Abstract
Fifty wheat genotypes were evaluated at nine diverse locations in India to identify high-yielding and stable genotypes. The analysis of variance based on additive main effects and multiplicative interaction (AMMI) indicated significant genotype, environment and genotype - environment (GE) interactions, with a total variation of 5.99, 20.23 and 73.77%, respectively. A biplot-AMMI analysis and yield stability index incorporating the AMMI stability value and yield in a single non-parametric index were used to discriminate the genotypes with highest and stable yield; the genotypes G135, G125, G104, G112 and G144 were found to be promising. Two mega environments (ME) were identified based on GGE (genotype and GE interaction) biplot analysis and the genotypes G119 and G120 and G107, G148 and G146 performed best in the mega-environments ME I and ME II, respectively. Both approaches allowed the identification of stable genotypes (G112 and G135), which can be included in the national testing program, with a view to release a new variety.
Keywords:
AMMI; biplot; GGE; stability; wheat
INTRODUCTION
Worldwide as well as in India, bread wheat (Triticum aestivum L.) is the second most important food grain crop after rice. The importance of a sustained increase in wheat production and productivity for food security is well recognized in India, where wheat is a major staple food crop for the ever-increasing human population. The development of high-yielding genotypes coupled with resistance/tolerance to diverse biotic and abiotic stresses will be decisive to meet the demand for food grain. Multilocation trials are a key component of selection for stable and best-performing genotypes in different environments (Ahmadi et al. 2012Ahmadi J, Mohammadi A and Najafi-Mirak T (2012) Targeting promising bread wheat (Triticum aestivum L.) lines for cold climate growing environments using AMMI and SREG GGE Biplot analyses. Journal of Agricultural Sciences and Technology 14: 645-657., Oral et al. 2018Oral E, Kendal E and Dogan Y (2018) Selection the best barley genotypes to multi and special environments by AMMI and GGE biplot models. Fresenius Environmental Bulletin 27: 5179-5187., Tekdal and Kendal 2018Tekdal S and Kendal E (2018) AMMI model to assess durum wheat genotypes in multi- environment trials. Journal of Agricultural Sciences and Technology 20: 153-166.). The grain yield, the final product of any crop, is determined by the genotypic potential (G), environmental effect (E) and the genotype x environment (GE) interaction (Yan and Kang 2002Yan W and Kang MS (2002) GGE-biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, 288p. ). In case of an effect of the GE interaction, the selection of genotypes based on the mean yield is inadequate (Sharifi et al. 2017Sharifi P, Hashem A, Rahman E, Ali M and Abouzar A (2017) Evaluation of Genotype × Environment Interaction in Rice Based on AMMI Model in Iran. Rice Science 24: 173-180.).
A wide variety of methods to detect genotypes with a stable performance across environments were described in the literature. Most of them use regression analysis, sum of squared deviations from regression, Principal Component Analysis (PCA), cluster analysis and Additive Main effects and Multiplicative Interaction models (AMMI). The AMMI model uses ANOVA (analysis of variance) to test the main effects of both genotypes and environments and PCA to analyse the residual interaction component. The GGE biplot is a powerful model, with a graphical representation of identification of the best performing cultivars across the environments (Yan and Kang 2002Yan W and Kang MS (2002) GGE-biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, 288p. ). These graphical options facilitate the identification of high-yielding, stable genotypes, particularly in multi-environment trials. Moreover, yield stability and wide adaptation are increasingly important as the climate at specific locations is becoming more variable over the years. The AMMI biplot approach has been used for the identification of stable genotypes in multi-environment trials of wheat and barley (Oral et al. 2018Oral E, Kendal E and Dogan Y (2018) Selection the best barley genotypes to multi and special environments by AMMI and GGE biplot models. Fresenius Environmental Bulletin 27: 5179-5187., Tekdal and Kendal 2018Tekdal S and Kendal E (2018) AMMI model to assess durum wheat genotypes in multi- environment trials. Journal of Agricultural Sciences and Technology 20: 153-166.). Both GGE and AMMI models have also been used to study the interaction component in multi-environment trials to identify stable wheat genotypes (Ahmadi et al. 2012Ahmadi J, Mohammadi A and Najafi-Mirak T (2012) Targeting promising bread wheat (Triticum aestivum L.) lines for cold climate growing environments using AMMI and SREG GGE Biplot analyses. Journal of Agricultural Sciences and Technology 14: 645-657., Kendal and Sener 2015, Vaezi et al. 2017Vaezi B, Pour-Aboughadareh A, Mohammadi R, Armion M, Mehraban A, Hossein-Pour T and Dorii M (2017) GGE Biplot and AMMI Analysis of Barley Yield Performance in Iran. Cereal Research Communications 45: 500-511., Oral and Kendal 2018Oral E, Kendal E and Dogan Y (2018) Selection the best barley genotypes to multi and special environments by AMMI and GGE biplot models. Fresenius Environmental Bulletin 27: 5179-5187.). In this study, genotypes derived from the CIMMYT Elite Spring Wheat Yield Trial (ESWYT) were evaluated for grain yield across different environments to stratify the wheat genotypes according to the environmental conditions, for specific recommendations. The objectives of this study were to: i) analyse the G×E interaction on the grain yield of 50 wheat genotypes using AMMI and GGE biplot models; ii) identify high yielding and stable wheat genotype(s) across environments and to; iii) identify suitable genotype(s) for each environment.
MATERIAL AND METHODS
Experimental material and multi-environment wheat trials
Elite Spring Wheat Yield Trials (ESWYT) consisting of 50 genotypes including one local check were planted at nine test locations (Tables 1 and 2) in India in the winter (Rabi) growing season of 2016/17. The trial was arranged in a randomized complete block design, with two replications per location. The code and pedigree of all genotypes are listed in Table 3. Each genotype was planted in a plot with six 6-m rows, with a row-to-row and plant-to-plant distance of 20 cm and 10 cm, respectively. The recommended management practices were followed for aising strong and healthy crops. The grain yield data were recorded as total grain weight per plot after harvesting and the values extrapolated to kg ha-1.
Statistical analysis
The AMMI analysis was carried out with the adjusted mean grain yield to assess the relationships among genotypes, locations and G×E interaction, based on the model described by Zobel et al. (1988Zobel RW, Wright MJ and Gauch HG (1988) Statistical analysis of a yield trial. Agronomy Journal 80: 388-393) and Crossa (1990Crossa J (1990) Statistical analysis of multi-location trials. Advances in Agronomy 44: 55-85.). The AMMI and GGE biplot package in R software (R Core Team 2013R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: < Available at: http://www.R-project.org/
>. Accessed on April 14, 2019.
http://www.R-project.org/...
) were used for the analyses. The AMMI stability indices; AMMI distance (Di) and AMMI Stability Value (ASV) were calculated by the procedure proposed by Zhang et al. (1998Zhang Z, Lu C and Xiang ZH (1998) Analysis of variety stability based on AMMI model. Acta Agronomica Sinica 24: 304-309.) and Purchase et al. (2000Purchase JL, Hatting H and Vandeventer CS (2000) 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: 101-107.), respectively. Stability per se might not be the only selection parameter because the most stable genotypes do not necessarily have the best yield performance (Mohammadi and Amri 2007Mohammadi R and Amri A (2007) Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica 159: 419-432. ). We decided to incorporate both yield and stability in a single index to classify stable genotypes. The genotype stability index (GSI) considered the ranks of the genotype yields across environments and AMMI stability values. This index incorporates the yield mean and stability index in a single criteria and is calculated as:
GSI = RASV+RY
where RASV is the rank of ASV and RY the rank of mean genotype yield of all environments.
The data were graphically analysed to interpret the GxE interaction to identify stable and adaptive genotypes by the GGE biplot, as described by Yan and Tinker (2006Yan W and Tinker NA (2006) Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86: 623-645.). The biplots were generated from the first two PCAs, without scaling, centering (2) or singular value partitioning (SVP) (2). The lines that connect the test environment to the biplot origin are called environment vectors and the cosine of the angle between the vectors of two environments approximates the correlation between them (Yan et al. 2007Yan W, Kang MS, Ma B, Woods S and Cornelius P (2007) GGE Biplot vs. AMMI analysis of genotype-by-environment data. Crop Science 47: 643-655.).
RESULTS AND DISCUSSION
GE analysis by AMMI model
The average grain yield of the genotypes over locations ranged from 4936 kg ha-1 (115) to 6279 kg ha-1 (107). Genotype G106 yielded highest at two locations (Karnal and Hisar); G119 at three (Karnal, Pantnagar and Gurdaspur) and G107 at two locations (Indore and Pune) (Table 4). The AMMI model is widely used in stability analysis as it provides an initial diagnosis of the model to be fit into multi environmental evaluation, allows a partitioning of the GxE interaction and explains patterns and relationships between genotypes and environments (Zobel et al. 1988Zobel RW, Wright MJ and Gauch HG (1988) Statistical analysis of a yield trial. Agronomy Journal 80: 388-393, Crossa et al.1990Crossa J (1990) Statistical analysis of multi-location trials. Advances in Agronomy 44: 55-85.). The AMMI analysis of variance for grain yield showed that 73.77% of the total sum of squares was attributable to environmental, only 5.99% to genotypic and 20.23% to GxE effects (Table 5). A large sum of squares for environments indicated that the environments were diverse, with large differences among environmental means causing most of the variation in grain yield, indicating that environment has a strong influence on grain yield (Tonk et al. 2011Tonk FA, Ilker E and Tosun M (2011) Evaluation of genotype x environment interactions in maize hybrids using GGE biplot analysis. Crop Breeding and Applied Biotechnology 11: 1-9., Munaro et al. 2014Munaro LB, Benin G, Marchioro VS, Franco FA, Silva RR, Lemes da Silva C and Beche E (2014) Brazilian spring wheat homogeneous adaptation regions can be dissected in major mega environments. Crop Science 54: 1-10., Alam et al. 2015Alam MA, Sarker ZI, Farhad M, Hakim MA, Barma NCD, Hossain MI, Rahman MM and Islam R (2015) Yield stability of newly released wheat varieties in multi- environments of Bangladesh. International Journal of Plant & Soil Science 6: 150-161.). The magnitude of the GxE interaction sum of squares was 3.37 times higher than that for genotypes, indicating that there were substantial differences in genotypic response across environments, in agreement with previous reports (Tonk et al. 2011, Alam et al. 2015, Vaezi et al. 2017Vaezi B, Pour-Aboughadareh A, Mohammadi R, Armion M, Mehraban A, Hossein-Pour T and Dorii M (2017) GGE Biplot and AMMI Analysis of Barley Yield Performance in Iran. Cereal Research Communications 45: 500-511.). The multiplicative variance of the treatment sum of squares due to interaction was partitioned into seven significant interaction principal components. The first two PCs explained 46.62% of the total variation, in which the contribution of PC1 was 27.94% and that of PC2 18.68. Therefore, AMMI1 (IPCA1 vs additive main effects) and AMMI2 (IPCA2 vs IPCA1) biplots were generated to illustrate the genotype and environment effects simultaneously (Figure 1). The AMMI 1 biplot indicated that the environments E1, E4, E6, E9 and E3 were high yielding locations with high additive genotypic main effects, while the yields in the other environments were below the environmental mean. The scatter plot of the genotypes in this biplot indicated that genotype G107, followed by G119, G120, G130, G106, G104, G117, G136, G150, G101, G125 were the 10 highest yielding genotypes. The AMMI2 biplot indicated that the environments E2, E4 and E6 were discriminatory and located far away from the biplot origin. The genotypes G111, G135, G137, G134 and G130 were located close to the origin and proved highly stable, although their mean yields were on the lower side and should therefore not be recommended. Similar results regarding the stability of genotypes due to low IPCA1 values were recorded elsewhere (Mohammadi et al. 2013Mohammadi R and Amri A (2013) Genotype x environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192: 227-249., Oral et al. 2018Oral E, Kendal E and Dogan Y (2018) Selection the best barley genotypes to multi and special environments by AMMI and GGE biplot models. Fresenius Environmental Bulletin 27: 5179-5187.). The genotypes G128, G146, G148, G105, G141, G103, G124, G108, G127, located far away from the origin, were highly unstable and expressed a higher GE interaction (positive or negative).
AMMI bi-plot model showing relationship among: a) test environments and genotypes based on grain yield; and b) among IPC-1 and grain yield.
Eight sectors were observed and genotype G143 clustered with E9 and E3, indicating repeatable performance. Genotype 110 clustered with E3, 144 with E5 and 146 with E2, indicating that these genotypes are stable in the respective environments. The genotypes 111 and 137 were relatively closer to the biplot origin and could be good enough for E7 while 117, 130 and 144 were relatively closer to the biplot origin and could be good enough for E5, with average adaptation. Environment (E7) contributed most to the phenotypic stability of these genotypes (Figure1b), whereas E5, E9 and E3 contributed most to the GxE interactions. Tekdal and Kendal (2016Tekdal S and Kendal E (2018) AMMI model to assess durum wheat genotypes in multi- environment trials. Journal of Agricultural Sciences and Technology 20: 153-166.) reported 10 sectors with respect to the mega environments and few lines were recommended for each environment.
The AMMI-based stability parameter, the AMMI stability value (ASV), was calculated based on the first two PCAs to produce a balanced measurement between them, and can be useful in situations where the two first IPCs explain a considerable part of the GxE interactions (Table 5). According to ASV, genotypes G111, G131, G112, G129, G137, G132, G105, G126, G135 and G142 were identified as stable for having lower ASV values, whereas genotypes G115, G123, G120, G107 and G119 were identified as being more unstable (Table 6). According to parameter Di, the values of G137, G111, G135, G132 and G112 were the lowest, whereas that of G146 was highest, followed by G124, G115, G119 and G123. The genotypes G125, G135, G104, G112, G136, G144, G105, G132, G126 and G129 were the most stable and high-yielding genotypes based on the genotype stability index, which takes both the overall mean yield and ASV into consideration (Table 6). The AMMI based AMMI stability parameters were used to screen durum wheat for identification of stable lines and were found to be adequate for the identification of stable genotypes (Mohammadi and Amri 2013Mohammadi R and Amri A (2013) Genotype x environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192: 227-249., Alam et al. 2015Alam MA, Sarker ZI, Farhad M, Hakim MA, Barma NCD, Hossain MI, Rahman MM and Islam R (2015) Yield stability of newly released wheat varieties in multi- environments of Bangladesh. International Journal of Plant & Soil Science 6: 150-161.). Similarly, AMMI stability parameters were also used to identify stably performing barley lines in Iran and were found to be promising in the identification of stable barley lines (Vaezi et al. 2017Vaezi B, Pour-Aboughadareh A, Mohammadi R, Armion M, Mehraban A, Hossein-Pour T and Dorii M (2017) GGE Biplot and AMMI Analysis of Barley Yield Performance in Iran. Cereal Research Communications 45: 500-511.).
GGE biplot analysis
The GGE biplot analysis was used to identify the best line of each environment and assess the stability of the lines. The most attractive feature of GGE biplots is the ‘which-won-where’ analysis, in which crossover GE interaction, mega-environment differentiation and specific genotype adaptation are graphically represented (Rakshit et al. 2014Rakshit S, Ganapathy KN, Gomashe SS, Swapna M, More A, Gadakh SR, Ghorade RB, Kajjidoni ST, Solanki BG, Biradar BD and Prabhakar A (2014) GGE biplot analysis of genotype × environment interaction in rabi grain sorghum [Sorghum bicolor (L.) Moench]. Indian Journal of Genetics and Plant Breeding 74: 558-563., Oral et al. 2018Oral E, Kendal E and Dogan Y (2018) Selection the best barley genotypes to multi and special environments by AMMI and GGE biplot models. Fresenius Environmental Bulletin 27: 5179-5187.). The visualization of a ‘which-won-where’ pattern in multi-environment trials is essential to study the possible existence of different mega-environments in a region (Yan and Tinker 2006Yan W and Tinker NA (2006) Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86: 623-645.). The vertex genotypes were the most responsive for being located at the greatest distance from the biplot origin. The genotypes with either the best or poorest performance in one or all environments were considered responsive (Yan and Tinker 2006Yan W and Tinker NA (2006) Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86: 623-645.), falling within the sectors. In the biplot, the equality l.ine divides the graph into six sectors and nine environments were retained in two sectors (Figure 2), probably due to latitudinal and longitudinal differences. The test locations could be partitioned into two mega environments, one with E1, E8, E3, E4 and E7 and the second with E5, E2 and E6. In the first mega environment, the genotypes G119 and G120 were the winning genotypes and genotypes G112, G107, G148 and G146 in the second. There were strong correlations between environments located within the same sector, and variation in the genotype performance within environments indicated strong environmental influence and the existence of mega environment (Oral et al. 2018Oral E, Kendal E and Dogan Y (2018) Selection the best barley genotypes to multi and special environments by AMMI and GGE biplot models. Fresenius Environmental Bulletin 27: 5179-5187.). The GGE biplot is a tool of data visualization that allows an evaluation of environments due to the discriminative ability and representativeness of the GGE view, which is an advantage over the AMMI biplot analysis (Yan et al. 2007Yan W, Kang MS, Ma B, Woods S and Cornelius P (2007) GGE Biplot vs. AMMI analysis of genotype-by-environment data. Crop Science 47: 643-655., Aktas 2016Aktas H (2016) Tracing highly adapted stable yielding bread wheat (Triticum aestivum L.) genotypes for greatly variable South-Eastern Turkey. Applied Ecology and Environmental Research 14: 159-176.).
GGE bi-plot showing: a) “which-won-where” pattern for genotypes and environments; b) discriminating ability and representativeness of environments for grain yield; c) the relationship between mean grain yield and wheat stability.
Identification of ideal genotype based on GGE biplot analysis
The relationship among test environments was studied based on environment-centered (centering, 2) and environment-metric preserving (SVP, 2) without scaling option. Regarding grain yield, E2 and E6 were the most discriminating environments, whereas E5, E1 and E8 were the most representative environments, indicating their adequacy as test environments for multi-environmental trials (Fig. 2b). Environment E8 was closest to the mean environment, followed by E1 and E5. The genotype ranking in the closest to average, i.e., the most representative environments (E1 and E8), showed that genotype G107 yielded highest, followed by G120, G106 and G130. For selection of generally adapted genotypes, E2 was found most suitable based on both descriptiveness and representativeness, while E1 and E8 were found to be most suitable based on representativeness for grain yield.
According to Yan and Tinker (2006Yan W and Tinker NA (2006) Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86: 623-645.), an ideal genotype should have both high mean yield and high stability within a mega-environment. In fact, an ideal genotype should have the highest PC1 score (high yielding ability) and lowest (absolute) PC2 score (high stability) (Rakshit et al. 2014Rakshit S, Ganapathy KN, Gomashe SS, Swapna M, More A, Gadakh SR, Ghorade RB, Kajjidoni ST, Solanki BG, Biradar BD and Prabhakar A (2014) GGE biplot analysis of genotype × environment interaction in rabi grain sorghum [Sorghum bicolor (L.) Moench]. Indian Journal of Genetics and Plant Breeding 74: 558-563., Yan and Tinker 2006Yan W and Tinker NA (2006) Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86: 623-645.). The genotypes were ranked for ideal grain yield performance, in other words, with high yield performance and stability across the nine locations (Figure 2c). The biplot defined genotypes with longest vectors coupled with zero G×E, represented by dots and arrows, as stable and high yielding. The ideal genotype 129 was stable as its projection on the AEA was close to zero. Other promising genotypes near the ideal genotype were G111, G131, G135 and G112. The low yielding genotypes (G115, G123, G143, G102, G139 and G113) were located far away from the ideal genotype. Kendal and Sener (2015Kendal E and Sener O (2015) Examination of genotype × environment interactions by GGE biplot analysis in spring durum wheat. Indian J. Genetics 75: 341-348.) also identified mega environments for durum wheat in Turkey and identified suitable stable and ideal genotypes for grain yield and quality traits. Both the AMMI and GGE Biplot approaches proved equally effective in the identification of stable and high yielding genotypes (G129, G111, G131, G135 and G112), as also reported by Aktas (2016Aktas H (2016) Tracing highly adapted stable yielding bread wheat (Triticum aestivum L.) genotypes for greatly variable South-Eastern Turkey. Applied Ecology and Environmental Research 14: 159-176.). On the other hand, the GGE biplot has the advantage of a higher discriminative ability and representativeness of the GGE plot than the AMMI biplot.
CONCLUSION
The GE interaction along with the genotype and environment main effects among 50 genotypes evaluated at nine locations were found to be significant.
Both approaches, AMMI and GGE biplot, allowed the identification of common genotypes (G129, G111, G131, G135 and G112) that are stable and high yielding across all locations.
The genotypes G112 and G135 were identified as high yielding and stable across all nine locations. Therefore, these lines can be included in the national testing program, to be released as a variety.
ACKNOWLEDGEMENT
The authors acknowledge the support of CIMMYT, for providing advanced breeding lines from the CIMMYT Elite Spring Wheat Yield trial (ESWYT). Authors are also thankful to wheat breeders for their support in conducting ESWYT trials at different locations in India.
REFERENCES
- Ahmadi J, Mohammadi A and Najafi-Mirak T (2012) Targeting promising bread wheat (Triticum aestivum L.) lines for cold climate growing environments using AMMI and SREG GGE Biplot analyses. Journal of Agricultural Sciences and Technology 14: 645-657.
- Aktas H (2016) Tracing highly adapted stable yielding bread wheat (Triticum aestivum L.) genotypes for greatly variable South-Eastern Turkey. Applied Ecology and Environmental Research 14: 159-176.
- Alam MA, Sarker ZI, Farhad M, Hakim MA, Barma NCD, Hossain MI, Rahman MM and Islam R (2015) Yield stability of newly released wheat varieties in multi- environments of Bangladesh. International Journal of Plant & Soil Science 6: 150-161.
- Crossa J (1990) Statistical analysis of multi-location trials. Advances in Agronomy 44: 55-85.
- Kendal E and Sener O (2015) Examination of genotype × environment interactions by GGE biplot analysis in spring durum wheat. Indian J. Genetics 75: 341-348.
- Mohammadi R and Amri A (2007) Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica 159: 419-432.
- Mohammadi R and Amri A (2013) Genotype x environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192: 227-249.
- Munaro LB, Benin G, Marchioro VS, Franco FA, Silva RR, Lemes da Silva C and Beche E (2014) Brazilian spring wheat homogeneous adaptation regions can be dissected in major mega environments. Crop Science 54: 1-10.
- Oral E, Kendal E and Dogan Y (2018) Selection the best barley genotypes to multi and special environments by AMMI and GGE biplot models. Fresenius Environmental Bulletin 27: 5179-5187.
- Purchase JL, Hatting H and Vandeventer CS (2000) 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: 101-107.
- R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: < Available at: http://www.R-project.org/ >. Accessed on April 14, 2019.
» http://www.R-project.org/ - Rakshit S, Ganapathy KN, Gomashe SS, Swapna M, More A, Gadakh SR, Ghorade RB, Kajjidoni ST, Solanki BG, Biradar BD and Prabhakar A (2014) GGE biplot analysis of genotype × environment interaction in rabi grain sorghum [Sorghum bicolor (L.) Moench]. Indian Journal of Genetics and Plant Breeding 74: 558-563.
- Sharifi P, Hashem A, Rahman E, Ali M and Abouzar A (2017) Evaluation of Genotype × Environment Interaction in Rice Based on AMMI Model in Iran. Rice Science 24: 173-180.
- Tekdal S and Kendal E (2018) AMMI model to assess durum wheat genotypes in multi- environment trials. Journal of Agricultural Sciences and Technology 20: 153-166.
- Tonk FA, Ilker E and Tosun M (2011) Evaluation of genotype x environment interactions in maize hybrids using GGE biplot analysis. Crop Breeding and Applied Biotechnology 11: 1-9.
- Vaezi B, Pour-Aboughadareh A, Mohammadi R, Armion M, Mehraban A, Hossein-Pour T and Dorii M (2017) GGE Biplot and AMMI Analysis of Barley Yield Performance in Iran. Cereal Research Communications 45: 500-511.
- Yan W and Kang MS (2002) GGE-biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, 288p.
- Yan W and Tinker NA (2006) Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86: 623-645.
- Yan W, Kang MS, Ma B, Woods S and Cornelius P (2007) GGE Biplot vs. AMMI analysis of genotype-by-environment data. Crop Science 47: 643-655.
- Zhang Z, Lu C and Xiang ZH (1998) Analysis of variety stability based on AMMI model. Acta Agronomica Sinica 24: 304-309.
- Zobel RW, Wright MJ and Gauch HG (1988) Statistical analysis of a yield trial. Agronomy Journal 80: 388-393
Publication Dates
-
Publication in this collection
31 Oct 2019 -
Date of issue
Jul-Sep 2019
History
-
Received
21 Jan 2019 -
Accepted
16 Apr 2019
