Indian Journal of Agricultural Research

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Indian Journal of Agricultural Research, volume 57 issue 4 (august 2023) : 416-420

AMMI and GGE Biplot Analysis for G×E Interaction of Wheat Genotypes under Different Irrigation and Sowing Condition

Ragini Dolhey1,*, V.S. Kandalkar1
1Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior-474 002, Madhya Pradesh, India.
Cite article:- Dolhey Ragini, Kandalkar V.S. (2023). AMMI and GGE Biplot Analysis for G×E Interaction of Wheat Genotypes under Different Irrigation and Sowing Condition . Indian Journal of Agricultural Research. 57(4): 416-420. doi: 10.18805/IJARe.A-5603.
Background: AMMI analysis showed that genotype, environment and genotype-environment interaction had a highly significant variation for 20 wheat genotypes analyzed over four environments. ASV ranking revealed G15 (RVW-4275) as a stable genotype while G3 (RVW-4263) and G9 (RVW-4269) as unstable genotypes. GGE biplot analysis for environment interrelationship revealed that E1 (Irrigated timely sown), E2 (Restricted irrigation timely sown) were correlated forming one group and E3 (Irrigated late sown), E4 (Restricted irrigation late sown) were correlated forming another group. Polygon view showed that G9 (RVW-4269) was found stable and better performing in E1, G12 (RVW-4272) was stable under the E2 environment and G3 (RVW-4263) was stable in E3. Ideal genotype graph with concentric circles having ideal genotype at the center and genotypes G12(RVW-4272), G18(RVW-4278), G13(RVW-4273), G11(RVW-4271), G10 (RVW4270) present in a concentric circle close to the center can to considered as stable and desirable genotypes.

Methods: In the present study the plant material comprised of 20 wheat genotypes. These genotypes were randomly allocated in different replication under different environmental condition. The field trial was evaluated at four different environments viz., E1- Irrigated timely sown, E2- Restricted irrigation is timely sown (RI- 2 irrigation), E3- Irrigated late sown, E4- Restricted irrigation late sown during Rabi season of 2016-2017 at research farms, college of agriculture, Gwalior, MP. The genotype main effects and genotype × environment interaction effects (GGE) model and additive main effects and multiplicative interaction (AMMI) model were two statistical approaches used to determine stable genotype in R software.

Result: Highly significant difference was seen for genotype and G×E interaction in our study, revealing that genotype yield output was highly impacted by G×E. In all four environments and G3, G9 as unstable genotypes in all four environments, ASV ranking revealed G15 as a stable genotype. For further breeding, these genotypes G12, G18, G13, G11, G2, G10 and G15 may be used to grow genotypes adapted to conditions of partial irrigation or drought stress.
Around the world, wheat is an important food crop and it is the second widely cultivated crop after rice. It also has high nutritional value for the food security of livelihood. It contributes 30% to food crops and is cultivated in large areas of India. Since the last decade, India, being the second-largest wheat producer globally, having produced around 93.50 million tonnes, is a major contributor to India’s food security system, occupying nearly 30.23 million hectares, producing 93.50 million tonnes and productivity 30.93 q/ha. In Madhya Pradesh, it is cultivated in 5.911 million hectares, with 17.689 million tonnes and productivity of 29.93 q/ha. (Anonymous, 2015-2016).

The cause of annual wheat production decrease is abiotic and biotic stress in which heat and drought stress play a crucial role. 65 million ha of wheat production area in the world has been reported under drought stress (FAO 2013). Around 66% of the wheat crop in India receives only partial (1-2) irrigation, which causes a decline in grain yield due to water stress conditions (Joshi et al., 2007). Therefore, genotypes that can withstand drought and give better yield are needed to develop.

Abiotic stress plays an important role; therefore, there is a need that drought and heat tolerance genotypes should be developed through a breeding program. Plant breeders aim to develop high yielding cultivars with wider adaptability by incorporating better adaptation and stability for yield in wheat genotypes. However, attaining this goal is difficult due to genotype-environment interactions (GEI). (Gauch and Zobel.,1996).
In the present study the plant material comprised of 20 wheat genotypes G1 (RVW-4261), G2 (RVW-4262),G3 (RVW-4263), G4 (RVW-4264), G5 (RVW-4265), G6 (RVW-4266), G7 (RVW-4267), G8 (RVW-4268), G9 (RVW-4269), G10 (RVW-4270), G11 (RVW-4271), G12 (RVW-4272), G13 (RVW-4273), G14 (RVW-4274), G15 (RVW-4275), G16 (RVW-4276), G17 (RVW-4277), G18 (RVW-4278), G19 (RVW-4279), G20 (RVW-4280). These genotypes were randomly allocated in different replication under different environmental conditions. The experiment was conducted at the Research field of AICRP on wheat, College of Agriculture, Gwalior located in the Gird region (Agro-climatic zone No 6, wheat-pearl millet crop zone). The maximum temperature ranged from 19.80C to 43.50C and the minimum temperature ranged from 6.00C to 26.60C during the overall wheat season. Total rainfall of 14mm was recorded from October 2016 to April 2017. The overall season was favorable for crop growth. The experiment was conducted in a randomized complete block design with two replications in a 2-row plot of 2.5 m length at research farms, college of agriculture, Gwalior, MP. The seeds were sown in rows with spacing of 20 cm apart and 4-6 cm within a row on November 15th (Timely sown environment 2016-17) and December 3rd (Late sown environment 2016- 17). The field trial was evaluated at four different environments viz., E1- Irrigated timely sown, E2- Restricted irrigation is timely sown (RI- 2 irrigation), E3- Irrigated late sown, E4- Restricted irrigation late sown during Rabi season of 2016-2017 at research farms, college of agriculture, Gwalior, MP. India. Five plants were randomly selected in all genotypes in each replication and data were recorded for Biological yield (g).

Plant breeders aim to develop high yielding cultivars with wide adaptability. Genotype-environment interaction plays a crucial role in determining stable and widely adapted genotypes. Different statistical approaches include a parametric and non-parametric method to determine genotype x environment interaction. However, at present, no such method is introduced that everyone could accept (Kaya et al., 2006).

The genotype main effects and genotype × environment interaction effects (GGE) model and additive main effects and multiplicative interaction (AMMI) model are two mostly used methods for agriculture research because two-way data obtained from several kinds of agriculture experiment and these models relate to two-way data matrices. AMMI is used in multi-environment trials (MET) and it splits the GEI matrix into individual genotype and environment scores. A similar illustration was given by Zobel et al., (1988). The stability ranking of genotypes is done using AMMI stability value (ASV) (Purchase et al., (2000). ASV has been found as a suitable method for explaining the stability of genotype. AMMI and GGE showed similar results with the equal capability to gain accuracy in research (Gauch et al., 2008) and both were found useful in research work for representing mega-environment.

There is a difference between GGE and AMMI as GGE biplot analysis is based on environment centered PCA and AMMI analysis is based on double centered PCA. GGE biplot model gives a complete visual analysis of data as biplot shows mean performance, stability and represents mega-environment simultaneously (Ding et al., 2007; Kang, 1993; Yan, 2001; Yan and Kang, 2003). Which won where the pattern of GGE biplots shows which genotype is stable and high yielding, it also depicts discriminating and representing the environment (Yan et al., 2001). This study aimed to identify stable genotype on stress (E3 and E4 environment) and non-stress (E1 and E2 environment) conditions by evaluating G×E interaction through AMMI and GGE biplot analysis of wheat grain yield.
AMMI analysis
 
As shown in Table 1, a combined analysis of variance and AMMI analysis revealed that environment, genotype and G×E interaction had a highly significant difference. Significant variance was found for grain yield showing that yield was significantly affected by the environment (Table 1). Variation was seen among the environment as a high sum of the square was seen for the environment. Therefore, due to genotypic and environmental effects, plant grain yield differed.

Table 1: Analysis of Variance Table over four environments.



AMMI stability value (ASV) ranks the genotype using the AMMI model (Purchase et al., 2000). As shown in Table2, the ASV ranking value reveals that genotype 15 was found to be most stable, having the lowest ASV, whereas genotype 3 and 9 were found unstable with higher ASV.

Table 2: Stability and yield ranking of 20 genotypes over four environments, Mean grain yield, AMMI stability values (ASV).



AMMI model shows that G×E interaction is present and this interaction is spitted among the first and second IPCA (interaction principal component axis) Fig 1. Through the biplot graph, genotype 15 lying near the center was found stable in all four environments.

Fig 1: AMMI biplot graph for PCA 1 and PCA 2. Blue color represents genotypes (G1-G20) and red color marks environments (E1-E4).


 
Genotype and genotype-environment analysis (GGE analysis)
 
GGE biplot based on environment focused scaling and is used for estimating the pattern of environments. Environment PC1 score had negative and positive scores, which shows the difference in the genotypes yield over different environments resulting in cross-over G ×E interactions. 

The vector view of the GGE biplot (Fig 2) represents a summarized view of interrelationships among environments. Two groups of the environment were formed having E1, E2 environment in one group and E3, E4 environments in other groups. A positive correlation was found between E1and E2, having an angle less than 90. The correlation was seen within the environment group (E1, E2) and group (E3, E4), revealing that indirect selection can be made across these environments. For example, a genotype having adaptation in E1 may also show adaptation in E2.

Fig 2: Relationship among environments.



That won where the GGE biplot pattern shows which genotype was found best in each environment and forms environment groups (Fig 3). It also represents a polygon view of GGE biplot. Genotypes scores present at the furthest point from the origin are joined to form a polygon and within it, remaining genotypes are present. Genotype won is found based on genotypes relationship with site scores (Yan et al. 2000). At polygon vertices, eight genotypes viz, G9, G12, G3, G7, G8, G15, G16 and G5 are present. Genotypes present at the polygon vertex are the best desirable genotypes for the environment present in the same sector where vertex genotype is present (Yan 2002; Yan and Tinker 2006). Equity lines form eight sectors in a polygon and all four environments are present in three sectors, hence forming three mega-environment. G9 genotype won in first mega-environment E1, G12 genotype performs best in second mega-environment E2 and G3 was found winning in third mega-environment E3, E4 (Fig 3).

Fig 3: Which won where the pattern for genotypes and environments in polygon view.



The concentric circles on the biplot picture the length of the environment vectors, which measures the environments’ discriminating ability. Therefore, the most discriminating (informative) environments where E3, E1 and E2 and E4 least discriminating (Fig 4). The average environment (represented by the small circle at the end of the arrow) has the average coordinates of all test environments. AEA is the line that passes through the average environment and the biplot origin.  E2 was the most representative having a smaller angle with the AEA (Fig 4), whereas E4 was the least representative. E2 environment was both discriminating and representative; thus, E2 was found to be good test environments for selecting generally adapted genotypes. (Yan and Tinker, 2006).

Fig 4: Discrimitiveness v/s representativeness.



Genotypes should be evaluated on both mean performance and stability across environments in a single mega-environment. In Fig 5, the GGE biplot shows the average-environment coordination (AEC) view.  AEC abscissa (or AEA) points to higher mean yield across environments. Thus, G3 had the highest mean yield, followed by G18, G11, G10, G12 and G13, etc. G1 had a mean yield similar to the grand mean and G15 had the lowest mean yield. Another line is the AEC ordinate it points to greater variability (poorer stability) in either direction. Thus, G9 was highly unstable, whereas G1, G13, G15 were highly stable. G9 was highly unstable because it had lower than expected yield in environments E3 and E4 but higher than expected yield in E1. (Yan and Tinker, 2006).

Fig 5: Average environment coordination (AEC) views environment-focused scaling GGE-biplot for the means performance and stability of genotypes.



The ideal genotype graph shows genotype ranking for grain yield and stable performance (Fig 6).

Fig 6: Comparison of the genotype with ideal genotype based on genotype-focused scaling GGE biplot.



A high stable genotype with high performance over the different environments is said to be the ideal genotype (Yan and Tinker 2006). A Pointing arrow in a graph represents an ideal genotype (Fig 6). Taking the ideal genotype at the center, concentric circles are drawn. These concentric circles locate the genotype’s distance from an ideal genotype (Yan and Tinker 2006). A desirable genotype is present near the ideal genotype. Genotype G12 next to it G18, G13, G11, G2 and G10 were present near-ideal genotypes; therefore, these genotypes ranked up for high mean yield and stable performance. These genotypes were found most desirable over four environments (Karimizadeh et al., 2013).

In this study, GGE biplot and AMMI was used to evaluate yield stability and different environment condition representativeness for wheat genotypes. G12, G18, G13, G11, G2, G10 and G15 showed higher grain yields and stability compared with other genotypes for partial irrigation and late sowing condition. On the other hand, two groups of the environment were formed having E1, E2 environment in one group and E3, E4 environments in other groups. A positive correlation was found between E1and E2, having an angle less than 90. The correlation was seen within the environment group (E1, E2) and group (E3, E4), revealing that indirect selection can be made across these environments. E2 environment was both discriminating and representative; thus, E2 was found to be good test environments for selecting generally adapted genotypes.
A high significant difference was seen for genotype and G×E interaction, revealing that G×E highly affected genotype yield performance. ASV ranking revealed G15 as a stable genotype in all four environments and G3, G9 as unstable genotypes in all four environments. GGE biplot showed environment E1, E2 correlation and E3, E4 correlation. Genotypes G9, G12 and G3, were found stable in E1, E2 and E3. E3 was found a good test environment for selecting generally adapted genotype. It was found that genotype G12, G18, G13, G11, G2, G10 and G15 can be considered stable and desirable genotypes in four all four environment conditions. These genotypes G12, G18, G13, G11, G2, G10 and G15 may be used in further breeding to develop genotypes adapted to partially irrigated or drought stress conditions.

  1. Ding, M., Tier, B. and Yan, W. (2007). Application of GGE biplot analysis to evaluate genotype (G), environment (E) and GxE interaction on P. radiata: case study. Australasian Forest Genetics Conference, 11 - 14 April 2007, the Old Woolstore, Hobart, Tasmania, Australia.

  2. FAO. (2013). World food and agriculture. Statistical year book. Rome: Food and agriculture organization of the United States.

  3. Gauch, H.G. and Zobel, R.W. (1996). AMMI analysis of yield trials. In Genotype-by-environment Interaction (Kang, M.S. and H.G. Gauch, eds.), CRC Press, Boca Raton. FL: 85-122. 

  4. Gauch, H.G. (2006). Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 46:1488-1500.

  5. Gauch, H.G., Piepho, H.P. and Annicchiarico, P. (2008). Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Sci. 48:866-889.

  6. Joshi, A.K., Mishra, B., Chatrath, R., Ferrara, G.O. and Singh, R.P. (2007). Wheat improvement in India: Present status, emerging challenges and future prospects. Euphytica. 157: 431-446.

  7. Kang, M.S. (1993). Simultaneous selection for yield and stability in crop performance trials: Consequences for growers. Agron. J. 85:754-757.

  8. Karimizadeh, R., Mohammadi, M., Sabaghni, N., Mahmoodi, A.A., Roustami, B., Seyyedi, F., et al. (2013). GGE biplot analysis of yield stability in multi-environment trials of lentil genotypesunder rainfed condition. Notulae Scientia Biologicae. 5: 256.

  9. Kaya, Y., Aksura, M., Taner, S. (2006). CGE-Biplot analysis of multienvironment yield trials in bread wheat. Bahari Daðdaþ International Agricultural Research Institute, Turk J Agric For. 30: 325-337.

  10. Purchase, J.L., Hatting, H. and Van Deventer, C.S. (2000). Genotype x environment interaction of winter wheat (T.aestivum) in South Africa: Stability analysis of yield performance. S. Afr. J. Plant Soil. 17(3):101-107.

  11. Reynolds, M.P., Sayre, K.D., and Rajaram, R. (1999). Physiological and genetic changes in irrigated wheat in the post green revolution period and approaches for meeting projected global demand. Crop Science. 39: 1611-1621.

  12. Yan, W. (2001). GGE Biplot-A Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron. J. 93:1111-1118.

  13. Yan, W. (2001). GGE biplot: A windows application for graphical analysis of multi-environment trial data and other types of twoway data. Agronomy Journal. 93: 1111-1118.

  14. Yan, W. (2002). Singular-value partitioning in biplot analysis of multienviron-ment trial data. Agronomy Journal. 94: 990-996.

  15. Yan, W. and Kang, M.S. (2003). GGE Biplot analysis: a graphical tool for breeders, geneticists and agronomists. CRC Press, Boca Raton, Florida.

  16. Yan, W., and Tinker, N.A. (2006). Biplot analysis of multienvironment trial data: Principles and application. Candian Journal of Plant Science. 86: 623-645.

  17. Zobel, R.W., Wright, M.G. and Gauch, H.G. (1988). Statistical analysis of a yield trial. Agron J. 80: 388-393.

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