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.

Introduction and Objective: Existence of genotype × Environment interaction for quantitative traits such as grain yield can limit the selection of superior Genotypes for the development of improved cultivars. In order to calculate the interaction of genotype × Environment, breeders evaluate Genotypes in several Environments to identify Genotypes with high yield and stability. This experiment was performed to investigate the interaction of genotype in the Environment on 11 bean Genotypes using parametric and nonparametric methods and GGE bioplot model to evaluate Genotypes and Environments, determine the relationship between Genotypes and Environments and identify the ideal genotype. Material and Methods: In this experiment, 9 lines of pinto beans along with Ghaffar cultivars and Cos16 lines (11 lines in total) were performed in a randomized complete block design with three replications in order to achieve high yield and marketable bean cultivars. Parametric and non-parametric methods were used to select stable Genotypes with high yield and GGE bioplot analysis was used to select superior Genotypes that are compatible with regional Environments. Results: There was a significant interaction between genotype and Environment, indicating significant differences in the response of Genotypes to different Environments of the experiment. In parametric methods G4, G8, G9 and to some extent G2 Genotypes and in nonparametric methods G2, G8, G3, G4 and to some extent G9 Genotypes were introduced as stable cultivars. Biplot analysis showed that G1 genotype in Zanjan in first and Second years and G2 genotype in Khomein n first and second Environments showed the highest yield. G11 and G7 Genotypes with the longest distance to the ATC line had low yield and low yield stability. None of the Genotypes were found to be desirable Genotypes with average yield and high yield stability, but G2 genotype and later, G4 genotype was a short distance from the ideal genotype. None of the studied Environments is close to the ideal Environment and therefore none of them can be considered as representative of Environments for genotype segregation. Results: Zanjan in first and Second years and G2 genotype in Khomein n first and second Environments have the highest yield. No ideal Genotypes were observed, but two Genotypes, G1 and G4, can be introduced as superior Genotypes.

The aim of this research was to determine the effect of cold stress levels on growing period, yield and some yield components of 15-bread wheat (Triticum aestivum. L) cultivar grown Azerbaijan in Iran. Experiment was carried out in split plot on randomized complete block design with tree replications in two years 2009-2010. Tree levels of cold stress were planted with the first of 22 October and the others followed by 30 days intervals.15 cultivar (five Genotypes with types winters names Sayson, Martin, Gaskogen, C-82-12, C-81-14, 5 genotype with type intermediate including Alvand, Mahdavi, Zarrin, Marvdasht, Tous and five Genotypes with type spring names Shiraz, Pishtaz, M-79-7, M-81-13 and Kavir) were the sub plot and cold stress level were the main plot. According to the results of two years. Cold stress had a significant effect on grain number per spike, plant high, 1000- kernel weight (1000-KW) and grain yield. Grain yield obtained from the first cold stress level, consequently the period of first cold stress level planting time and growth condition on the first cold stress treatment level was suitable for tillering that the variety have enough time to tiller their tolerance to cold stress and could be concluded as optimum sowing period for maximum grain yield for the region.

The goal of this research was to assess the stability and yield performance of 150 durum wheat Genotypes in multi-Environment trials in two locations (Diyarbakir and Kiziltepe), in 2011-2012, and 2012-2013 growing seasons. The trials were designed by Lattice Experimental Design with two replications (incomplete block design). The AMMI (Additive Main Effects and Multiplicative Interaction) and GEI (Genotype×Environment Interaction) analysis were used in the study to estimate GEI effects on grain yield, because of plant breeders’ great interestin these models for breeding programs. AMMI evaluation indicated that Genotypes made the most important contributions to treatments Sum of Squares (59.8%), Environments (3.5%), and GEI (36.7%), respectively, suggesting that grain yield had been affected by Environment. IPCA 1 and IPCA 2 axes (Principal Component) were significant as P<0.01 and explained 63.8 and 36.2%, respectively. Results showed that Kiziltepe 2013 was more stable and high yielding, meanwhile Diyarbakir 2012 and Diyarbakir 2013 Environments were unstable and low yielding. According to stability variance, usually the province lines were more productive and stable than some old cultivars and many landraces/ Genotypes. Moreover, genotype G24 was more effective in all Environments. The GEI model according to AMMI analysis suggested that this genotype can be considered as a candidate, due to extensive adaptability and high performances in all Environments.

Multi-Environment trials (METs) of crop Genotypes are costly and require efficient test sites for cost effectiveness. This study aimed to identify efficient test sites for METs of sugarcane (Saccharum spp.) Genotypes in Thailand, utilizing data from 10 sugarcane Genotypes conducted at nine locations covering different sugarcane growing regions of the country for two crop-classes. Cluster analysis and the genotype plus genotype´Environment (GGE) biplot method were used to group these sites into five subsets, based on their similarity in genotypic responses of cane and sugar yields of the planted crop and the first ratoon crop. The results showed a fair agreement between the two methods, but inconsistent results were obtained from groupings that were based on different yield traits and crop-classes. Locations appearing more consistent in certain groups were chosen as the representatives of the respective groups to constitute the set of efficient test sites. Cluster analysis and the GGE biplot, however, identified different sets of test sites that were equally effective in retaining the G´L interaction and the performance ranking of the test Genotypes as the original nine test sites. The selected locations by cluster analysis which included Nakhon Ratchasima, Ratchaburi, Kamphaeng Phet, Tha Phra, Khon Kaen and Udon Thani are preferred because of their wider geographical distribution. Four sites could thus be omitted, which would substantially reduce the costs and time and greatly improve the efficiency of the METs of sugarcane Genotypes in Thailand.

The objective of this study was to determine genotype × Environment (GE) interaction and yield stability of ten cotton Genotypes. The trials were conducted at randomized complete block design (RCBD) with four replications at six locations during-cropping seasons. Combined analysis of variance (ANOVA) revealed significant difference among Genotypes (G), Environment (E) and genotype × Environment interactions (GE) for yield, boll weight, boll number, earliness index and monopod branches number. The K performed high yielding genotype ( kgh-) followed by NSK, SKSH-, BC-and GKTB-. According to yield, stability parameters and adaptability analysis, the NSK, SKSH-and GKTBwere ideal Genotypes with broad adaptability. Specific adaptation Genotypes (e. g. K ) are ideal for maximizing yield and yield stability in stress conditions. Stability parameters similarity for genotype selection ranged from to percent. Significant relationships were found between yield and stability parameters (except for regression coefficient, bi). Finally, our results recommend the NSK-for release as commercial cultivar followed by GKTB-and SKSH-in cotton breeding program of Iran.

The effects of genotype by Environment interactions on the grain yield of twelve promising durum lines along with a durum (Zardak) and a bread (Sardari) wheat as checks were investigated at three locations, representative for multi-location durum yield trials, in Iran under rain-fed and supplementary irrigation conditions for three cropping seasons.Main effects due to E, G and GE interaction as well as four interaction principal component axes (IPCA 1-4) were found to be significant (P<0.01). AMMI biplot was able to distinguish Genotypes, with wide and specific adaptation, and Environments, with high and low genotype discrimination ability. Genotypic responses were similar in Kermanshah and Ilam in two (2005-06 and 2008-09) out of three years. In Shirvan genotypic responses generally were similar. Based on AMMI analysis, the check cultivars were more adapted to rainfed conditions while the promising Genotypes G8, G2, G7, G4 and G6 were adapted to supplementary irrigation conditions. According to AMMI and some stability parameters, the Genotypes G3, G10 and G5 were more stable and were more adapted to the whole target regions.

The sugar beet crop has always been attacked by various pests and diseases. Rhizomania viral disease, which has spread in different regions of sugar beet cultivation, has become a disease of prime importance for the crop in the last three decades. Resistant cultivar usage is the only reliable way to manage rhizomania disease. In order to identify promising Genotypes, eleven sugar beet Genotypes with natural infection to rhizomania, in a company with three controls, were assessed in a Randomized Complete Block Design (RCBD) with four replications. The experiment was conducted in six research stations of Karaj, Khoy, Kermanshah, Mashhad, Miandoab, and Shiraz for two cropping seasons (2020 and 2021). Based on the rhizomania score, all Genotypes had acceptable resistance to the disease. The Additive Main Effects and Multiplicative Interaction (AMMI) stability analysis illustrated that the first five principal components were significant and specified 88.8% of the total genotype by Environment interaction variance. Gen-7, Gen-10, Gen-11, and Gen-2 were selected as stable Genotypes based on the AMMI model. Genotype plus Genotype by Environment Interaction (GGE) biplot results also confirmed the superiority of Gen-10 and Gen-11 regarding sugar yield and stability in disease-infected Environments. According to the results of the Multi-Trait Stability Index (MTSI), Genotypes Gen-4, Gen-1, Gen-2, and Gen-11 were identified as stable Genotypes under rhizomania-infected conditions. By applying different stability measurement methods, in addition to identifying the Genotypes’ adaptation to different Environments, accurate decisions for future breeding or cultivar registration can be achieved.

Limitation of water resources and salinity of water and soil are important factors in the development of olive cultivation and production. Therefore, it is very important to select and introduce salinity tolerant olive cultivars. This study was carried out using six olive cultivars and promising Genotypes (Conservolia, Koroneiki, Amin, D1, Ds8 and Ozineh2) and three salinity treatment (0, 50, 200 mMl-1 NaCl) as factorial experiment in completely randomized design with three replications in controlled Environment. The results showed that with increasing salinity, the Na+ content increased in roots, stems and leaves tissues. The concentration of Na+, Cl-and K+ in these tissues was affected by salinity levels and olive Genotypes. As the salt concentration increased, the potassium content and K+/Na+ ratio significantly decreased in roots, stems and leaves tissues. Transfer of Cl-from root to stem and leaves in susceptible cultivars and Genotypes was higher than in tolerant Genotypes. Cultivar Conservolia and Genotypes and D1 genotype had the lowest leaf area. Salt injury index of showed that cv. Amin and Ds8 genotype had no injury symptom. However cv. Conservolia and D1 genotype showed the greatest injury symptoms by salinity stress. According to the results of this research, cv. Amin and Ds8 genotype were identified as tolerant, Ozineh2 promising genotype relatively susceptible, cv. Koroneiki as susceptible, and cv. Conservolia and D1 genotype as very susceptible to salt stress.

Introduction and Objective: Considering the increase in per capita consumption of rice in the country and the need to increase rice production per unit area, it is very important to introduce new high-quality varieties with high yield and stable grain yield. The grain yield depends on the genotype and its response to Environmental conditions. To increase the quantity and quality of rice, this research was conducted to evaluate the interaction between genotype × Environment and to determine the stability of the grain yield of rice Genotypes. Materials and Methods: In this experiment, 8 quality rice lines were carried out including Kadus, Ali Kazemi, and Champa local cultivars in the form of Randomized Complet Block Design with three replications in Cheram and Basht regions during 2017 and 2018. In each year, the performance of tested Genotypes was tested separately using simple variance analysis and using Duncan's method, and at the end of the second year, combined analysis was performed to determine the compatibility. To analyze the stability and compatibility of lines, Shukla's stability variance, Francis and Kanenberg's coefficient of Environmental changes, Wrickes ecovalence, deviation from Eberhart and Russell's regression line, Finley and Wilkinson's regression coefficient and Pintos' coefficient of identification were used. Results: The results showed a great diversity between the investigated Genotypes in terms of grain yield and other agricultural traits. Composite variance analysis showed that there is a significant difference between years at the 5% probability level. The stability analysis of the Genotypes by calculating the stability parameter shows that the highest stability was related to the local Champa variety and lines 7, 8, 5, and 6. Based on the calculated Eberhart & Russell Method, Genotypes 7, 6, and 8 and the local Champa variety were favorable in both test Environments. In terms of Wrickes ecovalence, and stability parameter, the local Champa cultivar and Genotypes 6 and 5 were the best. Based on the results of the analysis and comparison of the average of the treatments, the superiority of the grain yield was related to lines 7 and 5 with an average yield of 9.60 and 8.85 tons per hectare. The mentioned lines were recognized as superior Genotypes due to their average yield, conversion efficiency, high percentage of whole rice, and average amylose content. Conclusion: Based on the results obtained from the stability methods of lines number 7 and 5, respectively, with an average yield of 9.60 and 8.85 tons per hectare and having stability variance, Environmental change coefficient, and intra-location variance less than one, as well as the coefficient of the regression line equal to one they are recommended as stable Genotypes for both regions and other similar regions.