جستجوی مقالات مرتبط با کلیدواژه « genotype by environment interaction » 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.

Chickpea is one of the most important legumes in Iran and accounts for almost 84% of dietary legumes. Chickpea seed yield is strongly influenced by environments, and breeders often determine the stability of high-yielding genotypes across different environments before being introduced as a cultivar. Accurate study of the nature of genotype × environment allows breeders to identify stable and compatible genotypes and has always been one of the important issues in the production and release of new stable and high-yielding cultivars in breeding programs. . Adaptation of chickpea genotypes to environmental conditions is important for crop production stability in different years and places. Existence of the interaction of genotype and environment affects the value of genotypes in different places. The present study was conducted to investigate the effect of genotype by environment interaction on grain yield of chickpea genotypes and cultivars in four environments and to identify stable and high-yielding genotypes under rainfed conditions as autumn planting.

In this study, twelve cultivars and advanced genotype of chickpea were planted during two cropping years (2016-2018) in a randomized complete block design with three replications in semi-warm (Kuhdasht) and temperate (Khorramabad) areas of Lorestan province. Various nonparametric methods such as non-parametric statistics of Si (1), Si (2), Si (3) and Si (6), Thennarasus statistics of NPi (1), NPi (2), NPi (3) and NPi (4), mean rank stability statistics (R), stability statistics of Ketata et al (σr, σmy), Kang stability statistics (Ysi), Fox stability statistics (TOP, MID and LOW) and Genotype Stability Index (GSI) were used for estimating the stability of genotypes. the principal component analysis method was used to better understand the relationships between different statistics..

The results of combined analysis of variance showed that the main effects of environment (including location, year and location ×year) and genotype ×environment were significant at the 1% probability level and the main effects of genotype, location × genotype and year × genotype were significant at the 5% probability level. The effects of genotype, environment and the genotype by environment interaction accounted for 6.48, 77.4 and 13.03% of the total squares, respectively.The biplot of the first principal component (PC1) versus the second principal component (PC2) classified the studied nonparametric stability statistics into three groups.Based on the statistics of NPi (1), NPi (2), NPi (3) and NPi (4), the genotypes with the lowest values are considered as stable genotypes. According to NPi (1) statistics, G1, G6 and G9 genotypes were identified as the most stable and G3 and G5 genotypes as the most unstable genotypes. Based on NPi (2) and NPi (4) parameters, G1, G10 and G9 genotypes were the most stable and G5, G12 and G4 genotypes were the most unstable. Based on the results, the first two principal components explained 68% (42% and 26%, respectively), of the variance of the main variables). Cluster analysis using mean seed yield and non-parametric statistics, placed chickpea genotypes in two main groups. The first cluster included mean seed yield, TOP, MID, σr and σmy. The second cluster consisted of four sub-clusters .

Based on Si (1), Si (2), Si (3) and Si (6) statistics, G1, G10 and G9 genotypes with the lowest values were identified as the most stable genotypes. G1 and G9 genotypes with the lowest GSI were recognized as the best genotypes in terms of grain yield and stability. Based on the parameters with the dynamics concept of stability, genotypes G1, G10 and G9 were identified as stable genotypes with high yield.

Chickpea (Cicer arietinum L.) is one of the most important legumes in the world after pea and bean and is rich in protein (21.7-23.4%), minerals (iron, phosphorus, calcium, zinc, potassium and magnesium), carbohydrates (41.1-47.4%) and vitamins (B1, B2, B3, B5, B6, B9, C, E, K). The interaction of genotype by environment, as a response of genotypes to the environmental variation is a source of complexity for breeding programs and preparation of high yielding and stable genotypes. One of the most important ways to discover the nature of genotype by environment interaction is stability analysis, which identified the stable or compatible genotypes. Different methods for investigation of genotype by environment interaction and determination of stable genotypes have been reported, which generally include uni-variate and multivariate methods. One of the multivariate methods is AMMI analysis. The purpose of present study is the evaluation of the stability of chickpea genotypes using AMMI indices and biplots. 

Eighteen selective advanced genotypes of chickpea from ICARDA with two check verities (Arman and Azad) evaluated across four locations (Gachsaran, Ilam, Gonbad and Khoramabad) at three growing seasons, in a completely randomized block design with three replications. The data of 3rd year in Gonbad were lost and therefore, the analysis of data performed on 11 environments. Average seed yield of genotypes estimated at each environment (combination of location and growing season) and used for analysis. Statistical analyses including simple analysis of variance, combined analysis of variance and stability analysis carried out by metan (Multi environment trial analysis) R package. Five AMMI stability indices including ASV (AMMI stability value), SIPC (Sum of IPCs scores), EV (Eigenvalue stability parameter of AMMI), Za (Absolute value of the relative contribution of IPCs to the interaction), WASS (Weighted average of absolute scores) and simultaneous selection index (ssi) of these parameters was used for stability evaluation of genotypes. 

Combined analysis of variance indicated environment, genotype and genotype by environment interaction accounted 79.2, 2.4 and 10.0% of the phenotypic variation of seed yield, respectively. The significant effect of genotype indicated the wide genetic background of genotypes, while the significant effect of genotype by environment interaction is indicated the diversity of genotypes in test locations and growing years and exhibited the necessary of evaluation of genotypes in multiple environments. According to the significant effect of genotype by environment interaction, AMMI analysis was carried out by principal components analysis and the results indicated the significant effects of five principal components on seed yield. The first two principal components contributed to 42.5 and 19.4% of genotype by environment interaction. Genotype G3 (1663 kg ha-1) followed by G1, G17, G20 and G5 had the highest seed yield. According to the ASV and WAAS indices, G3, G4 and G13; EV and ZA indices, G3, G14, G16 and G6; and SIPC, G14, G3, G11, G4 and G16 were the most stable genotypes. Selection of these genotypes was done only on stability aspect of genotypes, therefore simultaneous selection index (ssi) based on any of these parameters was used to evaluate the simultaneously selection of genotypes based on seed yield stability and performance. Genotypes G3, G1, G4, G13 and G16 selected as superior genotypes by ssiASV, ssiZA and ssiWAAS indices; while based on ssiSIPC and ssiEV, G3, G16 and G20 were as the best genotypes. The other applications of AMMI stability analysis are selection of best genotypes in any of environments. According to this procedure, genotype G3 was placed in the first order in E1, E5 and E7; in the second order in E2, E3 and E9; in the third order in the E6 and E7; and in the fourth order in the E5. The AMMI1 biplot (IPCA1 vs grain yield) identified G1, G3, G17 and G13 as high yielding and stable genotypes with seed yield higher than total mean and lowest IPCA1 values. This biplot was also indicated environments E1, E9, E5, E2 and E3 had the lowest contribution in genotype by environment interaction interaction. The first principal components explained only 42.5% of genotype by environment interaction, and therefore, it seems that using the AMMI2 biplot is more efficient to identify the superior genotypes. In AMMI2 biplot (IPCA1 vs IPCA2), genotypes G4, G3, G13, G1 and G10 had high general stability. The environments E3, E4 and E10 with long vectors, had high discriminating ability and can estimate the relative efficiency of genotypes well.

In general, based on different indices, G3, G1 and G13 had high yield in most of environments, and in most methods had good stability and could be candidates for introduction of new cultivars.

Study of genotype (G) × environment (E) interaction and identifying of stable and high yielding cultivars are important and essential component of cotton breeding programs dedicated for cultivar development.

Nine new promising cotton genotypes (Gossypium hirsutum L.) along with two commercial cultivars (control) were evaluated in a randomized complete block design (RCBD) with four replications at eight locations including Hashemabad, Karkandeh, Darab, Khodafarin, Garmsar, Varamin, Isfahan and Moghan during two crop seasons (2018-2019).

Combined analysis of variance (ANOVA) across environments indicated significant variation among genotypes and G × E interaction for seed cotton yield. Significant G×E variation allowed for subsequent analysis of genotype stability statistics and AMMI analysis. Regarding AMMI analysis, two principle components contributed for 81.0 % of the variation among the G×E interaction. The Varamin and 92-34 genotypes have shown general adaptability with average yield across environments. The Golestan, ANBK and ANB414 were identified as high yielding and the most stable genotypes based on parametric models (Cvi, bi and Xi), AMMI and GGE Biplot models. It was concluded that AMMI biplot clearly facilitate identification of mega-environments and cultivars for specific recommendations.

The results of study indicated that ANB414, ANBK and Golestan could be suited for cultivation across the test environments.

Lentil as a legume crop is one of the most important food crops in developing countries (Karimizadeh & Mohammadi, 2010). In most cases, the interaction between environment and genotype occurs, complicating selection for improved yield among genotypes (Sabaghpour et al., 2004). A cultivar or genotype is considered to be more adaptive or stable if it has a high mean yield but a low degree of fluctuation in yielding ability when grown in diverse environments (Finlay & Wilkinson, 1963). The response of different genotypes in different environments and thus the evaluation of genotype interaction in the environment are of particular importance to researchers in plant genetics and breeding, which can help plant breeders to evaluate genotypes more accurately and select the best one. The purpose of this study was to identify and introduce superior genotypes in terms of yield and yield stability among the lines obtained from preliminary yield test.

Sixteen advanced lentil genotypes along with the control genotypes i.e. Kimia and Gachsaran selected from the advanced yield trial of the 2012-13 cropping year were used as planting material in Gachsaran, Khorramabad and Ilam areas for three years (2013-2016) in a randomized complete block design with three replications. Analysis of variance was performed separately in each environment and then Bartlett test was used to evaluate the homogeneity of experimental errors. Then the combined analysis of variance was performed on seed yield. Stability analysis were performed using environmental variance (S2i), coefficient of variation (CVi), Shukla's variance (2i), Wrick equivalence (Wi), Plaisted statistic ( ), Plaisted and Peterson statistic ( ) and superiority index (Pi) and nonparametric methods, , , , TOP and mean of rank.

Simple analysis of variance showed genetic differences among the genotypes. The combined analysis of variance was performed after Bartlett test, which confirmed variance homogeneity of experimental errors (χ2 = 9.5; P = 0.33). The combined analysis of variance indicated the significant effects of genotype, year, location and interactions of year × location, genotype × location and genotype × year × location. The mean seed yield of genotypes showed that out of 18 studied genotypes, seven genotypes produced higher yields than the average yield of genotypes in the all environments (1566.39 kg.ha-1), so that the highest seed yield were seen in the genotypes 15 and 16, followed by genotypes 8, 12, 11, 5 and 2. Based on environmental variance (S2i), the genotypes 3, 7, 6 and 13 and based on the coefficient of environmental variation (CVi), the genotypes 3, 7, 6 and 15 were identified as stable genotypes. Plaisted and Plaisted and Peterson methods identified the genotypes 4, 9, 2, 10 and 3 as stable genotypes. Wrick equivalence and Shukla variance also introduced the genotypes 4, 9, 2, 10, 3 and 12 as stable genotypes. The Lin and Binns superiority index identified the genotypes 16, 8, 15, 12 and 11 as the most stable genotypes. The genotypes 4, 2, 3, 10 and 9 were the most stable genotypes based on nonparametric index. Based on the index, the genotypes 1, 2, 3, 4, 6, 7, 9, 10 and 18 and based on statistics, the genotypes 3, 4, 10, 9, 1, 7, 2 and 6 were stable genotypes. Based on Fox nonparametric index, the genotypes 16, 11, 2, 8, 12, 13 and 14 were stable genotypes. The genotypes 12, 2, 9, 16, 11, 8, 4 and 3 were more stable based on the total Kong rank. The principal component analysis to evaluate the relationship between seed yield and stability indices showed that seed yield had the highest correlation with MID, TOP and PI. Therefore, these three indices can be used as the best indices to identify superior genotypes in terms of seed yield and stability.

In general, the genotypes 2, 5, 8, 11, 12, 13, 15 and 16 gave higher average yield or equal to Gachsaran control seed yield and were also stable. The genotype 16 and 11 were the most stable genotypes based on MID, TOP and PI and also had the highest seed yield and could be candidates to be released as new cultivars.

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.

Selection of desirable genotypes with high yield and stability is the main goal of most soybean breeding programs. The aim of this study was to evaluate the grain yield and stability of 19 pure soybean lines along with Williams’s control (20 genotypes) during two cropping years (2014-2015) in four regions: Karaj, Gorgan, Moghan and Shahrekord. A randomized complete block design with three replications was used in all experimental areas. The AMMI and GGE Biplot methods were used to determine the compatibility and stability of performance. Combined analysis of variance showed significant effects of environment, genotype, genotype × environment interaction, and components of IPCA1 and IPCA2 (AMMI analysis) at 1% level of significance. Genotype × environment interaction variance accounted for 42% of the total variance and the two components of IPCA1 and IPCA2 accounted for 81% of the total variance. Based on AMMI criteria (IPCA1, IPCA2 and ASV) genotype G17 (Williams x Steel / L3) with yield of  2449 kg ha-1 was determined as the most stable genotype while based on GGE biplot G18 (Williams x Steel/L4) with 2865 kg ha-1 and G20 (Williams/Check) with 2927 kg ha-1 yield  were identified as the most favorable genotypes. Also environments: E7 and E8 (first and second year Shahrekord) were selected as the most favorable environments and genotypes: G18 and G20 based on GGE biplot were also among the top four genotypes of the two environments. Two mega environments were identified in the bi-plot analysis that the first mega environment includes the E3 (Gorgan first year), E5 (Moghan first year) and E6 (moghan second year) and the second mega environment includes the E1 (Karaj first year) and E2 (karaj second year) and E4 (Gorgan second year).

The genotype by environment interaction is a major challenge in the study of quantitative characters because it complicates the interpretation of genetic experiments and do predictions difficult, also it reduces grain seed yield stability in different environments. In order to determine the yield stability, adaptability and analysis of the genotype × environment interaction of oily sunflower inbred lines under normal and salt stress conditions, 100 genotypes coming from different geographical regions were evaluated using a randomized complete block design with three replications for two successive years (2006 to 2007). In stability analysis using statistics such as environmental variance and coefficient of variation, lines 71 and 33 showed minimum variations compared to other lines. Based on Eberhart and Russell regression method, lines 71, 45, 40 and 25 was the most stable genotypes. AMMI statistics revealed lines with code numbers of 71, 77, 93 and 51 as the most stable genotypes. Biplot technique was used to identify the appropriate genotypes for special environments. Based on this method, lines 71, 61 and 17 showed the lowest interaction and considered as the most stable genotypes. However, line 71 showed highest seed yield compared two other lines. The line with code number of 50 showed special stability and high yield under normal conditions whereas lines 90, 48 showed special stability and high yield under salt stress conditions. In conclusion, based on different stability analysis statistics and AMMI analysis the line 71 are introduced as most stable line under normal and salt stress conditions.

Biplot related to the effects of genotype and genotype × environment is called GGE biplot. This method is used to assess the stability of genotypes. In this methodology, selection of sustainable varieties is based on genotype and genotype by environment interaction effects. In this research, the stability of 18 bread wheat genotypes was investigated in six environments (three years and two environmental conditions, irrigated and rainfed). The experiment was carried out in randomized complete block design with three replications under both conditions in experimental field of Faculty of Agriculture, Razi University, Kermanshah, Iran, during three years (2010-2012 and 2014-2015). The results of combined analysis of variance showed that the environment, genotype and genotype by environment interaction effects were 75%, 5.9% and 14.9% of total variance, respectively. Pishtaz and WC-4530 genotypes were the nearest genotypes to ideal genotype. Due to the high correlation between E1 (rainfed condition, 2010-2011) and E2 (irrigated condition, 2010-2011) and among E5 (rainfed condition, 2014-2015), E3 (rainfed condition, 2011-2012) and E4 (irrigated condition, 2011-2012), these environments were identified as similar environments. Simultaneous evalution of stability and yield of the studied genotypes showed that WC-4530 is stable and high yielding genotype. View of polygon graph revealed five superior genotypes and three mega-environments, and the compatible genotypes were determined for each mega-environment. All experimental environments had good differentiation ability.

Salinity stress is one of the serious threats for high productivity of crops, especially in wheat as a key staple food for world population. Understandingthe inheritance of yield and yield components is very important in wheat breeding programs for salinity tolerance. In this research, to evaluatethe gene action of yield and yield components, seven Iranian wheat cultivars including Falat, Bam, Ghods, Roshan, Arg, Kavir and Pishtaz along with their crosses were cultivated in randomized complete block design with three replications under both normal and salinity stress conditions at National Salinity Research Center,Yazd province, Iran,in 2014. All initial assumptions for the traits were indefeasible, therefore combined analysis of variance was done based on Jinks-Hayman approach. The results of analysis of variance showed that all traits including grain yield, biological yield, tiller number, peduncle length, plant height and 100 grain weight had significantly simple additive effect “a” and “a × environment”interaction effect. Also, the simple effect of b3 which is equal to specific combining ability in method 1 diallel analysis, and its interaction with environment were significant for all studied traits. Significant “a” and “b3” terms indicated the important role of additive and dominance effects in the inheritance of trait in both conditions. In this research, the cultivar Roshan had the best combining ability for yield, biomass and tiller number. Generally, the gene type for controlling traits, gene action, potential of dominance and recessive genes in parents,maternal effects and additive and dominance effects were different under twoenvironmental conditions, non-stress and salinity stress. Estimated high broad sense and moderate to high narrow sense heritabilities indicated that thestudied genetic materials could be promisingly used to improve these traits under non-stressconditions aa well as salinitytolerance.Therefore, it ispossibleto select tolerant genotypes to salinity stress in Iranian bread wheat cultivars used in this research.

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 objectives of this study were to analyze genotype by environment (GE) interactions effects on the forage yield of 8 corn hybrids by computing AMMI analysis and evaluating genotype (G), environments (E) and GE interactions using stability parameter i.e., AMMI stability value(ASV) and Wrick,s ecovalance (W2i). Experiments were conducted at five regions (karaj, Neysahbur, Gorgan, Brojerd and Isfahan Research Station), during 2005-2006 crop season. The result’s of AMMI analysis for forage yield indicated that the Genotype (G) main effects, environment (E) and GE interactions as well as three first interaction principal components (IPCA1-3) were significant. AMMI biplot was able to distinguish stable genotypes and environments with high discrimination ability from low one,s. AMMI ANNOVA selected different hybrids for each regions: No. 5, 8 and 1 for Karaj , 5, 2 and 8 for Neyshabur, 1 and 5 for Gorgan, 8, 5and 1 for Brojerd and finally hybrids No. 5, 8 and 2 for Isfahan. According to the AMMI analysis and the parameters investigated, genotype 5, with forage yield higher than grand mean, was the most stable hybrid and with high adaptation to the environment was studied, meanwhile Hybrids No. 7 and 8 had the greatest influence on the interaction but were the unstable genotypes tested in all years and areas.

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

Selection of favorite cultivars and lines affected with genotype- environment interaction dramatically. In order to study of genotype × environment interaction in rice، 12 lines with 2 commercial cultivars as check (Neda and Fajr) was studied in 9 different environments (3 location and 3 years). The experiment was randomized complete block design (RCBD) with three replications. The differences were significant among genotypes، environments and their interaction. The yield stability was studied by AMMI method. The results showed two principal components were significant and explain 67 percent of interaction variance. Results revealed that grain yield was highly influenced by environmental factors. The line 9 had yield higher than mean and with the lowest for first interaction principal component thus distinctive as stable line. Biplot of two first interaction component revealed that line 9 in Sari (2008) and Amol (2008) had specific adaptability. Lines 4 and 8 in Tonkabon (2008 and 2009) and in Amol (2010) had specific adaptability. Lines 6، 7، 10 and Neda cultivar in Sari (2009، 2010) had specific adaptability. Finlay the lines 1، 2، 3 and 5 in Tonkabon (2010) had specific adaptability. Lines 2، 5، 7 and 12 and fajr and Neda cultivars had the highest common adaptability to environments.

In order to select high yielding and most adaptable chickpea lines under dryland conditions, seven desi type chickpea genotypes together with one check cultivar (Pirouz) were evaluated in three research stations (Gerize, Kharke and Ghamlu) of Kurdistan province, during three years (2001-03) in RCB design with four replications. Bartlett test suggested that there was homogeneity in error of variances. Thus, analyses of variance were done to study the genotype-environment interactions, and analysis of stability was done to determine the performance of lines in varying environments. Results, of ANOVA showed that there were significant differences among environments, mean yield of genotypes and the G E interaction. Stability analysis after Eberhart and Russell method showed that there were significant variations due to genotypes, environment and their interactions for one hundred seeds weight, and due to environment for seed yield. On the basis of stability parameters (P < sub>i, bi, S2di, and CV %), genotype ICCV 91006 with the highest seed yield (821 kgha-1) was selected as the most desirable and stable genotype .

In order to study the adaptability and yield stability of dryland promising durum wheat genotypes, this experiment was conducted with 22 genotypes of durum wheat plus 2 bread wheat cultivars as checks in RCBD for three cropping seasons (1998- 2001) in six cold research stations in dry land areas. In each location simple ANOV A and combined ANOV A for three years were implemented. Because in combined" analysis (3 years in each location) and according to Hartly Fmax test, the error mean squares (MSe) at different locations were significant, therefore for uniformity of MSe, the experimental locations were divided into two groups. Linn and Binns parameter, coefficient of variation of grain yield and rank method were used for determination of stable cultivars/lines. Results of combined ANOV A in the first group (Maragheh, Sararood and Ardebil) showed that interaction effects of year x location and year x cultivar were significant (P ? 0.01), and also differences among cultivais/lines in grain yield were significant at 5% probability level. Bread wheat cultivars had the highest grain yield and, in durum wheat, lines no. 2 and 4 with 1585 and 1566-1 kgha,respectively were the superior lines. Lines no.2 and 18 had the most stability and grain xield among genotypes. In this group line no.18 with facultative growth habit is recommended for these regions. In the second group (Shirvan, Uromieh and Ghamloo) the effects of year, location, cultivar and also interaction effects of year x location and year x location x cultivar on grain yield were significant. After bread wheat cultivars that had the highest grain yield, durum wheat lines no. 9 and 18 with 756 and 734 kgha-1, respectively were the highest yielding genotypes. Regarding grain yield, results of stability and adaptability analysis and growth habit, these two lines are recommendable for growing in these locations.