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GGEbiplot Analysis of Wheat CultivarsEvaluated in a Multi-Environment TrialMuhanad W. Akash a , Manjit S. Kang b c & Gerald O. Myers ca The University of Jordan, Faculty of Agriculture, Department ofHorticulture and Crop Science , Amman, Jordanb Punjab Agricultural University , Ludhiana, Indiac Louisiana State University Agricultural Center, School of Plant,Environmental and Soil Sciences , Baton Rouge, Louisiana, USAPublished online: 28 May 2009.

To cite this article: Muhanad W. Akash , Manjit S. Kang & Gerald O. Myers (2009) GGEbiplot Analysisof Wheat Cultivars Evaluated in a Multi-Environment Trial, Journal of New Seeds, 10:2, 88-97, DOI:10.1080/15228860902892232

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Journal of New Seeds, 10:88–97, 2009Copyright © Taylor & Francis Group, LLC ISSN: 1522-886X print/1522-9025 onlineDOI: 10.1080/15228860902892232

WJNS1522-886X1522-9025Journal of New Seeds, Vol. 10, No. 2, April 2009: pp. 1–16Journal of New Seeds

GGEbiplot Analysis of Wheat Cultivars Evaluated in a Multi-Environment Trial

GGEbiplot Analysis of Wheat CultivarsM. W. Akash et al.

MUHANAD W. AKASH1, MANJIT S. KANG2,3, and GERALD O. MYERS3

1The University of Jordan, Faculty of Agriculture, Department of Horticulture and Crop Science, Amman, Jordan

2Punjab Agricultural University, Ludhiana, India3Louisiana State University Agricultural Center, School of Plant, Environmental

and Soil Sciences, Baton Rouge, Louisiana, USA

Effective breeding relies on a good understanding of genotype byenvironment interaction (GEI). Six cultivars of wheat wereevaluated at four locations. The ANOVA indicated a significantGEI, which was taken into consideration using the GGEbiplotsoftware.In this paper, we apply and discuss a biplot approach thatfacilitates visual genotype (G) + GEI = GGE analysis, determinesthe best cultivar for each environment and determines discrimi-nating ability and representativeness of test environments. Thefirst two principal components (PC1 and PC2), used to create atwo dimensional GGEbiplot, explained 83.5 and 13.9% of GGEsum of squares, respectively. Cultivars ‘Acsad-65’ performed best atJubaiha and Gwair, ‘H-nawawi’ at Maru, and ‘Om-quais’ atRamtha. ‘Acsad-65’ was an ideal cultivar (in mean performanceand stability) and Jubaiha was an ideal environment (in repre-sentativeness and discriminating ability). No single cultivar couldbe recommended for all test environments; rather, specific superiorcultivars were identified for specific environments based on bothaverage yield and stability.

KEYWORDS discriminating ability, genotype by environmentinteraction, GGEbiplot, multi-environment, wheat

The authors acknowledge the financial support of the Deanship of Academic Researchat The University of Jordan.

Address correspondence to Muhanad W. Akash, The University of Jordan, Faculty ofAgriculture, Department of Horticulture and Crop Science, Amman 11942, Jordan. E-mail:makash@ju.edu.jo

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GGEbiplot Analysis of Wheat Cultivars 89

INTRODUCTION

Breeders regularly deal with genotype by environment interaction (GEI) whentesting cultivars across different environments. Such interaction reduces thecorrelation between phenotype and genotype, hindering selection progress(Comstock and Moll 1963). An understanding of the causes of this interactioncan help identify superior cultivars for a target environment (Kang 2002).

Observed yield for a genotype in an environment is a measure ofenvironment main effect (E), genotype main effect (G), and GEI (Yan andKang 2003). The GEI refers to yield variation that cannot be explained byG or E alone (Kang and Gauch 1996; Yan and Hunt 2001). Several methodshave been used to statistically analyze GEI, e.g., coefficient of variability(Francis and Kannenberg 1978), mean variance component for pairwise GEI(Plaisted and Peterson 1959), ecovalence (Wricke 1962), stability variance(Shukla 1972), and regression coefficient and sum of squared deviationsfrom regression (Finlay and Wilkinson 1963; Eberhart and Russell 1966;Perkins and Jinks 1968). However, it is often not easy to resolve the geno-typic response across different environments without graphical methods(Yan et al. 2001). The biplot technique helps solve the problem (Gabriel1971). A GGEbiplot displays GGE, as the part of variation that is relevant tocultivar evaluation (Yan 2002). The E main effect is removed, as it is irrele-vant to cultivar evaluation (Yan and Kang 2003). The GGEbiplot analysis isa multivariate analytical technique that graphically displays any two-waydata such as genotype by environment, genotype by trait, diallel cross, andgenotype by marker data for quantitative trait loci mapping (Yan and Hunt2002; Yan and Rajcan 2002; Yan and Kang 2003).

The aim of this study was to use the GGEbiplot methodology for graphi-cal analysis of six wheat cultivars evaluated at four different locations orenvironments, identify the best cultivar(s) for each environment, and deter-mine discriminating ability and representativeness of the test environments.

MATERIALS AND METHODS

Field Experiment

Six most commonly used wheat cultivars in Jordan, viz., ‘Acsad-65’,‘Deir-alla’, ‘H-27’, ‘H-nawawi’, ‘Om-quais’, and ‘Sham’, were evaluated atfour sites (Table 1) in 2006/7 growing season. At each site, the experimentwas laid out as a randomized complete-block design with three replications.The plot size was five rows of 2 m length, with 0.25 m spacing betweenrows. Seeding rate was 240 seeds per square meter. One square meter washarvested from each plot and a random sample (around 0.5 kg) was takento estimate grain yield. In total, 60 samples were threshed and grain yield(kg ha−1) was estimated by weighing the cleaned grain.

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Statistical Analysis

In the analysis of variance, environment and cultivar main effects wereregarded as fixed effects and analyzed using the “mixed procedure” of SASversion 9 (SAS Institute 2002). The biplot graphs were made using GGEbi-plot software (Yan 2001; Yan and Kang 2003), which is based on principalcomponent analysis (PCA). The PCA constructs a set of p normalizedorthogonal linear combinations of data in such a way that the first linearcombination has variance as large as any possible single linear combinationcan have for the data. The first two PCs generally explain largest amount ofvariation. This technique is a dimension-reduction tool. For further study ofthe methodology, the reader is referred to Yan and Kang (2003) and Yanand Tinker (2006).

RESULTS AND DISCUSSION

Analysis of Variance

Environment and cultivar main effects for grain yield were significant atprobability of 0.001 and their interaction was significant at probability of0.05 (Table 2). This finding justified the use of GGEbiplot analysis.

Which-Won-Where

One of the most attractive features of the GGEbiplot software is its ability toshow the “which-won-where” pattern via a “polygon view” that facilitatesthe visualization of the interaction patterns between cultivars and environ-ments (Yan and Kang 2003). This polygon is drawn such that cultivarsthat are farthest from the biplot origin are connected and all other cultivarsare contained within the polygon (Figure 1). Then lines are drawn that are

TABLE 1 General Description, Average Annual Rainfall, Cumulative Rainfall and MeanGrowing Season Temperature Occurring Between December 1 and June 30 in 2006/07Growing Season

Environment PositionElevation

(m) Soil type

Averageannualrainfall (mm)

Cumulative rainfall

in 2006/07 (mm)

Mean growing season

temperature in 2006/07

(°C)

Ramtha 32°46’ N 36°45’ E 590 Silty clay 220 164 20.9Ghweir 31°14’ N 35°45’ E 820 Silty clay 240 330 20.8Jubeiha 32°2’ N 35°88’ E 980 Heavy clay 410 404 19.4Maru 34°40’ N 32°36’ E 620 Heavy clay 390 330 20.7

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GGEbiplot Analysis of Wheat Cultivars 91

perpendicular to each polygon side. This partitions the polygon into sectors(Figure 1) (see Yan 2001).

There are four sectors in Figure 1, with cultivars ‘Acsad-65’, ‘H-nawawi’,‘Om-quais’, and ‘Sham’ as vertex cultivars. Environments Jubaiha and Gwairfell in the sector in which ‘Acsad-65’ was the vertex cultivar, indicating that‘Acsad-65’ was the best cultivar for Jubaiha and Gwair. Thus, the GGEbiplotmethodology allowed the selection of most suitable cultivar for two targetenvironments. No environments fell into sectors with ‘Sham’ and ‘H-27’,indicating that these two cultivars were not the best cultivars for any envi-ronment (however, see later discussion on stability).

TABLE 2 Mean Squares of Three Replications (Rep)for Grain Yield for Six Wheat Cultivars (Cul)Evaluated Under Four Different Environments (Env)in 2006/07

Source of variation df Mean squares

Env 3 2834131***Cul 5 211167***Cul *Env 15 67340*Rep (Env) 7 76664Residual Error 29 29343

*Significant at 0.05 probability level.***Significant at 0.001 probability level.

FIGURE 1 GGEbiplot polygon of the six wheat cultivars using the mean grain yield obtainedfrom four different environments in 2006/07.D

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Mean Performance and Stability of Cultivars

An ideal cultivar should have both high grain yield and high stability.The center of the concentric circles is where an ideal cultivar should be;the smaller the distance from a cultivar to this center, the more ideal thecultivar. The average yield of cultivars is approximated by the projec-tions of their markers on to the average tester coordinate (ATC) x-axis.Thus, cultivar ‘Acsad-65’ had the highest average yield, and ‘Sham’ hadthe lowest. The stability of the cultivars is measured by their projectionon to the ATC y-axis; the greater the absolute length of the projectionof a cultivar, the less stable it is. Thus, ‘Om-quais’ was the leaststable cultivar; ‘Sham’ and ‘Hurani-27’ were the most stable cultivars(Figure 2A). These two cultivars did not have any environments in theirsector (see Figure 1), suggesting these were not the best performers inany environment. In fact, these two cultivars had below average grainyield (Figure 2A). A cultivar that is stable and poor in performance is nouse to the farmer. ‘Acsad-65’ was closest to the concentric center, i.e.,the most ideal cultivar (highest yield and most stable) followed by‘H-nawawi’, ‘Deir-alla’, ‘Om-quais’, ‘H-27’, and ‘Sham’ as the worst cultivar(Figure 2A).

Representativeness and Discriminating Ability of Environments

Representativeness and discriminating ability are two important measures ofa test environment (Yan and Kang 2003). An ideal environment is the mostrepresentative of all environments and the most discriminating among culti-vars. In Figure 2B, such environment is represented by the small circle withan arrow pointing to it.

The vector length, the distance from the plot origin to the marker of anenvironment (Figure 2B), represents that environment’s discriminatingability. The longer the vector, the more discriminating the environment.The absolute length of the projection from the marker of an environmentonto the ATC y-axis is a measure of its representativeness. The longer theprojection, the less representative the environment. The ATC x-axis passesthrough the biplot origin and the marker of the average environment, whichis defined by the average PC1 and PC2 scores across all environments (Yanand Tinker 2006). Environment Jubaiha was the most representative, as ithad a small projection on to the ATC y-axis, and also highly discriminating,as it had the longest vector. Environments Maru and Gwair had good discrim-inating ability (far away from the origin) but were not highly representative ofenvironments used (large projection onto the ATC y-axis). EnvironmentRamtha was representative but not discriminating (short vector length)(Figure 2B).

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GGEbiplot Analysis of Wheat Cultivars 93

As the center of the concentric circles represents the ideal environment,Jubaiha was the best environment, followed by Maru and Gwair as a group(Figure 2B). Ramtha was the poorest test environment. This informationcould be important in the long run; Ramtha could be dropped as a test siteshould monetary resources become limited.

FIGURE 2 The average environment coordinate (AEC) showing (A) the mean performanceand stability of the six wheat cultivars and their ranking, and (B) the discriminating abilityand representativeness of the four environments and their ranking.

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Cultivars Performance at Gwair

Mean separation using LSD at the p = .05 level of significance was used.Gwair had significantly greater yield than other test environments, exceptMaru (Table 3).

To rank the cultivars based on their performance at Gwair, an axis wasdrawn that passed through the biplot origin and Gwair marker. This axisrepresented the ranking of the cultivars, which was in the following order:‘Acsad-65’ had the highest yield, followed by ‘Om-quais’, ‘H-nawawi’, ‘Deir-alla’, and ‘H-27’; ‘Sham’ had the lowest yield (Figure 3A).

The perpendicular line in Figure 3A separated cultivars that performedbelow average from those that performed above average at Gwair. There-fore, cultivars ‘Acsad-65’ and ‘Om-quais’ had above-average performance,whereas the other four cultivars, on the other side of the perpendicular line,had below-average performance.

Relative Performance of ‘Acsad-65’ in Different Environments

‘Acsad-65’ cultivar had significantly greater overall grain yield than the othercultivars, except ‘H-nawawi’ (Table 3). To study the specific adaptation of‘Acsad-65’, an axis was drawn that passed through the biplot origin and‘Acsad-65’. Thus, ‘Acsad-65’ performed the best in Jubaiha, followed byGwair, Maru, and Ramtha (Figure 3B).

The perpendicular line separated environments in which ‘Acsad-65’performed above average from those in which ‘Acsad-65’ performed belowaverage. Thus, ‘Acsad-65’ yielded above average in all test environments(Figure 3B). This was also declared as the ideal cultivar (Figure 2A).

Comparing Performance of Two Cultivars Across Environments

To compare the performance of two cultivars (e.g., ‘Acsad-65’ and‘Om-quais’) a line joining the two cultivars and a perpendicular line that

TABLE 3 Mean Separation of Grain Yield (Kg ha−1) of the Six Used WheatCultivars Evaluated Under Four Different Environments in 2006/07

CultivarGrain yield (Kg ha−1) Environment

Grain yield (Kg ha−1)

‘Acsad-65’ 1331.33 A Gwair 1436.35 A‘Deir-alla’ 1038.61 B Jubaiha 1109.04 B‘H-27’ 1025.01 B Maru 1263.28 AB‘H-nawawi’ 1179.54 AB Ramtha 512.41 C‘Om-quais’ 1061.32 B‘Sham’ 845.82 CLSD (0.05) 170 252

Note: Means, in each column, followed by similar letters are not significantlydifferent according to LSD (0.05 probability level).

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GGEbiplot Analysis of Wheat Cultivars 95

FIGURE 3 GGEbiplots showing (A) the performance of the six wheat cultivars grown atGwair, (B) the performance of ‘Acsad-65’ cultivar in different environments and (C) the com-parison of two cultivars (‘Acsad-65’ and ‘Om-quais’) grown in the four test environments.

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passed through the plot origin were drawn (Figure 3C). This graph showedthat environments Jubaiha, Gwair, and Maru were on the ‘Acsad-65’ side ofthe perpendicular line, meaning that ‘Acsad-65’ was better than ‘Om-quais’in these three environments. However, ‘Om-quais’ was better than ‘Acsad-65’in Ramtha environment.

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