Yield Stability Analysis of Bread Wheat Lines using AMMI Model

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1 AGRICULTURAL COMMUNICATIONS, 2015, 3(1): Yield Stability Analysis of Bread Wheat Lines using AMMI Model FATEMEH BAVANDPORI *, JAFAR AHMADI AND SEYED MOHSSEN HOSSAINI Department of Crop Production and Breeding, Imam Khomeini International University, Qazvin, Iran. *Corresponding Author: (Accepted: 27 Nov. 2014) ABSTRACT In order to determine stable bread wheat genotypes with high grain yield via a single parameter, field experiments were conducted with 20 genotypes for 3 consecutive years ( ) under two different conditions (irrigated and rainfed) in a randomized complete block design with three replications in each environment. Combined analysis of variance showed highly significant differences for the GE (genotype-environment) interaction indicating the possibility of selecting stable entries. The results of AMMI (additive main effect and multiplicative interaction) analysis indicated that the first two AMMI (AMMI1 AMMI2) were highly significant (P<0.01). The partitioning of total sum of squares exhibited that the environment effect was a predominant source of variation followed by GE interaction and genotype effect. The GE interaction was three times higher than that of the genotype effect, suggesting the possible existence of different environment groups. AMMI stability value (ASV) discriminated genotypes G12, G18, G13, G14 and G11as the stable genotypes, respectively. As stability per SE is not a desirable selection criterion, because the most stable genotypes would not necessarily give the best yield performance, hence, simultaneous consideration of grain yield and ASV in a single non-parametric index were entitled. Genotype Selection Index (GSI) discriminated G12 and G18 with general adaptability and high grain yield for rainfed and irrigated conditions which was in agreement with the results of biplot analysis. Keywords: ASV, Biplot, GSI, PCA, variation. Abbreviations: AMMI: additive main effect and multiplicative interaction; ANOVA: analysis of variance; ASV: AMMI stability value; GE: genotype-environment; GEI: genotype-environment interaction; IPCA: interaction principal component axes; PCA: principal component analysis; TSS: total sum of squares; GSI: Genotype Selection Index. INTRODUCTION Bread wheat (Triticum aestivum L.) is a major food grain in Iran, therefore improving yield and yield stability is the primary objective of wheat breeding programs in this country (Ram et al., 2007). To identify wheat genotypes with wide or specific adaptation to different environments, multi-location yield trails are grown each year. These have led to empirical identification of superior cultivars, some of which have been released in several counties (Asenjo et al., 2003; Basford et al., 2004). The environments involve a wide range of photoperiods and temperatures which could cause large genotype (G) environment (E) interactions (GEI), especially in the semi-arid areas. Large real crossover-type GEI, especially among high yielding lines invalidates recommendations to farmers of the cultivar(s) giving the highest average yield across all test environments. Quantification of GEI and understanding its physiological bases are needed to breed efficiently for superior environments (Vergas et al., 2001; Thomason and Phillips, 2006). Most yield trails are used only to determine which cultivars give the highest average seed yield, and therefore merit recommendation for planting by farmers. Multi-location yield trials facilitate quantification of the environment and GEI effects. However, a fact not generally recognized is that, every yield trial by analysing processes that determine yield can inexpensively quantify the genetic, physiological and environmental controls that result in yield differences among cultivars, seasons and locations (Tarakanovas and Rusgas, 2006). Various methods of GE interaction analysis exist, including parametric and non-parametric approaches. Parametric approaches are: (1) univariate analysis (regression analysis and stability variance analysis) and (2) multivariate analysis (principal component analysis, factor

2 BAVANDPORI ET AL. analysis, canonical component analysis, cluster analysis and biplot analysis) (Sharma, 1996; Roy, 2000; Chahaland, 2002). The ordinary form of ANOVA is an additive model and therefore describes only the main effect (Snedecor and Cochran, 1989). Principal component analysis is a multiplicative model and has the opposite problem of not describing the additive main effects. Linear regression models (Mandel, 1969; Finlay and Wilkinson, 1963) combine additive and multiplicative components and thus analyse both main effects and interaction, but in general they confound the interaction with the main effects (Wright, 1971), reducing its power for general significance testing. The additive main effects and multiplicative interaction (AMMI) model is a powerful multivariate method for multi -environmental trials (Romagosa and Fox, 1994). For detailed study of underlying patterns of interactions that classical analysis of variance is not effective, therefore, for a more in-depth analysis of interactions, the additive main effects and multiplicative interaction (AMMI) model has been found to be an effective tool (Zobel et al., 1988). AMMI is essentially effective where the assumption of linearity of responses of genotypes to a change in environment is not fulfilled (Zobel et al., 1988; Farshadfar and Sutka, 2006) which is required in stability analysis techniques (Finlay and Wilkinson, 1963; Eberhart and Russel, 1966). This technique, also called FANOVA (Gollob, 1968), incorporates both additive and multiplicative components into an integrated powerful least squares analysis (Gauch, 1982; Voltas et al., 1999; Farshadfar and Sutka, 2003). The AMMI model does not require this assumption. It is a hybrid statistical model which incorporates both additive and multiplicative components of the two-way genotype-environment data structure. It separates the additive main effects from the interaction which is analysed as a series of multiplicative components using principal component analysis and helps to indicate the interaction pattern (Farshadfar and Sutka, 2003). Complex relationships among locations or among genotypes can be adequately represented in a scatter gram (Crossa et al., 1991). Plots showing both the genotypes and the environments simultaneously can be of great assistance in this respect, and are called biplots (Gabriel, 1971; Farshadfar and Sutka, 2003; Rubio et al., 2004). A new approach known as genotype selection index (GSI) was recommended by Farshadfar (2008).Using AMMI stability value (ASV) and mean yield, GSI incorporates both mean yield and stability in a single criterion. Low value of this parameter shows desirable genotypes with high mean yield and stability. The objectives of this study were (i) to identify bread wheat genotypes that have both high mean yield and stable yield performance across different environments for semiarid areas of Iran, and (ii) to study the relationships, similarities and dissimilarities among yield - stability statistics carried out to quantify GE interaction effects on yield and to determine stable entries within the genotypic pool used in this study. MATERIALS AND METHODS Experimental Layout and Genetic Materials: In order to determine stability of 20 bread wheat genotypes field experiments were conducted for three consecutive years ( ) under two different conditions (irrigated and rainfed) (Table 1) at Kermanshah, Iran (34 21 N latitude, 47 9 E longitude and 1319 m altitude). The experimental layout at each environment was randomized complete block design with three replications. Climate of the region is classified as semi-arid with mean annual rainfall of mm. Minimum and maximum temperatures at the research station were 5.9 and 22.6 C, respectively. Each plot consisted of five rows with 5 meter length. Rows distance was 20cm with seed density 400 per m 2. Data on seed yield were taken from the middle two rows of each plot. The seed yield was determined for each genotype at each test environments. The environments were considered as random factors while genotypes as fixed factors. Statistical Analysis: Combined analyses of variance, Bartlett s test for additive effect on grain yield and mean comparison with Duncan s multiple range test were done using MSTAT-C and SPSS statistical softwares. The additive main effect and multiplicative interaction (AMMI) analysis was performed using the model suggested by Crossa et al. (1991) as: Yij = μ + g i+ e j+ Σn=1h λn αni.γnj + Rij. Where Yij is the yield of the i th genotype in the j th environment, μ is the grand mean, gi is the mean of the i th genotype minus the grand mean, ej is the mean of the j th environment minus the grand mean, λn is the square root of the Eigen value of the principal component analysis (PCA) axis, αni and γnj are the principal component scores for PCA axis n of the i th genotype and j th environment and Rij is the residual. A biplot based on the singular value decomposition (SVD) of GE contains only the GE interaction and can be referred to as a GE biplot. In contrast a biplot based on the SVD of G and GE contains only G plus GE, and will be characterized as a GGE biplot (Weikai et al., 2000). The GE biplot was projected for 20 genotypes tested at six environments. Clustering was computed for the genotype score using an agglomerate hierarchical alogarithm based on 9

3 AGRICULTURAL COMMUNICATIONS Ward s method (Farshadfar, 1998) and the result of cluster grouping for the genotype PCA score was projected in the biplot of PCA1 and PCA2, and the biplot of PCA1 and mean yield. The IRRISTAT software was used for combined analysis of variance and AMMI analysis. AMMI Stability Value (ASV): The AMMI stability value (ASV) as described by Purchase et al. (2000) was calculated as follows: ASV = IPCA1sumofsquar e 2 2 ( IPCA1score ) + ( IPCA 2score ) IPCA 2sumofsquar e Where SS IPCA1 SS IPCA2 is the weight given to the IPCA1-value by dividing the IPCA1 sum of squares by the IPCA2 sum of squares. The larger the IPCA score, either negative or positive, the more specifically adapted a genotype is to certain environments. Smaller ASV scores indicate a more stable genotype across environments. Genotype Selection Index (GSI): Based on the rank of mean grain yield of genotypes (RYi) across environments and rank of AMMI stability value (RASVi) a selection index called GSI was calculated for each genotype which incorporates both mean grain yield and stability index in single criteria (GSIi) as: GSIi= RASVi+RYi Table1. Genotype code and the name of 20 bread wheat genotypes studied in this research. No. Code Name 1 G1 Geravandi-17 2 G2 WC G3 WC G4 WC G5 WC G6 WC G7 Pishgam-1 8 G8 WC G9 Pishgam-2 10 G10 WC G11 WC G12 WC G13 WC G14 WC G15 WC G16 Pishtaz 17 G17 Moghan-3 18 G18 WC G19 WC G20 WC RESULTS AND DISCUSSION Combined Analysis of Variance: The results of combined analysis of variance (Table 2) over locations (irrigated and rainfed) and years resulted in highly significant differences (P<0.01) in the interaction of genotypes environments (locations and years). The significant interactions of genotypes environments (locations and years) suggest that grain yield of genotypes varied across irrigated and rainfed conditions. Significant differences for genotypes, environments and GE interaction indicated the effect of environments in the GE interaction, genetic variability among the entries and possibility of selection for stable genotypes. Chandra et al. (1974) reported that GE interaction with location is more important than GE interaction with year. As GE interaction was significant, therefore we can further proceed and estimate phenotypic stability (Farshadfar and Sutka, 2006; Osiru et al., 2009). Mean comparison using Duncan s multiple rang test revealed that maximum grain yield belonged to genotypes G19, G16, G7 and G12 with average seed yield of , , and g respectively. The minimum grain yield was attributed to genotype G6 ( g) in irrigated conditions. In rainfed conditions, maximum grain yield belonged to genotypes G19 and G12 (with and gm 2-1, respectively) and minimum grain yield was attributed to genotype G6, G7 and G3 (with , and gm 2-1, respectively). Genotypes of annual crops evaluated for grain yield on a multi-locational, multi-year basis frequently show GE interaction that complicates the selection or recommendation of materials. Coping with genotype-year or genotype-locationyear interaction effects is possible only by selection for yield stability across environments defined as location year combinations (Annicchiarico, 1997). Table 2. Combined analysis of variance for grain yield over different rainfed and irrigated conditions. Source of Degree of freedom Mean squares variation Year (Y) ** Location (L) ** L Y ** Error Genotype (G) ** G Y ** G L ** G L Y ** Error % C.V **: significant at 1% level of probability. AMMI Analysis of GE Interaction: The advantages of the AMMI model or its variants are that, they use overall fitting, impose no restrictions on the multiplicative terms and result in least square fit (Freeman, 1990). Within limits, any model may be expected to fit the data from 10

4 BAVANDPORI ET AL. which it was derived. However, the AMMI model has a good chance of being able to predict for new sites and new years, thus contributing a real advance (Gauch, 1988). Gauch and Zobel (1996) showed that AMMI1 with IPCA1 and AMMI2 with IPCA1 and IPCA2 are usually selected and the graphical representation of axes, either as IPCA1 or IPCA2 against main effects or IPCA1 against IPCA2 is generally informative. The AMMI method is used for three main purposes. The first is model diagnoses, AMMI is more appropriate in the initial statistical analysis of yield trials, because it provides an analytical tool of diagnosing other models as sub cases when these are better for particular datasets (Gauch, 1988). Secondly, AMMI clarifies the G E interaction and it summarizes patterns and relationships of genotypes and environments (Zobel et al., 1988; Crossa et al., 1990). The third use is to improve the accuracy of yield estimates. Gains have been obtained in the accuracy of yield estimates that are equivalent to increasing the number of replicates by a factor of two to five (Zobel et al., 1988; Crossa, 1990). Such gains may be used to reduce testing cost by reducing the number of replications, to include more treatments in the experiments or to improve efficiency in selecting the best genotypes. Using ANOVA, yield sum square was partitioned into genotype, environment and GE interaction. GE interaction was further partitioned by principal component analysis (Table 3). The results of AMMI analysis indicated that 11.54% of total variability was justified by GE interaction, 81.05% by environment and 3.83% by genotype. The partitioning of total sum of squares indicated that the environment effect was a predominant source of variation followed by GE and genotype effect. The GE interaction was three times higher than that of the genotype effect, suggesting the possible existence of different environment groups. Table 3. AMMI analysis of grain yield in bread wheat over rainfed and irrigated conditions. S.O.V DF SS %SS MS Genotype(G) a ** Environment(E) a ** G E a ** IPC b ** IPC b ** IPC b ** IPC b 8825 * noise b 2861 Error a 3335 Total a: total sum of squares percent, b: GE sum of squares percent. *; ** significant at the 5% and 1% probability levels, respectively. The ordination technique revealed high significant differences for IPC1, IPC2, and IPC3and IPC4. The first interaction principal component (IPC1) explained 42.54% of the variability of GE, followed by IPC2 (34.68%), IPC3 (15.31%) and IPC4 (5.80%).Therefore, 77.22% of the GE sum of squares percent was justified by AMMI1 and AMMI2. The values of interaction principal components (IPCA1, 2, 3 and 4) for genotypes and environments are presented in Tables 4 and 5. The contribution of IPC1 to the GE interaction was greater than that of IPC2, IPC3 and IPC4, the greatest interaction being found for genotype 19 and the least interaction for genotype 12. Table 4. Values of interaction principal components (IPC) for bread wheat genotypes. Genotype IPC1 IPC2 IPC3 IPC4 G G G G G G G G G G G G G G G G G G G G Table 5. Values of interaction principal components (IPC) for environments. Environment IPC1 IPC2 IPC3 IPC4 E E E E E E AMMI Stability Value (ASV): The AMMI model does not make provision for a quantitative stability measure, such a measure is essential in order to quantify and rank genotypes according to their yield stability, the ASV measure was proposed by Purchase et al. (2000) to cope with this problem. In fact, ASV is the distance from zero in a two dimensional scatter gram of IPCA1 11

5 AGRICULTURAL COMMUNICATIONS (interaction principal component analysis axis 1) scores against IPCA2 scores. Since the IPCA1 score contributes more to GE sum of square (Table 6), it has to be weighted by the proportional difference between IPCA1 and IPCA2 scores to compensate for the relative contribution of IPCA1 and IPCA2 total GE sum of squares. The distance from zero is then determined using the theorem of Pythagoras (Purchase et al., 2000). In general the importance of AMMI model is in reduction of noise even if principal components do not cover much of the GESS (Gauch and Zobel, 1996; Gauch, 1992). AMMI stability value (ASV) discriminated genotypes G12, G18, G13, G14 and G11 as the stable genotypes, respectively. As stability per se is not a desirable selection criterion, because the most stable genotypes would not necessarily give the best yield performance, hence, simultaneous consideration of grain yield and ASV in a single non-parametric index entitled. Table 6. First and second IPC scores, ASVi and GSIi of genotypes investigated. Mean Genotype IPC1 IPC2 ASVi GSIi yield G G G G G G G G G G G G G G G G G G G G Genotype Selection Index (GSI): Stability per se should however not be the only parameter for selection, because the most stable genotypes would not necessarily give the best yield performance (Mohammadi et al., 2007), hence there is a need for approaches that incorporate both mean grain yield and stability in a single criteria. In this regard, as ASV takes into account both IPCA1 and IPCA2 that justify most of the variation of GE interaction, therefore the rank of ASV and mean grain yield (RYi) are incorporated in a single selection index namely Genotype Selection Index (GSI). The least GSI is considered as the most stable with high grain yield. Genotype Selection Index (GSI) discriminated G12 and G18 with general adaptability and high grain yield for rainfed and irrigated a condition which was in agreement with the results of biplot analysis. Pattern Analysis: The AMMI2 biplot (Fig. 1) explained 77.22% of the GE interaction, making it a useful test for interaction. It was observed that most of the genotypes and environments were dispersed around the biplot. Genotypes farther from the centre of biplot show specific adaptation. In order to estimate specific adaptation and study their stability, biplot diagram was used. Mohammadi and Amri (2008) in a study of genotype environment interaction in durum wheat revealed that those genotypes which are far from the centre of biplot, have high G E interaction and those genotypes that nearest to centre of biplot, have high stability. Fig. 1. Biplot analysis of GE interaction based on AMMI2 model for first two interactions principal component scores. Genotypes yield in rainfed environment 2011 year (E2) had different approach in all environments and had the most positive interaction. Furthermore, two rainfed environment in years (E4 and E6), had earned negative and almost intermediate interaction. This was while irrigated environment in 2012 year (E3), had least interaction and it value was near to zero. Environments E3, E4, E6, E2 and E1 had longer vectors and were further from the centre of the biplot. Genotype G12 base on first principle, had low and positive interaction and it had high stability. Genotype G19 had high and negative interaction principle and low stability. If two vector of genotype have small angle, two environments have high concentration. Genotype in centre of biplot axes means that genotype has general 12

6 BAVANDPORI ET AL. adaptation with environment. Vertical length of genotype vector on environment vector shows the amount of deviation of that genotype from means of that environment. When length of genotype vector is longer, the effect of that genotype interaction with environment is more (Nachit et al., 1992). The genotype G3, G15 and G17 has specific with environments E4 and E6 because their angle is less than 90 and their GE interaction is positive. According to the explained issues in Fig. 1, biplot vector which comes from first and second principles, interaction of the 20 studied genotypes in six environments have three processes between environments. Irrigated and rainfed environments in 2011 year (E3 and E4), and rainfed environment in 2013 year (E6) had same process. Two irrigated environments in 2011 and 2013 (E1 and E5) had almost environment construction and rainfed environment of 2011 year had a process for itself. In other words, Genotypes G20, G2 and G19 had specific adaptation with environment E2 (rainfed environment 2011 year), which that genotypes had almost desirable grain yield. While genotypes G9, G16, G7 and G10 had specific adaptability with environments E1 and E5 (irrigated environment 2011 year and irrigated environment 2013 year respectively), genotypes G6, G8, G15, G3 and G17 had specific adaptation with environments E3, E4 and E6 (irrigated environment 2012 year, rainfed environment 2012 year and rainfed environment 2013 year respectively), genotypes G5 and G4 had specific adaptation with environment E2 (rainfed environment 2011 year) whereas these two genotypes didn t have acceptable grain yield and at the end genotype G1 has specific adaptation with environment E5 (irrigated environment 2013 year). The genotypes G11, G18, G14, G13 and G12 had negative GE interaction. Genotypes toward the centre of biplot have zero interaction; therefore have general adaptation with different mean grain yield. Only genotypes G18 can be considered as stable with high performance and favourable yield. Based on the AMMI2 model, genotypes G12 and G18 can be recommended as the most stable genotypes for rainfed and irrigated conditions. In pattern analysis genotypes are judged in grouping form and therefore save time and precision in interpretation and selection (Wade et al., 1995; Alagarswamy and Chandra, 1998; Farshadfar and Sutka, 2003). CONCLUSION Genotype-by-environment (GE) interaction has been an important and challenging issue among plant breeders, geneticists and agronomists engaged in performance testing. The genotypeenvironment interaction reduces association between phenotypic and genotypic values and leads to bias in the estimates of gene effects and combining ability for various characters sensitive to environmental fluctuations. Such traits are less amenable to selection. Both yield and stability of performance should be considered simultaneously to reduce the effect of GE interaction and to make selection of genotypes more precise and refined. The results of this investigation proved that stability analysis by AMMI model indicated that environment and genotype environment interactions effects were significant. Analysis of the genotype environment interactions showed that four principal components were significant and 77.2 percent of interaction sum of square was related to the first two components. Based on the biplot of AMMI2, genotypes 11 (WC-47359), 18 (WC-47472), 14 (WC-4611), 13 (WC-47388), 12 (WC-47403) had general adaptability. Based on ASV and GSI indices genotypes 12 (WC-47403) and 18 (WC-47472) revealed the highest stability. ACKNOWLEDGMENT We would like to thank the Razi University of Kermanshah for their support in providing suitable facilities and field in order to complete this project. Our sincere thanks also goes to Prof. Ezatollah Farshadfar for his valuable comments and supports during this study. Alagarswamy, G. and S. Chandra Pattern analysis of international sorghum multi-environment trials for grain-yield adaptation. Theoretical and Applied Genetics. 96: Annicchiarico, P Additive main effects and multiplicative interaction (AMMI) analysis of genotype location interaction in variety trials repeated over years. Theoretical and Applied Genetics. 94: Asenjo, C.A., R. Benzus and H. Acciaresi Genotype-environment interactions in rice (Oriza sativa L.) in temperate region using REFERENCES the joint regression analysis and AMMI methods. Cereal Research Communication. 31: Basford, K.E., W.T. Federer and I.H. Delacy Mixed model formulations for Multi-environment trails. Agronomy Journal. 96: Chahal, G.S. and S.S. Gosal Principles and Procedures of Plant Breeding. Alpha Science International Ltd., Pang Bourne, India. 149 p. Chandra, S., M.S. Sohoo and K.P. Singh Genotype environment interaction for yield in ram. Journal of Research. 8:

7 AGRICULTURAL COMMUNICATIONS Crossa, J Statistical analysis of multi-location trials. Advances in Agronomy. 44: Crossa, J., H.G. Gauch and R.W. Zobel Additive main effect and multiplicative interaction analysis of two international maize cultivar trials. Crop Science. 30: Crossa, J., P.N. Fox, W.H. Pfeiffer, S. Rajaram and H.G. Gauch AMMI adjustment for statistical analysis of an interactional wheat yield trail. Theoretical and Applied Genetic. 81: Eberhart, S.A. and W.A. Russel Stability parameters for comparing varieties. Crop Science. 6: Farshadfar, E Application of biometrical genetic in plant breeding. Vol 2. Razi University Press. Kermanshah, Iran. pp: Farshadfar, E Incorporation of AMMI stability value and grain yield in a single non-parametric index (GSI) in bread wheat. Pakistan Journal of Biological Sciences. 11(14): Farshadfar, E. and J. Sutka Locating QTLs controlling adaptation in wheat using AMMI model. Cereal Research Communications. 31: Farshadfar, E. and J. Sutka Biplot analysis of genotype- environment interaction in durum wheat using the AMMI model. Acta Agronomica Hungarica. 54: Finlay, K.W. and G.N. Wilkinson The analysis of adaptation in plant-breeding program. Australian Journal of Agricultural Research. 14: Freeman, G.H Modern statistical methods for analyzing genotype-environment interactions. In: M.S. Kang (ed.), Genotype Environment Interaction and Plant Breeding, pp Louisiana State University Agricultural Center, Baton Rouge, LA, USA. Gabriel, K.R The biplot graphic display of matrices with application to principal component analysis. Biometrika. 58: Gauch, H.G Noise reduction by Eigen vector ordinations. Ecology. 63: Gauch, H.G Model selection and validation for yield trials with interaction. Biometrics. 44: Gauch, H.G Statistical analysis of regional yield trials. AMMI analysis of factorial designs. Elsevier, New York, NY, USA. Gauch, H.G. and R.W. Zobel AMMI analysis of yield trials. In: Kang MS, Gauch HG (eds.) Genotype byenvironment interaction. CRC Press. Boca Raton, FL, USA. Gollob, H.F A statistical model which combines features of factor analysis and analysis of variance techniques. Psychometrica. 33: Mandel, J The partitioning of interaction in analysis of variance. Mathematical Science. 73: Mohammadi, R. and A. Amri Analysis of Genotype Environment interaction for grain yield in durum wheat. Crop Science. 49: Mohammadi, R., A. Abdulahi, R. Haghparast and M. Armion Interpreting genotypeenvironment interactions for durum wheat grain yields using non-parametric methods. Euphytica. 157: Nachit, M.M., G. Nachit, H. Ketata, H.G. Gauch and R.W. Zobel Use of AMMI and linear regression models to analyze genotype-environments interaction in Durum wheat. Theoretical and Applies Genetics. 83: Osiru, M.O., O.M. Olanya, E. Adipala, R. Kapinga and B. Lemaga Yield stability analysis of Ipomoea batatus L. cultivars in diverse environments. Australian Journal of Crop Science. 3(4): Purchase, J.L., H. Hatting and C.S. Vandeventer Genotype environment interaction of winter wheat (Triticum aestivum L.) in South Africa: Stability analysis of yield performance. South African Journal of Plant and Soil. 17: Ram, C., G. Sharma, O. Ferrara, J. Crossa, M.R. Bhatta and M.A. Sufian What grain yield and stability assessed through regional trials in the Eastern Gangetic Plains of Sought Asia. Euphityca. 157: Romagosa, I. and P.N. Fox Genotype environment interaction and adaptation. In: Hayward, M.D., Bosemark, N.D., Romagosa, I. (eds.). Plant Breeding Principles and Prospects. Chapman and Hall, Boca Raton, FL, USA. Roy, D Plant Breeding, Analysis and Exploitation of Variation. Alpha Science International Ltd. Pang Bourne, India, pp: Rubio, J., F. Flores, M.T. Moreno, J.I. Cubero and J. Gil Effects of the erect/bushy habit, single/double pod and late/early flowering genes on yield and seed size and their stability in chickpea. Field Crops Research. 90: Sharma, J.R Principles and Practice of Plant Breeding. Tata McGraw-Hill Publishing Company Limited, New Delhi, India. Snedecor, G.W. and W.G. Cochran Statistical Methods. Iowa State University Press, Ames, IA, USA. Tarakanovas, P. and V. Rusgas Additive main effect and multiplicative interaction analysis of grain yield of wheat varieties in Lithuania. Agronomy Research. 4: Thamson, W.E. and S.B. Philips Methods to evaluate wheat cultivar testing environment and improve cultivar selection protocols. Field Crops Research. 99: Vergas, M., J. Crossa., F.V. Eeuwijk., K.D. Sayre and M.P. Reynolds Interpreting treatment environment interaction in agronomy trials. Agronomy Journals. 93: Voltas, F.A., A. Sombrero, A. Lafarga, E. Igartua and I. Romagosa Integrating statistical and ecophysiological analyses of genotype by environment interaction for grain filling of barley I. Field Crops Research. 62: Wade, L.J., S. Sarkarung, C.G. Melran, A. Guhey, B. Quader, C. Boonrite, S.T. Amarante, A.K. Sarawgi, A. Hauge, D. Harnpichitritaya, A. Pamplona and M.C. Bhamri Genotype by environment interaction and selection method for identifying improved rainfed lowland rice genotypes. International Rice Research Institute. Manila. Philippines. 14

8 BAVANDPORI ET AL. Weikai, Y., L.A. Hant, S. Qinglai and Z. Szalvincs Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science. 40: Wright, A.J The analysis and prediction of some two factor interactions in grass breeding. Agricultural Science. 76: Zobel, R.W., M.J. Wright and H.G. Gauch Statistical analysis of a yield trial. Agronomy Journal. 80: