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Published online 2 March 2006
Published in Agron J 98:388-393 (2006)
DOI: 10.2134/agronj2004.0310
© 2006 American Society of Agronomy
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Barley

Biplot Analysis of Genotype by Environment Interaction for Barley Yield in Iran

H. Dehghania,*, A. Ebadia and A. Yousefib

a Dep. of Plant Breeding, Faculty of Agriculture, Tarbiat Modares Univ., Tehran, Iran
b Seed and Plant Improvement Institute, Karaj, Iran

* Corresponding author (dehghanr{at}modares.ac.ir)

Received for publication December 17, 2004.

   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
Cultivar evaluation and mega-environment identification are the most important objectives of multienvironment trials (MET). The objective of this study was to explore the effect of genotype and genotype x environment interaction on the grain yield of 19 barley (Hordeum vulgare L.) genotypes via GGE (genotype plus genotype x environment) biplot methodology. Experiments were conducted using a randomized complete block design with four replications for 3 yr at 10 locations. The biplot analysis identified three barley mega-environments in Iran. The first mega-environment contained locations Khoy, Mashhad, Miandoab, Karaj, and Nyshabour, where genotype Bahtim7-D1/79-w40762 was the winner; the second mega-environment contained locations Tabriz, Hamedan, Ardabil, and Arak, where genotype Walfajre/W1-2291 was the winner. The location of Zanjan made up the other mega-environment, with 73-M4-30 as the winner. Genotypes Bahtim7-D1/79-w40762 and Walfajre/W1-2291 had the highest mean yield and genotype K-201/3-2 had the poorest mean yield. The estimated relative yield of genotypes at Karaj station shows that genotype Bahtim7-D1/79-w40762 had the highest yield and genotype Owb70173-2H-OH had the poorest. The performances of genotypes Star/Alger and K-201/3-2 were highly variable, whereas genotypes Cossak/Gerbel/Harmal and Toji"S"/Robur were highly stable. The results of this study indicate the possibility of improving progress from selections under diverse location conditions by applying the GGL (genotype plus genotype x location) biplot methodology.

Abbreviations: ARAK, Arak Station • ARDA, Ardabil Station • E, environment main effect • G, genotype main effect • GE, genotype x environment interaction • GGE, genotype plus genotype x environment interaction • GGL, genotype plus genotype x location interaction • HAMA, Hamedan Station • KARA, Karaj Station • KHOY, Khoy Station • L, location main effect • MASH, Mashhad Station • MET, multienvironment trials • MIAN, Miandoab Station • NYSH, Nyshabour Station • TABR, Tabriz Station • Y, year main effect • ZANJ, Zanjan Station


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
MULTIENVIRONMENT TRIALS (MET) are conducted for all major crops throughout the world. In a MET, a number of cultivars are tested in a number of environments. The development of cultivars that are adapted to a wide range of diversified environments is a major goal of plant breeders in an improvement program. A cultivar or genotype is considered to be more adaptive or stable if it has a high mean yield but a low degree of fluctuation in yielding ability when grown in diverse environments (Arshad et al., 2003). The primary goal of METs is to identify superior cultivars for a target region. A secondary, but important, goal is to develop an understanding of the target region and, in particular, to determine if the target region can be subdivided into different mega-environments (Yan et al., 2000). The term mega-environment has been defined in different ways. Taking a global perspective of wheat (Tritcum aestivum L.) breeding, CIMMYT defined a mega-environment "as a broad, not necessarily contiguous area, occurring in more than one country and frequently transcontinental, defined by similar biotic and abiotic stresses, cropping system requirements, consumer issues, and for convenience by volume of production" (Braun et al., 1996). Investigation of mega-environments, which has been an important issue in MET research, is a prerequisite for meaningful cultivar evaluation and recommendation (Yan and Hunt, 1998, 2003).

The measured yield of each cultivar in each test environment is a measure of an environment main effect (E), a genotype main effect (G), and the genotype x environment (GE) interaction (Yan and Kang, 2003). Typically, E explains 80% or higher of the total yield variation; however, it is G and GE that are relevant to cultivar evaluation (Yan, 2002). The term GE interaction commonly refers to yield variation that cannot be explained by G or E alone (Yan and Hunt, 2001). Significant GE interaction results from changes in the magnitude of the differences among genotypes in different environments or from changes in relative ranking of the genotypes (Allard and Bradshaw, 1964). The GE interaction reduces the correlation between phenotype and genotype and selection progress (Comstock and Moll, 1963). The GE interaction has been studied by different researchers, and several methods have been proposed to analyze it, e.g., univariate methods such as Francis and Kannenberg's (1978) coefficient of variability, Plaisted and Peterson's (1959) mean variance component for pairwise GE interactions, Wricke's (1962) ecovalence, Shukla's (1972) stability variance, Finlay and Wilkinson's (1963) regression coefficient, Perkins and Jinks's (1968) regression coefficient, and Eberhart and Russell's (1966) sum of squared deviations from regression. Usually a large number of genotypes are tested across a number of sites and years, and it is often difficult to determine the pattern of genotypic response across environments without the help of graphical display of the data (Yan et al., 2001). The biplot technique provides a powerful solution to this problem (Gabriel, 1971). Biplot analysis is a multivariate analytical technique that graphically displays the two-way data and allows visualization of the interrelationship among environments, genotypes, and interactions between genotypes and environments. Biplots are useful in summarizing and approximating patterns of response that exist in the original data (Gabriel, 1971). Two types of biplots, the AMMI biplot (the statistical model of additive main effect and multiplicative interaction; Gauch, 1988; Zobel et al., 1988) and the GGE biplot (genotype main effect plus genotype x environment interaction; Yan et al., 2000) have been used to visualize genotype x environment two-way data.

Yan et al. (2000) presented standard biplots of the site regression model to select the best performing cultivars in subsets of sites. In analyzing Ontario winter wheat performance trial data, Yan and Hunt (2001) used the GGEbiplot software (Yan, 2001) to obtain the first two principal components (PC1 and PC2) derived from PC analysis of environment-centered yield data. GGEbiplot can be useful in some major aspects. The first is to display the "which-won-where" pattern of data that may lead to the identification of high-yielding and stable cultivars and the second is to identify discriminating and representative test environments (Yan et al., 2001).

Iran is currently one of the world's largest net importers of agricultural products, importing about 30% of its requirements. Rapid population growth is expected to increase the demand for food, since the countrywide average precipitation is about 250 mm yr–1. Iran is working toward increasing its agricultural efficiency. To increase its efficiency, the agricultural sector of Iran is attempting to improve barley production through identification and introduction of stable and adaptive cultivars.

A major challenge of plant breeding is finding the useful information within the quantities of data. The GGE biplot graphically displays G and GE of a MET in a way that facilitates visual cultivar evaluation and mega-environment identification. The GGEbiplot software was chosen to facilitate the application of the GGE biplot methodology in MET data analysis and the analyses of two-way data.

Our objective was to use the GGE biplot technique to examine the possible existence of different mega-environments in barley-growing regions in Iran. Other objectives were to apply this method to determine the best genotype for each mega-environment and determine discriminating ability and representativeness of the environments.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 REFERENCES
 
Experiments
Data analyzed in this study were obtained from sets of barley yield trials conducted for 3 yr. Each year, 19 genotypes were tested at 10 different research stations in Iran. All research stations are located in cold regions of Iran, including Mashhad (MASH), Khoy (KHOY), and Nyshabour (NYSH) stations in the northeast; Ardabil (ARDA), Miandoab (MIAN), and Tabriz (TABR) stations in the northwest; Hamedan (HAMA), Zanjan (ZANJ), and Arak (ARAK) stations in the west; and Karaj (KARA) station in the north-central part of Iran. These locations have an elevation of >1000 m above sea level and mean annual minimum temperature of less than –14°C; the number of days with freezing temperatures is 90. The names of the genotypes, their pedigrees, and their origins are given in Table 1. At each location, a randomized complete block design with four replications was used. Plots were 1.2 m wide and 6 m long. Grain yield was obtained from a sample of 6 m2 from the center of each plot in each year and location. Following harvest, seed yield was determined for each genotype in each test environment, and mean yield average was computed in accordance with the experimental design. Data analyses for yield were done using the GGEbiplot software (Yan, 1999, 2001). Analysis of variance was conducted using SAS (SAS Institute, 1996) to determine the effect of E (consisting of year [Y], location [L], and Y x L interaction), G, and all possible interactions among these factors, on grain yield.


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  		Table 1. Name, pedigree, and origin of 19 barley genotypes.

 
Correlation coefficients between pairs of environments were computed using SAS (SAS Institute, 1996).


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
REFERENCES
 
Analysis of Variance
Analysis of variance (Table 2) indicated significant GE interaction (P < 0.01) and showed the influence of changes in environment on the yield performance of the genotypes evaluated. The E effect was significant (P < 0.01). The L and G effects were significant at P < 0.05 and P < 0.01, respectively; however, the year effect was not significant. The large yield variation due to L, which is irrelevant to genotype evaluation and mega-environment investigation (Gauch and Zobel, 1997), justified the selection of SREG (site regression) as the model for analyzing the MET data (Yan et al., 2000). Mean squares associated with the GL and G x Y interaction were significant (P < 0.10) and not significant, respectively.


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  		Table 2. Analysis of variance on grain yield.

 
The results of analyses of variance for the yearly data are presented in Table 3, which gives an overall picture of the relative magnitudes of the G, L, and GL variance terms. Location was always the most important source of yield variation, accounting for 65 to 83% of G + L + GL. The magnitude of the GL interaction relative to G suggested the possible existence of different mega-environments. The large yield variation due to L, which is irrelevant to cultivar evaluation and mega-environment investigation (Yan et al., 2000), justifies the selection of a GGL biplot as the appropriate method for analyzing the MET data. A two-dimensional, symmetrically scaled GGL biplot graphically approximates the location-centered yield data (Fig. 1 and 2) . The GGL biplots for 3 yr were similarly constructed and are not presented. Each year the locations fell into different groups and the pattern of the location groupings varied across years. The GGL biplots based on the averaged data for 3 yr (Fig. 1 and 2) was used. The same implication was previously exploited by Yan et al. (2000).


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  		Table 3. Genotype (G), Location (L), and Genotype x Location (GL) variance terms for barley yield.

 

Figure 1
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  		Fig. 1. Biplots (A) of the mega-environments and their winning genotypes, (B) of genotype ranking based on both average yield and stability, (C) comparing the environments with the ideal environment based on both discrimininating ability and representativeness of the target environment, and (D) comparing the genotypes with the ideal genotype for both mean yield and stability.

 

Figure 2
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  		Fig. 2. Biplots (A) of the performance of different genotypes at the best location (KARA), (B) comparing the performance of a given genotype (G11) at different environments, (C) comparing two genotypes (G5 and G7) in different environments, and (D) of the correlation between environments.

 
In a cultivar x location x year experiment, one of the problems associated with cultivar evaluation is that the effect of location can vary considerably from year to year. This is usually evidenced by a significant location x year interaction in the analysis of variance. The presence of such an interaction presents a serious problem to anyone wishing to recommend a cultivar to a region. Because the environment factor in this analysis is a combination of locations and years, it is not helpful when recommendations of cultivars to specific locations are required. Lin and Binns (1988) stated that the environmental effect on a genotype depends on two main elements: soil and weather. The soil element is usually persistent from year to year and can be regarded as fixed. The weather element is more complex because it has a persistent part represented by the general climatic zone, and an unpredictable part represented by time variation (e.g., year to year). The environmental effect has been conceptually subdivided into predictable and unpredictable components; a similar subdivision can be made for the GE interaction. The separation of the environment effect into these two components was first advocated by Allard and Bradshaw (1964). Lin and Binns (1988) suggested that, in a cultivar x location x year experiment, we can assume that the cultivar x location mean averaged across years is the biological equivalent of cultivar x predictable variation, and years within location is the equivalent of cultivar x unpredictable variation. Lin and Butler (1988) used the set of cultivar x location means averaged across years with the assumption that the mean across years at each location was representative of the fixed component. The assumption is that, if the locations are grouped based on their fixed component, the consistency of the GE interaction structure across years may be substantially improved. Since cultivar x predictable variation is controllable, this also justifies the using the GGL biplot.

Winning Genotype and Mega-environment
Visualization of the "which-won-where" pattern of MET data is important for studying the possible existence of different mega-environments in a region (Gauch and Zobel, 1997; Yan et al., 2000, 2001). The polygon view of a biplot is the best way to visualize the interaction patterns between genotypes and environments and to effectively interpret a biplot (Yan and Kang, 2003). The vertex genotypes in this investigation were Bahtim7-D1/79-w40762, Walfajre/W1-2291, Vavilon, Star/Alger, Owb70173-2H-OH, K-201/3-2, and Kavir/Badia"s" (Fig. 1A). The vertex genotype for each sector is the one that gave the highest yield for the environments that fall within that sector. Another important feature of Fig. 1A is that it indicates environmental groupings, which suggests the possible existence of different mega-environments. Thus, based on biplot analysis of 3 yr of data, three mega-environments are suggested in Fig. 1A. The first mega-environment contains locations KHOY, MASH, MIAN, KARA, and NYSH, with genotype Bahtim7-D1/79-w40762 being the winner; the second mega-environment contains locations TABR, HAMA, ARDA, and ARAK, with genotype Walfajre/W1-2291 being the winner. The location of ZANJ makes up another mega-environment, with 73-M4-30 the winner.

Mean Performance and Stability of Genotypes
In Fig. 1B, genotypes Bahtim7-D1/79-w40762 and Walfajre/W1-2291 had the highest mean yield and genotype K-201/3-2 had the poorest mean yield. Mean yields of the genotypes were in the following order: Walfajre/W1-2291 > BAHTIM7-D1/79-W40762 > L.124//L.640/L.527 > Cossak/Gerbel/Harmal {approx} 73-M4-30 > C1-10143/Walfajre > Toji"S"/Robur {approx} Kozir > Kavir/Badia"s" {approx} Gerbel/Alger {approx} Star/Alger > L.1242/Kossak {approx} Dundy {approx} Arass/Cyclon > L.131//cg/CM {approx} Schulyer/L.640 > Owb70173-2H-OH > Vavilon > K-201/3-2.

The performance of genotypes Star/Alger and K-201/3-2 were the most variable (least stable), whereas genotypes Cossak/Gerbel/Harmal and Toji"S"/Robur were highly stable. An ideal genotype is one that has both high mean yield and high stability. The center of the concentric circles (Fig. 1C) represents the position of an ideal genotype, which is defined by a projection onto the mean-environment axis that equals the longest vector of the genotypes that had above-average mean yield and by a zero projection onto the perpendicular line (zero variability across environments). A genotype is more desirable if it is closer to the ideal genotype. Therefore, genotypes Bahtim7-D1/79-w40762 and L.124//L.640/L.527 are more desirable than other genotypes (Fig. 1C).

Discriminating Ability and Representativeness of Environments
Discriminating ability is an important measure of a test environment. Another equally important measure of a test environment is its representativeness of the target environment. An ideal environment should be highly differentiating of the genotypes and at the same time representative of the target environment.

The ideal environment represented by the small circle with an arrow pointing to it (Fig. 1D) is the most discriminating of genotypes and yet representive of the other test environments. Therefore, ARDA, TABR, and HAMA are more desirable test environments than ZANJ, KARA, or MASH.

Comparing Genotype Performance at the Best Location
Mean comparison of yield at 10 locations (during 3 yr) indicated that Karaj had the highest yield (Table 4). Figure 2A is a graphic comparison of the relative performance of all genotypes at Karaj. This figure indicates that at Karaj, genotype Bahtim7-D1/79-w40762 had the highest yield and Owb70173-2H-OH had the poorest yield. The perpendicular line separates cultivars that performed below average from those performing above average at Karaj: cultivars Owb70173-2H-OH, Schulyer/L.640, Star/Alger, L.131//cg/CM, Vavilon, L.1242/Kossak, and Gerbel/Alger performed below average, whereas the other cultivars, on the same side of the perpendicular line as Karaj, performed equal to or above average. The order of the genotypes at Karaj was Schulyer/L.640 < Star/Alger < L.131//cg/CM < Vavilon < L.1242/Kossak < Gerbel/Alger < Kozir < 73-M4-30 {approx} K-201/3-2 < Dundy < Toji"S"/Robur < Arass/Cyclon {approx} Walfajre/W1-2291 < Kavir/Badia"s" {approx} Cossak/Gerbel/Harmal < C1-10143/Walfajre < L.124//L.640/L.527.


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  		Table 4. Mean comparison of yield at 10 locations and correlation coefficients among test environments.

 
Comparing Relative Genotype Performance in Different Locations
Figure 2B compares the relative performance of genotype L.124//L.640/L.527 at all locations. The environments were ranked in the direction of the L.124//L.640/L.527 axis, and the parallel lines help visualize the ranking of the environments relative to the performance of L.124//L.640/L.527. Thus, L.124//L.640/L.527 performed the best in KHOY, followed by ARDA, TABR, MIAN, HAMA, MASH, NYSH, KARA, ARAK, and ZANJ.

Comparing Performance of Two Genotypes at All Locations
In Fig. 2C, genotypes Bahtim7-D1/79-w40762 and Owb70173-2H-OH are compared. Bahtim7-D1/79-w40762 was better than Owb70173-2H-OH at ARAK, ARDA, HAMA, KARA, KHOY, MASH, MIAN, NYSH, and TABR, while Owb70173-2H-OH was better at ZANJ.

All of the results of the comparison indicate that the order at different locations was not similar; there are several environmental factors such as preseason rainfall, cropping season rainfall, minimum and maximum temperature, and relative humidity that contributed to the GE interaction sum of squares. Saeed and Francis (1984) also found that cropping season rainfall and temperature had a significant effect on yield and contributed to the GE interaction. This was the greatest reason for the change in performance order at different locations in this study.

The climatic conditions in many regions of Iran are typical of arid and semiarid regions; however, much of the arable land is devoted to growing wheat and barley, which are staple foods and the main sources of protein for the average Iranian. Barley production is mostly possible only with irrigation, due to low rainfall and high evaporation rates. An estimated 32 Mha of unused land are potentially suitable for barley production, but the shortage of water limits their agricultural role. For barley production, recommendations are based on the premise that regional characteristics are persistent and that breeding locally adapted cultivars can be helpful toward increasing agricultural efficiency; on this basis, we have used the GGL biplot.

Correlation among Environments
The correlation coefficients among the 10 test environments are presented in Table 4. The vector view of a GGE biplot provides a succinct summary of the interrelationships among the environments (Yan, 2002). Figure 2D is the vector view of the GGE biplot, in which the environments are connected with the biplot origin via lines. This view of the biplot aids understanding of the interrelationships among the environments. The cosine of the angle between the vectors of two environments approximates the correlation coefficient between them. Therefore, the most prominent relations were: (i) near-zero correlations between MIAN and ZANJ, between ARDA and KARA, and between ARAK and MASH as indicated by the near-perpendicular vectors (r = cos90 = 0); and (ii) positive associations among TABR, HAMA, and ARDA, and between KHOY and TABR as indicated by acute angles.

The vector view of a biplot can be used to identify different mega-environments; test environments from different mega-environments should have large angles or low or negative correlations. Another useful property of the vector view of the biplot is that the length of the environment vectors approximates the standard deviation within each environment, which is a measure of their discriminating ability. Thus, ARDA and KHOY were most discriminating and KARA and NYSH were least discriminating (Fig. 2D). The vector view of a biplot helped identify redundant test environments. Environments with small angles between them were highly positively correlated, and they provided similar information on genotypes.

Obtaining similar information by using fewer test environments should reduce the cost of testing and increase breeding efficiency. In the MET data of barley in all 3 yr, locations KHOY and MASH were closely correlated (Fig. 2D), suggesting that these two locations provide redundant information about genotypes. Therefore, we suggest that one of the two locations be dropped to reduce the cost of testing.

In short, this study indicates the possibility of improving progress from selections under diverse location conditions by applying a GGL biplot.


   ACKNOWLEDGMENTS
 
We would like to acknowledge Professor W. Yan both for development of the GGE biplot methodology in MET data and GGEbiplot software and for valuable insights.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 




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