Search

Potatoes (Solanum tuberosum L.) are an important plant crop, whose yield may vary significantly depending on pedo-climatic conditions and genotype. Therefore, the analysis of the genotype × environment interaction (GEI) is mandatory for the setup of high-yielding and stable potato genotypes. This research evaluated the tuber yield (t ha−1) and yield characteristic of nine potato cultivars over 3 years and 4 organic farms in Poland by additive main effects and multiplicative interactions (AMMIs) and genotype plus genotype environment interaction (GGE) biplot analyses. The results of these analyses indicated significant differentiation of tuber yield among genotypes in individual environments. It was found that the environment (E, where E = L (localization) × Y (year)), genotype (G) and GEI, but not replication, significantly affected tuber yield. The AMMI analysis showed that the environment factor explained the most considerable part of tuber yield variations (52.3%), while the GEI and G factors explained a much lower part of the variations. The AMMI and GGE analyses identified five cvs.: Twister (46.4 t ha−1), Alouette (35.8 t ha−1), Kokra (34.8 t ha−1), Levante (33.1 t ha−1), and Gardena (30.4 t ha−1), as leading cultivars in the studied organic farms due to their high productivity coupled with yield stability. The statistical measure Kang (YSi) showed that these cvs. can be considered as adaptable to a wide range of organic environments. In the case of morphological traits of tubers (tuber shape and depth of tuber eyes), the most important factor influencing both these traits was genotype (G). Influence of other factors, like localization (L), year (Y), and all interactions (double and triple), were much less significant or insignificant. In case of taste and non-darkening of tuber flesh, the main effects which significantly affected the values of these traits were genotype (G) and localization (L). We observed that cooking type can vary depending on the year (Y) and the localization (L). [ABSTRACT FROM AUTHOR]

Potatoes (Solanum tuberosum L.) are an important plant crop, whose yield may vary significantly depending on pedo-climatic conditions and genotype. Therefore, the analysis of the genotype × environment interaction (GEI) is mandatory for the setup of high-yielding and stable potato genotypes. This research evaluated the tuber yield (t ha−1) and yield characteristic of nine potato cultivars over 3 years and 4 organic farms in Poland by additive main effects and multiplicative interactions (AMMIs) and genotype plus genotype environment interaction (GGE) biplot analyses. The results of these analyses indicated significant differentiation of tuber yield among genotypes in individual environments. It was found that the environment (E, where E = L (localization) × Y (year)), genotype (G) and GEI, but not replication, significantly affected tuber yield. The AMMI analysis showed that the environment factor explained the most considerable part of tuber yield variations (52.3%), while the GEI and G factors explained a much lower part of the variations. The AMMI and GGE analyses identified five cvs.: Twister (46.4 t ha−1), Alouette (35.8 t ha−1), Kokra (34.8 t ha−1), Levante (33.1 t ha−1), and Gardena (30.4 t ha−1), as leading cultivars in the studied organic farms due to their high productivity coupled with yield stability. The statistical measure Kang (YSi) showed that these cvs. can be considered as adaptable to a wide range of organic environments. In the case of morphological traits of tubers (tuber shape and depth of tuber eyes), the most important factor influencing both these traits was genotype (G). Influence of other factors, like localization (L), year (Y), and all interactions (double and triple), were much less significant or insignificant. In case of taste and non-darkening of tuber flesh, the main effects which significantly affected the values of these traits were genotype (G) and localization (L). We observed that cooking type can vary depending on the year (Y) and the localization (L).

The three cultivars of Gayo Arabica coffee (GAC) are distributed widely in the Gayo Highlands, Aceh Province, Indonesia, between 900 and 1,700 m above sea level (masl). The difference in altitude affects the yield and quality of coffee, and Arabica coffee has biennial bearing characteristics, so there are always annual yield fluctuations. This study aimed to determine the yield performance and stability level of the three GAC cultivars, Gayo 1, Gayo 2, and Gayo 3 (G3), across six different environments. The study used a randomized complete block design with three replications. The six environmental conditions are the combinations of two altitudes (900 and 1,500 masl) and 3 years of production (2019, 2020, and 2021). The estimation of coffee yields is based on the fruit value, which is calculated by the number of productive branches per tree, nodes per branch, berries per node, and the weight of a single cherry. An investigation of the genotype-by-environmental interaction using a combined analysis of variance and the yield performance and stability analysis performed using the Eberhart and Russell method, AMMI (additive main effects and multiplicative interaction) and GGE (genotype + genotype × environment) biplot analysis. Results showed that the yield performance of GAC was significantly affected by the interaction between genotype and environment. G3 was an ideal cultivar because it had high yield performance, was stable, and could adapt to broader environments in Gayo Highland. Therefore, G3 deserves high priority for Arabica coffee development in that region. [ABSTRACT FROM AUTHOR]

• The performance of the hybrids was primarily affected by the environment, planting dates, and their interactions. • The magnitudes of the GEI sum of squares were more than five times that of the hybrid sum of squares for the grain yield. • The stability of hybrids did not show the same trend across environments among the planting dates. • AMMI, Pi, GGE, YSI, and ASV analysis methods effectively identified stable and high-yielding hybrids. • Dealing with GEI is a valuable strategy to optimize resources in maize breeding programs. Maize is one of the most important field crops for feed, food, and raw material in South Africa. It is the main staple food for the majority of the people impoverished households in South Africa. Maize is susceptible to drought, and erratic rainfall and extreme climate variation are major maize production constraints in South Africa. This study aimed to determine the influence of planting dates on the grain yield stability of six maize genotypes in five environments for three (2019/20 to 2021/22) consecutive seasons. The additive main effect and multiplicative interaction (AMMI) model indicated that the environment contributed 59.20 % and 47.76 % of the total variation in grain yield at planting dates one (PD1) and two (PD2), respectively. The magnitudes of the genotype x environment interaction sum of squares were 5.20 for PD1 and 12.30 for PD2 times that of the genotype (genotype) sum of squares for the grain yield (GEI). The first principal component accounted for 53.14 % (PD1) and 47.76 % (PD2) of the total variation in the dataset. The AMMI genotype superiority measure (Pi), genotype and genotype by environment interaction (GGE), yield stability index, and AMMI stability value analyses identified genotypes DKC75–65BR and DKC78–45BRGEN as stable and high yielding across environments and planting dates. Genotype DKC71–44B was the least stable at both planting dates. Bothaville (E7) for PD1 and Coligny 2021/22(E13) for PD2 had high yield potential and were favorable environments, whereas Potchefstroom 2019/20 and 2021/22 were the least favorable environments for PD1 and PD2, respectively. [ABSTRACT FROM AUTHOR]

The identification of high-yielding and stable genotypes for cultivation across differential production regions is among the key breeding objectives in cowpea improvement programs. This study was aimed to determine genotype-by-environment interaction (GEI) for grain yield to select high-yielding and stable cowpea genotypes for production in South Africa and identical agro-ecologies, and for cultivar development. Fifty cowpea genotypes were tested for grain yield across seven environments of South Africa using a 10 × 5 alpha lattice design replicated three times, during the 2019/2020 and 2020/2021 planting seasons. Grain yield data were subjected to analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI) and the genotype-by-environment interaction (GGE) biplot analyses. ANOVA and AMMI showed significant genotype, environment and GEI effects. High grain yield was recorded for genotypes G35 (0.47 t ha−1), G1 (0.45 t ha−1) and G47 (0.43 t ha−1) across test environments. AMMI stability values (ASV); identified Acc-Cowp44 as the most stable genotype across all sites, recording the lowest ASV of 0.03. The comparison view of GGE biplot revealed Acc-Cowp29, Acc-Cowp38 and Acc-Cowp5 as ideal genotypes, possessing high grain yield of 0.19, 0.47 and 0.36 t ha−1, respectively. The identified genotypes are recommended for production and inclusion in subsequent breeding activities.

The demand by industries for large-seeded peanuts is increasing in Thailand and Southeast Asia. New large-seeded peanut lines were recently developed in Thailand to respond to the demand. In this study, a multilocation yield trial was performed to identify the best genotype(s) in Thailand's central region and investigate the genotype-environment interaction (GEI) on peanut production. Twelve promising largeseeded peanut lines and two check varieties (KU50 and KK6) were planted at 12 different planting locations during the dry and rainy seasons of 2018 and the dry season of 2019. This study found significant yield potential variability in the promising lines of peanuts evaluated at different planting locations. A combined analysis of variance presented that the environment and genotypes had a considerable impact (p < 0.001) on the pod and seed yield. The GEI showed a high impact (p < 0.01) on pod yield and an effect (p < 0.05) on seed yield. The environment presented the most significant influence on pod and seed yield variations, followed by genetics and GEI. The total variation in seed yield was 64.22%, composed of PC1 and PC2 values at 45.71% and 18.51%, respectively. The GGE biplot analysis of the yield potentials at each location indicated that KUP12BS029-1-1-3 was the ideal genotype, with a high yield potential and most stability at multilocations, followed by KUP12BS030-3-4-1 and KUP12BS030-1-4-3. These promising lines will be released as new peanut varieties in central Thailand and are recommended as parental lines in breeding programs for large-seeded and yield potential in Thailand and Southeast Asia. [ABSTRACT FROM AUTHOR]

Analysis of genotype-by-environment interaction (GEI) is critical in the local potato breeding and selection programme to obtain information on the performance of the genotypes for yield adaptability and stability. The objective of this study was to assess the marketable tuber yield of 18 advanced potato clones compared to the commercial variety Spunta at four locations (Bigara, Réduit, St. Antoine and Rivière du Poste), representative of four major soil types in Mauritius. They were analysed for adaptability and stability using the additive main effects and multiplicative interaction (AMMI) model and genotype main effect and genotype x environment interaction (GGE) biplot. Five clones gave significantly the highest marketable tuber yield in terms of overall ranking with yield increase of 47.4% to 59.6% over the control variety Spunta. AMMI analysis of variance detected significantly (P < 0.001) higher proportion of variation in marketable tuber yield due to environment (42%); followed by genotype x environment interaction (21%) which justified multi-locational testing. AMMI1 biplot demarcated clones 142/161/2 and 142/161/5 as high yielding and most stable while AMMI 2 biplot identified the winning genotypes for a specific environment. Thus, clones142/161/4 and 161/142/16 had specific adaptation to Bigara, 29/5/2 and 21/5/3 were adapted to St. Antoine, 21/5/10 to Rivière du Poste whereas 29/5/3 was adapted to Réduit. The GGE biplots identified clones 142/161/2 and 142/161/5 as the two most desirable genotypes close to the “ideal genotype”. The “which- won- where” view of the GGE biplot further pointed to the presence of two mega-environments, which corresponded to the sub-humid irrigated/humid environments (Réduit, St. Antoine and Rivière du Poste) and the high altitude super-humid environment (Bigara). These results showed that in future both AMMI and GGE methods can be integrated in the local potato breeding programme to select superior genotypes through multi-year and multi-locational yield evaluation. [ABSTRACT FROM AUTHOR]

The demand by industries for large-seeded peanuts is increasing in Thailand and Southeast Asia. New large-seeded peanut lines were recently developed in Thailand to respond to the demand. In this study, a multilocation yield trial was performed to identify the best genotype(s) in Thailand’s central region and investigate the genotype–environment interaction (GEI) on peanut production. Twelve promising large-seeded peanut lines and two check varieties (KU50 and KK6) were planted at 12 different planting locations during the dry and rainy seasons of 2018 and the dry season of 2019. This study found significant yield potential variability in the promising lines of peanuts evaluated at different planting locations. A combined analysis of variance presented that the environment and genotypes had a considerable impact (p < 0.001) on the pod and seed yield. The GEI showed a high impact (p < 0.01) on pod yield and an effect (p < 0.05) on seed yield. The environment presented the most significant influence on pod and seed yield variations, followed by genetics and GEI. The total variation in seed yield was 64.22%, composed of PC1 and PC2 values at 45.71% and 18.51%, respectively. The GGE biplot analysis of the yield potentials at each location indicated that KUP12BS029-1-1-3 was the ideal genotype, with a high yield potential and most stability at multilocations, followed by KUP12BS030-3-4-1 and KUP12BS030-1-4-3. These promising lines will be released as new peanut varieties in central Thailand and are recommended as parental lines in breeding programs for large-seeded and yield potential in Thailand and Southeast Asia.

The identification of high-yielding and stable genotypes for cultivation across differential production regions is among the key breeding objectives in cowpea improvement programs. This study was aimed to determine genotype-by-environment interaction (GEI) for grain yield to select high-yielding and stable cowpea genotypes for production in South Africa and identical agro-ecologies, and for cultivar development. Fifty cowpea genotypes were tested for grain yield across seven environments of South Africa using a 10 × 5 alpha lattice design replicated three times, during the 2019/2020 and 2020/2021 planting seasons. Grain yield data were subjected to analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI) and the genotype-by-environment interaction (GGE) biplot analyses. ANOVA and AMMI showed significant genotype, environment and GEI effects. High grain yield was recorded for genotypes G35 (0.47 t ha−1), G1 (0.45 t ha−1) and G47 (0.43 t ha−1) across test environments. AMMI stability values (ASV); identified Acc-Cowp44 as the most stable genotype across all sites, recording the lowest ASV of 0.03. The comparison view of GGE biplot revealed Acc-Cowp29, Acc-Cowp38 and Acc-Cowp5 as ideal genotypes, possessing high grain yield of 0.19, 0.47 and 0.36 t ha−1, respectively. The identified genotypes are recommended for production and inclusion in subsequent breeding activities. [ABSTRACT FROM AUTHOR]

Development of high-yielding dual-purpose sweetpotato (Ipomoea batatas [L.] Lam) genotypes is useful for cultivation in mixed crop-livestock farming systems. The objective of this study was to determine genotype-by-environment interaction (GEI) for storage root yield (SRY), vine yield (VY), root: vine ratio (RVR), total above-ground and below-ground biomass (TABGB) and root dry matter content (RDMC) among newly developed sweetpotato clones to select promising dual-purpose clones. Forty-five sweetpotato genotypes, of which 32 were newly developed candidate clones, were evaluated across six diverse environments in Rwanda. Additive main effects and multiplicative interaction (AMMI) and genotype and genotype-by-environment (GGE) biplot analyzes were used to analyze GEI. AMMI analysis revealed a relatively larger proportion of the GEI effect for SRY (31.21%), VY (37.17%), RVR (41.73%), TABGB (35.24%) and RDMC (36.23%) than for genotype and environmental effects, suggesting differential response of test genotypes across environments for the assessed traits. GGE-biplot analysis explained total variation of 72.98% for SRY, 83.26% for VY, 74.03% for RVR, 78.82% for TABGB and 56.78% for RDMC. Clone 21 with enhanced SRY and TABGB, and clone 42 for SRY had broad adaptation. For better VY, clone 733 was the best performer in environments E2 and E6 which are designated as mega-environment (MGE) 2, and clone 321 in E1, E3 and E4 (MGE 3). For RVR, clone 577 had best adaptation in E3, E4 and E5 (MGE 1), while clone 460 in E2 and E6 (MGE 2). Clones 42 and 381 were best performers for RDMC with specific adaptation in environments E3 and E1, respectively. [ABSTRACT FROM AUTHOR]

A critical comparison of stability models is essential to give an insight to breeders for developing relatively stable wheat cultivars. A multi-environment trial (MET) was conducted to assess the pattern of genotype by environment interaction (GEI) effects on yield using multiple stability models viz. Additive Main effect and Multiplicative Interaction (AMMI), GGE biplot analysis and stability parameters. Eighty-one wheat genotypes were evaluated during three consecutive years (2013/14, 2014/15 and 2015/16) across nine environments (sites x year combination) in Khyber Pakhtunkhwa, Pakistan. Graphical stability approaches such as AMMI and GGE provided almost similar results for identifying the high-yielding and stable wheat genotypes. The AMMI analysis identified G-58 and G-79, whereas GGE biplot identidified G-79 as the most stable and high yielding genotype. Numerical stability parameters like Eberhart and Russell's model and Francis coefficient of variation (CV) declared G-79 as top-ranked genotype while Shukla stability value (σi²) and Wrick' s ecovalence (Wi) identified G-80 (check cultivar Janbaz), G-52 and G-79 as leading wheat genotypes based on grain yield. Spearman's rank correlation revealed significant positive correlations of AMMI stability value (ASV) with CV, σi² with Wi, and Wi with CV indicating that these parameters could invariably be used for identifying stable wheat genotypes depending upon the nature of the experiment, breeding material, and the complexity of data. Different stability models identified G-79 as high yielding and stable genotype and thus could be recommended for commercialization in the province of Khyber Pakhtunkhwa, Pakistan. Furthermore, stability parameters can supplement the use of AMMI and GGE biplot analysis to get more credible and reliable scrutiny of wheat genotypes in METs. [ABSTRACT FROM AUTHOR]

Finger millet (Eleusine coracana (L.) Gaertn. subsp. coracana ) production has become stagnant over the years and one of the possible ways to increase the production can be spread of widely adaptable high yielding cultivars. Five national finger millet cultivars were grown in randomized complete block design at ICAR-Vivekananda Institute of Hill Agriculture for six consecutive years to evaluate the grain yield stability. The grain yield data were subjected to AMMI and GGE biplot techniques for assessing the stability and patterns of GE interaction in finger millet National cultivars. The combined ANOVA showed that finger millet grain yield was significantly affected by environment, which explained 54.67% of the total treatment (G+E+GE) variation, whereas the G and GEI accounted for 10.38% and 34.96%, respectively. The partitioning of GEI sum of squares using AMMI analysis indicated that the first two PCAs were highly significant. The first IPCA axis (IPCA1) accounted for 50.3% of the G×E interaction sum of squares. The second IPCA axis accounted for 38.2% of the interaction sum of squares. Both represented a total of 88.5% variation. AMMI 1 biplot indicated the general adaptation of genotype HR 374 across the environments, whereas the other genotypes showed specific adaptation to one or other environments. GGE-biplot graphical analysis further confirmed the results and revealed that HR 374 as an ideal genotype in terms of high yield and stability followed by RAU 8 as desirable genotype. In our research, both of AMMI and biplot models were successful in assessing the performance of genotypes and the selection of best genotype was identical in both of them.

The uniformity of genotypes are significant for crop breeding program decisions to improve new varieties. The AMMI (Additive main effects and multiplicative interaction) analysis and Genotype x Environment Interaction (GEI) is make to estimation grain yield and understands GxE interaction patterns by researches as differential ranking of variety yields in multi-environment trials. Therefore, fifteen barley advanced line and six national cultivars and four foreign varieties (registered in abroad) were used in the study. The experiments were performed according to a complete randomized block design with four replications at five environments during two years. The stability and superiority of genotypes for yield and other traits were determined using AMMI and GGE biplot analysis. Factors (G, GE, and GEI) were found to be highly significant (P < 0.01) for grain yield. AMMI analysis indicated that the major contributions to treatment sum of squares were environments (98.52%), GE (0.45%) and genotypes (1.02%), respectively, suggesting that grain yield of genotypes were effected environmental conditions. The GGE biplot indicated that PCA 1 axes (Principal component) was significant as P<0.01 and supplied to 49.36% of complete GxE interaction. The AMMI indicated that G8 and G23 desirable and stabile genotypes for grain yield in multienvironment. Moreover, E2 and E5 (irrigated environments) were high yielding, while E3 (drought stress) low yielding as forecast. On the other hand, GGE biplot indicated that three group were occurred among traits, first group (GY: grain yield, CC: crude cellulose, CD: cold damage), second group (PC: , HW: hectoliter weight, TGW: thousand grain weight, SH: seed humidity), third group (LOD: lodging, PH: plant height, HT: heading time). Moreover: the study showed that G3, G6, G7, G8, G13 and G21 were the best genotypes both grain yield and other traits. The results of AMMI model and GGE biplot indicated that G8 is suitable to recommend for release and G23 desirable origin for yield stability and G7 valuable source for quality to use in barley breeding program. [ABSTRACT FROM AUTHOR]

Abstract The study was undertaken with the objective to examine the nature and to quantify the magnitude of genotype × environment interaction (GEI) effects on marketable leaf yield of fluted pumpkin, Telfairia occidentalis (Hook. F.) and to determine the winning genotype (s) for the test environments in Southwest Nigeria. The experiment was conducted using twenty-five landraces of fluted pumpkin in four environments [comprising two different locations (Abeokuta and Akure) for two consecutive years (2012 and 2013)]. Randomized complete block design with three replicates was employed. The combined analysis of variance over environments explained that fluted pumpkin marketable leaf yield was significantly (p < 0.001) affected by environments, genotypes and GEI. The result showed differential performance of fluted pumpkin landraces at different test environments and hence the interaction was crossover type. The genotype main effect plus genotype × environment interaction (GGE) biplots were applied to analyse and visualize pattern of the interaction components. The first two PCs explained 86.40% (PC1 = 66.93%, PC2 = 19.47%) of the total variation of the GGE model (i.e. G + GE). Landraces Fts34, Ftn44 and Ftk20 were most stable but Ftd1 and Ftw21 were more desirable. Landraces Fta39 (398.80g) and Ftw21 (299.60g) were high yielding and are adapted to Akure and Abeokuta respectively. Akure is considered a better location for the evaluation of fluted pumpkin for marketable leaf yield. [ABSTRACT FROM AUTHOR]

Soybean [Glycine max (L.) Merrill] is major oilseed crop globally. It is grown in diverse agro-ecological conditions and the performance of quantitative traits often varies due to significant genotype × environment interaction (GEI) therefore, the integration of yield and stability is one of the common objective of soybean breeding. The present investigation was carried out to study genotype × environment interaction (GEI) through GGE biplot and AMMI analysis over four environments (Majhera, Palampur, Bajaura and Almora) with 32 genetically diverse genotypes for four traits viz., grain yield, days to 50% flowering, days to maturity and 100 seed weight under rainfed condition of North Western Himalayan hills using randomised complete block design. The analysis of variance revealed that environments (E), genotypes (G) and genotype × environment interactions (GEI) accounted about 19.61%, 26.18% and 40.71% of the total variation, respectively. GGE biplot graphically displayed interrelationships between test locations as well as genotypes and facilitated visual comparisons through two-dimensional biplot between the first two principal components (PCI and PCII) which explained 74.40% variation for grain yield, 91.98% for days to 50% flowering, 83.27% for days to maturity and 84.68% for 100 seed weight. The GGE biplot suggested suitability of all the four test locations to be used for multi-location trials on the basis of discrimination ability and representativeness. Genotypes, 'C 17' ('PS 1556') was found the best performing genotypes in terms of grain yield followed by 'C 11' ('VLS 89'), 'C 4' ('PS 1550') and 'C 10' ('DS 3102') whereas, in terms of high grain yield and stability both 'C 11' ('VLS 89') was found as the ideal genotype. In the test locations Majhera, Palampur and Almora, winning genotypes for grain yield were 'C 17' ('PS 1556') and 'C 11' ('VLS 89') while, 'C 34' ('VLS 59') was the winning genotype at Bajaura. [ABSTRACT FROM AUTHOR]

The explication of candidate line and new varieties is uncomfortable the affect of genotype-by-environment interaction (GEI). The AMMI (Additive main effects and multiplicative interaction) analysis and GEI is making to estimation grain yield and understands GxE interaction patterns by researches as differential ranking of variety yields in multi-environment trials. So, a candidate of release and three spring barley varieties which registered in different years were reclaimed at seven environments. The experiments were performed according to a complete randomized block design with four replications. The stability and superiority of genotypes for yield was determined using AMMI and GGE biplot analysis. Factors (G, GE, and GEI) were found to be highly significant (P < 0.01) for grain yield. AMMI analysis indicated that the major contributions to treatment sum of squares were environments (79.4%), GE (13.9%) and genotypes (6.4%), respectively, suggesting that grain yield of genotypes were effected environmental conditions. The GGE biplot indicated that PCA 1 and PCA 2 axes (Principal component) were significant as P<0.01 and supplied to 86.3% of the complete GxE interaction. The GGE biplot indicated that four mega-environment were occured in terms of varieties. The varieties and candidate line took part in an independent mega-environment. On the other hand, Candidate line showed general adaptability (E1, E2, E4, E5 and E7), while Altikat and Sur 93 showed specific adaptation to E3 and E6 respectively. According to both techniques, especially Sahin 91, Sur 93 and Altikat came forward with low yielding, while candidate line with high yielding, stability and general adaptability. Statistical results indicated that AMMI and GGE biplot are informative techniques to compare varieties with candidate lines to discover general stability, adaptation pattern for practical recommendations. [ABSTRACT FROM AUTHOR]

Genotype×environment interactions (GEIs) can affect breeding programs because they often complicate the evaluation and selection of superior genotypes. This drawback can be reduced by gaining insights into GEI processes and genotype adaptation. The objectives of this research were to evaluate: (1) the yield stability of promising wheat lines across locations and (2) the relationship among the test environments for selecting superior lines within the cold climate mega-environments of Iran. A total of 35 wheat promising lines were grown at 7 locations during the 2008-2009 cropping season. Combined analysis of variance showed that the environment (E) accounted for 75.7% of the model sum of squares. The magnitude of the GEI sum of squares was about three times larger than that for genotypes. To determine the effects of GEI on yields, the data were subjected to the additive main effects and multiplicative interaction (AMMI) and genotype+(genotype×environment) interaction (GGE) biplot analysis. The AMMI1 model was found to explain up to 88% of the main and interaction effects. According to the AMMI1 and GGE biplots, the lines G5 and G4 were found to produce high and stable yields across environments. There were three mega-environments (Euromieh and Ardebil as mega-environment I, Mashhad, Arak, Hamedan and Jolgerokh as mega-environment II, and Karaj as mega-environment III) according to the site regression genotype (SREG) GGE model. Application of AMMI and GGE biplots facilitated visual comparison and identification of superior genotypes for each target set of environments. [ABSTRACT FROM AUTHOR]