جستجوی مقالات مرتبط با کلیدواژه « اثر متقابل ژنوتیپ و محیط » در نشریات گروه « زراعت »

It is quite effective to identify high-yielding and stable genotypes and evaluate different genotypes in various environmental conditions.

This study was conducted to evaluate the response of 12 imported faba bean genotypes with the application of gibberellic acid hormone (environmental factor) by the GGE-biplot method. For this purpose, genotypes were evaluated in a complete randomized design with three replications during autumn of 2018 in the research greenhouse of Shahrekord University. Gibberellic acid was sprayed at concentrations of 0, 10, and 30 ppm from the 2-leaf to flowering stages on a weekly basis.

The analysis of variance showed that the effects of genotypes, gibberellic acid, and their interaction effects were significant in grain yield. The genotypes by the gibberellic acid sum of squares explained 22.33% of total grain yield variations. Using the GGE-biplot model, the first two components accounted for 86.5% of total grain yield variations due to the effect of genotype and the interaction effects between genotypes and gibberellic acid hormone.

Based on the analysis of GGE- biplots, the grain yield of genotypes C2, C4, C10, and C6 was higher than other genotypes. Furthermore, they had appropriate relative responses to the application of gibberellic acid hormone than other genotypes. Regarding the need for genotypes with high yield potential to increase in seed yield, they can be used for breeding this product. The eight remaining genotypes had lower grain yields with the most inappropriate responses to the application of gibberellic acid hormone, identified as undesirable genotypes.

Barley (Hordeum vulgare) as one of the small grain cereals is widely cultivated in Iran and the world. Hence, the achievement of high-yielding and stable varieties is one of the main breeding proposed. The present study aimed to investigate a set of international genotypes of barley in terms of grain yield and some agronomic traits using the best linear unbiased predictor (BLUP) model under climatic conditions in Iran.

In this study, a set of genotypes including 24 international barley genotypes provided from ICARDA were investigated for grain yield and some agronomic traits including the number of days to heading, number of days to physiological maturity, grain filling period, plant high, spike length, and thousand grains weight in a randomized block design in the three research stations located at Karaj, Birjand, and Zabol during the 2020-2021 cropping season. After collecting and analyzing the experimental data, the best genotypes were identified based on BLUP model.

The results obtained from experimental data showed that the genotype and genotype-by-environment interaction effects were significant for grain yield and other measured traits (except days to heading and spike length). The highest values of genetic variance and heritability were estimated for spike length, 1000-grains weight, grain yield, and plant height. The results of the based-BLUP (best linear unbiased predictor) statistics revealed that genotypes G3, G6, G10, and G23 had the highest yield performance and stability compared with other genotypes across different environments. Taking into account the role of each measured trait on grain yield stability, genotypes G10, G16, G22, and G24 were identified as the best barley genotypes using the MTSI index. 

In conclusion, it can be stated that the MTSI index has high efficiency in breeding programs to use other traits related to grain yield to select stable and high-yield genotypes.

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.

Drought stress is one of the results of climate change that has a negative impact on the growth and yield of crops. Breeding for drought tolerance and production stability are important goals of crop breeding programs in arid and semi-arid regions.

In order to evaluate the stability of grain yield and drought tolerance at barley genotypes in temperate climates of Iran, 20 cultivars and barley lines, in a randomized complete block design with three replications during the two cropping years 2016-2017 and 2017-97 in four stations including Karaj, Varamin, Birjand and Yazd were evaluated under two conditions, normal irrigation and drought stress at the end of the season (irrigation interruption in 50% of heading).

Combined analysis of variance showed that the effects of genotype, year × location, location × genotype and genotype × year × location on grain yield was significant. In order to study the interactions of genotype × environment more precisely and to determine genotypes with stable and drought tolerant yield, different stability parameters and drought tolerance and susceptibility indices were calculated for each genotype. Calculation of stability parameters showed that lines 20, 4, 16 and 15 were recognized as genotypes with stable yield and suitable adaptation, respectively. In terms of drought tolerance indices in the first year, lines 4, 17, 16, 20 and 11 and in the second year, lines 7, 3, 16, 4, 20 and 5, the top genotypes were drought tolerant at the end of the season.

The use of drought tolerance and susceptibility indices, three-dimensional charts and biplot in the first and second years showed that lines 4, 16 and 20 were jointly selected as the best genotypes in terms of drought tolerance in both years, so this the results showed that these lines had good repeatability for drought tolerance. On the other hand, stability statistics also indicated the stability of yield of these lines. so due to grain yield stability, drought tolerance and high yield potential, three lines 4, 16 and 20 were superior and adaptable  lines for normal and terminal drought stress conditions in the temperate climate of Iran that after supplementary  tests can be considered to help production in these areas.

Improper distribution of rainfall and reduced rainfall are major factors in reducing lentil yield per unit area . Therefore, the use of genotypes adapted to adverse environmental conditions can play an important role in increasing yield in such conditions. Awareness of genotype × environment interactions helps breeds to be more accurate in evaluating genotypes and choosing the best genotypes.

This study was conducted during two years (2019-2020 and 2020-2021) in two stations in the cold dry areas of the country (Qeydar Zanjan, Maragheh). The experiment consisted of 17 advanced lentil genotypes along with three control cultivars Kimia, Bilesvar and Senna (20 genotypes in total) which was performed in a randomized complete block design with 3 replications.

The results of combined analysis showed a significant difference in the level of one percent probability for environment, genotype and genotype × environment interaction. The results of combined analysis of variance showed that the environment, genotype and genotype by environment interaction effects were 79.5%, 2.25% and 18.23% of total variance, respectively The results of GGE biplot indicate the existence of 42.1% of the total changes related to the first component and 26% of the total changes related to the second component, which together explain 68.1% of the total changes. According to the obtained results, there is a high correlation between E1 and E3 environments and between E2 and E4 environments and they can be introduced as similar environments. In biplot study, genotypes 7 (FLIP2013-29L), 13 (FLIP 2012-262 L) and 11 (FLIP 2012-207L) had higher performance and stability at the same time.

Genotypes 13, 7 and 11 were introduced as high yielding and stable genotypes and can be used to select or recommend a variety.

Wheat (Triticum aestivum) is the largest food crop to cover the earth’s surface in the world. Environmental influences are considered as the measurement criteria for genetic stability.  Sensitivity of a crop genotype to the environmental factors gives rise to genotype - Environment Interaction (GEI). Breeders’ objective is to develop varieties that give high productivity under available environmental conditions.

In this study, stability and adaptation of eighteen promised bread wheat lines along with Morvaread and Gonbad as control cultivars, were evaluated in randomized complete block design with three replications at four regions in Gorgan, Gonbad, Sari and Mogan, during two cropping seasons of 2015-2017.  

Combined analysis of variance indicated significant effects of genotypes and year × location × genotype interactions. Significant interaction effects indicated different reactions of genotypes in various locations. Due to the significant effects of this interaction, genotypes × environment interaction effect was evaluated using GGE Biplot stability analysis. Results of Biplot that revealed mean and stability of genotypes, line11stayed on, in the positive direction of axis had the highest grain yield and line 2 stayed on, in the negative direction of axis had the lowest grain yield. This biplot analysis diagnosed of Mega-environment. In base of this study, among of Gorgan, Gonbad and Sari environments had strong positive correlation and genotypes had the same reactions. Mogan had next to zero correlation with three other locations and reaction of genotypes was independent. The results showed the possibility of selecting stable genotypes with high yield compared to experimental controls, which indicates genetic improvement in bread wheat breeding programs in the northern climate of the country.

Consequently, genotypes 11, 13, 7 and 8 with high grain yield and strong stability were selected.

To investigate adoptability and pattern of G × E interaction, 15 faba bean lines as well as four check cultivars (including Barekat, Saraziri, Baloochi and Zereshki) were evaluated using a randomized complete block design with three replications in four agricultural research field stations of Gorgan, Dezful, Brojerd and , Iranshahr, Iran during two cropping seasons (2015-2016 and 2016-2017). Combined analysis of variance showed significant effects of location, year, year × location interaction, genotype × location interaction, genotype × year interaction and year × location × genotype interaction on grain yield. Stability in performance of the 19 genotypes was tested using GGE-Biplot approach across eight environments. GGE- Biplot analysis using a genotype × environment interaction (GEI) model explained 91.5% of total interaction effect variance. View of polygon graph revealed three superior mega-environments and the compatible genotypes were determined for each mega-environment; Gorgan (Line G15), Brojerd-Iranshahr (Line G3) and Iranshahr (Line G19). Lines G15, G7 and G13 with average seed yield of 3439, 3128 and 3094 kg ha-1, respectively, had higher seed yield and yield stability. Based on GEI and GGE- Biplot analysis, Gorgan and Brojerd experimental environments had good differentiation ability. Finally, genotypes G12, G14 and G15 were the most stable genotypes with wider adaptation to all the tested environments and can be recommended as the superior genotypes for being released as new commercial faba bean cultivars.

The purpose of this study was to study the stability and adaptability of 12 advanced lines of rice with two control varieties (Fajr and Neda cultivars). These genotypes were evaluated over 3 years of cultivation (2011-2013) in a randomized complete block design with three replications and three locations including Amol, Sari and Tonekabon. In this research, the stability statistics including Romer’s environmental variance, Francis and Kannenberg coefficient of variation, Wrick’s ecovalance, Shukla stability variance, AMMI stability value, lin and Binns parameter, within location coefficient of variation and Tai regression method were used to evaluate genotypes stability. Based on environmental variance and coefficient of variation, lines 7, 9 and Neda cultivar were stable and showed acceptable yield. According to Shukla stability variance, wrik’s eco-valance parameters and yield, lines 1, 7, 9, 12 and Fajr cultivar were the best genotypes. The results of variance and Lin and Binns parameter indicated that lines 10, 7 and 1 were the best genotypes. Based on AMMI stability parameter, lines 1, 2, 4, 7, 12 and Neda variety showed the best stability and yield. Tai regression results indicated that lines 7, 9, 12, and Fajr and Neda cultivars had the best stability and yield. Based on all methods, lines 7, 9 and Fajr cultivar were identified stable and with the  yield of 6032.44, 5879.59 and 5755.81 kgr/ha respectively stayed above the average of all genotypes.

Due to inadequate agricultural water in warm area of Iran, Identification and introduction of stable and adaptable genotypes with high grain yield in non-stress and drought stress conditions is the most important issue in durum wheat breeding program. In order to study adaptability and grain yield stability of durum wheat genotypes in non-stress and terminal drought stress conditions, 19 durum line/cultivars along with Mehregan, as a check commercial bread wheat cultivar, were used. Genotypes assessed in four locations, i.e. Ahvaz and Darab for drought stress conditions and Dezful and Khorramabad for normal conditions in two cultivation years, 2017-18 and 2018-19. The experiments were conducted in a RCBD design with three replications. For grain yield, combined analyses of variance and stability analysis using parametric and non-parametric methods such as AMMI, Ranking and STDEV of Ranking were undertaken. Based on the results, Genotypes with specific adaptation were included: D-96-4 and D-96-6 for Khorramabad; D-96-5, D-96-9 and D-96-18 for Ahvaz; D-96-3(Mehregan) and D-96-16 for Darab and Dezful. Genotypes D-96-11, D-96-17, D-96-18 and D-96-20 were selected as stable and wide adaptable based on mean yield and ASV parameter in AMMI1 model and genotypes D-96-13 and D-96-18 were selected based on ranking and STDEV of ranking. Finally, based on the results of two methods, Genotype D-96-18 with high grain yield and the low G×E interaction was selected as a superior durum wheat line in both non-stress and drought stress conditions.

In order to study the genetic variability based on the physiologic and drought tolerance indices of 20 genotypes of durum wheat, an experiment was carried out in research farm and physiology laboratory of Agricultural and Natural Resources Campus of Razi University of Kermanshah, Iran, in a randomized complete block design with three replications under stress and non-stress conditions in 2013-2014. The results of analysis of variance for different agronomic, physiological and biochemical traits showed that the effect of genotype on the characteristics of water stored in cut leaves, water use efficiency, stomata conductance, chlorophyll b, total chlorophyll, grain yield, activity of catalase enzymes, peroxidase and superoxide dismutase were significant. Comparison of mean of different traits showed that genotypes 20, 5, 3, 1, 12 and 13 had the highest values. The correlation analysis in drought stress revealed that the grain yield with chlorophyll b and total chlorophyll as well as relative water content had a significant positive correlation with photochemical efficiency of photosystem II. Significant negative correlation was observed between the water stored trait of cut leaves and the level of activity of the enzyme superoxide dismutase. There was a significant positive correlation between chlorophyll a content with chlorophyll b and chlorophyll content as well as between chlorophyll b and total chlorophyll. The GGE-BIPLOT showed that the genotypes 5, 6, 20, 9, 2, 3, 10, 4, and 19 were identified as superior genotypes for studied traits. Such comprehensive information on genotypes is important for converting them to cultivars and introducing them to farmers, as well as choosing parents for hybridization.

Selection of adapted genotypes with high seed yield and yield stability is the goal of soybean breeding programs. To evaluate the adaptability and seed yield stability of soybean promising lines, 19 promising lines and cv. Williams as check were evaluated using randomized complete block design with four replications in four locations: Karaj, Gorgan, Moghan and Khoramabad in Iran during two growing seasons (2013 and 2014). GGE biplot analysis was employed to evaluate the adaptability and seed yield stability. Combined analysis of variance showed thatyear, location, genotype, year × location, year × genotype, location × genotype and genotype × location × year interaction effects were significant on studied traits. The contribution of year, location and genotype variance to total variance was 0.01, 0.60 and 0.02, respectively, indicating considerable contribution of location variance. The first two components of PC1 and PC2 explained overall 58% of total observed variation of genotype and genotype × environment (G + GE). In this study, three mega-environments were identified. The first mega-environment included: E2 (Karaj 2014), E5 (Moghan 2013) and E8 (Gorgan 2014) and G16 was the superior genotype in this mega-environment. The second mega-environment included: E3 (Khorramabad 2013) and E4 (Khorramabad 2014) and G8 was the superior genotype in this mega-environment. Third mega-environment consisted: E1 (Karaj 2013) and E7 (Gorgan 2013) and G17 was the superior genotype in this mega- environment. Biplot analysis showed that genotypes: G17 (L85-3059) with 2702 kg.ha-1 and G16 (L12/Chaleston × Mustang) with 2750 kg.ha-1 were highly adapted genotypes with high seed yield and yield stability. The E7 environment (Gorgan, 2013) was the most desirable environment in respect to its discriminating ability among soybean genotypes and the best representative of the target environments.

Drought stress affects 40-60% of agricultural lands all over the world(Bray, 2002). Having 240 mm precipitation, Iran is located in arid and semi-arid parts of the word. Due to shortage of rainfall in Iran, a big share of crop breeding is devoted to drought tolerance. It has been resulted in stable varieties. Although barley is more tolerant to drought stresscompared to other cereals, however it is susceptible to moisture shortage during tillering and grain formation which would result in yield losses (Nevo and Chen, 2010). Yield is the most direct index for evaluation of response to stress. Although grain yield is affected by environmental factors but it is an index to evaluate response of cereals to environmental stress. The propose of drought tolerant genotypes is to provide relatively tolerant varieties so that showing less yield losses compared to other genotypes. Different yield based indices have been suggested. All these indices consider yield under two condition that is stress and no stress (Fernandez, 1992).
Material and twenty varieties and promising lines of barley were compared in a randomized complete block design with three replication under two irrigation conditions (normal and water lack after 50% of ear emergence).The experiment was carried out at research station of Torogh for two years. Farm preparation, cultivation and sowing were implemented as standard protocol for experiment. Under normal condition, 3-4 irrigation ere applied according to crop water demand while irrigation was cut after 50% of ear emergence under stress condition. Stress tolerance indices were calculated based on two years means which areMP, GMP, STI, SSI, TOL, YI, YSI, RDI. Data were analyzed and Duncan mean comparison as done (SAS 9.1.3.).Biplot analysis was adopted by GEA-R software.
Result and applying GIBplot based on studied indices made it easy to compare genotypes. There was no significant difference between yield correlations under normal and stress conditions. Genotype*environment interaction was significant over two years of experiments which shows provoking high level of stress. Blum (1999) and Panthuwan et al. (2002), belived that potential yield could only affect yield under normal condition or low stress but under highly stress, genotype*environment interaction would significantly decide level of crop yield.Biplot correlation equations could explain relation between grain yield and calculated indices where GMP, MP and STI were the best ones under both conditions. Lines 6, 20 and 15 were identified as superior gentypes based on GIBplot polygon. Keeping in view both yield and stability adopting GGEBIplot same results were obtained. Line 6 was ideal genotype in biplot. Results showed that GIBplot and GGEBiplot were suitable methods for identifying superior genotypes based on biplot indices considering both yield and stability

To study the stability of seed yield of winter oilseed rape genotypes in target areas and to compare the types of stability parameters, experiments were conducted in randomized complete block design with 15 genotypes and a control cultivar (total of 16 genotypes) with three replications during the two growing seasons of 2015-2017 in four cold and semi cold locations (Kabutarabad-Isfahan, Torogh-Mashhad, Islamabad-e-Gharb, Karaj), Iran. Results of analysis of variance showed a significant difference between the oilseed rape genotypes in some locations. After confirming the homogeneity of variance of experimental errors in different environments, combined analysis of variance was performed with the assumption of the fix effect of genotypes and random effect of location and year. Results showed that the environment, genotype and genotype×environment effects were significant at 1% probability level and genotypes had different performance in different environments. The greatest contribution of the variations was related to the environmental effect (32.83%). The statistical methods consisted of; environmental variation coefficient, stability variance, ecovalence, environmental variance, mean square deviations from regression line, coefficient of determination, regression coefficient Tai regression (Alpha and Lambda) and simultaneous selection index were used to determine the stable and high yielding genotypes. Although there was a possibility of selection for performance and stability by a functional criterion, but due to the existence of low and variable Spearman correlations between different types of indices, except for the simultaneous selection index and grain yield, a combination of different methods of stability was used and G4 (ES Alonso), G7 (Es Kamilo) and G16 (Okapi) genotypes were identified as stable and high yielding genotypes. It concluded that the selected genotypes could be used for cultivation in cold and mild cold regions of the country.

In order to evaluate oil yield stability in oilseed rape genotypes and genotype´environment interaction, 22 oilseed rape genotypes were evaluated using RCBD design with 4 replications in Agricultural Research Station of Islam Abad-e-Gharb during 3 cropping seasons in normal and delayed sowing date conditions. Combined variance analysis showed that genotype, environment and genotype´environment interaction were statistically significant. Proportion of environment effect followed by genotype´environment interaction was so greater than genotype effect. Based on biplot of two first genotype´environment interaction components, that explains proportion of oilseed rape genotypes in genotype´environment, genotypes Parade, Kristinia, Goliath, Shiralee, Kimberly and Elect were located next to center of biplot so that had the least proportion for the Genotype´Environment effect and were stable from the viewpoint of oil production. AMMI stability values (ASV) of genotypes confirmed the above mentioned results. Hyola401 and Zarfam were located away from the center of biplot so that were unstable genotypes for oil production. Biplot display indicated that environments E1, E3 and E4 had the most scores for genotype´environment interaction whereas E2 had the least proportion in genotype´environment interaction effect. Dissimilarity in ranking of selected genotypes from the viewpoint of oil yield based on other stability parameters suggests that it would be better using of numerous stability parameters to reliable and accurate selection of stable genotypes.

To evaluate the genotype × environment interaction and determine the stable genotypes of wheat, 30 genotypes of bread wheat along with two controls namely Chamran and Chamran 2 were studied in 6 locations (Ahwaz, Darab, Dezful, Iranshahr, Khorramabad, Zabul) and two years (from 2013 to 2015), in each using an alpha lattice design with 4 replications. The results obtained from AMMI analysis demonstrated that the main effects of genotype, environment, genotype × environment interaction and the first four principal components were highly significant. The first four principal components justified around 90.9% of the sum of squares of the interactions. By using the stability of the figures of the statistics lasting value AMMI (ASV), genotypes 2, 6, 14, 28 had the lowest (ASV) values. Genotypes 2, 6 and 14 with higher yields than the overall mean were identified as high yielding genotypes with stable performance. Drawing the biplot of the first principal component and the average yield for genotypes and environments suggested that genotypes 9, 28, 25, 12, 14, 10, 2 and 6 had low interactions, but genotypes 14, 10, 2 and 6 with higher than average yields and desired stability were selected. Biplot of the first two principal components showed that the interaction between genotypes 2, 6, 7 and 14 due to higher grain yield than the average of the total, were identified genotypes with good compatibility. Genotype grouping them into three groups based on the model SHMM placed in the first group of 22 genotypes, genotypes 9 in the second group and the third group was the only genotype 20.

Genotype-environment interaction for plant breeders has been important as it is a complex issue in breeding for high yield varieties and releasing new genotypes. In order to assess adaptability and stability of sunflower varieties in different climate conditions, twelve cultivars were investigated in Karaj, Shiraz, Birjand, Kashmar and Arak in randomized complete block designs with three replications. To study genotype by environment interaction, additive main effects and multiplicative interaction (AMMI) model was used. Analysis of additive main effects (analysis of variance) and multiplicative interaction effects (principal components analysis) revealed that the effect of environment at 1% probability level and effect of genotype - environment interaction at 5% probability level were significant and tow first interaction principal components explained 84% of the interaction sum squares. Biplot of first interaction principle and mean yield revealed that Progress genotype had adequate yield and lowest value for first principle component. Therefore this genotype selected as a high yield and stable genotype; Record and Gabur genotypes were stable and high yield following Progress genotype. AMMI2 graph indicated that Progress, Master and Zargol in Arak and Karaj, Favorit, Record and Azargol in Shiraz, Gabur and SHF81-90 in Kashmar and Armaverski, Lakomka, Zaria and Sor in Birjand had specific adaptability. Among all genotypes, Zaria, Lakomka, SHF81-90, Gabur and Zargol had the highest general adaptability.

This study was carried out to adaptability evaluation of the 50 grapevine varieties introduced from Russia from 2008 to 2013 in the Qazvin and Urmia provinces. The experimental design was randomized complete block 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. Recorded traits were: yield per plant, length and width of berry, total soluble solids of juice (TSS), juice pH, juice titratable acid (TA) and time of harvest. 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.

To study genotype, environment, genotype × environment interaction effects for grain yield on red bean, ten red bean genotypes were evaluated at five locations (Borujerd, Zanjan, Shahrekord, Tabriz, and Khomein) for two cropping seasons (2012- 2014). The experiment was conducted using a randomized complete block design with three replications. Combined analysis of variance for grain yield showed that the main effects of both genotypes and environments, and the genotype × environment interaction effect, were highly significant. Average environmental grain yield across genotypes ranged from lowest of 1615.8 kg.ha-1 in Shahrekord in 2012 to the highest of 4069.8 kg.ha-1 in Borujerd in 2013. Environment main effect was the main source of variation that accounted for 61% of the total yield variation. Analysis of variance of additive main effects and multiplicative interaction (AMMI) showed that eight IPCAs were significant and residue effects (noise) was considered zero. The first three main components of AMMI model explained 79.8% of the total yield variation. Genotype KS31290 was identified as the most stable genotype based on the values of the first three main components, ASV and D parameter. Genotype KS31285 with the highest ASV and D parameter, and the most distance from the center of biplot was unstable genotype. However, this genotype had higher yield (2900 kg.ha-1). Based on the results of this experiment, KS31290 can be considered as a promising line for being released in areas with similar climatic conditions as those in experimental sites.

In order to determine the stability and reaction of barley genotypes in different climatic conditions, grain yield of ten barley cultivars were evaluated in five locations. Studied areas were Karaj, Birjand, Kashmar, Sanandaj and Shiraz. The experiment conducted in a randomized complete block designs with three replications in 2013-2014. Genotype by environment interaction effect was significant for grain yield. To investigate the stability of the cultivars, stability parameters including environmental variance, Shukla stability variance, Wricke ecovalence, Lin and Bains coefficient variation and the variation coefficient stability parameter were assessed. According to these methods, Nosrat, Makuei and Gorgan cultivars with optimum performance were more stable than other genotypes. GGE - biplot graph in grain yield explained 91.54 percent of variations. According to GGE – biplot method Strain, Gorgan, Kavir and Nosrat had the high mean values of grain yield and stability. In contrast, Rayhan and Zarjou had the low mean values of grain yield and stability. For grain yield, studied locations divided to two mega-environments. The first mega-environment was Karaj and Birjand and the second mega-environment was Shiraz, Sanandaj and Kashmar. The second mega-environment regarded as the best mega-environment because of more number of varieties in with high grain yield. Karaj, Sanandaj and Shiraz had the most discrimination power in genotype recognition.

The existence of genotype × environment interaction necessiate the assessment of genotypes yields in wide range of environmental conditions, so that obtained information could increase their selection and introduction efficiency. In this study to determine the yield stability and analysis of genotype × environment interaction pattern, 66 mutant lines of rice with 5 check cultivars were evaluated using a randomized block design with three replications for three years (2012-2014). Analysis of additive main effects (analysis of variance) and multiplicative interaction effects (principal components analysis) revealed that the effects of genotype, year, and interaction between of them were highly significant. Total interaction effect was divided into one significant interaction principal component at 5% probability level and residual (noise) via AMMI model. The first principal component explained 57.57% of the total variation. Biplot of the first principal component and mean yields (AMMI1 model) for evaluated genotypes and environments (years) revealed that high yielding genotypes of G7, G41 and G69 with the least genotype × environment interaction effect were more stable genotypes and G63, G20 and G33 with yield lower than average recognized as unstable genotypes.