Genotype By Environment Research Articles

Genotype, environment, and genotype by environment interaction for seed isoflavone concentration in soybean grown in soybean cyst nematode infested and non-Infested environments

Soybean (Glycine max [L.] Merr.) is a major isoflavone producing legume. Isoflavones and their derivatives function as phytoalexins that may function in protecting plants against pathogens. They are also known as phytoestrogens in the human diet, which have putative positive human health effects such as reduced risk of breast and prostate cancers, cardiovascular disease, and high blood cholesterol levels. Therefore, increasing isoflavone concentration could be a desirable target for developing added value for food-grade soybean cultivars. Soybean cyst nematode (SCN; Heterodera glycines) is the most yield-limiting pathogen for soybean world-wide. The objectives of this study were to: 1) determine genotype (G) and genotype-by-environment (GE) interaction variation for seed isoflavone concentration and, 2) determine the effects that SCN resistance may have on seed isoflavone concentration in SCN infested and non-infested environments. A population of 109 recombinant inbred lines (RILs), derived from a cross between DH4202 and RCAT1004, along with 11 commercial check cultivars were grown in two SCN infested and two non-infested fields in southern Ontario, Canada, in 2015 and 2016. The genotypes were categorized into resistant, moderately resistant, and susceptible lines based on SCN female indices calculated from SCN infested environments. Significant G, environment (E), and GE interaction effects were observed for total isoflavone concentration as well as for yield. Higher isoflavone concentrations were observed in non-infested environments when compared with SCN infested environments for all three levels of resistance. Within SCN infested environments, resistant genotypes had significantly higher isoflavone concentrations than susceptible genotypes, indicating a potential role of isoflavones in plant defense from SCN in resistant genotypes. SCN infested environments were found to be more reliable for genotype evaluation of isoflavone concentration than non-infested environments due to similar relative performance of genotypes in all SCN infested environments. A strong relationship between the level of SCN resistance and relative yield among the genotypes was observed in SCN environments. When evaluating genotypes, soybean breeders should consider the negative impact SCN has on seed isoflavone concentration, the positive relationship between SCN resistance and isoflavone concentration in SCN environments, as well as the greater stability in isoflavone concentration across genotypes within SCN environments compared to the non-infested ones.

GGE biplot application for adaptability of African yam bean grain yield to four agro-ecologies in Nigeria

Genotypes respond to environments differently; therefore it is important that their response to growing environment should be investigated in order to identify most suitable genotype(s) for specific environment. The objective of this study was to understand the adaptability of some accessions of African yam bean to different agro-ecologies of Nigeria. Thirty African yam bean (AYB) ( Sphenostylis stenocarpa (Hochst ex. A. Rich) Harms) accessions were evaluated for grain yield adaptability to four agro-environments in Nigeria. Genotype by environment (GE) interaction was significant (P < 0.01) for grain yield, with a range of 239.29 kg ha -1 (TSs 89) to 4130.46 kg ha -1 (TSs 33). Two principal component axes generated in the biplots explained 70.9% of the total variation due to the accessions and their interaction with the environments. The superior genotypes for each location were TSs82 (Ibadan), TSs116 (Mokwa), TSs24 (Ikenne) and TSs33 (Ubiaja). Three mega-environments arose from the study; namely Ikenne, Ubiaja and Mokwa (Ibadan merged with Mokwa to form a single megaenvironment). Ikenne was a more representative environment for AYB production, having a mean grain yield of 1074.53 kg ha -1 , closest to the grand mean of 1075.57 kg ha -1 . However, Ubiaja was most supportive for grain production of AYB. Some of the accessions identified with high yielding, adaptable/stable in the study included TSs101, TSs111, TSs93, TSs94, TSs57, TSs104B and TSs109. Key Words: Accessions, mega-environment, principal components, Sphenostylis stenocarpa

Marker Association Analysis with Three Agronomic Traits in Hard Winter Wheat Lines under Diverse Environments

Genetic performance for a quantitative trait is often controlled by gene-by-gene and genotype-by-environment (GE) interaction effects. As an important component, a GE interaction effect is highly related to crop stability. As genetic association mapping has been widely used to determine markers associated with traits of interest, stability analysis based on genetic markers/genes of importance could help develop widely adapted or specifically adapted cultivars. With linear mixed model approach, in this study we analyzed a wheat data set from the USDA Hard Winter Wheat Regional Nursery Program in 2010. The data included 32 genetic markers and 48 genotypes with three important agronomic traits: grain yield, plant height, and heading date, which were measured under multi-environments. Our results showed that four important DNA markers: Rht2, PPO18NED, Lr34JagTM, and Waxy-A1-AFC-AR2FAM, were significantly associated with all these three traits. Among these, Rht2 contributed 50.34%, 78.65%, and 53.90% of the phenotypic variation for grain yield, plant height, and heading date, respectively. Compared to their main effects, however, genotype-by-environment interaction effects were less important under these diverse environments.

Comparing Bayesian and Frequentist Approaches for GGE Bi-plot Analysis in Multi-Environment Trials in Sorghum

The GGE stands for genotype main effect (G) added to genotype by environment (GE) interaction. GGE-biplot a graphical tool was applied to study adaptation of 18 sorghum genotypes to the six environments using data from a multi-environment trials (MET) conducted in randomized complete block designs at two locations during 2009-2012 in Sudan. Analysis of variance was used to test the significance of GE interactions, estimates of variance components and predicted means were obtained using restricted maximum likelihood REML method. A number of priors for the variance components of the model were considered for Bayesian analysis using R2WinBUGS software. The best set of priors was selected using the deviance information criterion (DIC). Thus, the predicted estimates of GE means using REML method for frequentist approach and posterior estimates for the Bayesian approach were used for the graphical presentation of the genotypes and the environments. In frequentist approach, the first two principal components accounted for 64% of variation in total GGE interactions where the individual two principal components accounted for PC1=43% and PC2=23% respectively. The Bayesian approach accounted for 89% variation in the total GGE interaction with PC1=58% and PC2=31% respectively. The Bayesian GGE biplot analysis explained much larger proportion of variation in GGE interaction in comparison with frequentist approach, and thus resulted in a more powerful inference on the adaptation of genotypes to the environments considered.

Genotype by Environment Interaction of Soybean (Glycine Max L. Merrill) For Yield and Yield Components in the Savanna

Genotype-by-environment interaction was exploited using ten genotypes in four locations covering Northern and Southern Guinea Savanna ecological zone in Nigeria. In each of the locations the experiment was laid out in a randomized complete block design (RCBD) with three replications for two years 2013 and 2014 during the rainy season.GGE (genotype main effect (G) and genotype by environment (GE) interaction) bi-plot graphical tool was used to analyze the multi-environment trials (MET) data. The partitioning of the GE interaction showed that PC1 and PC2 accounted for 59.2% and 26.2% of the GGE sum of squares respectively. This also explained a total of 85.4% of the GEI variation. Two mega-environments were characterized with genotypes TGX 1904-6F, TGX 1935-3F, TGX 1440-1E and TGX 1984-22F falling into them. TGX 1448-2E and TGX 1895-35F appeared to be overall best genotypes combining high stability and yield though they did not fall into mega-environment could be recommended for use in breeding programmes.

GGE biplot analysis of genotype by environment interaction of spring barley varieties

The article presents results of genotype (G) main effect and genotype by environment (GE) interaction (G × GE) biplot analysis of a multi environmental trial (MET) data in 17 varieties of spring barley conducted in 2013–2014. The objective of this study was to determine the effects of genotype, environment and their interaction on grain yield and to identify stable spring barley genotypes. The experimental layout was a randomized complete block design with three replications in each environment. Combined ANOVA indicated that the main effects due to environments, genotypes and genotype by environment interaction were highly significant. The contribution of environment, genotype and genotype by environment interaction to the total variation in grain yield was about 88.05, 3.61 and 7.54 %, respectively. The G × GE effects were further partitioned using a GGE biplot model. The first two principal components obtained by singular value decomposition of the centred data of yield accounted for 88.50% of the total variability caused by G × GE. Out of these variations, PC1 and PC2 accounted for 51.38% and 37.12% of variability, respectively. The first mega-environment contains environments E1, E3 and E4 with variety ‘Donetskyy 15’ producing the highest yield; the second mega-environment only contains environment E2 with variety ‘Vzirets’ being the most yielding. According to the biplot analysis of the “ideal” environment, it was concluded that E1 was the closest to the “ideal” environment and, therefore, the most desirable of all the test environments. GGE biplot view of this study identified the most productive and stable varieties – ‘Donetskyy 14’, ‘Kosar’ and ‘Alegro’. Variety ‘Stepovyk’ is noticeable for its maximum fidelity to the “ideal” genotype in terms of performance and stability.

Genotype-by-environment interaction on genetic relationships between lactation persistency and conception measures in Japanese Holstein cows

The objective of this study was to examine the genetic relationships between first-lactation persistency and four measures of conception accounting for heterogeneous genetic (co)variances over a wide range of milk production and fertility management environments. The combination of genotype and management environment conditions (genotype-by-environment (GE) interaction) was studied by using a reaction norm model. Data were obtained on 421,999 cows from 9296 herds for first parity and 257,815 cows from 8318 herds for second parity. Conception status during the first two parities from calving to 70 days in milk (DIM) and 180 DIM were evaluated, leading to a total of four conception measures. Under poor reproductive management, the genetic correlations between first parity lactation persistency and conception to 70DIM were 0.408 and 0.242 for the first and second parities, respectively, compared with 0.178 and 0.116 for the respective genetic correlations between lactation persistency and conception to 180DIM. These positive correlations indicate that increased persistency would help to improve conception to 70DIM or 180DIM. The genetic correlation between persistency and conception to 180 DIM during the first parity was −0.211 under a high level of reproductive management and moderately high milk production, suggesting that conception to 180DIM for low-persistency cows of first parity was increased by improving reproductive management under this situation. In contrast, the genetic correlation between persistency and conception to 180DIM in second parity was 0.351 under high management of milk production and reproduction. The genetic correlations between lactation persistency and conception to 70DIM or 180DIM for the first two parities ranged from negative to positive, depending on the combination of genotype and management environment conditions. Therefore, it is important to consider GE interaction when combining lactation persistency, conception measures, and milk yield to select for sustainable milk production systems.

Genotype x Environment Interaction and Yield Stability of Several Yield Components Among Adapted Rice Cultivars in West Sumatra

Genotype by environment (GE) interaction and genotype stability of a trait in rice (Oryza sativa L.) are very important for plant breeders in making decision regarding the development and evaluation of new cultivars as well as for farmers in selecting suitable cultivars to be planted for commercial purpose. Yield components including panicles number plant–1, number of grains panicle–1, 1000-grain weight, and grain yield of five locally adapted cultivars of rice were evaluated at three locations in West Sumatera. The data were used to determine GE interaction variance components and stability of the traits. There were significant effects of locations on yield and some yield components except number of panicles plant–1. The cultivars differed significantly in all yield components but not in grain yield. The influence of GE interaction was highly significant on all yield components and grain yield. The magnitude of GE interaction variance component was greater than that of location for all traits. These data suggested that genotypes performed differently among the locations and were not stable with respect to the locations, so that farmers should select a suitable cultivar to be grown in the area of production.

Using Additive Main Effect and Multiplicative Interaction Model for Exploration of Yield Stability in Some Lentil (Lens Culinaris Medik.) Genotypes

AbstractThe additive main effect and multiplicative interaction (AMMI) analysis has been indicated to be effective in interpreting complex genotype by environment (GE) interactions of lentil (Lens culinarisMedik.) multienvironmental trials. Eighteen improved lentil genotypes were grown in 12 semiarid environments in Iran from 2007 to 2009. Complex GE interactions are difficult to understand with ordinary analysis of variance (ANOVA) or conventional stability methods. Combined analysis of variance indicated the genotype by location interaction (GL) and three way interactions (GYL) were highly significant. FGH1and FGH2tests indicated the five significant components; FRatioshowed three significant components and F-Gollob detected seven significant components. The RMSPD (root mean square predicted difference) values of validation procedure indicated seven significant components. Using five components in AMMI stability parameters (EVFI, SIP-CFI, AMGEFI and DFI) indicated that genotypes G5 and G6 were the most stable genotypes while considering three components in of AMMI stability parameters (EVFII, SIPCFII, AMGEFII and DFII) showed that genotypes G8 and G18 were the most stable genotypes. Also genotypes G2, G5 and G18 were the most stable genotypes according to AMMI stability parameters which calculated from seven components (EVFIII, SIP-CFIII, AMGEFIII and DFIII). Among these stable genotypes, only genotypes G2 (1365.63 kg × ha-1), G11 (1374.13 kg × ha′1) and G12 (1334.73 kg × ha-1) had high mean yield and so could be regarded as the most favorable genotype. These genotypes are therefore recommended for release as commercial cultivars.

Genotype × environment interaction for grain yield of some lentil genotypes and relationship among univariate stability statistics

Lentil (Lens culinaris Medik.) is traditionally grown as a rain fed crop, particularly in the Middle East; its seed is a rich source of protein for human consumption in developing countries such as Iran and others. The stability of 11 different lentil genotypes was investigated using 19 univariate stability parameters. Field experiments were conducted in 20 rain-fed environments in Iran’s lentil producing areas to characterize genotype by environment (GE) interactions on seed yield of 11 lentil genotypes. Combined analysis of variance across environments indicated that both environment and GE interactions significantly influenced genotype yield. Several statistical methods and techniques were used to describe the GE interaction and to define stable genotypes in relation to their yield. The results of these different stability methods were variable. However, most showed genotype FLIP 92-12L was stable and genotype Gachsaran was unstable. Genotypes identified as superior differed significantly from local cultivars and can be recommended for use by farmers in semi-arid areas of Iran. Principal component analysis was used to obtain an understanding of relationships among stability techniques. It showed the parameters studied could be grouped in five distinct classes. Clustering of the genotypes indicated that there were two genotypic groups in this group of genotypes.

Assessing the agronomic potential of linseed genotypes by multivariate analyses and association mapping of agronomic traits

High prices of fish oil make linseed attractive for aquaculture and animal feed. To ensure a constant supply of linseed, the development of stable cultivars is of strategic importance. In this study, 35 linseed genotypes were evaluated in five Chilean environments (E) from 2009 to 2012. The additive main effect and multiplicative interaction analysis (AMMI), genotype (G) plus genotype by environment (GE) interaction (GGE) biplot analysis and three stability parameters were tested with the aim of identifying adapted genotypes for the development of linseed cultivars. An association mapping (AM) analysis was also conducted for four agronomic traits and the stability of the associated markers was evaluated using the QQE (QTL main effect and QTL by environment interaction) approach. Combined analysis of variance for yield, seeds per boll (SPB), plant height (PH) and days to flowering (DTF) were significant for G, E and GE (P < 0.001). The combined stability analysis identified some Canadian, Argentinean and Chilean accessions to be the best adapted and highest yielding genotypes. Coancestry analysis indicated that crossing Canadian and Chilean genotypes could maximize transgressive segregation for yield. Significant associations for DTF, PH and SPB explained up to 59 % of the phenotypic variation for these traits. The QQE and AM analyses were consistent in identifying marker LGM27B as the most stable and significant across all environments with the largest effect in reducing DTF. The combined application of the stability, AM and QQE analyses could accelerate the development of marketable linseed cultivars adapted to Southern Chile.

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 Multi-Environment Trials of Rainfed Barley in Warm Regions of Iran

Multi-environment trials (MET) are commonly conducted in plant breeding programs to identify superior genotypes and to determine mega-environments in a targeted region. The main objectives of the study were to graphically analyze MET data from rainfed barley (Hordeum vulgare L.) and interpret genotype-by-environment (GE) interaction using GGE biplot methodology and Kang's two parameters, i.e., yield-stability statistic (YSi) and rank-sum (RS). The analyses were performed on grain yield data of 18 rainfed barley genotypes, derived from an Iran/ICARDA joint project, grown at three representative rainfed barley locations in warm areas of Iran during three cropping seasons (2007–2009). Combined ANOVA indicated that the environment accounted for 87.8% of total variation, followed by GE interaction. The large magnitude of the GE interaction relative to genotypic effect suggested the possible existence of sub-environmental groups for the genotypes. Collective analyses of yearly and combined GGE biplots were used to identify high-yielding genotypes and their areas of adaptation, and suggested the existence of two rainfed barley mega-environments. The “discriminating power vs. the representative view” of the GGE biplot identified Moghan as the location with high discriminating ability and greater representativeness, suggesting the possibility of testing genotypes adapted to warm rainfed areas at this location. The YSi and RS statistics as well as GGE biplot gave similar results for simultaneously selecting for high yield and stability. Based on the simultaneous selection index, the genotypes G9 (ChiCm/An57//Albert/3/Alger/Ceres362-1-1) and G13 (Onslow/Arda2) could be recommended for commercial release in warm rainfed areas of Iran. The study also indicated that YSi and RS were as efficient as the GGE biplot method in selecting high-yielding and stable genotypes under variable environments.

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.

EVALUATION OF EXPERIMENTAL MAIZE HYBRIDS TESTED IN MULTI- LOCATION TRIALS USING AMMI AND GGE BIPLOT ANALYSES

Multi-environment yield trials are essential in estimation of genotype by environment (GE) interaction and identification of superior genotypes in the final selection cycles. The objective of this study was to evaluate stability and adaptability of grain yield of experimental maize hybrids by GGE (Genotype and Genotype by Environment Interaction) biplot and AMMI (Additive Main Effects and Multiplicative Interaction) analyses. This study comprised 19 experimental NS (Novi Sad) maize hybrids tested across twelve environments in northern Serbia in a randomized complete block design. The ANOVA (Analysis Of Variance) indicated significant effects of genotypes (G), environments (E) and their interaction (GE). On the same time, the highest percentage of variation was explained by E (77.83%) while G and GE effects together explained the rest of variation (<30%). Joint effects of genotype and interaction (G+GE) was partitioned using GGE biplot analysis where the first two components were significant, explaining 62.40% (44.34% PC1 and 18.06% PC2) of the GGE sum of squares. The two principal components in AMMI analysis were significant, explaining 53.99% (30.87% PC1 and 23.12% PC2) of interaction variation. Stability of analysed genotypes was similar in both methods. It is concluded that there is no large difference between the AMMI and GGE biplot analyses in evaluation of experimental maize hybrids in different climatic conditions and that both methods can be used equally successfully.

Grouping genotypes and test environments by some cluster methods regarding genotype × environment interaction in multi-environment trials

Lentil (Lens culinaris Medik.) is an important source of protein and carbohydrate food for people of developing countries and is popular in some developed countries where they are perceived as a healthy component of the diet. Ten lentil genotypes were tested for grain yield in five different environmental conditions, over two consecutive years to classify thes genotypes for yield stability. Seed yield of lentil genotypes ranged from 989.3 to 1.367 kg ha-1 and the linear regression coefficient ranged from 0.75 to 1.18. The combined analysis of variance showed that the effect of environment (E) and genotype by environment (GE) interaction were highly significant while the main effect of genotype (G) was significant at 0.05 probability level. Four different cluster procedures were used for grouping genotypes and environments. According to dendograms of regression methods for lentil genotypes there were two different genotypic groups based on G plus GE or GE sources. Also, the dendograms of ANOVA methods indicated 5 groups based on G and GE sources and 4 groups based on GE sources. According to dendograms of regression methods for environments there were 5 different groups based on G plus GE sources while the dendograms of ANOVA methods indicated 9 groups based on G and GE sources and 3 groups based on GE sources. The mentioned groups were determined via F-test as an empirical stopping criterion for clustering. The most responsive genotypes with high mean yield genotypes are G2 (1145.3 kg ha-1), G8 (1200.2 kg ha-1) and G9 (1267.9 kg ha-1) and could be recommended as the most favorable genotypes for farmers.

Evaluation of durum wheat experimental lines under different climate and water regime conditions of Iran

In the Mediterranean region, grain yield of durum wheat is frequently limited by both high temperature and drought during grain filling. A total of six sets of paired trials, including 18 durum experimental lines and two durum and bread wheat landraces, were conducted in the field under three moderate (no stress), warm (stress) and cold (stress) climate conditions and two different water regimes [i.e. rain-fed (terminal stress) and two supplemental irrigations (no stress)] conditions for two cropping seasons (2005–06 and 2006–07) in Iran. Several stress indices (i.e. drought, heat, and cold) obtained from grain yield data under different climate and water regime conditions were used to analyse relationships between genotypic grain yield data of 20 genotypes and their tolerance to different stresses. The additive main effect and multiplicative interaction (AMMI) analysis was also used to capture a large portion of the genotype by environment (GE) interaction sum of squares and to separate main and interaction effects. The combined ANOVA revealed significant differences between locations, water regimes (rain-fed v. irrigation) and their effects in discriminating among the genotypes. The genotypic yields in both rain-fed and irrigated conditions were positively associated (P &lt; 0.01) at the moderate and warm locations; but were adversely correlated (P &lt; 0.05) at cold locations, suggesting that a high-yield potential under two supplemental irrigations does not necessarily result in improved yield under rain-fed conditions. The results verified that grain yield increased in response to irrigation, but some genotypes were more sensitive to environmental differences depending on year and location. Relative reduction of grain yield due to drought was a promising trait to improve drought tolerance indirectly in warm and moderate locations. Estimates of broad-sense heritability also varied with climate conditions. Based on principal component analysis (PCA), the different stress indices tended to discriminate genotypes in dissimilar fashions. The results also indicate that it was possible to identify superior genotypes based on single and multiple abiotic stresses (drought, heat and cold). The genotypes G4 and G5 had general tolerance to the three abiotic stresses while the G19 and G15 were tolerant to drought and cold stresses and G18 and G17 were tolerant to heat stress. According to AMMI biplot analysis, the genotypes G4 and G9 were the best in terms of both mean yield and minimal GE interaction, indicating that selecting for improved yield may increase yield in a wide range of environments.

Pattern in interaction in the maize yield trial

In making decisions on the development of varieties and the identification of certain growing regions, the effects of genotype (G) and genotype by environment (GxE) are very important. Although the main genotype effect and the interaction effect should be partitioned, there is still a need for their integration in the yield, as both simultaneously affect a ranking of the particular genotype within a certain environment. The AMMI (Additive Main Effect and Multiplicative Interaction) analysis, as a hybrid model, provides appropriate partitioning. The AMMI2 model, capturing 98.4% of the treatment sum of squares, was used, and 12 observed environments were classified into three target environments. Hence, out of 15 commercial maize hybrids, used in the trial, the hybrids ZP-677, ZP-570 and ZP-732 were selected as carriers of maximum yields in three target environments. In this way, it was shown that even in a relatively small region a narrow adaptation of genotypes can be exploited.

Analysis of Genotype-by-Environment Interaction for Agronomic Traits of Durum Wheat in Iran

Grain yield of durum wheat (Triticum turgidum L. var. durum) under Mediterranean conditions is frequently limited by both high temperature and drought during grain filling. Genotype-by-environment (GE) interaction and genotype-by-trait (GT) data were analyzed for agronomic performance of durum wheat breeding lines. Data were obtained from 18 durum wheat breeding lines and two cultivar checks (Zardak and Sardari) for their agronomic performance under three different climate locations (moderate, warm and cold winters) and two moisture regimes (rainfed and two supplemental irrigations conditions) in two cropping seasons (2006 and 2007) in Iran. Analysis of GE interaction data based on multiple traits showed that the environment (combination of year-location- moisture regimes) effect was always the most important source of trait variation, accounting for 58.6 to 98.4% of the total variation. Biplot analysis of the studied traits revealed that (i) the locations tended to discriminate genotypes in dissimilar fashions, and (ii) the relationships among traits were not consistent over the locations, where they facilitated visual genotype comparisons and selection at each location.

Analysis of genotype-by-environment interaction for grain yield of rainfed durum wheat genotypes in warm winter areas of Iran

This study was performed for pattern analysis of genotype-by-environment (GE) interaction on 20 durum wheat genotypes grown in 15 testing environments during 2004–06 in Iran. Combined analysis of variance showed significant genotypes (G), environments (E), and GE interactions (P < 0.01), with environmental main effects being the predominant source of variation, followed by GE interaction. The results showed various patterns of genotype responses to different environment groups and assisted in structuring the durum wheat testing locations with identification of two major-environment groups with high genotype discrimination ability. The locations (Gachsaran and Ilam) corresponding to warm and semi-arid aresa were similar in genotype discrimination and showed no association with the other testing locations (Gonbad, Moghan, and Khoramabad) representing the Mediterranean area, indicating they differ in rankings of genotypes. The top-yielding genotypes, G13, G14 and G9, were highly adapted to warm and semi-arid environments, but those corresponding to the Mediterranean area had a high ability to discriminate the genotypes G16, G11, and Saimareh. The stability and adaptability of specific genotypes were assessed by plotting their nominal grain yields at specific environments in an ordination biplot, which aided in the identification of environment groups. Appropriate check genotypes for all environments or for specific environments were also identified. Pattern analysis allowed a sensible and useful summarization of GE interaction data set and helped to facilitate selecting superior genotypes for target-growing sites.