Identification Of Mega-environments Research Articles

Envirotype-based delineation of environmental effects and genotype × environment interactions in Indian soybean (Glycine max, L.)

Soybean is a rainfed crop grown across a wide range of environments in India. Its grain yield is a complex trait governed by many minor genes and influenced by environmental effects and genotype × environment interactions. In the current investigation, grain yield data of different sets of 41, 30 and 48 soybean genotypes evaluated during 2019, 2020 and 2021, respectively across 19 locations and twenty years’ data on 19 different climatic parameters at these locations was used to study the environmental effects on grain yield, to understand the genotype × environment interactions and to identify the mega-environments. Through analysis of variance (ANOVA), it was found that predominant portion of the variation was explained by environmental effects (E) (53.89, 54.86 and 60.56% during 2019, 2020 and 2021, respectively), followed by genotype × environment interactions (GEI) (31.29, 33.72 and 28.82% during 2019, 2020 and 2021, respectively). Principal Component Analysis (PCA) revealed that grain yield was positively associated with RH (Relative humidity at 2 m height), FRUE (Effect of temperature on radiation use efficiency), WSM (Wind speed at 2 m height) and RTA (Global solar radiation based on latitude and Julian day) and negatively associated with VPD (Deficit of vapour pressure), Trange (Daily temperature range), ETP (Evapotranspiration), SW (Insolation incident on a horizontal surface), n (Actual duration of sunshine) and N (Daylight hours). Identification of mega-environments is critical in enhancing the selection gain, productivity and varietal recommendation. Through envirotyping and genotype main effect plus genotype by environment interaction (GGE) biplot methods, nineteen locations across India were grouped into four mega-environments (MEs). ME1 included five locations viz., Bengaluru, Pune, Dharwad, Kasbe Digraj and Umiam. Eight locations—Anand, Amreli, Lokbharti, Bidar, Parbhani, Ranchi, Bhawanipatna and Raipur were included in ME2. Kota and Morena constitutes ME3, while Palampur, Imphal, Mojhera and Almora were included in ME4. Locations Imphal, Bidar and Raipur were found to be both discriminative and representative; these test locations can be utilized in developing wider adaptable soybean cultivars. Pune and Amreli were found to be high-yielding locations and can be used in large scale breeder seed production.

AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments.

This paper reports an evaluation of eleven oat genotypes in four environments for two consecutive years to identify high-biomass-yielding, stable, and broadly adapted genotypes in selected parts of Ethiopia. Genotypes were planted and evaluated with a randomized complete block design, which was repeated three times. The additive main effect and multiplicative interaction analysis of variances revealed that the environment, genotype, and genotype-environment interaction had a significant (p ≤ 0.001) influence on the biomass yield in the dry matter base (t ha-1). The interaction of the first and second principal component analysis accounted for 73.43% and 14.97% of the genotype according to the environment interaction sum of squares, respectively. G6 and G5 were the most stable and widely adapted genotypes and were selected as superior genotypes. The genotype-by-environment interaction showed a 49.46% contribution to the total treatment of sum-of-squares variation, while genotype and environment effects explained 34.94% and 15.60%, respectively. The highest mean yield was obtained from G6 (12.52 kg/ha), and the lowest mean yield was obtained from G7 (8.65 kg/ha). According to the additive main effect and multiplicative interaction biplot, G6 and G5 were high-yielding genotypes, whereas G7 was a low-yielding genotype. Furthermore, according to the genotype and genotype-environment interaction biplot, G6 was the winning genotype in all environments. However, G7 was a low-yielding genotype in all environments. Finally, G6 was an ideal genotype with a higher mean yield and relatively good stability. However, G7 was a poor-yielding and unstable genotype. The genotype, environment, and genotype x environment interaction had extremely important effects on the biomass yield of oats. The findings of the graphic stability methods (additive main effect and multiplicative interaction and the genotype and genotype-environment interaction) for identifying high-yielding and stable oat genotypes were very similar.

AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments

Cassava (Manihot esculenta Crantz) is a staple food and generates income for smallholder farmers in southern Ethiopia. The performance of cassava genotypes varies in different growing environments; thus, the evaluation of genotypes tested in various environments plays an essential role in developing strategies to delineate environments, explore unstable genotypes in target environments, and identify stable genotypes for multiple environments. In this regard, there needs to be more information on the identification of mega-environments and stable genotypes with high yields for wide adaptation. Thus, this study aimed to identify mega-environment and high-yielding cassava genotypes for multiple environments using AMMI and GGE biplots. A total of 25 genotypes were evaluated in six environments using a RCBD during the 2020–2021 cropping season. The AMMI analysis of variances revealed that environments, genotypes, and genotype-environment interaction had a significant ( P ≤ 0.001 ) influence on cassava fresh storage root yield (t·ha−1), showing genetic variability among genotypes by changing environments. The genotype-by-environment interaction showed a 61.36% contribution to the total treatment SS variation, while the environment and genotype effects explained 28.16% and 10.48% of the total treatment SS, respectively. IPCA1 and IPCA2 accounted for 33.42% and 23.5% of the GE interactions SS, respectively. The GGE biplot showed that the six environments used in this study were delineated into three mega-environments, namely, the first (Tarcha and Disa), the second (Wara and Areka), and the third (Jimma and Bonbe). Those mega-environments could be helpful for genotype evaluation and effective breeding. The GGE biplot indicated that the vertex genotypes were G16, G17, and G25. They are regarded as specifically adapted genotypes since they are more responsive to environmental change. The GGE biplot also revealed that Tarcha was ideal, having the most discriminating and representative environment, while G10 was the ideal and the overall winning genotype for the current study. Moreover, the genotypes G10 and G14 were identified as being the most stable, with a higher fresh storage root yield than the grand mean. Thus, G10 and G14 were selected as superior genotypes that could be promoted to advanced yield trials to develop stable cultivars with better storage root yield of cassava.

On-farm multi-location evaluation of genotype by environment interactions for seed yield and cooking time in common bean

Common bean variety choice by farmers in Uganda is driven by seed yield plus end-use quality traits like market class and cooking time. Limited genotype by environment information is available for traits valued by consumers. This research evaluated yield, seed size, hydration properties, and cooking time of 15 common bean genotypes within market classes recognized by consumers along with three farmers’ checks at nine on-farm locations in Uganda for two seasons. Yield ranged from 71 to 3,216 kg ha−1 and was largely controlled by location (21.5% of Total Sums of Squares [TSS]), plus the interaction between location and season (48.6% of TSS). Cooking time varied from 19 to 271 minutes with the genotypes Cebo Cela and Ervilha consistently cooking fastest in 24 and 27 minutes respectively. Comparatively, the local checks (NABE-4, NABE-15, and Masindi yellow) took 35 to 45 minutes to cook. Cooking time was largely controlled by genotype (40.6% of TSS). A GGE biplot analysis uncovered the presence of two mega-environments for yield and one mega-environment for cooking time. Identification of mega-environments for these traits will help expedite common bean breeding, evaluation, and variety selection through reduction of number of test environments needed for phenotype evaluations. The high yielding and fast cooking genotypes from this study can be targeted as parental materials to improve existing common bean germplasm for these important traits.

GGE Biplot Analysis of Genotype by Environment Interaction and Grain Yield Stability of Bread Wheat Genotypes in Central Ethiopia

GGE biplot is an effective method based on principal component analysis to fully explore mega-environments trials data. The study conducted was to identify the best performing, high yielding stable advanced bread wheat genotype for selection environments, the identification of mega-environments and analysis of the ideal genotype and environment by GGE biplot method. Fifteen bread wheat genotypes were evaluated using RCBD with four replications at six locations in Ethiopia. The results of combined analysis of variance for grain yield of fifteen bread wheat genotypes indicated that genotype, environment and GEI were highly significant (P0.001). The factors explained showed bread wheat genotypes grain yield was affected by environment (35.28%), genotype (33.46%) and GEI (31.45%). The first two PC axes of GGE explained 88.7% of G+GEI and divided the six locations into three major groups: Group1 included Asasa, Kulumsa and Arsi Robe (moderately discriminating locations); Group2 had the highland wheat producing locations Holeta and Bekoji (most discriminating locations), while Group3 contain Dhera (least discriminating location), a moisture stress location in the rift valley. Locations within the same group were closely correlated and provided redundant information about the genotypes. Testing can be performed in any one of the locations within a group. Genotype ETBW8078 and ETBW8459 were more stable as well as low yielding. Considering simultaneously yield and stability, genotype ETBW9045 and Hiddase showed the best performances suggesting their adaptation to a wide range of environments. Lemu, ETBW8084 and ETBW8065 were considered as desirable. Genotype ETBW8075 was the least stable with low yield and had a large contribution to the GEI, having the longest distance from the average environment. ETBW9470 was specifically adapted to Group1 locations while ETBW8070 was adapted to Group2 environments. Based on yield performance advanced lines ETBW9470 and ETBW8070 are recommended to be included in variety verification trials for further release.

GGE-Biplot Analysis of Multi-Environment Yield Trials of Common Bean (Phaseolus vulgaris L.) in the southern Ethiopia

The present study was conducted on thirty-six common beans (Phaseolus vulgaris L.) Genotypes across six contrasting environments defined for its different soil fertility status and located at the southern Ethiopia. The genotypes were arranged in 6 x 6 triple lattice design and executed for two successive main cropping seasons with the objectives to evaluate yield performance of common bean genotypes and identification of mega environments. GGE (i.e., G = genotype and GE = genotype by environment, interaction) bi-plot methodology was used for graphical presentation of yield data after subjecting the genotypic means of each environment to GGE Bi-plot software. The first two principal components (AXIS 1 and AXIS2) were used to display a two-dimensional GGE bi-plot. Thus, genotypic AXIS1 scores >0 classified the high yielding genotypes while AXIS2 scores <0 identified low yielding genotypes. Unlike genotypic AXIS1, genotypic AXIS2, scores near zero showed stable genotypes whereas large AXIS2 scores classified the unstable ones. The environmental AXIS1 were related to crossover nature of GEI while AXIS2 scores were associated with non-cross over GEI. The six test environments in the southern region were divided in to two distinct mega environments (Mega-1 and 2). Mega-1 constituted GOHF13, ARMF12 and ARLF13 while genotype 14 (SCR10) being the best winner, on the other hand, Mega-2 contained GOHF12 and while common bean genotype 20(SCR17) being the best winner. The results of this study indicated that breeding for specific adaptation should be taken as a breeding strategy in southern region to exploit positive GEI to increase production and productivity of common bean.

Identification of mega-environments and rice genotypes for general and specific adaptation to saline and alkaline stresses in India

In the present study, a total of 53 promising salt-tolerant genotypes were tested across 18 salt-affected diverse locations for three years. An attempt was made to identify ideal test locations and mega-environments using GGE biplot analysis. The CSSRI sodic environment was the most discriminating location in individual years as well as over the years and could be used to screen out unstable and salt-sensitive genotypes. Genotypes CSR36, CSR-2K-219, and CSR-2K-262 were found ideal across years. Overall, Genotypes CSR-2K-219, CSR-2K-262, and CSR-2K-242 were found superior and stable among all genotypes with higher mean yields. Different sets of genotypes emerged as winners in saline soils but not in sodic soils; however, Genotype CSR-2K-262 was the only genotype that was best under both saline and alkaline environments over the years. The lack of repeatable associations among locations and repeatable mega-environment groupings indicated the complexity of soil salinity. Hence, a multi-location and multi-year evaluation is indispensable for evaluating the test sites as well as identifying genotypes with consistently specific and wider adaptation to particular agro-climatic zones. The genotypes identified in the present study could be used for commercial cultivation across edaphically challenged areas for sustainable production.

Maize hybrid stability in environments under water restriction using mixed models and factor analysis.

In several crops, the water deficit is perhaps the main limiting factor in the search for high yields. The objective of this study was to evaluate the phenotypic stability of maize hybrids in environments with and without water restriction using the analytical factor (AF) approach. We evaluated 171 maize hybrids in 14 environments, divided into environments with (A1, A2, A3, A4, A5, A6, and A7) and without (A8, A9, A10, A11, A12, A13, and A14) water restriction, over a period of 7 years. Each year, 36 hybrids were evaluated. A square lattice design (6 x 6) was used, with common treatments between years. The characteristics of grain yield (GY), male flowering (MF) and female flowering (FF), plant height (PH), and ear height (EH) were evaluated. Phenotypic adaptability and stability of the hybrids were also verified. Hybrids G66, G99, G86, and G26 were the most stable and showed potential for use in environments with and without water restriction. The AF models showed to be useful for evaluating hybrids over many years, allowing selection of better hybrids with adaptability, specific and general stability, and correlation of hybrids with their production components, in addition to allowing identification of mega-environments that permit stability in the response of the adapted hybrids.

Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data

The genotype x environment interaction is frequently observed in many crops and studies on environmental stratification and genotype adaptability have been proposed to understand it. The aim of this study was to carry out factor analysis in data from multi-environment experiments by the mixed model approach (REML/BLUP). Instead of adjusted phenotypic means, a matrix containing the genotypic effects added to the effects of the genotype x environment interaction (G+GE) was used, predicted via REML/BLUP in joint analysis (designated as R-FGGE). In the study, data from 36 common bean lines evaluated in 15 environments were used. By this proposal, 46.7% of the environments were gathered in two groups, one with four and the other with three environments. The R-FGGE has the same characteristics as the previous proposals, that is, ease of identification of mega-environments and genotypes with broad adaptability, along with the advantages associated with the mixed model methodology.

PARAMETRIC MODEL BASED ASSESSMENT OF GENOTYPE X ENVIRONMENT INTERACTIONS FOR GRAIN YIELD IN DURUM WHEAT UNDER IRRIGATION

The objectives of this study were to assess the genotype (G) by environment (E) interaction of grain yield of durum wheat(Triticum durum Desf.) based on parametric models, additive main effects and multiplicative interaction (AMMI) and joint linear regression models; and compare the relative efficiency of the two models in explaining the GE effects. Twenty-three genotypes were evaluated across 12 environments (location-year combinations) in 2003 and 2004. Combined analysis of variance showed that the environment (E) accounted for a high percentage of sums of squares (remaining after removing the sums of squares due to error and replications). The genotypic variability of grain yield among genotypes was small. The best genotype 6 (DBSP02/8) out yielded the check by 0.24 ton/ha. Based on full AMMI model analysis, AMMI-1 was found to explain up to 94% of the main and interaction effects, and AMMI-2 was found to fully capture target percentage sums of squares in the GE interaction pattern. The biplot based on the first bilinear AMMI model terms indicated that genotype 7 (DBSP02/9) and genotype 19 (DBSP03/16) could be suited for cultivation across the test environments. However, no genotype had superior performance in all environments. Reitrivier normal planting date in 2003 (E403) was the most favorable environment for yield, whereas, Upington in 2003 (E603) was the least favorable one. Model comparison criteria showed that AMMI model was superior to joint regression model in terms of its predictive accuracy and efficiency of explaining the pattern of GE sum of squares. It was concluded that AMMI biplot clearly facilitate identification of mega-environments and cultivars for specific recommendations. The differential response of genotypes observed in this study reaffirms the necessity of multi location evaluations to identify superior and stable genotypes. However, trends in specific adaptation could be detected using the which-won-where pattern of the AMMI analysis, and site-specific breeding may be exploited when feasible.

Analysis of genotype × environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods

Mohammadi, R. and Amri, A. 2012. Analysis of genotype × environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods. Can. J. Plant Sci. 92: 757-770. Multi-environment trials (MET) are conducted annually throughout the world in order to use the information contained in MET data for genotype evaluation and mega-environment identification. In this study, grain yield data of 13 durum and one bread wheat genotypes grown in 16 diversified environments (differing in winter temperatures and water regimes) were used to analyze genotype by environment (GE) interactions in rain-fed durum MET data in Iran. The main objectives were (i) to investigate the possibility of dividing the test locations representative for rain-fed durum production in Iran into mega-environments using the genotype main effect plus GE interaction (GGE) biplot model and (ii) to compare the effectiveness of the GGE-biplot and several non-parametric stability measures (NPSM), which are not well-documented, ...

Analysis of genotype × environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods

Mohammadi, R. and Amri, A. 2012. Analysis of genotype × environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods. Can. J. Plant Sci. 92: 757–770. Multi-environment trials (MET) are conducted annually throughout the world in order to use the information contained in MET data for genotype evaluation and mega-environment identification. In this study, grain yield data of 13 durum and one bread wheat genotypes grown in 16 diversified environments (differing in winter temperatures and water regimes) were used to analyze genotype by environment (GE) interactions in rain-fed durum MET data in Iran. The main objectives were (i) to investigate the possibility of dividing the test locations representative for rain-fed durum production in Iran into mega-environments using the genotype main effect plus GE interaction (GGE) biplot model and (ii) to compare the effectiveness of the GGE-biplot and several non-parametric stability measures (NPSM), which are not well-documented, for evaluating the stability performance of genotypes tested and the possibility of recommending the best genotype(s) for commercial release in the rain-fed areas of Iran. The results indicate that the grain yield of different genotypes was significantly influenced by environmental effect. The greater GE interaction relative to genotype effect suggested significant environmental groups with different top-yielding genotypes. Warm environments differed from cold environments in the ranking of genotypes, while moderate environments were highly divergent and correlated with both cold and warm environments. Cold and warm environments were better than moderate environments in both discriminating and representativeness, suggesting the efficiency and accuracy of genotype selection would be greatly enhanced in such environments. According to the NPSM, genotypes tend to be classified into groups related to the static and dynamic concepts of stability. Both the GGE-biplot and NPSM methods were found to be useful, and generally gave similar results in identifying high-yielding and stable genotypes. In contrast to NPSM, the GGE-biplot analysis would serve as a better platform to analyze MET data, because it always explicitly indicates the average yield and stability of the genotypes and the discriminating ability and representativeness of the test environments.

Faba bean adaptation to autumn sowing under European climates

Faba bean (Vicia faba L.) is an important grain legume widely grown as a spring crop to avoid frost damage. However, there is interest in winter types for the expected benefits in grain yield as compared to spring ones. In the current experiments, we compared field performance in autumn sowings of 15 faba bean winter-type cultivars that were sown in two consecutive autumns in 12 climatically contrasting sites in Austria, France, Germany, Spain, and the UK. GGE biplot analyses (genotype plus genotype-by-environment interaction) were conducted to evaluate yield performance of faba bean genotypes and identification of mega-environments. Crossover genotype × environment was large and mainly due to the geoclimatic area. GGE biplot allowed identification of three mega-environments, namely continental, oceanic, and Mediterranean. Due to the climatic diversity of the environments, no cultivar performed well in all environments. Cultivars Clipper, Castel, Target, Wizard, and Gabl-107 performed well in oceanic mega-environment, whereas cultivars Castel, HIX, and Target performed well in continental mega-environment. None of the studied cultivars were suited to Mediterranean environments, and only Irena was able to give some modest yield at Cordoba. The average tester coordinate (defined by the average of first and second principal components of all environments) allowed to evaluate cultivars for their yielding ability and stability and to evaluate environments for their discriminating ability and to be more representative of the mega-environment. Thus, Wizard and Gabl-107 were the highest yielding cultivars being relatively stable over oceanic and continental environments. In contrast, the cultivars Irena and Divine yielded poorly at all environments. The results support the specific breeding for each major geoclimatic zone based on distinct genetic bases and selection environments.

Graphic Analysis of Genotype by Environment Interaction for Lentil Yield in Iran

Selection of lentil (Lens culinaris Medik) cultivars with wide adaptability across diverse farming environments is important, before recommending them to achieve a high rate of cultivar adoption. Seed yield of 11 lentil genotypes, tested in a randomized complete‐block design with four replicates across 20 environments in Iran, was analyzed using site regression (SREG) stability model. The biplot technique facilitates a visual evaluation of superior genotypes, which is useful for cultivar recommendation and megaenvironment identification. A substantial amount of genotype × environment (GE) interaction for lentil grain yield was detected. Location (L) and genotype × location (GL) variabilities were the predominant components of total yield variation. The first two principal components (PC1 and PC2) of the SREG model accounted for 76% of the total GE interaction. There were four winning genotypes and three megaenvironments according to the SREG model. The best genotype in one location was not always so in other test locations. According to the ideal‐genotype biplot, genotype G5 was better than all other genotypes; G5 exhibited both high mean yield and high stability of performance across environments. According to G + GE sources of variations, the genotypes (G4, G7, G9, and G10) were the most suitable varieties for the lentil‐producing regions in Iran.

Targeting Cultivars onto Rice Growing Environments Using AMMI and SREG GGE Biplot Analyses

The identification of the highest yielding cultivar for a specific environment on the basis of both genotype (G) and genotype × environment (GE) interaction would be useful to breeders and producers since yield estimates based only on G and environment (E) effects are insufficient. The objective of this study was to demonstrate the usefulness of additive main effects and multiplicative interactions (AMMI) model analysis and G plus GE interaction (GGE) biplots, obtained from sites regression (SREG) model analysis in interpreting GE grain yield data. Replicated grain yield data of six rice (Oryza sativa L.) cultivars (Cocodrie, Cypress, Jefferson, Lemont, Saber, and Wells) from three main cropping seasons (2000, 2001, and 2002) at four locations in Texas, USA (Bay City, Eagle Lake, Ganado, and Beaumont) were obtained and used for this purpose. Through AMMI model analysis, the magnitude and significance of the effects of GE interaction and its interaction principal components relative to the effects of G and E were estimated. The stability and adaptability of specific cultivars were assessed by plotting their nominal grain yields at specific environments in an AMMI biplot, which aided in the identification of megaenvironments (environments with the same highest yielding cultivar). Appropriate check cultivars for all locations or for specific locations were identified. Through GGE biplots of SREG model analysis results, the relative yield performance of cultivars at a specific environment were illustrated, the performance of a cultivar at different environments was compared, the performance of two cultivars at different environments were compared, the highest yielding cultivars at the different megaenvironments were identified, and ideal cultivars and test locations were identified.

Use of biplot analysis and factorial regression for the investigation of superior genotypes in multi-environment trials

For cultivar evaluation in recommendation trials, grain yield is the combined result of effects of genotype (G), environment (E) and genotype × environment interaction (GE). In this framework, GGE biplot and factorial regression analyses represent different approaches for GE investigation. The biplot facilitates a visual evaluation of ‘which wins where’ patterns, useful for cultivar recommendation and mega-environment identification. Factorial regression, alternatively, involves a description of cultivar reaction to the environment in terms of biophysical variables that directly affect crop yield. A combination of both methods can be used to assist in the detection and characterisation of superior genotypes. Initially, winning cultivars are detected using features of GGE biplot analysis. By applying a factorial regression model, these cultivars are subsequently characterised in relation to key environmental factors and physiological systems involved in yield determination. This strategy was illustrated in the analysis of Spanish winter and spring wheat recommendation trials for 2002. In both cases, GGE biplots identified three winning cultivars along with the corresponding subsets of trials where each winning cultivar showed yield superiority. Factorial regression then enhanced the understanding of circumstances under which winning cultivars performed better and identified phenotypic traits favouring a higher yield. For winter wheat, differences among environments in (i) pre-flowering thermal time, (ii) low temperatures prior to flowering and (iii) drought incidence during grain filling caused genotype-dependent responses in grain yield. In spring wheat, genotypes reacted differentially to changes in (i) pre-flowering thermal time, (ii) pre-flowering drought incidence and (iii) high temperatures during grain filling. The combination of relevant environmental variables allowed specific winning niches for each superior cultivar to be defined from an ecophysiological perspective. Such an outcome could be regularly employed in the future to delineate predictive, more rigorous recommendation strategies as well as to help define meaningful mega-environments for recommendation purposes in Mediterranean areas.

Cultivar Evaluation and Mega-Environment Investigation Based on the GGE Biplot

Cultivar evaluation and mega-environment identification are among the most important objectives of multi-environment trials (MET). Although the measured yield is a combined result of effects of genotype (G), environment (E), and genotype × environment interaction (GE), only G and GE are relevant to cultivar evaluation and mega-environment identification. This paper presents a GGE (i.e., G + GE) biplot, which is constructed by the first two symmetrically scaled principal components (PC1 and PC2) derived from singular value decomposition of environment-centered MET data. The GGE biplot graphically displays G plus GE of a MET in a way that facilitates visual cultivar evaluation and mega-environment identification. When applied to yield data of the 1989 through 1998 Ontario winter wheat (Triticum aestivum L.) performance trials, the GGE biplots clearly identified yearly winning genotypes and their winning niches. Collective analysis of the yearly biplots suggests two winter wheat mega-environments in Ontario: a minor mega-environment (eastern Ontario) and a major one (southern and western Ontario), the latter being traditionally divided into three subareas. There were frequent crossover GE interactions within the major mega-environment but the location groupings were variable across years. It therefore could not be further divided into meaningful subareas. It was revealed that in most years PC1 represents a proportional cultivar response across locations, which leads to noncrossover GE interactions, while PC2 represents a disproportional cultivar response across locations, which is responsible for any crossover GE interactions. Consequently, genotypes with large PC1 scores tend to give higher average yield, and locations with large PC1 scores and near-zero PC2 scores facilitates identification of such genotypes.