Analysis of Genotype, Environment and Genotype Environment Interaction in Bread Wheat Genotypes Using GGE Biplot

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1 AGRICULTURAL COMMUNICATIONS, 2016, 4(3): 1 8. Analysis of Genotype, Environment and Genotype Environment Interaction in Bread Wheat Genotypes Using GGE Biplot MOHTASHAM MOHAMMADI 1, TAHMASEB HOSSEINPOUR 2, MOHAMMAD ARMION 3, HASAN KHANZADEH 4 AND HASAN GHOJOGH 5 1 Dryland Agricultural Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Gachsaran, Iran. 2 Seed and Plant Research Department, Lorestan Agricultural and Natural Resources, Research and Education Center, Agricultural Research, Education and Extension Organization, AREEO, Khoramabad, Iran. 3 Seed and Plant Research Department, ILam Agricultural and Natural Resources, Research and Education Center, Agricultural Research, Education and Extension Organization, AREEO Ilam, Iran. 4 Seed and Plant Research Department, Ardabil Agricultural and Natural Resources, Research and Education Center, Agricultural Research, Education and Extension Organization, AREEO, Moghan, Iran. 5 Seed and Plant Research Department, Golestan Agricultural and Natural Resources, Research and Education Center, Agricultural Research, Education and Extension Organization, AREEO, Gonbad, Iran. *Corresponding Author: mohtashammohammadi@yahoo.com (Accepted: 3 April 2016) ABSTRACT Growing adapted cultivars with high yield stability is an effective strategy for reducing environmental effects on wheat production in rainfed areas. This research was designed to evaluate changes in bread wheat adaptation and yield stability to identify genotypes with high yield ability and superior yield stability across a range of test environments in semi arid areas of Iran and the evaluation of environment relationships. The plant materials used in this investigation included 18 diverse new advance genotypes of bread wheat including KOOHDASHT cultivar as reference with four replications in a RCBD design during three years ( ) in 12 environments. The sites regression (SREG) model was used for genotype environment interaction (GEI) studies and analysing multienvironmental trials. The genotypes studied indicated both crossover and non crossover types of GEI, thereby genotypic selection will be difficult for the rain fed conditions of tropical dryland research stations of Iran. However, The GGE model aided in determination of the relative performance of genotypes at different environments. The results indicated G15 (THELIN/3/BABAX/LR42//BABAX/4/BABAX/LR42//BABAX CGSS 02Y00083T), and G12 (SUNCO/2*PASTOR) were more desirable due to both high mean yield and high stability. It was revealed that G15, which fell into the centre of concentric circles, was ideal genotype in terms of higher yielding ability and stability, compared to the rest of the genotypes. Moreover, based on the results of this research Gachsaran station was identified as closest location to ideal environment which can be considered as more suitable environment under limited resources. Keywords: Adaptability, breeding, drought, heat, site regression, stability. Abbreviations: DARI: Dryland Agricultural Research Institute; GEI: Genotype Environment Interaction; GGE: Genotype by Environment; MET: Multi Environment Trials; SREG: Sites Regression Model. INTRODUCTION Targeting improved genotypes onto its growing locations is the ultimate interest of all plant breeding programs (Annicchiarico, 2002). The new genetically improved genotypes needed to be evaluated in multi environmental trials in order to test their yield performance across different environments. Developing the high yielding crop genotypes as well as more stable genotypes is very important for plant breeders (Gauch et al., 2008; Yang et al., 2009, Sabaghnia et al., 2013). Also, it has been shown that new improved genotypes are more responsive to changes in environmental conditions than old and local native genotypes (Sabaghnia et al., 2012). Thus, the GE interaction affects inspiration of breeding progress because it makes the evaluation, utilization and selection of superior improved genotypes difficult (Mohammadi, 2014a).

2 AGRICULTURAL COMMUNICATIONS In the presence of GE interaction, mean yield is less predictable and cannot be interpreted based on genotypic or environmental effects alone (Ebdon and Gauch, 2002). Meanwhile, generalized indication of genotypes for cultivation in good and poor locations may causes in wrong choices due to specific adaptation of genotypes to specific locations (Mohammadi et al., 2013). Plant breeders perform multi environment trials (MET) across test environments (several locations and over years) to select favorable genotypes based on both mean yield and performance stability; and to determine whether a test environment is homogeneous or should be divided into various mega environments (Gauch, 2006; Yan and Kang, 2002). There are different methods for analyzing and interpreting GE interaction and facilitate genotype recommendations in MET. These models have been classified as univariate versus multivariate approaches or parametric versus nonparametric methods (Flores et al., 1998; Karimizadeh et al., 2012; Lin et al., 1986). Multivariate statistical approaches explore multi directional aspects of GE interaction and attempt to extract more information from GE interaction components (Gauch and Zobel, 1996; Gauch et al., 2008). Multiplicative models have an additive component (i.e., main effect of environments and/or genotypes) and a multiplicative component (i.e., GE interaction) (Mohammadi, 2014b). The multiplicative GE interaction is far too complex to be summarized by one or two stability parameters using univariate measures of stability. Meanwhile advances in computer science have made it possible to use multivariate statistical procedures of data analysis with fast and precise algorithms (Annicchiarico, 1997; Gauchet al., 2008). The GGE biplot model is a multiplicative model that absorbs the genotypes main effects, plus the GE interaction, which are the important factors in yield stability (Yan and Tinker, 2006). This model uses the primary and secondary effects from GGE biplot analysis and is useful in megaenvironment identification (Yan et al., 2007; Yang et al., 2009). A GGE biplot as a data visualization tool is able to graphically demonstrate a GE interaction pattern and permits visualization on the interrelationships among genotypes, environments and their interaction. It is an powerful tool for effective analysis and interpretation of MET data to identify a mega environment, genotype evaluation based on the both yield and stability; and evaluation of test environments from a discrimination aspect. A GGE biplot indicating both genotype and environment based on a site regression (SREG) model have been used to demonstrate a GE interaction pattern as well as possible (Yan and Tinker, 2005; Yan et al., 2007). Because these procedures are statistically more complex, a specialized statistical package is sometimes needed, and results lead to interpretations that are more consistent with the reality of the trials. The GGE biplot procedure has been successfully employed to determine the relationship among genotypes, environments, mega environments and suitable genotypes (high yielding, with good yield stability) all over the world (Dehghani et al., 2006; Sabaghnia et al., 2008). The GGE biplot procedure has been employed successfully in determining mega environments as well as the most favorable genotypes of wheat (Farshadfar et al., 2012; Farshadfar and Sadeghi 2014; Mohammadi et al., 2011; Mohammadi et al., 2012); barley (Dehghani et al., 2006; Mohammadi, 2015), lentil (Sabaghnia et al., 2008; Karimzadeh 2013a, b), chick pea (Farshadfar et al., 2011) and maize (Dehghani et al., 2009) in Iran. The objectives of the this research were to (1) determine the magnitude of the GE interaction effects and effectiveness of GGE biplot methodology useful displaying of presence variation among genotypes and environments (2) identify favorable bread wheat genotypes that have both high yield and stable performance across test environments of Iran s semiarid areas (3) determine the best test environments (representative and discriminating) and evaluation relationships between test environments. MATERIALS AND METHODS Plant Material: Eighteen diverse new advance genotypes of bread wheat including Koohdasht cultivar as reference (were tested during growing seasons in 12 environments at four different locations including Gachsaran, Gonbad, Khoramabad and Ilam, Iran. Experimental Environments: These bread wheat elite lines were drawn from DARI s (Dryland Agricultural Research Institute) wheat breeding program (their pedigrees are given in Table 1). The trials were conducted in randomized complete block design with four replications on well prepared soil at each location every year. Different agro geographic properties of these test locations are summarized in Table 2. Automatic sowing machine was used for seeding based on 300 kernels per square meter for each genotype on plot size of m consisting of six rows of m lengths. Sowing was done from 20 th December to 10 th December in accordance with the optimum time recommended 2

3 MOHAMMADI ET AL. for each test location. According to nutrient requirements, appropriate fertilizers of N and P2O5 were used. Analyses of variance were performed for individual environments to plot residuals and identify outliers. The Anderson Darling normality and Bartlett s test was used to determine the homogeneity of variances among environments to determine the validity of the combined analysis of variance. A combined analysis of variance using SAS software was performed on the data set to partition out environment (E), genotype (G) and G E interaction. Genotype was regarded as a fixed effect, while environment was regarded as a random effect. Code G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15 G16 G17 G18 Table 1. Parentage and Pedigree of 18 bread wheat genotypes. Variety/Line/Pedigree Source PASTOR/TILHICMSS00Y01316S 030Y 030M 030WGY 16M 0Y FRET2/TUKURU//FRET2CGSS00B00158T 099TOPY 099M 099Y 099M 9CEL 0B RL6043/4*NAC//PASTOR/3/CROC_1/AE.SQUARROSA (224)//OPATACMSS97M03174T 040Y 020Y 030M 040SY 020M 2Y 010M 0Y 0SY PASTOR/BAV92/3/BJY/COC//PRL/BOWCMSS97M03293T 040Y 020Y 030M 040SY 020M 11Y 010M 0Y 0SY BABAX//IRENA/KAUZ/3/HUITESCMSS99M01622T 040Y 040M 040Y 15M 3CVLFY 3M 0Y CROC_1/AE.SQUARROSA(224)//OPATA/3/PASTOR/4/PASTOR *2/OPATA CMSS98Y03432T 040M 0100M 040Y 020M 040SY 23M 0Y 0SY CROC_1/AE.SQUARROSA (224)//OPATA/3/ALTAR 84/AEGILOPS SQUARROSA (TAUS)// OPATA/4/PASTOR CMS S98Y03433T 040M 0100M 040Y 020M 040SY 12M 0Y 0SY SCA/AE.SQUARROSA (409)//PASTOR/3/ PASTOR CMS S99 Y03439T 040M 040Y 040M 040SY 040M 23Y 010M 0ZTB 0SY MILAN/SHA7/3/CROC_1/AE.SQUARROSA (224)//OPATA CMS S99Y 00339S 040Y 040M 040SY 040M 4Y 010M 0ZTB 0SY MILAN/SHA7/3/CROC_1/AE.SQUARROSA (224)//OPATA CMS S99Y 00339S 040Y 040M 040SY 040M 14Y 010M 0ZTB 0SY SUNCO/2*PASTORCMSS99Y05530T 10M 040Y 040M 040SY 040M 6Y 010M 0ZTB 0SY SUNCO/2*PASTORCMSS99Y05530T 10M 040Y 040M 040SY 040M 7Y 010M 0ZTB 0SY TIECHUAN 1*2/3/HE1/3*CNO79//2*SERI CMSS99 M01648F 040Y 040M 040SY 040M 040SY 15M 0ZTB 0SY THELIN#2//ATTILA*2/PASTOR/3/PRL/2*PASTORCGSS02Y00096T 099B 099M 099Y 099M 42Y 0B THELIN/3/BABAX/LR42//BABAX/4/BABAX/LR42//BABAXCGSS02Y00083T 099B 099B 099Y 099M 48Y 0B BABAX/LR42//BABAX*2/3/TUKURUCGSS01B00050T 099Y 099M 099M 099Y 099M 64Y 0B WBLL1*2/BRAMBLINGCGSS01B00066T 099Y 099M 099M 099Y 099M 8Y 0B KOOHDASHT (Reference) Table 2. Geographical properties of test locations. Location Longitude/ Latitude Altitude (m) Soil Texture Annual Rainfall(mm) Gachsaran E/ N 710 Silty Clay Loam Gonbad E / N 45 Silty Clay Loam Khoramabad E / N 1148 Silt Loam Ilam E / N 975 Sandy loam Statistical Analysis: To explore G plus GE variability in grain yield dataset, the SREG model was used presented by the following equation: Yij= µ+ßj+ k n=1 +λnξinηin+εij Where Yij is the mean of genotype i in environment j; µ is the overall mean; ßj is the environment j main effect ; n is the singular value that permits the imposition of or the normality constraints on the singular vectors for genotypes and environments; λn is the singular value of genotypes and environments, ξin and ηin are the singular vectors for the genotypes and environments respectively and εij is the residual error. The GGE biplots were draw using the first two symmetrically scaled principal components for generating average tester coordinate and polygon view graphs (Yan and Kang, 2003), which as an excellent graphical tool has many applications other than determining stability of performance. In this study, GGE biplots were used to identify the highest yielding genotypes with the most stability at the different environments, finding more suitable environments for genotype evaluation and identify ideal cultivars and test environments. To visualize the associations among locations, a vector view biplot was obtained. RESULTS AND DISCUSSION The combined analysis of variance showed that bread wheat grain yields were significantly affected by E, which explained 93.7% of the total (G + E + GEI) variation, whereas GEI, which 3

4 AGRICULTURAL COMMUNICATIONS were significantly (P < 0.01) accounted for 5.3%. However G effect with 1% justified variation role was not significant (Table 3). Gauch and Zobel (1997) reported that, in normal multienvironments trials (MEYTs), E accounts for about 80% of the total variation, while G and GE each account for about 10% under normal conditions. The partitioning of GGE through GGEbiplot analysis showed that PC1 and PC2, explaining 45% and 31% of GGE sum of square, respectively. Together, they accounted for 76% of GG ESS (Table 3). Although the measured yield is a combined result of the main effects of the genotype, main effects of the environment and GE interaction, Yan et al. (2000; 2007) emphasized that genotype main effect and GE interaction are the two important sources of grain yield variation and must be considered in MEYTs data for genotypes evaluation and mega environment identification. Therefore, these results from considered semiarid conditions of Iran have showed that although grain yield has formed by genetic characteristics, this is modified under environmental factors and their conditions similar to cold regions of Canada (Yan and Rajcan, 2003). It is very common for MEYTs data to have a mixture of crossover and non crossover types of GEI. In this study, different genotypes produced the highest grain yield in different environments. Genotypes G5, G10, G12 and G13 possessed the highest yield in environments E12 (Ilam 3), E2 (Gonbad 1), E11 (Khoramabad 3) and E4 (Ilam 1), respectively. These differential rankings of genotypes across test environments revealed that there exists possible crossover GEI. However, crossover GEI is not always the case. Genotype G15 (THELIN/3/BABAX/LR42//BABAX/4/BABAX/LR4 2//BABAX) was the highest yielding in environments E1 (Gachsaran 1), E6 (Gonbad 2) and E9 (Gachsaran 3). Moreover, genotype G1 (PASTOR/TILHI) exhibited the highest yield in environments E5 (Gachsaran 2) and E10 (Gonbad 2), whereas genotype 18 (Check: KOOHDASHT) was the highest in environments E3 (Khoramabad 1) and E7 (Khoramabad 2) (Table 4). These results in differential change of yield mean but not of ranking of genotypes showed that GEI may also have a noncrossover nature. The results of grain yield comparison among these 18 bread wheat genotypes indicated 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, relative humidity, beginning time of occurrence, duration and intensity of heat and drought stresses and different soil properties that contributed to the GE interaction. Table 3. The combined analysis of variance on grain yield of 18 bread wheat genotypes tested in 12 environments in Iran during S.O.V DF SS MS F Explained (%) Environment (E) ** 93.7 Genotype (G) ns 1.0 G E ** 5.3 Error Total **: Significant at 1% probability level; ns : non significant. The GGE Biplot analysis graphic of the eighteen bread wheat cultivars in twelve environments (four locations over three agricultural years), is displayed in Figure 1. Yan et al. (2000) stated that in the graphic analysis, the first principal component (PC1) presents cultivar productivity, and the second principal component (PC2) was related to genotypic stability or instability. Thus, based on the graphic interpretation, the genotypes with the highest PC1 values were G15, G5, G2 and G12 with the highest yield and G10 had the poorest mean yield. The most stable genotypes were G12, G14, G15, G11 and G17, since near zero PC2 scores showed genotypic stability. Genotype G13 identified as the most variable genotype. Therefore, G15 and G12 were more stable as well as high yielding. Yield performances consist of mean yield and stability concepts. Plant breeders explore genotypes that indicate yield stability well as high yield across environments (Kang, 2002). The visualizing graphic genotype based on both mean performance and their stability displayed different genotype groups which a classified four groups (Figure 1). The best ones (G15 and G12) is highly desirable which are high yield and high stability. The group with high yield but low stability (G2 and G13) are desirable for specific selection, whereas low yield and low stability group (G4 and G9) is possible for special breeding purposes, e.g. drought resistance selection. The most undesirable group is low yield but high stability (G3, G11 and G14). The results are presented as which wonwhere pattern (shown in Figure 2) to identify the best genotypes in each environment and groups of 4

5 MOHAMMADI ET AL. environments (Yan and Hunt, 2002). Moreover, Visualization of the which won where pattern of MEYTs data is important for studying the possible existence of different megaenvironments (ME) in a region (Gauch and Zobel, 1997; Yan et al., 2000; 2001). The polygon is formed by connecting the markers of the genotypes that are furthest away from the biplot origin such that all other genotypes are contain in the polygon. The rays are lines that are perpendicular to the sides of the polygon or their extension (Yan, 2002). In Figure 2, ray 1 is perpendicular to the side that connects G13 and G15, ray 2 is perpendicular to the side that connects G15 and G2, so on. These five rays divide the biplot into six sections, and 4 environments fall into three of them. The vertex genotype for each sector is the one that gave the highest yield for the environments that fall within that sector. The highest yield in Gachsaran and Ilam is related to G15. Moreover, G13 showed highest yield in Khoramabad. An ideal genotype is one that has the highest mean performance and be absolutely stable (i.e. perform the best in all environments). The centre of the concentric circles (Figure 3) 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). Although such an ideal genotype may not exist in reality, it can be used as a reference for genotype evaluation. A genotype is more desirable if it is closer to the ideal genotype. Therefore, genotype G15 (THELIN/3/BABAX/ LR42//BABAX/4/ BABAX/LR42//BABAX) compared with the rest of the genotypes is the most desirable (Figure 3). In addition, G5 (BABAX//IRENA/KAUZ/3/HUITES), and G12 (SUNCO/2*PASTOR), located on the next concentric circle, may be regarded as desirable genotypes. Genotypes G2, G8, G16 and G17 were situated at the third concentric circle. Fig. 1. GGE biplot for ranking of 18 bread wheat genotypes based on mean and stability. Fig. 3. GGE biplot based on both average yield and stability for comparison of the genotypes with ideal genotypes. Fig. 2. Polygon view of the GGE biplot show which won where pattern for genotypes and environments. Fig. 4. GGE biplot for relationship between four sites. 5

6 AGRICULTURAL COMMUNICATIONS Table 4. Mean grain yield (kg ha 1 ) of 18 wheat cultivars tested at Gachsaran (E1, E5 and E9), Gonbad (E2, E6 and E10), Khoramabad (E3, E7 and E11) and Ilam (E4, E8 and E12) stations during years. Geno type E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 Mean b c 1071ab 2800j g 1149de bc cd bc a cd d cde k ab a cd b a 3680a ab ab 1211a b cd c abc cd bc bc a a cde bc bc c 969abc 3340d ab d bc de abc abc bc d def efg bcd ab cde cd de 3585b a 2829j ab f bc fg cd abc abc e bcd k cd ab cde d bc 4066a a 3371d ab 2203ab b de bc abc abc cd b ef bcd ab ab b a 985abc 2843j ab a bc cd bc abc cd bcd def ki a ab cde bcd ab 3885a a 1023ab 3109f cd 3011 b cd bcd abc abc b bcd cdef ghi abc ab e bcd ef 3725a a 3515b ab ef b g bcd bc abc d bcd cd bcd ab cde bcd c b 3033j ab fg 860cdef bc d bc abc e cd hi e a cde d ab 3552b c ab def 2678k c c cd c abc cd d bcd ab cd d de 3852a b 1031ab k 1274de bc f bcd bc a cd cd cdef cd a cd ab 3539b a 1109ab 3136e cd 3113 bc cde cd ab abc bc bcd cd fgh ab a e abc de 3971a a 867bcd 3414c ab 3081 b fg bcd a bc bc bc ef de e a cde bc cd 3527b abc 3062g ab d bc ef cd bc abc cd def hij d ab cde d ab 4158a a 985abc ab a a cde b bc abc a bc de bcd a c a de 4044a a 1125ab 3704a a b fg bc bc abc cd bcd cd b cd ab bc bc 3571b a 1192ab 3542b e b de cd bc bc bc bc c cd cd a bcd de e bc a 2549a 813def b fg c abc ab fgh bcd b de cd The total mean values followed by common letters are not significantly different at 1% or 5% level of probability. A favourable test environments for experimental evaluation is one with high PC1 value (more discriminating of the genotypes) and PC2 value close to zero (more representative of the overall environment mean) (Yan et al., 2001). Thus, the environments Gachsaran and Ilam (favourable environments) had the highest PC1 values, and only the Gachsaran environment had a PC2 value close to zero. The location Gachsaran was the most discriminating as indicated by the longest distance between their marker and the origin (Figure 4).However, due to their large first or secondary scores (PC1 or PC2). Under limited resources and the need to conduct MEYTs in a limited number of environments, Gachsaran and Ilam may be better test environments. Gachsaran and Ilam were more representative of the overall environments and more powerful to discriminate genotypes than the other ones. The relationship between the environments was shown in Figure 4. The lines that connect the biplot origin and the markers for the environments are called environment vectors. The angle between the vectors of two environments approximates the correlation coefficient between them (Yan and Rajcan, 2002). Two environments are positively correlated if the angle between their vectors is <90, negatively correlated if the angle is >90, independent if the angle is 90. Based on the angles of environment vectors, the four sites are located in one group. All environments were positively correlated because all angles among them were smaller than 90. Figure 4 suggested that Khoramabad and Ilam were the most closely correlated environments. The ideal test environment should be highly differentiating of the genotypes and at the same time representative of the target environment. It should have large PC1 scores (more power to discriminate genotypes in terms of the genotypic main effect) and small (absolute) PC2 scores (more representative of the overall environments). The ideal environment represented by the small circle with an arrow pointing to it (Figure 5) 6

7 MOHAMMADI ET AL. is the most discriminating of genotypes and yet representative of the other test environments. Fig. 5. GGE biplot both discriminating ability and representativeness of the target environment for comparison of environment with the ideal environment. Although such an ideal environment may not exist in reality, it can be used as a reference for genotype selection in the MEYTs. An environment is more desirable if it is located closer to the ideal environment. Figure 5 indicated that GACH (Gachsaran), which fell into the edge of concentric circles, was an ideal test environment in terms of being the most representative of the overall environments and the most powerful to discriminate genotypes. Favourable environment was Ilam. Therefore, Gachsaran, Ilam, and Khoramabad stations are more desirable test environments than Gonbad. Using the ideal environment as the centre, concentric circles were drawn to help visualize the distance between each environment and the ideal environment (Yan et al., 2000; Yan and Rajcan, 2002). Similar to past results (e.g. Karimizadeh et al., 2013), the GGE biplot, in compare to the conventional procedures of yield stability analysis, complexes some features from all of them such as a visual interpretation of the G E interaction. Determinations of more suitable environments as well as the favourable genotypes are important aspects of a biplot analysis. CONCLUSION The GEI is a common phenomenon in genotype selection experiments and its presence usually complexes genotype selection and release decision. Particularly, due to different environmental factors, improving a rain fed wheat variety is a persistent challenge. However, the GGE biplot showed many visual interpretations, particularly allows visualization of any crossover G E interaction, that is usually essential to breeding program. This study demonstrated that the GGE model was very effective for studying the pattern of GEI and interpreting of bread wheat grain yield data from multi environment trials. By considering among the genotypes studied, there were desirable ones (G15 and G12) in terms of both stability and high yielding ability or adaptability. Meanwhile, the graphic of interrelationships among environments displayed fair correlation among test environments and indicated Gachsaran and Ilam stations can be the more suitable test environments under limited conditions that we have to do research in less environments. Acknowledgement We would like to highly appreciate and acknowledge all supports provided by Dryland Agricultural Research Institute (DARI) for conducting the field trials. The author also wish to thank Dr. Wei Kai Yan (Eastern Cereal Oilseed Research Centre of Agriculture and Agri Food Canada) for making available a time limited version of GGE biplot as test Biplotxlsx and highly appreciate to Dr. Naser Sabaghnia (Maragheh University) for helping to use this software. Annicchiarico, P Joint regression vs AMMI analysis of genotype environment interactions for cereals in Italy. Euphytica. 94: Annicchiarico, P Defining adaptation strategies and yield stability targets in breeding programmes p. In: Kang, M.S. (Ed.). Quantitative genetics, genomics and plant breeding. CABI, Wallingford, UK. 417 p. Dehghani, H, A. Ebadi and A. Yousefi Biplot analysis of genotype by environment interaction for barley yield in Iran. Agronomy Journal. 98: Dehghani, H., N. Sabaghnia and M. Moghaddam Interpretation of genotype by environment REFERENCES interaction for late maize hybrids grain yield using a biplot method. Turkish Journal of Agricultural and Forestry. 33: Ebdon, J.S. and H.G. Gauch Additive main effect and multiplicative interaction analysis of national turfgrass performance trials: I. Interpretation of genotype environment interaction. Crop Science. 42: Farshadfar, E. and M. Sadegi GGE Biplot Analysis of Genotype Environment Interaction in Wheat Agropyron Disomic Addition Lines. Agricultural Communications. 2(3):1 7. Farshadfar, E., R. Mohammadi, M. Aghaee and Z. Vaisi GGE biplot analysis of genotype environment 7

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