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Cultivar Evaluation and Mega-Environment Investigation Based on the GGE Biplot
Crop Science
Abstract
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 x 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.
... Genotype plus genotype-by-environment (GGE) biplot was used to visually analyze the genotypes in different environments (Yan et al., 2000;Yan et al., 2001). The first two principal components were used to construct a biplot using the model "GH" (column metric preserving) method of singular value partitioning (SVP), "testercentered G+GE" and "no scaling". ...
... On the contrary, the shorter vector projections from the arrowed AEC abscissa were recorded for G6, G10, G3, G23, G9, G15, G11, G12, G1, G20, G7, G5, and G22 while the remaining genotypes had intermediate projections from this abscissa. The lines parallel to the AEC ordinate help to rank the genotype's stability and genotypes with lower variability or higher stability have a smaller absolute length of the projection in either direction from the AEC abscissa and vice versa (Yan et al., 2000;Yan, 2001;Kaya et al., 2006;Farshadfar and Sadeghi, 2014). Accordingly, the projection from the AEC abscissa provides the stability performances of oat genotypes across environments. ...
... However, environments with large PC2 scores which are either positive or negative scores indicate the non-crossover (quantitative) type of the genotype by environment interaction. The result of this study showed that the environments such as E7 and E4 had higher PC2 scores with similar signs (negative) suggesting proportional yield differences across environments, which lead to a noncrossover type of interaction (Yan et al., 2000;Kaya et al., 2006). On the contrary, E6 and E4 had the highest PC1 scores with opposite signs. ...
The genotype by environment interaction (GEI) study was performed using the GGE biplot analysis to select high fodder yielding with consistently performing genotypes across environments in the central highlands of Ethiopia. Twenty-four oat genotypes were evaluated in nine environments during the 2020/21 cropping season using a randomized complete block design replicated three times at each environment. The pooled analysis of variance showed that fodder dry matter yield was significantly affected by genotype, environment, and GEI. The environment accounted for 67.45% of the total variance for fodder yield while the genotype and GEI explained 9.82 and 22.73%, respectively. Moreover, the first principal component (PC1) captured 35.49% of the total variation for GEI while 29.36% of the variation was captured by the second principal component (PC2). The result revealed that the higher fodder-yielding genotypes which located at the left side of the average-environment coordinate (AEC) ordinate an ideal environment which had higher discriminating ability and representativeness while E1 and E8 were favorable environments. Based on the performances of fodder yield and stability across environments, G6, G23, G10, and G9 were selected and recommended for verification in the central highlands of Ethiopia.
... Multiple environment trials have been used in the past to assess the response of genotypes across different locations (Yan et al. 2000). Thus, high yielding and adapted cereals varieties have been identified is the past (Arshadi et al. 2018). ...
... Using the rankings based on yield performance and the AMMI stability value, the yield stability index (YSI) was calculated as described by Farshadfar (2008) where YSI is the sum of Ranking based on grain yield and ranking from the ASV. Biplot graphs were drawn following the GGE biplot model defined by Yan et al. (2000). ...
... In plant breeding, defining the number of megaenvironments is complicated and requires the consistency of winning genotypes in each given mega-environment over several years (Yan et al. 2007). However, which won where analysis allows a breeder to identify the best genotypes for a given mega-environment (Yan et al. 2000). The six environments were divided into two mega-environments. ...
... If there is no recognizable pattern of G×E, then the target environment is a single mega environment with unpredictable G×E, and models addressing random sources of variation may be appropriate (Yan and Kang, 2003).This can be achieved by advent of biplot analysis, where biplot is a scatter plot that approximates and graphically displays a two-way table by both its row and column factors such that relationships among therow factors, relationships among the column factors, and the underlying interactions between the row and column factors can be visualized simultaneously (Yan and Kang, 2003;Hotti and Sadhukhan, 2020). More recently, the term "GGE biplot" was proposed and various biplot visualization methods developed to address specific questions relative to genotype byenvironment data (Yan et al., 2000). The main target of Genotype by Environment data evaluation by Biplot analysis is to out four major objectives: (i) Multi-years data to divide the target environment into meaningfulmega-environments so that some of the GE can be exploited;(ii) The data of Genetic and environmental covariates are required to address to identify the causes of GE;(iii) Identification of the best test environments(representative and discriminating) and(iv) Identifying the superior genotypes (both high and stableperformance within a megaenvironment). ...
... The parameric type includes univariate and multivariate analyses. Yan et al. (2000) took into consideration both G and G×E at a time which later came to be referred as biplots. The G×E generates a huge number of MET (Multi Environment variety Trial) data which can be reduced down for easier consideration by plotting them into biplots. ...
... Multiple genes control grain yield, and the substantial influence of the G×E interaction makes genotype evaluation hard (Elbasyoni 2018). Accordingly, plant breeders can identify genotypes suitable for specific environments while assessing their stability and adaptability across locations using GGE biplot and G×E interaction analysis through the AMMI model (Yan et al., 2000, Yan andTinker, 2006). The analysis of variance (ANOVA) for key yieldattributing traits of desi chickpea revealed significant effects of environment (E), genotype (G), and genotype-by-environment interactions (G×E) ( Table 2). ...
The present study evaluates the yield stability of eight chickpea genotypes across three locations in two seasons to identify high-yielding, stable genotypes suitable for Odisha. The analysis of variance (ANOVA) indicated significant effects of genotypes, environments, and their interactions (G×E) on the traits measured. Pooled ANOVA indicated highly significant differences for key agronomic traits such as days to 50% flowering, plant height, number of pods per plant, and 100-seed weight, while seed yield exhibited significant variation. The GGE biplot analysis was used to assess genotype stability and adaptability. Mean vs. stability analysis showed that ICCV 14102 had the highest mean grain yield, whereas ICCV 15114 was the most stable genotype. The ‘which-won-where’ biplot demonstrated three distinct mega-environments: the first, containing E6, was dominated by ICCV 15118 and JAKI 9218; the second, comprising E4 and E5, was led by ICCV 14108; and the third, covering E1, E2, and E3, identified ICCV 14102 as the best performer. When ranking genotypes in relation to an ideal genotype indicated ICCV 14108 as the most preferred, followed by ICCV 14102, ICCV 15115, and JAKI 9218. Similarly, environments were ranked based on their discriminating power and representativeness, with E4 emerging as the most ideal environment, followed by E5, E2, and others. The results highlight the effectiveness of GGE biplot analysis in genotype selection by providing insights into adaptability, stability, and performance across diverse environments. These findings provide valuable insights for the development of high-yielding and stable chickpea varieties in Odisha, with potential applications in other regions facing similar environmental challenges.
... Among the latest methodologies, the analysis utilizing the GGE (genotype main effects + genotype environment interaction) biplot model, as proposed by Yan et al. 6 , incorporates both the main effects of genotype and the interactions between genotypes and environments. The biplot method was developed by Gabriel 18 to graphically represent the results of the analysis of principal components 17 , bringing together information from different variables in a single plot and allowing a clearer visualization of these relationships. ...
... The second principal component (PC2) indicated phenotypic stability, being correlated to environments. The genotypes with the PC2 closest to zero are the most stable 6 . ...
... According to Yan (2000), both principal components must explain a total variation (G + GE) higher than 60% to indicate the correct application of the GGE biplot methodology. Figure 1 follows this approach, representing all variables, with values of PC1 + PC2 = 86.01% ...
The interaction of genotype by environment challenges material selection for specific regions. The GGE biplot methodology proves to be effective in detecting and addressing this complexity. This study evaluated the performance of eight maize hybrids in three locations in the North/Northwest of Rio de Janeiro during the winter 2021–2021 and summer 2021–2022 crop seasons. The results were subjected to analysis of variance and Tukey’s test, and the GGE biplot analysis, performed represented more than 80% of the total variation. Commercial hybrids (single and double) were more productive and more. adaptable. In contrast, UENF interpopulation hybrids presented lower productivity but higher stability, with lower-cost seeds and being more accessible, especially for producers in the North/Northwest regions of Rio de Janeiro, also becoming the most suitable. The results highlight the importance of stability and adaptability in the selection of cultivars for common corn. The use of UENF hybrids is recommended to optimize production in the region, with the GGE biplot method showing effectiveness in identifying the best genotypes, indicating that the productivity results are consistent with the proportion of heterosis explored, demonstrating that simple hybrids are the most productive, followed by double and interpopulational hybrids.
... METs usually produce a great deal of data with patterns too complex to explain through analysis of variance (ANOVA), which has limited ability to interpret and visualise how genotypic or environmental effects contribute to GEI as well as how GEI affects phenotypes of the genotypes for a trait (Oladosu et al. 2017;Khan et al. 2021). To overcome this, the GGE (genotype + genotype × environment interaction) biplot approach of visually presenting the analysis of MET datasets was proposed (Yan et al. 2000). ...
... The GGE biplot methodology relies on the effects caused only by genotype and GEI because they are directly pertinent to the evaluation of genotypes. GGE biplot analysis combines the genotype effect with the multiplicative effect of GEI at the same time (Yan et al. 2000) by means of principal component analysis (PCA) (Balestre et al. 2009). By appropriate singular value partitioning (SVP) to 'genotype' or 'environment', not only can the GGE biplot evaluate and effectively recognise promising genotypes across environments, it can also select optimal environments for field trials; likewise, it can be used to distinguish the particular genotypes for suitable megaenvironments (Yan and Tinker 2006). ...
... GGE biplot analysis was suggested by Yan et al. (2000) to enable visual evaluation of genotypes for specific traits and selection of ideotypes of outstanding trait performance for certain mega-environments based on MET data. The data are used in the analysis through singular value decomposition to obtain the first two PCs (PC1 and PC2). ...
Context
Well-organised leaf architecture produces compact canopies and allows for greater sunlight penetration, higher photosynthetic rates, and thus greater yields. Breeding for enhanced leaf architecture of sorghum (Sorghum bicolor L.), a key food source in semi-arid regions, benefits its overall production.
Aims
The study focuses on selecting useful genotypes with excellent leaf architecture for grain sorghum improvement.
Methods
In total, 185 sorghum genotypes were subjected to multi-environment trials. Leaf flagging-point length, leaf length, leaf width, leaf angle and leaf orientation value (LOV) were characterised under field conditions. Genotype + genotype × environment interaction (GGE) biplot analysis was used to identify the most stable genotypes with the highest LOV.
Key results
Statistical analysis showed significant effects of genotype × environment interaction (P < 0.001), and high broad-sense heritability for the traits. Correlation analysis demonstrated negative correlations (P < 0.001) between LOV and its components. Singular value decomposition of LOVs in the first two principal components explained 89.19% of the total variation. GGE biplot analysis identified G55 as the ideotype with the highest and most stable LOV.
Conclusions
Leaf architecture optimisation should be given greater attention. This study has identified a genotype with optimal and stable leaf architecture, laying the foundation for improvement in breeding to increase overall yields of sorghum.
Implications
Genotype G55 can be utilised as a parent with other parents that display economically important characteristics in breeding programs to produce offspring that can be planted densely to increase population yields. Genotypes identified with loose leaf architecture are useful in dissecting genes controlling leaf architecture by crossing with G55 to construct genetic mapping populations.
... GEI can be better understood by GGE biplot analysis, which in turn facilitates the identification of representative environments, detects the ability of test environments to discriminate, and identifies stable genotypes in METs (Yan and Tinker 2006). GGE biplots are based on environment-centred singularvalue decomposition and graphically depict both genotype and GEI based on the sources of variation associated with the genotype assessment (Yan et al. 2000). Such information provides a better understanding of the GEI being used in breeding programs of crops. ...
... The GGE model (Yan et al. 2000) was developed on the basis of biplots, which are popularly used for analysis of MET data and are an efficient tool for visualising two-way data. A GGE biplot can show genotype main effects and GEI effects from a two-way data table concurrently (Yan et al. 2000). ...
... The GGE model (Yan et al. 2000) was developed on the basis of biplots, which are popularly used for analysis of MET data and are an efficient tool for visualising two-way data. A GGE biplot can show genotype main effects and GEI effects from a two-way data table concurrently (Yan et al. 2000). The biplot was produced using the following parameters: standard deviation standardised (scaling = 0), environment centred (centring = 2), transformed (transform = 0), and singular-value partitioning = 2. ...
Context
Micronutrient enrichment of pearl millet (Pennisetum glaucum (L.) R.Br.), an important food source in arid and semi-arid Asia and Africa, can be achieved by using stable genotypes with high iron and zinc content in breeding programs.
Aims
We aimed to identify stable expression of high grain iron and zinc content in pearl millet lines across environments.
Methods
In total, 29 genotypes comprising 25 recombinant inbred lines (RILs), two parental lines and two checks were grown and examined from 2014 to 2016 in diverse environments. Best performing genotypes were identified through genotype + genotype × environment interaction (GGE) biplot and additive main-effects and multiplicative interaction (AMMI) model analysis.
Key results
Analysis of variance showed highly significant (P < 0.01) variations. The GGE biplot accounted for 87.26% (principal component 1, PC1) and 9.64% (PC2) of variation for iron, and 87.04% (PC1) and 6.35% (PC2) for zinc. On the basis of Gollob’s F validation test, three interaction PCs were significant for both traits. After 1000 validations, the real root-mean-square predictive difference was computed for model diagnosis. The GGE biplot indicated two winning RILs (G4, G11) across environments, whereas AMMI model analysis determined 10 RILs for iron (G12, G23, G24, G7, G15, G13, G25, G11, G4, G22) for seven for zinc (G14, G15, G4, G7, G11, G4, G26) as best performers. The most stable RILs across environments were G12 for iron and G14 for zinc.
Conclusions
High iron and zinc lines with consistent performance across environments were identified and can be used in the development of biofortified hybrids.
Implications
The findings suggest that AMMI and GGE, as powerful and straightforward techniques, may be useful in selecting better performing genotypes.
... Therefore, plant breeders have to conduct yield trials in diverse environments prior to finalize the superior genotype for the farmer's field (Abate et al., 2015). These multi-environment yield trials (METs) not only allow the breeders to identify superior genotype for a specific region but also assist in portioning the target region into different mega-environments (Yan et al., 2000). However, this is not an easy task as the complexities of GE interaction hinder the selection of the best performing and the most stable genotypes, thus, reducing the efficiency of the breeding program (Kaya et al., 2006). ...
... Numerous parametric and non-parametric statistical procedures such as regression coefficient (Finlay and Wilkinson, 1963), stability variance (Shukla, (Pinthus, 1973), coefficient of variability (Francis and Kanneberg, 1978), additive main effects and multiplicative interaction (AMMI) (Gauch and Zobel, 1988) and the genotype main effect (G), and GE interaction effect (GGE) model (Yan et al, 2000) have been proposed to estimate the genotypes and GE interactions for METs. Among these statistical approaches, the AMMI and GGE are frequently used in METs due to better diagnostics of G and GE variations, high accuracy, mega-environment delineation, and graphical presentation (Yan et al., 2007;Gauch, 2013). ...
... Among these statistical approaches, the AMMI and GGE are frequently used in METs due to better diagnostics of G and GE variations, high accuracy, mega-environment delineation, and graphical presentation (Yan et al., 2007;Gauch, 2013). Yan et al. (2000) proposed GGE-biplot method to graphically display the genotype and GE interaction of METs efficiently using the singular value decomposition (SVD) of environment-centered or within environment standardized data. The first principal component score of genotypes and that of environments are plotted against their respective scores of second principal component to construct which-won-where, mean performance versus stability of the genotypes, and the environmental evaluation biplots (Yan et al., 2007). ...
Plant breeders perform multi-environment yield trials to identify superior genotypes for a specific region and to partition the target region into different mega-environments. In this investigation, the GGE-biplot was used to evaluate 15 bread wheat advanced lines for yield performance across five locations of Sindh, Pakistan. The results of the combined analysis of variance revealed that the genotypes, locations, and their interaction significantly affected the grain yield. The polygon view of GGE-biplot indicated that G4, G6, G8, G9, G13, and G2 were the vertexed genotypes while three rays divided the five locations into two mega-environments. First mega-environment comprised of only one location E1 for which G6 and G4 were the winner genotypes. The second mega-environment consisted of four locations viz. E2, E3, E4, and E5 which contained G8 and G9 as the winner genotype. The ranking biplot designated G6 as an ideal genotype followed by G8 and G11. The least average yield across all the environments was observed in genotypes G13 and G2. Comparison biplot based on ideal genotypes ranked the other favorable genotypes as G4 > G11 > G8. The environment ranking biplot revealed that E2 was an ideal location since it had excellent power to discriminate all the genotypes based on of grain yield and was more appropriate to represent the overall environments. Among five test locations, the discrimination power of three locations E2, E4, and E5 was very similar in ranking the wheat genotypes as the environmental vectors of these locations overlapped another. Overall, the maximum average yield was recorded for G11 (5925.0 kg ha-1) followed by G6 (5852.5 kg ha-1) and G8 (5831.0 kg ha-1). Taken together, the wheat advanced lines G6, G8, and G11 showed good yield potential to become the candidate wheat lines for cultivation in Sindh province, Pakistan. Keywords: Wheat, genotype × environment interaction, GGE-biplot, Multi-environment yield trial (MET), Sindh Pakistan
... The Genstat 18 edition (VSN International Ltd., 2014) software was used for combined analysis of variance and AMMI analysis. GGE biplots summarize genotype and genotype-environment interaction effects on yield data using singular value decomposition (Yan, 2002;Yan et al., 2000). The study employed a GGE biplot to rank yield and stability genotypes. ...
... The GGE biplot graphically depicts the genotype main effect plus genotype-by-environment interaction G + (G × E), and simultaneously represents the mean performance and stability and facilitates the identification of suitable genotypes for specific mega-environments in multienvironment trial analysis (Yan et al., 2000;Yan WeiKai, 2011). The first two principal components extracted from the singular value decomposition of the environment-centered genotype data captured 62.14% of the total variation, of which the first principal component explained 42.21% while the second principal component explained 19.93%. ...
This study examines the yield stability and adaptability of large red bean genotypes across various bean-growing regions in Ethiopia. Sixteen genotypes were tested in fourteen different environments over four years (2015-2018) using a triple lattice design. Grain yield data were analyzed using several methods: Additive Main Effect and Multiplicative Interaction (AMMI), Genotype Main Effects plus Genotype × Environment Interaction (GGE), AMMI Stability Value (ASV), and Yield Stability Index (YSI). The results showed that grain yields were significantly influenced by the environment (77.03%), genotype (3.18%), and their interaction (10.26%) (P ≤ 0.01). GGE biplot analysis identified four mega-environments, with MS16 (MEISO in 2016) being the most discriminative and representative site. Genotypes G4, G14, G15, and G9 were the most stable and high-yielding according to GGE biplot, while G12, G13, and G8 were stable but low-yielding based on ASV analysis. YSI identified G15, G10, G13, G14, and G9 as high-yielding and stable. Overall, GGE biplot stability statistics and YSI highlighted G9 (DAB 544) and G14 (DAB 481) as superior genotypes, suitable for commercial cultivation in Ethiopia.
... The Genstat 18 edition (VSN International Ltd., 2014) software was used for combined analysis of variance and AMMI analysis. GGE biplots summarize genotype and genotype-environment interaction effects on yield data using singular value decomposition (Yan, 2002;Yan et al., 2000). The study employed a GGE biplot to rank yield and stability genotypes. ...
... The GGE biplot graphically depicts the genotype main effect plus genotype-by-environment interaction G + (G × E), and simultaneously represents the mean performance and stability and facilitates the identification of suitable genotypes for specific mega-environments in multienvironment trial analysis (Yan et al., 2000;Yan WeiKai, 2011). The first two principal components extracted from the singular value decomposition of the environment-centered genotype data captured 62.14% of the total variation, of which the first principal component explained 42.21% while the second principal component explained 19.93%. ...
... The Genstat 18 edition (VSN International Ltd., 2014) software was used for combined analysis of variance and AMMI analysis. GGE biplots summarize genotype and genotype-environment interaction effects on yield data using singular value decomposition (Yan, 2002;Yan et al., 2000). The study employed a GGE biplot to rank yield and stability genotypes. ...
... The GGE biplot graphically depicts the genotype main effect plus genotype-by-environment interaction G + (G × E), and simultaneously represents the mean performance and stability and facilitates the identification of suitable genotypes for specific mega-environments in multienvironment trial analysis (Yan et al., 2000;Yan WeiKai, 2011). The first two principal components extracted from the singular value decomposition of the environment-centered genotype data captured 62.14% of the total variation, of which the first principal component explained 42.21% while the second principal component explained 19.93%. ...
This study examines the yield stability and adaptability of large red bean genotypes across various bean-growing regions in Ethiopia. Sixteen genotypes were tested in fourteen different environments over four years (2015-2018) using a triple lattice design. Grain yield data were analyzed using several methods: Additive Main Effect and Multiplicative Interaction (AMMI), Genotype Main Effects plus Genotype × Environment Interaction (GGE), AMMI Stability Value (ASV), and Yield Stability Index (YSI). The results showed that grain yields were significantly influenced by the environment (77.03%), genotype (3.18%), and their interaction (10.26%) (P ≤ 0.01). GGE biplot analysis identified four mega-environments, with MS16 (MEISO in 2016) being the most discriminative and representative site. Genotypes G4, G14, G15, and G9 were the most stable and high-yielding according to GGE biplot, while G12, G13, and G8 were stable but low-yielding based on ASV analysis. YSI identified G15, G10, G13, G14, and G9 as high-yielding and stable. Overall, GGE biplot stability statistics and YSI highlighted G9 (DAB 544) and G14 (DAB 481) as superior genotypes, suitable for commercial cultivation in Ethiopia.
... where Yij Refers to the average productivity of the field pea genotype i in the environment j; yj is the general average of the genotypes in the environment j; y 1εi1ρj1 is the first principal component (PC1); y 2εi2ρj2 is the second principal component (PC2); y1 and y2 are the eigenvalues associated to IPCA1 and IPCA2, respectively; ε 1 and ε 2 are the values of PC1 and PC2, respectively, of the genotype i; ρ j1 and ρ j2 are the values of PC1 and PC2, respectively, for the environment j; and ε ij is the error associated with the model of the ith genotype and jth environment (Yan et al. 2000). ...
... In our study, GGE biplot analysis, which explained 48% of the total yield variation, identified six key vertices within the polygon biplot. Given that environmental factors accounted for 78% of the total yield variations, these biplot findings are critical for understanding the effects of both genotype (G) and GEI (Yan et al. 2000). The relatively higher contribution of GE compared to G suggests the likely presence of distinct mega-environments. ...
Understanding crop performance across diverse agro-ecologies is crucial for developing region-specific breeding strategies. This multi-location study examined the impact of diverse environments on crop eco-phenology and genotype-by-environment interactions (GEI) of tall-type field pea breeding lines. Empirical methods were employed to identify strategic locations that support higher yields and unique genotypic traits. The results revealed significant variations across locations, with coefficients of variation for key traits as follows: days to flowering (31%), days to maturity (20%), reproductive period (19%), yield (35%), and seed weight (31%). Environmental component accounted for the largest yield variation (78%), followed by GEI (13%). Correlation analysis indicated a significant influence of both temperature extremes, particularly maximum temperature during flowering, on crop yields. Higher minimum temperatures during flowering and reproductive period were associated with reduced yields, while extended crop duration in cooler regions also negatively impacted yields. A significant quadratic relationship between seed weight and yield underscored the importance of seed weight as a yield-stabilising trait. GGE-biplot analysis identified four mega-environments, and designated Faizabad, Pantnagar, Varanasi, and Kota as ideal testing sites for selecting genotypes with broader adaptability. These findings provide valuable insights for redesigning field pea breeding programmes at the national level.
... between variables or columns (Hongyu et al., 2014;Gauch, 2006). GGE Biplot analysis (Yan et al., 2000) displays both the genotype main effects (G) and the GEI effects from multi-environment trials (MET). Plant breeders have found GGE Biplots very useful in mega-environment analysis (Flores et al., 2013;Rubiales et al., 2014) and genotype evaluation (Araujo et al., 2022;Lal et al., 2022). ...
... Genotype as main effect and GEI were analyzed and visualized by GGE biplots for all investigated traits separately as described by Yan and Kang (2003). Results are presented in "which won where" biplots (Yan et al., 2007), an effective graphic tool in megaenvironment analysis and by the Average environment coordination (AEC) GGE view (Yan et al., 2000) that analyzes both performance and stability within each mega-environment. ...
Faba bean (Vicia faba L.) is an important pulse crop traditionally used for human nutrition and animal feeding. With a high protein content ranging from 24% to 35% of seed dry matter, considerable amounts of globulins, essential amino acids and minerals, faba bean is today an important source meeting the growing global demand for nutritious food. The objective of study was to investigate the variability of nine phenological, phenotypical and yield related traits in 220 faba bean accessions in multi-location trials across four representative European regions. Nine field trials were carried out from 2018 till 2020 in four representative European locations (Spain, Finland, Belgium and Serbia) using an augmented p-rep design containing 20 replicated checks. Significant differences among genotypes and environments were detected, being the genotype x environment interaction (GEI) the major source of variation in five of the nine evaluated traits. The “which-won-where” analyses identified two mega-environment namely South European mega environment (SE-ME) and North European mega environment (NE-ME), while the best performing and most stable genotypes according to the nine traits were identified using “means vs stability” analyses. According to the highest trait value in each mega environment several winning genotypes were identified showing better performances than some commercial varieties (controls) or checks. Our results suggest that the geographical locations falling into each mega-environment can be used as faba bean test locations. The genotype ranking for the multi-trait stability index (MTSI) revealed that the most stable and best ranking genotypes in SE-ME are G018, G086, G081, G170 and G015 while in the north mega-environment are G091, G171, G177 (Merkur), G029 and G027. Hierarchical cluster analysis and principal component analyses showed a clear correlation between the traits analysed and the botanical type. These findings indicate that botanical type is one of the most significant factors affecting development in any environment, and it must be taken into account in faba bean breeding activities. The information derived from this study provides a chance for breeding new resilient faba bean cultivars adapted to different agroecological European regions, a critical point for addressing Europe’s reliance on protein imports and enhancing sustainable agriculture practices.
... The cluster analysis realized to divide genotypes into groups based on similarity in yield by using Ward (Standardized by Column) method in JMP-Pro17 software. Graph analysis of the GGE biplot was realized by single-value decomposition according to the following formula reported by Yan et al. (2000). ...
... Yij: the mean of ith genotype in jth environment, μ mean of all genotypes βj; main effect of jth environment n; Singular value, λ1 and λ2; the special quantities for the first and second components i1 and i2; the special vectors of genotypes ηi1 and ηi2 ; Environment eigenvectors for n th interaction principal component, εij; the remaining quantity for the ith genotype in jth environment Yij − µ−βj = 1 ξi1ηj1 + 2ξi2ηj2 +εij (Yan, et al., 2000). ...
G×E interaction is critical for understanding how genetic and environmental factors affect plant performance, and this interaction is essential for developing more efficient and adaptive genotypes in plant breeding. In study, The GGE biplot analysis played a crucial role in determining effects on the yield performance of genotype × environment (G×E) interactions and comparing the stability and adaptability of genotypes in Diyarbakir and Kiziltepe. Additionally, cluster analysis was performed using the Ward method, which grouped the genotypes based on yield similarities and identified distinct groups adapted to different environmental conditions. The experiments were arranged by the factorial experimental design with four replications in each environment during the summer seasons of 2015 and 2016.Consequently, the significant differences were determined between genotypes and locations and their interactions. GGE biplot analysis found that the variations in the yield performance of genotypes were caused by 81.24% by the first principal component (PC1) and 18.76% by the second principal component (PC2). FLIP98-206C and FLIP98-143C genotypes were shown high yield potential and stability. In contrast, genotypes D1-3 and Azkan exhibited lower stability and yield performance. Therefore, the high-yielding and broadly adapted genotypes must be prioritized for experiments in regions Diyarbakir and Kiziltepe. However, narrower target regions must be identified for low-performing genotypes and large-scale experiments in these regions should be conducted to understand the long-term yield and stability performance of high-yielding genotypes.
... Where Y ij is the observed value of genotype i in environment j; μ is the constant associated with all the observations; β j is the main effect of environment j; y 1 and y 2 are the errors associated with the first (PC1) and second (PC2) principal component, respectively; εi1 and εi2 are the values of PC1 and PC2, respectively, for the genotype i; pj1 and pj2 are the values of PC1 and PC2, respectively, for the environment j; and ε ij is the error associated with the i-th genotype and with the j-th environment (Yan et al., 2000). ...
Sorghum (Sorghum bicolor L. Moench) has significant potential as a
raw material in the bioenergy sector. Consequently, sorghum breeding
programs have focused on developing cultivars with agronomic, chemical,
and industrial traits most suitable for biofuel production and adaptable to
diverse climate conditions. This study aimed to evaluate the adaptability
and stability of sweet sorghum genotypes intended for biofuel production
using Genotype and Genotype by Environment (GGE) biplots and select
the most adapted and stable. The experiments were conducted across six
environments located in Jaguariúna/SP, Nova Porteirinha/MG, Planaltina/DF,
Sete Lagoas/MG, Sobral/CE, and Vilhena/RO. Twenty-five genotypes were
assessed in a randomized complete block design with three replications,
with plots consisting of two 5-m rows. Tons of stalks per hectare (TSH) (t
ha-1), total soluble solids (TSS) (°Brix), and tons of Brix per hectare (TBH) (t
ha-1) were analyzed using analysis of variance, GGE biplots, and Scott-Knott
test. We found significant differences (p<0.01) for genotype, environment,
and genotype×environment interactions. The environments most effective
in discriminating the genotypes and their representativeness were Vilhena,
Planaltina, and Sete Lagoas for TSS; Vilhena and Sete Lagoas for TSH; and
Nova Porteirinha, Vilhena, and Sete Lagoas for TBH. Considering all traits, as
well as adaptability and stability, the genotypes with the best performance
were CMSXS5042, CMSXS5022, CMSXS5040, and CMSXS5041. Therefore,
GGE biplots successfully identified the environments and the most adapted,
stable, and promising sorghum genotypes for biofuel production.
... Instead of relying solely on the significance of GEI effects, we emphasized the application of multi-environment trial analytical techniques to better interpret genotype performance patterns. GGE biplot analysis proved valuable in identifying optimal test locations, defining mega-environments, and determining superior genotypes, all of which are crucial for guiding future breeding activities (Yan et al., 2000;Yan & Kang, 2003). Similar studies on rice have shown that environment, genotype, and GEI significantly influence yield, as demonstrated by Chandel et al. (2010), Suwarto and Nasrullah (2011), Akter and Hassan (2014), Rerkasem et al. (2015), Sharifi et al. (2017), Sadimantara et al. (2018), and Rahayu (2020). ...
The cultivation of high-yielding and stable rice varieties is essential for ensuring food security in Bangladesh. To achieve this, breeders often conduct multilocational trials to evaluate genotype performance across diverse environments. During the Aman rice season, six rice genotypes and three check varieties across nine locations were investigated in this study using a randomized complete block design. The primary aim was to identify superior genotypes using the Multi-Trait Stability Index (MTSI). Both mixed and fixed effect models were utilized in this study to achieve accurate and reliable results. The mean performance and stability (MPS) were effectively represented by the WAASBY (Weighted Average of Absolute Scores + Yield) biplot, which served as the superiority index in the analysis. The Likelihood Ratio Test (LRT) showed that genotype by environment interaction (GEI) and genotype had a substantial impact. Key findings showed that genotype-by-environment interaction (GEI) significantly influenced grain yield and related traits. While most traits positively correlated with yield, Thousand Grain Weight (TGW) did not. The WAASBY biplot effectively assessed performance and stability. Some genotypes, such as BRRI dhan33, BRRI dhan39, were stable but low yielding. Conversely, BRRI dhan49 and BR9786-BC2-119-1-1 were highly productive but less stable. Notably, BR9786-BC2-132-1-3 delivered the highest yield but exhibited moderate stability, making it promising yet sensitive to environmental changes. This study highlights BR9786-BC2-132-1-3 as a potential candidate for further evaluation due to its productivity, with ongoing research needed to improve its adaptability and resilience across diverse conditions.
... Through the biplot technique, the relationships between genotypes and traits can be examined with graphs obtained from mean values from different angles. The GT biplot plot shows the relationship between two traits, the relationship of one trait with other traits, or the relationship of genotypes with each other according to the traits using the angles between the trait vectors (Yan et al., 2000;Yan and Tinker, 2006;Baran et al., 2022). In this study, the performance of eight different sugar beet varieties in terms of the traits examined were presented with biplots. ...
Sugar beet is known globally as one of the most important sources of sucrose. Sugar beet, which provides raw materials to many industries, creating an important employment opportunity in the regions where it is cultivated. In this study, yield and quality parameters of eight different sugar beet varieties were determined by different analytical methods. The experiment was carried out in 2024 at the experimental field of the Faculty of Applied Sciences, Muş Alparslan University, utilizing a randomized block design with three replications. Following a seven-month vegetative period, yield and quality analysis of the harvested beets were carried out, allowing for the determination of relationships between variety and traits. Statistically significant and important differences were found among the sugar beet varieties in terms of the parameters analyzed. Notably, the Lamberta variety came to the forefront in terms of storage root yield parameters (root weight, root length, single plant weight). Consequently, this variety displayed the highest average root yield compared to other varieties. While the Agatella variety demonstrated high averages for dry matter content and polar sugar content, it exhibited lower storage root and sugar yields. These findings suggest a negative correlation between sugar content and storage root yield and sugar yield. Overall, the Lamberta variety stood out in terms of root yield, while the Annamira variety stood out in terms of sugar yield. As a result of the research, sugar beet varieties varied between root diameters of 9.11-15.41 cm, root lengths of 15.34-18.43 cm, root weights of 646-2892 g, dry matter content of 20.87-24.40%, polar sugar content of 16.68-19.41%, root yields of 5196-8229 kg/da, and sugar yields of 908-1348 kg/da. According to the “which-where-won” model of GGE biplot analysis, the studied traits were clustered under 3 mega environments.
... Principal component analysis was conducted to explore the interrelationships among all traits based on the overall data pattern. Biplot analysis was developed to provide a comprehensive visualization of the relationships among seed yield, drought indices, and genotype rankings, utilizing the cosine of the angle between the vectors 16 . All analyses were performed using software R version 4.3.1. ...
Chickpea, a protein-rich legume grown primarily in tropical and subtropical regions, faces significant challenges due to drought stress. A field study was conducted over two years (2020-21 and 2021-22) aimed to identify chickpea genotypes that are tolerant to drought. The study involved 25 chickpea genotypes subjected to irrigated (control) and water stress (drought) conditions and the experiment was arranged in a split-plot design. The study results indicated significant variation among the genotypes, water treatments, years, and their interactions. Multiple stress tolerance indices, association analysis, principal component analysis, biplot analysis, clustering, and ranking methods were employed to identify drought tolerant genotypes. The stress tolerance index (STI), mean relative performance (MRP), and relative efficiency index (REI) were identified as the most effective indicators for pinpointing genotypes with high yield potential under both experimental conditions. Genotypes viz., BDG75, BGD103, Digvijay, ICCV92944, ICC4958, and JG16 showed high drought tolerance, as evidenced by their favourable performance in terms of mean rank, standard deviation of ranks, and rank sum. Conversely, the genotypes ICCV96030, JG63, GNG1581, JG12, PG186, GG2, Pusa362, and SAKI9516 were found sensitive. Correlation analysis, ranking techniques, cluster analysis, PCA, and biplot analysis effectively distinguished between drought-tolerant and drought-sensitive genotypes. The biplot analysis further reconfirmed the notable drought tolerance of the BDG75, BGD103, Digvijay, ICCV92944, ICC4958, and JG16 genotypes. This study demonstrated that an index-based selection approach can be used to screen and identify chickpea genotypes that exhibit higher tolerance to water stress effectively and rapidly. Therefore, the findings underscore the potential of using selection indices as a viable strategy for enhancing drought tolerance in chickpeas.
... The BLUP values for Pal, LT, and SD were also entered into new double-entry spreadsheets, one for each trait, where the genotype was considered as independent and the environments as dependent factors. These data were also analyzed by PCA, adapted for the analysis of G × E interaction (GGE-Biplot), as proposed by Yan et al. [59]. The stability indices based on Finlay-Wilkinson regression slopes for Pal, LT, and SD were obtained by regressing the genotypes against the mean genotype value in each environment. ...
Leaf anatomical traits are influenced by environmental and genetic factors; however, studies that investigate the genotype × environment interaction on these traits are scarce. This study hypothesized that (1) the leaf anatomy of Coffea spp. genotypes is varied, and (2) interactions between managements and seasons significantly influence leaf anatomical traits, inducing a clear adaptation to specific environments. Possible modifications of leaf anatomy in Coffea spp. genotypes were investigated under different managements: full-sun monoculture at low-altitude (MLA), full-sun monoculture at high altitude (MHA), and low-altitude agroforestry (AFS), in winter and summer. The genotype influenced all leaf anatomical traits investigated, contributing to 2.3–20.6% of variance. Genotype × environment interactions contributed to 2.3–95.8% of variance to key traits. The effects of genotype × management interactions were more intense than those of genotype × season interactions on traits such as leaf thickness, palisade parenchyma thickness, abaxial epidermis, and polar and equatorial diameter of the stomata. The management AFS was more effective in altering leaf anatomical traits than the altitude differences between MLA and MHA, regardless of the season. These findings provide valuable insights for future research and for the development of strategies to improve the adaptation of coffee plants to changing environmental conditions.
... In the mega-environments (MGE), the genotypes near the polygon's vertices either had the best or worst performance. The polygon view of the GGE bi-plot was the most effective method for identifying winning genotypes and visualizing the patterns of interaction between genotypes and environments by Yan et al. (2000) and Yan and Kang (2003). The vertex (winning genotypes) in the sector where environments were placed in the MGE sector were genotypes G-11620, G-14486, and G-11612. ...
Animal feed is one of the main challenges facing livestock producers, due to inadequate nutrition, particularly during the dry season. The aim of this study was to identify Lablab genotypes performance in different midlands areas of Guji zones. A 3mx2m plot was used to seed twelve genotypes of Lablab purpureus, which were obtained from the International Livestock Research Institute Gene Bank, and a tick registered variety from Bako Agricultural Research Centre. During the main cropping rainy season in 2021-2022, three locations Dufa, Gobicha, and Kiltu sorsa, Adola subsite, and on farms in two (2) consecutive years, respectively were studied using randomized complete block designs (RCBD) with three replications. Information was gathered regarding the establishment, duration of various physiological stages, dry matter yield of fodder, chemical compositions, and additional relevant factors. AMMI and the SAS statistical analysis programmer, version (2002), were used to perform an analysis of variance on the gathered data. The list significant difference test was used to compare the means. The results of the AMMI analysis of variance for forage dry matter yield showed that there were substantial (P<0.01) variations in genotype and environment, but not in the effects of the G x E interaction. Both the representative testing site and the testing conditions (Adola woyu and Kiltu sorsa) were quite good at differentiating genotypes. The combined analysis of the data revealed that non-significant (P>0.05) differences for plant height and thousand seed weight, but significant (P ≤ 0.05) differences for days to flowering, days to maturity, number of branches, leaf to steam ratio, number of pods, and number of seeds across the tested environments. The results showed that, out of all the examined locations, G-11620 (15.43 t/ha) and G-14486 (11.12 t/ha) had the highest forage dry matter production. It was observed that the leaf to steam ratio was higher in both G-11486 and G-11620. All chemical compositions across the tested genotypes were found to be significantly different (p ≤ 0.05) among parameters, with the exception of DOMD and IVDMD, which did not showed significant (p >0.05) variations among genotypes. The recorded CP content ranged from 21.15% for G-14486 to 23.50% for G-11620, with the lowest value coming from typical cheek Gabis 10.8%. The highest and the lowest NDF were recorded from G-11620 (11.2%) and Gabis (22.23%) respectively. Generally the mean performance, yield and stability of the G-11620 and G-14486 were high and stable across the tested locations. Therefore, genotypes (G-14486 and G-11620) were promoted to variety verification for further evaluation and possible for release. JEL Classification Codes: F25, Q35, W22, M83.
... Multi-environment trials (METs) are usually conducted to select high-yielding and stable genotypes across environments (Yan et al., 2000). The yield performance of genotypes usually varies across environments due to the presence of strong genotype by environment interaction (GEI). ...
The agro-morphological traits and fodder nutritive values of oat (Avena sativa L.) genotypes usually demonstrate inconsistent performance in different environments due to variations in growing environments. Therefore, the genotype by environment interaction (GEI) study was executed using additive main effects and multiplicative interactions (AMMI) analysis model to select superior and stable fodder-yielding oat genotypes. The effects of GEI on fodder yield stability in twenty-four oat genotypes were investigated in nine environments using a randomized complete block design with three replications. For all traits and fodder nutritive values except ash content of the genotype main effect, the pooled analysis of variance revealed significant variations among genotypes, environments, and GEI. The AMMI analysis of variance for fodder yield also showed significant variation for genotype, environment, and GEI effects and the highest contributor for the total variation was the environment (67.45%) main effect followed by GEI (22.73%) and the genotype (9.82%) main effect. The first (44.11%) and the second (26.79%) interaction principal component axes were significant and cumulatively accounted for 70.91% of the total interaction variance. Based on the first two AMMI analyses, E6, E4, and E2 were located far from the biplot origin and had a high contribution to the total variation of GEI. The fodder yield of the genotypes G6, and G11 were above the grand mean. The fodder yield stability result obtained from the AMMI-2 is usually more accurate compared to the first AMMI. Consequently, genotypes which had mean fodder yield above the grand mean and relatively stable performance were observed for G23,
... Yield is a complex quantitative trait that is governed by many genes and it is influenced by the actions and interactions of different traits as well as the effects of genotype, environment, and their interactions (Tonk et al., 2011;Nowsad et al., 2016). Multi-environment trials (MET) are conducted to assess the yield stability performance of genotypes across environments (Delacy et al., 1996;Yan et al., 2000;Farshadfar et al., 2012a). The yield performance of genotypes usually fluctuates across environments due to the presence of genotype-by-environment interaction (GEI). ...
Oat (Avena sativa L.) is one of the most important dual-purpose cereal crops cultivated under diverse environmental conditions in Ethiopia. The fodder dry matter yield stability analysis was conducted using twenty-four oat genotypes across nine environments in a randomized complete block design with three replications. The study aimed to evaluate the magnitude of genotype by environment interaction and determine the stability of oat genotypes for fodder yield using 14 univariate stability parameters. The pooled analysis of variance revealed that the genotype, environment, and their interaction effects had variation (p < 0.001) for fodder yield. The contribution of environment for the total fodder yield variation was the highest (67.45%) followed by the interaction (22.73%) and genotypic (9.82%) effects. The results of stability analysis showed that high fodder yield-producing genotypes had stable performance using the stability parameters of genotypic superiority index (P i), yield stability index (YSI), coefficient of determination (R 2), and coefficient of variability (CV i), demonstrating that selection of oat genotypes using these stability parameters would be effective for fodder yield improvement. Moreover, Spearman's rank correlation coefficients indicated that the stability parameters of P i , YSI, R 2 , and CV i had a significant positive association with fodder dry matter yield (FDMY). On the contrary, the FDMY had non-significant inverse relations with the remaining stability parameters except B i , suggesting that the selection of oat genotypes using these stability parameters would not be effective for fodder yield improvement. Therefore, G6, G7, G9, G10, and G23 were desirable genotypes for fodder yield improvement programs in Ethiopia.
... Utilizing environment-centered data, the GGE biplot visually depicts the genotype (G) as well as genotypeenvironment interaction effects found in multi-environment trial data. The GEI analysis of multi-environment trial data utilizes a biplot to illustrate the components (G and GE) that are essential to genotype valuation along with as source of variation (Yan et al., 2000;Yan, 2001). GGE biplots are utilized for (i) Mega Environment Analysis (W hich-W on-Where Pattern): Identifying and recommending genotypes suitable for particular mega environments (ii) Genotype Evaluation: Recommending stable genotypes that perform consistently across all environments and (iii) Location Evaluation: describing how target environments can discriminate against the genotypes under investigation. ...
Background: Pigeonpea is a protein-rich vegetarian food crop predominantly cultivated in tropical and subtropical regions worldwide. It stands as the second-largest grain legume in India. Developing adaptable pigeonpea cultivars with stable yields throughout various environmental circumstances has been the primary goal of crop development efforts because of seasonal fluctuations and unpredictable rainfall patterns. Methods: To ascertain the grain yield performance of thirty pigeonpea genotypes, research was executed across three consecutive summer seasons in 2019, 2020 and 2021 at the Agricultural College and Research Institute, Tamil Nadu Agricultural University, located in Thanjavur, Tamil Nadu, India. The experimental setup employed a randomized block design replicated twice to estimate pigeonpea genotype’s yield stability, utilizing AMMI and GGE models. Result: The AMMI (Additive Main effects and Multiplicative Interactions) analysis showed genotype and environmental interactions had been primary factors influencing pigeonpea genotype’s grain yield performance. The initial 2 principal component axes (IPCA I and IPCA II) exhibited statistical significance, collectively explaining the total degrees of freedom associated with the interaction component. Genotype G30 was the best performer in the E3 environment, while G25, G6 and G22 were top performing pigeonpea genotypes in E1 environment. The environments E1 and E2 were closely related. Among the pigeonpea genotypes tested in this investigation, G25 and G6 were higher yielders with greater yield stability and can be recommended for cultivation in all three seasons. In contrast, G30 and G22 yielded higher but had unstable yield performance. Genotypes G16, G17, G12 and G15 showed greater stability but were poor yielders.
... There are several methods for dividing the TPE into ME. For example, the genotype main effect plus GEI (GGE) biplots (Yan et al., 2000) on soybean MET data was used by Zdziarski et al. (2019) to identify two ME in Midwestern Brazil with contrasting altitudes, levels of fertilizer, and incidence of soybean cyst nematode profiles. da and Krishnamurthy et al. (2017) also used GGE biplots to pinpoint ME for pre-commercial sorghum hybrids in Brazil and rice genotypes in India, respectively. ...
Soybean [Glycine max (L.) Merr.] provides plant‐based protein for global food production and is extensively bred to create cultivars with greater productivity in distinct environments through multi‐environment trials (MET). The application of MET assumes that trial locations provide representative environmental conditions that cultivars are likely to encounter when grown by farmers. A retrospective analysis of MET data spanning 63 locations between 1989 and 2019 was conducted to identify mega‐environments (ME) for soybean seed yield in the primary production areas of North America. ME were identified using data from phenotypic values, geographic, soil, and meteorological records at the trial locations. Results indicate that yield variation was mostly explained by location and location by year interaction. The phenotypic variation due to genotype by location interaction effects was greater than genotype by year interaction effects. The static portion of the genotype by environment interaction variance represented 26.30% of its total variation. The observed locations sampled from the target population of environments can be divided into two or three ME, thereby suggesting that improvements in the response to selection can be achieved when selecting directly within clusters (i.e., regions and ME) versus selecting across all locations. In addition, we published the R package SoyURT that contains the datasets used in this work.
... AMMI model (Gauch and Zobel, 1998) was used to capture and characterise the pattern of G E interaction. Genotype + Genotype Environment (GGE) biplot analysis (Yan et al., 2000) was used to graphically visualize which genotype performed best in specific environments and to identify high performing stable genotypes. ...
Background: Climate change has led to temperature variations affecting chickpea crops in dry and semi-arid regions. Late-sown crops are exposed to high temperatures during their reproductive phases, while early-sown crops experience low temperatures during their vegetative stages. A set of fourteen chickpea genotypes were evaluated for seed yield stability under five different sowing dates during 2022-23 to identify lines with heat and moisture stress tolerance. The results aim to determine the best sowing time for optimal growth and yield. Methods: A study was conducted during the Rabi season of 2022-2023 to assess the yield performance of fourteen chickpea genotypes, including Advanced Breeding Lines and released varieties, at ZARS, Kalaburagi. The genotypes were sown on five dates and evaluated using the AMMI model and GGE biplot analysis to examine genotype-environment interactions and yield stability. Result: Analysis of variance (ANOVA) across five environments (ENV-1 to ENV-5) revealed significant genotypic differences in all environments indicating the presence of substantial amount of variability among the evaluated genotypes. Further, the principal components (PCs) obtained from the genotype environment interaction studies were highly significant. The first principal component (PC1) accounts for 51.1% of the interaction variation, while PC2, PC3 and PC4 explain an additional 20.2%, 19.2% and 9.5%, respectively, together accounting for 100% of the interaction variation. Interaction principal component analysis (IPCA) scores from AMMI helped identify genotypes with consistent performance and those with specific adaptive responses. Based on all models, genotypes G1 (KCD-11) and G7 (GBM-2) emerge as the top performers, offering the highest yields and superior stability. Therefore, these genotypes could be recommended to farmers for various sowing dates.
... The GGE biplot model was used to display genotype main effects (G) and genotype×environment effects (GE) from a two-way data table in a biplot as suggested by Yan, et al. (2000). The first component of the GGE biplot (PC1) represented the genotype main effect (G) while the second component (PC2) indicated the proportion explained by genotype-environment interaction (GEI). ...
The study was conducted during the main cropping seasons of 2016, 2017 and 2018 at Kulumsa, Bekoji, Asasa and Kofele
in potential areas of South Eastern Ethiopia from June to November with the objective to assess the performance of faba
bean genotypes for grain yield and yield stability. Twelve faba bean genotypes were evaluated using randomized complete block
design with four replications under rain-fed conditions. The combined analysis of variance revealed that grain yield of faba bean
was significantly influenced by genotype (15.8%), environment (32.6%), and genotype by environment interaction (51.6%).
The highest mean grain yield was obtained from G-12 (3692.3 kg ha-1) and G-10 (369.0 kg ha-1) with an overall mean yield
of 3403.9 kg ha-1 across nine environments, while the lowest yield was recorded from G-8. The first two principal components of
AMMI biplot showed that PC 1 explained 47.8% and PC 2 accounted 19.6% of the genotype by environment interaction sum
of squares. Some genotypes, such as G-12, G-10, G-1, G-7 and G-5, exhibited significantly higher yields than the average.
while others had yields lower than the average. Genotypes G-10, G-6 and G-2 showed the highest stability consistently based
on most stability parameters, AMMI, and GGE biplot analysis. G-10 could be considered an ideal genotype due to its high
yield and stability, which was widely adaptable across environments. Finally, top-ranked genotypes G-10, G-7 and G-5 were
identified for both grain yield and seed weight.
... Both yield and disease resistance of a genotype are influenced by genetic factors (G), the environment (G), and their interactions (GEI). These interactions result in differential responses of genotypes under different environmental conditions [14][15][16][17][18][19][20][21][23][24][25]. The Genotype plus Genotype-by-Environment (GGE) biplot [25] is a graphical statistical tool that was developed to analyze data from experiments conducted across multiple environments [26]. ...
The faba bean (Vicia faba) is an important grain legume that, despite decades of decline, is regaining interest in the Mediterranean basin due to an increasing demand for plant-based proteins and other ingredients, particularly for the food industry. However, the crop’s sensitivity to weather conditions (mainly drought and heat) as well as its high susceptibility to diseases hinder its yield performance and stability. For this reason, in this study, we present the results of multi-environment field trials conducted in southern Spain, where the performance of six new elite faba bean cultivars, developed through local breeding programs focused on selection for increased yield and chocolate spot (Botrytis fabae) resistance, was compared with two popular commercial cultivars. Data analysis across six diverse environments showed the significant effects of environment, genotype, and genotype-by-environment interaction (GEI) on yield and several morphologic traits. Grain yield was positively influenced by rainfall and negatively affected by high temperatures, with no evidence of damage due to cold temperatures. Stress tolerance indexes helped identify cultivars Omeya, Faraon Negro, and Navio6, which excelled across all metrics. The trials were intentionally conducted in broomrape (Orobanche crenata)-free plots, where chocolate spot emerged as the major biotic constraint, with the infection level highly influenced by rainfall. Significant differences were observed among accessions in their response to chocolate spot, with the cultivar Arrechana showing resistance. Overall, cultivars Omeya, Arrechana, Faraon Negro, Navio6, and Quijote demonstrated outstanding grain yield and excellent adaptation to the region.
... and accession (2 levels Yan et al. (2000). The analysis was performed using R statistical software (Version 4.4.0). ...
Growing Sri Lankan traditional rice (Oryza sativa L.) in the short day (Maha) season is crucial for the flowering initiation of most of the accessions. Masuran is a traditional rice variety with a high yielding potential including health-promoting attributes and premium price. This study was conducted to determine the effect of selected environmental factors of the season on the number of days to flowering (DF), plant height at flowering (PH) and selected yield components of two accessions of the 'Masuran' variety (Acc. No. 4132 and Acc. No. 5530). Planting was carried out on the 05 th day of every month for a period of one year. Average daily rainfall (RF), temperature and photoperiod of each month were recorded. The effect of ______________________________________ This article is published under the terms of the Creative Commons Attribution 4.0 International License which permits unrestricted use, distribution and reproduction in any medium provided the original author and source are credited. environmental factors on selected agronomic traits was analysed. Days to flowering, PH, Total Effective Tiller Number (TTN) and Number of Spikelets per First Panicle (NSFP) varied from 65 to 110 days, from 85.7 to 158 cm, from 3 to 8 and from 142 to 199 in Acc. No. 4132, respectively while those parameters varied from 60 to 89 days, from 80.3 to 170 cm, from 3 to 16, and from 119 to 283 in Acc. No. 5530, respectively during the experimental period. The analysis of the effect of photoperiod, rainfall and temperature of the first month of each planting revealed that the effect of photoperiod and temperature is significant on DF. The first month of each planting was observed as the photoperiod-sensitive phase of rice accessions. Correlation analysis for DF and NSFP resulted in a strong positive relationship suggesting a multi-factor environmental effect on NSFP of the variety, Masuran when it was grown throughout the year. The above results will be important in introducing a novel aspect for optimizing planting schedules and selecting suitable genotypes to enhance rice productivity under different environmental conditions.
... The red dots represent the selected potato varieties based on varietal performance and preferred traits by the farmers. Yan et al. (2000) defined an "ideal" genotype based on both mean performance and stability, and the genotype types can be ranked based on their distance from the ideal genotype. The ideal test environment should be highly discriminative of the genotypes and representative of the megaenvironment (Aina et al., 2007). ...
The goal of potato breeding is to develop widely adaptable, highly productive cultivars that farmers would prefer. The objective of this study was to evaluate the performance and stability of potato varieties linking the preferences of smallholder farmers in rain-fed and irrigated environments. Using a randomized complete block design in three replicates, twelve potato cultivars were assessed in 10 settings in Southeast Ethiopia during the Meher and Belg seasons in 2019 and 2020. The environments, genotypes, and GEI all revealed significant differences (p<0.001) in the pooled analysis of the variance of tuber yield. The tuber yield variances for GEI, environment, and genotypic impacts were 15.48%, 7.61%, and 59.49% explained by the AMMI analysis, respectively. The environments were grouped into three distinct categories. A total of 99.6% of the variance was the cumulative contribution of PC1, PC2, PC3, PC4, and PC5 sharing 80.8%, 11.3%, 4.3%, 2.2%, and 1.0%, respectively High-yielding and widely adapted were Gera, Gudanie, Bubu, Belete, Shenkolla, Guassa, and Maracharre varieties, according to the AMMI, BLUP, GGE biplot, and WAAS. However, dynamic types that were particularly affected by environmental variations include Jalenie, Dagim, Gorebella, Awash, and Zemen. A stability measure of metric and preference based on various traits identified Gudanie and Guassa varieties. The scores of the small holder farmers were consistent throughout the test environments. The canonical correlation analysis indicated the significant association between the metric traits collected by the breeder and the small- holder farmer preferences. The study provides baseline data for potato breeding, and the varieties must be evaluated in the nation's mega-environments for additional recommendations. Int. J. Agril. Res. Innov. Tech. 14(2): 85-98, December 2024
... Selecting genotypes that present high average productivity and high stability is one of the objectives of most breeders and the GGE biplot graph "mean versus stability" (Figure 2) allows to visualize these two characteristics in a simplified way. By this method, the productivity and stability of the genotypes are evaluated from the average environment coordination (AEC) (Yan et al., 2000). ...
The development of new cultivars is a strategy used in breeding programs to increase food production with environmental sustainability. The genotype × environment interaction is a great challenge in the identification and selection of superior genotypes for different edaphoclimatic conditions. Due to this interaction, it is essential to select and develop materials that can provide not only high productivity but also wide adaptability and production stability. Given the above, this work aims to select bean pre-cultivars regarding grain productivity, adaptability and stability for the State of Rio de Janeiro. In the 2018 harvest, two inbred lines competition trials were carried out and three in the 2019 harvest. Eleven black bean genotypes were evaluated in five environments, and the experiments were set up in a randomized block design with three replications. The adaptability and genotypic stability were assessed via the GGE Biplot, Eberhart and Russell and Lin and Binns methodologies, with the aid of the GENES and R software systems. The methodologies based on simple linear regression and non-parametric statistical analysis were concordant in the identification of genotypes with production stability (BRS Esteio, BRS FP 403 and CNFP 16459), responsive to environmental improvement (BRS Esteio) and adapted to unfavorable environments (BRS Esteio). Furthermore, BRS Esteio was classified as the ideotype and presented the best adaptability, high stability and performance above the general average. Thus, the adaptability and stability analysis methodologies proved to be effective and consistent in identifying superior genotypes.
... This analysis was performed on the grain yield basis using the statistical software tool GenStat 64-bit version 18.1 according to the method described by [41]: ...
Smallholder wheat farmers of Ethiopia frequently use landraces as seed sources that are low yielders and susceptible to diseases due to shortage of seeds of adapted improved bread wheat varieties. Developing novel improved varieties with wider adaptability and stability is necessary to maximize the productivity of bread wheat. Hence, a multi-location field trial was conducted across four locations in south Ethiopia during the 2022/23 main cropping season with the objective of estimating the magnitude of genotype by environment interaction (GEI) effect, and determine the stable genotype among the 10 Ethiopian bread wheat advanced selections using a randomized complete block design (RCBD) with three replications. The data recorded from all plots on 13 agronomic traits and the three wheat rust diseases were computed using appropriate statistical software. The results showed that individual and combined analysis of variance (ANOVA) exhibited the presence of highly significant variability (P<0.01) among the locations, genotypes, and GEI effects for most of the traits including grain yield. The additive main effects and multiplicative interaction (AMMI) ANOVA for main effects; location, genotype and GEI revealed significant variation among the selections with 82.0%, 8.7% and 9.3% share of sum square variation, respectively. The genotype plus genotype by environment interaction (GGE) bi-plot analysis explained 92.44% of the total variation observed. AMMI and GGE-biplot analyses indicated G11, G9, G10, and G8 as high yielders and well-adaptive in the favourable locations. AMMI stability value (ASV) and Yield stability index (YSI) showed G5 and G8 as highly stable and adaptive selections across locations. Overall, the study identified that G8 as the most stable and adaptive selection, while G11 was the top yielder cultivar across locations. Therefor it was suggested that seeds of G8 can be grown across all the locations, whereas G11, G9, and G10 can be grown in the favourable environments and similar agro-ecologies in the east African region.
... The GGE biplot methodology [11,15,23] consists of a set of biplots interpretation methods, whereby important questions regarding genotype evaluation and test-environment evaluation can be visually addressed. The results of the GGE biplot revealed that the first two principal components explained 79.54% of the total yield variation across the tested environments ( Fig 2). ...
Cowpea is deemed as a food security crop due to its ability to produce significant yields under conditions where other staples fail. Its resilience in harsh environments; such as drought, heat and marginal soils; along with its nitrogen-fixing capabilities and suitability as livestock feed make cowpea a preferred choice in many farming systems across sub-Saharan Africa (SSA). Despite its importance, Cowpea yields in farmers’ fields remain suboptimal, primarily due to biotic and abiotic factors and the use of either unimproved varieties or improved varieties that are not well-suited to local conditions. Multi environment testing of genotypes is essential for recommending varieties suited for either specific or for wide cultivation. This study aimed, to identify and recommend cowpea breeding lines for wide or specific cultivation in the Sudan Savanna and Deciduous Forest zones of Ghana. The research utilized twenty early-maturing advance cowpea breeding lines and three check varieties (released varieties). The experiment was conducted in two locations: Bunso in the Deciduous Forest zone and Manga in the Sudan Savanna zone over 2020/2021 and 2021/2022 cropping seasons. Combined analysis of variance revealed a significant genotype-environment interaction (GEI) which accounted for 35.12% of the variation in yield. The environments were classified into three mega environments, with Bunso_2021 identified as the near-ideal environment where the genotypes exhibited their maximum genetic potentials. In terms of adaption, genotype UG_04 demonstrated broad adaption, showing high yield and stability across all test environments. Genotypes UG_01 and UG_02 performed particularly well in Bunso_2021 and Bunso_2022, while UG_04 and UG_14 excelled in Manga_2021. These findings provide valuable insights for selecting cowpea varieties that can enhance productivity and stability in diverse agro-ecological zones.
... Please see Table 1 for environments detail. where Yij Refers to the average productivity of the lentil genotype i in the environment j; yj is the general average of the genotypes in the environment j; y 1εi1ρj1 is the first principal component (PC1); y 2εi2ρj2 is the second principal component (PC2); y1 and y2 are the eigenvalues associated to IPCA1 and IPCA2, respectively; ε 1 and ε 2 are the values of PC1 and PC2, respectively, of the genotype i; ρ j1 and ρ j2 are the values of PC1 and PC2, respectively, for the environment j; and ε ij is the error associated with the model of the ith genotype and jth environment [35]. Mega-environments were identified using the "Which-Won-Where" feature of the GGE Biplot. ...
... The first principal component axis (PC1) accounted for 50.4% of the variation in GxE interaction, while the second axis contributed 25.2% to the overall variability. Numerous studies have indicated that the most accurate predictions from the AMMI model can be derived from the first two IPCAs (Yan, et al., 2000). In the context of AMMI analysis, the IPCA scores for a genotype serve as a measure of its stability across different environments (Gauch & Zobel, 1997, Purchase, 1997. ...
The experiment was conducted to select the best yielder and widely adaptable white cumin genotypes across different testing environments during the 2012, 2013, 2014, and 2015 main cropping seasons. Nine white cumin genotypes were evaluated in RCBD design using three replications at eleven environments. The combined mean performance recorded a higher mean seed yield from genotype G3 (1,031.3 kg ha-1) followed by genotype G7 (1,011.2 kg ha-1). The AMMI analysis of variance for seed yield showed a highly significant (p<0.01) difference among genotypes, environments, and genotype × environment. The environmental effect accounted for 64.03% of the total variation, whereas the genotype × environment and genotype effect accounted for about 1.65% and 10.27% of total sum squares respectively. The first IPCA captured about 50.4% of genotype × environmental interaction sum square, while the second IPCA explained about 25.2%. The two IPCs cumulatively explained 75.6% of genotype × environmental interaction sum square. Based on ASV scores G7 and G4 have the lowest ASV and they are the most widely stable genotypes across environments. In contrast, genotypes G5 and G1 score relatively the highest ASV and are considered unstable genotypes. E3 scored the least negative IPCA1 values, while environments E5, E8, and E9 scored maximum positive and negative IPCA1 values.
... GGE biplot analysis was further used to assess genotype by trait interactions (Payne et al. 2014). The GGE biplot model was used for genotype (G) and genotype by environmental interaction (GEI) and genotype by genotype by trait association derived from singular value decomposition (SVD) into two main components (Yan 2002;Yan and Hunt 2001;Yan et al. 2000). The GGE biplot model used for GEI associations (Yan 2002;Yan and Hunt 2001;Yan and Tinker 2006) is as follows: ...
The testing of spider plant (Cleome gynandra L.) landraces in multi-environmental trials on multiple traits is a critical step toward future use of this species. Despite its importance, there are no recommended varieties with known strengths in terms of production and future breeding. The study objectives were to evaluate spider plant landraces for yield and important traits. Ten landraces were assessed at four sites during the 2021/22 and 2022/23 summer seasons in Zimbabwe. Significant (p < 0.05) variations in genotypic, environmental, and genotype by environment interaction (GEI) effects for traits were found. The average edible fresh leaf yield (FY) ranged from 42.8 to 84.1 g plant−1, whereas the seed yield (SY) ranged from 9.7 to 15.12 g plant−1. The AMMI model showed a highly significant (p < 0.001) impact of environment, genotype, and GEI on FY and SY. The GGE biplot uncovered 88.31 and 75.08% of the underlying relationship for the FY and SY, respectively, while it explained 69.27% variation of the genotype by trait interaction. The Horticulture Research Institute proved to be ideal testing site for both edible fresh leaf and seed yield. The landraces G8>G1 and G3>G5 were associated with high mean and stability for FY and SY, respectively. The landraces with high FY values presented additional superiority in terms of longer and wider leaves, and flowered late. However, landraces with high SY values exhibited greater height and seeds per pod.
... Estimating heritability in traits is essential for breeding programs, as it aids in identifying and recommending superior genotypes Yan et al., 2000). The results of this study revealed high heritability for both PH and grain yield, which indicates that these traits are less influenced by environmental variability, making it easier to select for desirable characteristics in plant breeding (Li et al., 2019). ...
Rainfed rice (Oryza sativa L.) cultivation is crucial for meeting food demand in
regions like Bangladesh, where irrigation resources are scarce. A study was conducted in five rainfed districts during the 2022 Aman season. The objective was to assess grain yield performance, adaptation patterns, and identify a stable rice variety using multiple trait evaluations. The results showed that Binadhan-17 produced the highest grain yield, reaching 5.58 t/ha, with its maximum yield recorded in Gopalganj at 6.12 t/ha. Heritability estimates from a linear mixed model were high for plant
height (0.93) and grain yield (0.65), with genotypic variance contributing 92.70%
and 64.84% to these traits, respectively. A negative correlation (r = −0.43) indicated that shorter varieties tend to perform better for grain yield. Stability analysis using weighted average absolute scores and best linear unbiased prediction identified Binadhan-17 as the most stable for grain yield, while the genotype–genotype
environment biplot confirmed its adaptability across all locations. Factor analysis of the multi-trait stability index showed stability across traits, with selection gains from 1.49% to 6.99%. Binadhan-22 was identified as the most stable for average performance and multi‐trait stability. Given its high yield and consistent performance across environments, Binadhan‐17 is recommended for large‐scale cultivation, while Binadhan‐22 offers a reliable, stable alternative across various traits.
... Estimating heritability in traits is essential for breeding programs, as it aids in identifying and recommending superior genotypes Yan et al., 2000). The results of this study revealed high heritability for both PH and grain yield, which indicates that these traits are less influenced by environmental variability, making it easier to select for desirable characteristics in plant breeding (Li et al., 2019). ...
Rainfed rice (Oryza sativa L.) cultivation is crucial for meeting food demand in regions like Bangladesh, where irrigation resources are scarce. A study was conducted in five rainfed districts during the 2022 Aman season. The objective was to assess grain yield performance, adaptation patterns, and identify a stable rice variety using multiple trait evaluations. The results showed that Binadhan‐17 produced the highest grain yield, reaching 5.58 t/ha, with its maximum yield recorded in Gopalganj at 6.12 t/ha. Heritability estimates from a linear mixed model were high for plant height (0.93) and grain yield (0.65), with genotypic variance contributing 92.70% and 64.84% to these traits, respectively. A negative correlation (r = −0.43) indicated that shorter varieties tend to perform better for grain yield. Stability analysis using weighted average absolute scores and best linear unbiased prediction identified Binadhan‐17 as the most stable for grain yield, while the genotype–genotype environment biplot confirmed its adaptability across all locations. Factor analysis of the multi‐trait stability index showed stability across traits, with selection gains from 1.49% to 6.99%. Binadhan‐22 was identified as the most stable for average performance and multi‐trait stability. Given its high yield and consistent performance across environments, Binadhan‐17 is recommended for large‐scale cultivation, while Binadhan‐22 offers a reliable, stable alternative across various traits.
... However, limited emphasis is often placed on understanding the accession's interaction with diverse target environments, which can be unpredictable. The development of biplot methodology, especially AMMI and GGE biplots, has simplified complex GEI visualization, allowing researchers to observe genotype and environment interactions in a clear, graphical format [16][17][18]. By introducing climate-resilient proso millet genotypes, our research can significantly impact the UAE's food production, reduce import dependency, and contribute to a more sustainable and resilient food system. ...
Scarce water resources, high temperatures, limited rainfall, elevated soil salinity, and poor soil quality (98% sand) challenge crop production in the desert regions of the Middle East. Proso millet’s resilience under these stresses presents a potential solution for enhancing food security in arid environments. This field study evaluated 24 proso millet genotypes under three environments (100% freshwater, 50% freshwater, and 10 dS/m salinity) in the UAE during normal and summer seasons, aiming to identify genotypes resilient to water, heat, and salinity stresses and to assess genotype-by-environment (G × E) interactions and key traits associated with grain yield. ANOVA indicated significant G × E interactions. Genotypes G9 and G24 displayed high yield and stability across environments during the normal season. In the summer, genotypes G7 and G10 exhibited resilience with high yields under high-temperature stress alone, while combined stresses led to yield reductions across all genotypes, with greater susceptibility under cumulative stress. GGE biplot analysis identified G9 as ideal in the normal season, while G15 and G23 demonstrated stability under combined stresses in the summer season. High chaffy grain yield (CGY) observed under summer stress conditions suggests a shift in resource allocation away from productive grain formation. The reproductive phase was highly vulnerable to heat stress, with 88% of this period experiencing daytime temperatures exceeding 40 °C, with a peak reaching up to 49 °C. These extreme conditions, coinciding with the crop’s critical growth stages, triggered a significant increase in chaffy grain production, substantially reducing overall grain yield. Despite these challenges, genotypes G7, G10, and G12 exhibited notable resilience, maintaining yields above 0.75 t ha−1. Correlation analysis suggested that selecting for increased plant height, forage yield, and 1000-grain weight (TGW) could enhance grain yield under the normal and summer conditions. This study highlights the potential of proso millet genotypes as climate-resilient options for arid regions, providing a basis for developing stress-tolerant varieties and promoting sustainable agriculture in desert climates.
Biplot typu GGE jest graficzną metodą analizy tzw. efektów GGE, tzn. efektów głównych genotypów połączonych z efektami interakcji genotypowo-środowiskowych. W pracy metoda GGE biplot została zastosowana do interpretacji danych z 2004 roku, dotyczących plonów rodów pszenicy ozimej na terenie zachodniej Polski. Przedstawiono pewnego rodzaju analizę skupień badanych środowisk, polegającą na poszukiwaniu grup miejscowości (multiśrodowisk) o zbliżonym efekcie interakcji z wszystkimi analizowanymi genotypami. Metodę GGE biplot zastosowano następnie do oceny stabilności genotypów ze względu na wysokość plonu oraz do wskazania genotypów najbardziej zbliżonych do tzw. genotypu idealnego, definiowanego jako genotyp stabilny o największym średnim plonie poprzez środowiska.
Biplot typu GGE jest graficzną metodą analizy tzw. efektów GGE, tzn. efektów głównych genotypów połączonych z efektami interakcji genotypowo-środowiskowych. Odpowiednio skonstruowany rysunek składa się z punktów (wektorów) na płaszczyźnie, z których część reprezentuje genotypy, a część środowiska. Efekty typu GGE są funkcjami długości wektorów i kątów między nimi. Zatem analiza interakcji genotypowo-środowiskowych polega na interpretacji własności geometrycznych i trygonometrycznych obiektów płaszczyzny. W pracy przedstawiony został proces konstrukcji biplotu typu GGE oraz podstawowe możliwości interpretacyjne tego biplotu (porządkowanie efektów GGE, szukanie genotypu o największym efekcie GGE w danym środowisku, wyznaczanie genotypów stabilnych oraz tzw. genotypu idealnego). Metoda została przedstawiona na przykładzie analizy danych dla plonu czterech rodów pszenicy ozimej w jednorocznej serii doświadczeń w siedmiu miejscowościach.
W pracy przedstawiono podstawowe problemy teoretyczne i wyniki badań eksperymentalnych w zakresie znaczenia i wykorzystania efektów genotypowych i interakcji genotypowo-środowiskowych oraz interakcji środowiskowej (miejscowości × lata) dla danej cechy ilościowej w programie hodowlanym i rejestracji odmian roślin uprawnych. Znaczenie tych efektów można określić za pomocą ocen ich wariancji (komponentów wariancyjnych), na podstawie wyników z reprezentatywnej wielokrotnej i wieloletniej serii doświadczeń z genotypami. Rozważania teoretyczne uzupełniono wynikami własnych i innych badań eksperymentalnych nad oceną komponentów wariancyjnych wspomnianych efektów dla plonu ziarna pszenicy ozimej, jako najważniejszej rośliny uprawnej. Stwierdzono, że w różnych rejonach świata i pulach genowych wariancja efektów interakcji miejscowości × lata ma największy udział w zmienności plonu ziarna pszenicy ozimej. Natomiast efekty interakcji genotypy × miejscowości × lata mają największe znaczenie wśród wszystkich efektów interakcji genotypowo-środowiskowych. Te dwa rodzaje efektów interakcyjnych ograniczają powtarzalność interakcji genotypy × miejscowości w różnych latach, decydującej z kolei o powtarzalności, w różnych latach, stabilności i adaptacji genotypów w rejonie rolniczym. Efekty interakcji genotypy × miejscowości są zwykle mniej zmienne, niż efekty genotypowe i efekty interakcji genotypy × lata. Uzyskane oceny komponentów wariancyjnych wymienionych efektów dla plonu ziarna pszenicy ozimej w badaniach własnych wykorzystano do zobrazowania symulacji najważniejszych charakterystyk, określających dokładność średnich fenotypowych genotypów dla rodów hodowlanych w docelowym rejonie uprawowym, zależnie od miejscowości, lat i liczby powtórzeń w serii doświadczeń.
Context
The loss of insect biodiversity threatens our food security and ecosystem services. This decline is partially linked to intensive agriculture, including monocultures. Alternative cultivation systems with companion plants can create habitats for beneficial insects but may negatively impact crop productivity due to competition.
Objective and methods
To increase agrobiodiversity, we evaluated a cropping system incorporating companion flowering species in soybean cultivation. A total of 16 different flowering species were sown between the soybean rows and evaluated for their impact on the productivity of three soybean genotypes in a two-year trial in southern Germany. The effects of each species on the soybean traits grain yield, protein content, and time to maturity were compared. In addition, their influence on the soil microbiome was assessed to evaluate a further aspect of biodiversity.
Results
In the first year with higher water availability, grain yield was affected negatively by the vigorous species Sinapis arvensis, Sinapis alba and Trifolium resupinatum. In the second year with drought stress, most companion species led to significant yield losses. In addition, maturity was substantially delayed by well growing undersown species. However, protein content, representing grain quality, was not negatively affected in any environment. Furthermore, no significant effect on the soil microbiome could be detected.
Conclusion
In conclusion, a cropping system integrating undersowing of companion species between rows in soybean cultivation is for most species only feasible with sufficient water availability. However, some species, like Linum usitatissimum, seem to be compatible with soybean in such a cropping system even in years with less favourable weather conditions.
The yield stability of promisingpigeon pea genotypes across six locations was assessed using additive main effects and multiplicative interaction and Genotype plus genotype and environment interaction biplot models. The combined analysis of variance indicated that yield was significantly affected by genotypes, environments and genotype and environment interaction. The first two interaction principal componentsfrom the AMMI analysis accounted for 68.48% and from GGE accounted for 64.4% of variation due to GxE interaction. Both AMMI and GGE biplots identified G3 and G4 as most stable andG4 with the highest yield across six locations. The "What-won-where" biplot revealed that six environments fell into four sectors i.e.,E1and E2fell into one mega environment (ME 1), E3and E6another (ME 2), E4in one mega environment (ME 3) andE5 into another (ME 4). Environments E5and E1were most representative while E1could be regarded as least representative environment.
In the Sahel, groundnuts play a major role in human and animal nutrition, as well as in cropping systems. However, this crop is often grown on nitrogen-and phosphorus-poor soils, accompanied by pockets of drought during the cycle. This can have negative effects on its agronomic performance and ability to fix atmospheric nitrogen. The aim of this study was to examine the effects of mineral fertilization and environmental conditions on the agronomic performance and 76 nitrogen fixation of six groundnut genotypes. The experiments were carried out in the experimental field of the Faculty of Science and Technology of the André Salifou University and in the experimental site of the Institut Pratique de Formation in Matameye. The treatment applied in each locality comprised three modalities: F0 (unfertilized), F1 (with Di-Ammonium Phosphate input), and F2 (with NPK input). A factorial design with four replications was used. The results obtained show that F2 gave the best yields of tops and seeds in both localities. Nodulation was higher in Matameye than Zinder, irrespective the treatment. The results showed significant interactions between sites, fertilization treatments and genotypes for the parameters studied. The results show that good fertilization improves peanut productivity and nitrogen fixation capacity.
To identify high-yielding, adaptable, and regionally representative sugar beet varieties, this study applied analysis of variance, the additive main effects and multiplicative interaction (AMMI), best linear unbiased prediction (BLUP) analysis, and the genotype main effects plus genotype–environment interaction (GGE) biplot method. We evaluated the sugar content and root yield of 16 sugar beet varieties grown at seven different trial locations in 2021. The results indicated significant differences between the uncorrected data and the BLUP values, with the latter being more accurate and reliable in representing breeding values. Among the tested varieties, KWS 7748–1 (V6) exhibited the highest and most stable yield across all three models, demonstrating strong adaptability and high yield potential in various environments. This suggests that KWS 7748–1 (V6) has high potential for stable yields, even in regions with fluctuating climatic conditions. Therefore, it could become an important resource in breeding programs, particularly for improving yield adaptability and stress resistance. Its reliability in diverse environments makes it an ideal candidate for widespread cultivation, contributing to increased sugar beet production and food security. Furthermore, Jialaid Banner (E7) was identified as the most discriminative and representative trial location. The combined application of analysis of variance, AMMI model, BLUP analysis, and GGE biplot method enhanced the scientific rigor, comprehensiveness, and effectiveness of the regional trials for sugar beet varieties.
Multilocation trials are often used to analyse the adaptability of genotypes in different environments and to find for each environment the genotype that is best adapted; i.e. that is highest yielding in that environment. For this purpose, it is of interest to obtain a reliable estimate of the mean yield of a cultivar in a given environment. This article compares two different statistical estimation procedures for this task: the Additive Main Effects and Multiplicative Interaction (AMMI) analysis and Best Linear Unbiased Prediction (BLUP). A modification of a cross validation procedure commonly used with AMMI is suggested for trials that are laid out as a randomized complete block design. The use of these procedure is exemplified using five faba bean datasets from German registration trails. BLUP was found to outperform AMMI in four of five faba bean datasets.
To maximize yield throughout a crop's heterogeneous growing region , despite differences in cultivar rankings from place to place due to genotype-environment interactions, frequently it is necessary to subdivide a growing region into several relatively homogeneous mega-environments and to breed and target adapted genotypes for each mega-environment. The objectives of this study are to identify relevant criteria for evaluating mega-environment analyses and to apply the Additive Main Effects and Muitiplicative Interaction (AMMI) model to mega-environment analysis. The proposed analysis is illustrated using a Louisiana corn (Zea mays L.) trial. Statistical strategies for identifying mega-environments should meet four criteria: flexibility in handling yield trials with various designs, focus on that fraction of the total variation that is relevant for identifying mega-environments, duality in giving integrated information on both genotypes and environments , and relevance for the primary objective of showing which genotypes win where. The AMMI model meets these criteria effectively when the usual biplots are supplemented with several new types of graphs designed to address questions about mega-environments. Preliminary results indicate that a small and workable number of mega-environments often suffices to exploit interactions and increase yields.
Use of appropriate nursery environments will maximize gain from selection for yield of wheat (Triticum aestivum L.) in the target population of environments of a breeding program. The objective of this study was to investigate how well-irrigated (low-stress) nursery environments predict yield of lines in target environments that varied in degree of water limitation. Fifteen lines were sampled from the preliminary yield evaluation stage of the Queensland wheat breeding program and tested in 26 trials under on-farm conditions (Target Environments) across nine years (1985 to 1993) and also in 27 trials conducted at three research stations (Nursery Environments) in three years (1987 to 1989). The nursery environments were structured to impose different levels of water and nitrogen (N) limitation, whereas the target environments represented a random sample of on-farm conditions from the target population of environments. Indirect selection and pattern analysis methods were used to investigate selection for yield in the nursery environments and gain from selection in the target environments. Yield under low-stress nursery conditions was an effective predictor of yield under similar low-stress target environments (r = 0.89, P < 0.01). However, the value of the low-stress nursery as a predictor of yield in the water-limited target environments decreased with increasing water stress (moderate stress r = 0.53, P < 0.05, to r = 0.38, P > 0.05; severe stress r = -0.08, P > 0.05). Yield in the stress nurseries was a poor predictor of yield in the target environments. Until there is a clear understanding of the physiological-genetic basis of variation for adaptation of wheat to the water-limited environments in Queensland, yield improvement can best be achieved by selection for a combination of yield potential in an irrigated low-stress nursery and yield in on-farm trials that sample the range of water-limited environments of the target population of environments.
Yield trials frequently have both significant main effects and a significant genotype X environment (GE) interaction. Traditional statistical analyses are not always effective with this data structure: the usual analysis of variance (ANOVA), having a merely additive model, identifies the GE interaction as a source but does not analyze it; principal components analysis (PCA), on the other hand is a multiplicative model and hence contains no sources for additive genotype or environment main effects; and linear regression (LR) analysis is able to effectively analyze interaction terms only where the pattern fits a specific regression model. The consequence of fitting inappropriate statistical models to yield trial data is that the interaction may be declared nonsignificant, although a more appropriate analysis would find agronomically important and statistically significant patterns in the interaction. Therefore, agronomists and plant breeders may fail to perceive important interaction effects. This paper compares the above three traditional models with the additive main effects and multiplicative interaction (AMMI) Model, in an analysis of a soybean [ Glycine max (L.) Merr.] yield trial. ANOVA fails to detect a significant interaction component, PCA fails to identify and separate the significant genotype and environment main effects, and LR accounts for only a small portion of the interaction sum of squares. On the other hand, AMMI analysis reveals a highly significant interaction component that has clear agronomic meaning. Since ANOVA, PCA, and LR are sub‐cases of the more complete AMMI model, AMMI offers a more appropriate first statistical analysis of yield trials that may have a genotype X environment interaction. AMMI analysis can then be used to diagnose whether or not a specific sub‐case provides a more appropriate analysis. AMMI has no specific experimental design requirements, except for a two‐way data structure.
The wheat area in developing countries, including China, is around 100 million ha. To address the needs of these very diverse wheat growing areas, CIMMYT has defined 12 wheat mega-environments (ME). A ME is defined as broad, not necessarily continuous often transcontinental area with similar biotic and abiotic stresses, cropping systems and consumer preferences. The factors describing each ME are presented.
CIMMYT’s breeding methodology is centered around the development of widely adapted germplasm with high and stable yield across a wide range of environments. Segregating populations are alternating screened in two diverse environments in Mexico. One key requirement is that all germplasm is tested under near optimum conditions for its yield potential. The second one is multi-locational testing of advanced lines at sites that represent a given ME (key locations) and careful screening of germplasm for tolerance to abiotic and biotic stresses specific to that environment. This methodology has permitted the pyramiding of a large number of multiple resistance genes for use against a wide spectrum of diseases and tolerance to abiotic stresses within each ME. In addition, the widespread testing of lines allows the identification of traits which are beneficial in several environments. Data from international nurseries are used to further delineate environments within an ME. This approach has proven to be successful since around 70% of the spring wheat area in developing countries (excluding China) is planted to varieties derived directly or indirectly from CIMMYT germplasm. The performance of the bread wheat cultivar Pastor in international trials is given as an example for a wide adaptation.
The additive main effects and multiplicative interaction (AMMI) model first applies the additive analysis of variance (ANOVA) model to two-way data, and then applies the multiplicative principal components analysis (PCA) model to the residual from the additive model, that is, to the interaction. AMMI analysis of yield trial data is a useful extension of the more familiar ANOVA, PCA, and linear regression procedures, particularly given a large genotype-by-environment interaction. Model selection and validation are considered from both predictive and postdictive perspectives, using data splitting and F-tests, respectively. A New York soybean yield trial serves as an example.
Previous papers have developed the shifted multiplicative model with one multiplicative term (SHMM1) as a model for clustering yield trial sites or cultivars into groups in which cultivar rank changes are statistically negligible. Properties of SHMM1 are proportionality of predicted cultivar differences within sites, and of site differences within cultivars. The latter constraint is relaxed if the sites regression model with one multiplicative term (SREG1) is used instead of SHMM1. Dendrograms for the two methods are identical, but SHMM and SREG analyses of clusters suggested by the dendrogram may lead to different conclusions concerning acceptability of a particular cluster. This study compared SREG clustering to SHMM clustering in two international maize (Zea mays L.) cultivar trials, when the data to which models were fitted were original unscaled cell means, and, as a way to cope with site to site heterogeneity of error variance, cell means scaled by dividing by the standard error of a cultivar mean within the particular site. Results of both trials confirmed our expectation that SREG clustering would occasionally allow clusters to merge which would not be statistically acceptable under SHMM analysis. This occurred at a cost of a modest increase in percentage and magnitude of significant crossover interactions within the clusters. Both trials exhibited significant site to site heterogeneity of error variances. Scaling of data resulted in more effective removal of significant rank-change interactions from within clusters, provided that the test criterion was based on the assumption of heterogeneous variance. Besides occasionally allowing larger clusters, advantages for SREG clustering of sites are (i) all solutions (including constrained non-crossover solutions) exist in closed form and (ii) the analysis of scaled data is equivalent to a weighted least squares analysis, neither of which holds for SHMM.
The additive main effects and multiplicative interaction (AMMI) model has been recommended for cultivar trials repeated across locations and/or years. Previous studies, using approximate F-tests introduced by Gollob, have declared more AMMI interaction principal components (PCs) significant than cross validation could show to be predictively useful. This study used Monte Carlo simulation to investigate whether such a result in an international maize (Zea Mays L.) yield trial of nine cultivars in 20 environments could be wholly or partially explained by liberality of the Gollob tests and also to compare properties of Gollob tests and several more conservative procedures. Gollob tests were found extremely liberal (Type I error rate as high as 66% when the first interaction PC in a 9 by 20 table is null) and AMMI users are warned not to rely on them. Tests known as F-GH1 and F-GH2 were essentially equivalent and effectively controlled Type I error rates at or below the intended level, but were conservative for any component for which the previous component was small. Simulation tests and iterated simulation tests with greater power than F-GH1 and F-GH2 but apparently with adequate control of Type I error rates, were developed. Simulation results suggest that F-GH1 or F-GH2 could usually be used to choose a predictive model with only a small loss in accuracy, and sometimes a gain, as compared to the expected model choice by cross validation with half of the data used for modeling and the other half for validation. In some cases cross validation is likely to choose a model with fewer PCs than the optimal truncated model obtainable from the full data set. If cross validation is used to choose a model, it is recommended that all but one replication should be used for modeling and only one for validation.
Gabriel (1971) proposed a technique for displaying the rows and columns of a twoway table as a two-dimensional biplot so that any element of the table can be approximated by the inner product of vectors corresponding to the appropriate row and column. The technique is useful for investigating the pattern of response of varieties over different environments, and substantially increases the information available from the more familiar methods of regression and principal component analysis without need for additional computation.
In plant breeding yield trials the environmental range often exceeds the genotypic range. In such instances environmental main-effects (mean yields) may confound characterization of selection environments, as the general productivity of an environment may be unrelated to tendencies in the relative performance (ranking) of genetic material grown in that environment.
This paper assesses methods for removing environmental main-effects to provide environmental descriptions with direct relevance to selection and evaluation in plant breeding. Relationships between environments using squared Euclidean distances based on raw, coded, ratio, and standardized data were compared with a rank change measure. The associated results of pattern analyses using the four differently calculated measures of squared Euclidean distance were also compared.
Standardization, giving each environment a mean of zero and a unit phenotypic standard deviation, was found to be the most suitable data transformation from theoretical considerations and in practice. Irrespective of environmental mean yield levels, standardized analyses result in the association of environments which rank lines similarly (and so provide similar selection information).
Any matrix of rank two can be displayed as a biplot which consists of a vector for each row and a vector for each column, chosen so that any element of the matrix is exactly the inner product of the vectors corresponding to its row and to its column. If a matrix is of higher rank, one may display it approximately by a biplot of a matrix of rank two which approximates the original matrix. The biplot provides a useful tool of data analysis and allows the visual appraisal of the structure of large data matrices. It is especially revealing in principal component analysis, where the biplot can show inter-unit distances and indicate clustering of units as well as display variances and correlations of the variables.
- Gauch