Genotype by Environment Interaction (GEI)

Breeders usually test a diverse array of genotypes under different environmental conditions, which implies genotype × environment interaction (GEI). According to Haldane (1947), GEI is important only if genotypes ranks differ from one environment to another. Since the 1970s, various attempts have been made to jointly capture the effects of G and GE interaction. Several methods have been developed to analyze GEI and to select genotypes that perform consistently across many environments (Becker and Leon, 1988; Kang, 1990; Kang and Gauch, 1996; Weber et al., 1996). The earliest approach was the linear regression analysis (Yates and Cochran, 1938).

Finlay and Wilkinson (1963); Eberhart and Russell (1966) and Tai (1971) popularized variations of the regression approach, assuming an

24

expected linear response of yield to environments. The merits and demerits of several methods were discussed by Kang and Miller (1984). Kang et al. (1987) concluded that Shukla’s (1972) stability variance and Wricke’s (1962) ecovalence were equivalent methods and they ranked genotypes identically for stability. These types of measures are useful to breeders and agronomists, as they provide the contribution of each genotype to total GEI. They can also be used to evaluate testing locations by identifying those locations with a similar GEI pattern (Glaz et al., 1985). Other statistical methods that have received significant attention are pattern analysis (DeLacy et al., 1996); the AMMI model (Gauch and Zobel, 1996), the shifted multiplicative model (Crossa et al., 1996), the non-parametric methods of Huhn (1996), which are based on cultivar ranks, the probability of outperforming a check (Eskridge, 1996) and Kang’s rank-sum method (Kang, 1988 &1993). The methods of Kang (1988, 1993) integrate yield and stability into one statistic that can be used as a selection criterion.

Among all the methods/models of stability analysis, GGE biplot (genotype and genotype × environment effect) technique is a versatile statistical/quantitative genetic methodology has recently been elucidated by Yan et al. (2000). The GGE biplot approach has captured the imagination of plant breeders and production agronomists like no other approach ever have. In addition to dissecting genotype × environment interactions, GGE biplot helps to analyze genotype-by-trait data, genotype-by-marker data, and diallel cross data (Yan et al., 2000; Yan, 2001; Yan and Hunt, 2001, 2002; Yan and Rajcan, 2002). The relationship among the test environments and their comparison with respect to ideal environments can be evaluated. Stability and ranking of genotypes based on which won where pattern and comparison among genotypes with respect to ideal genotypes helps breeders to select genotypes with location specific adaptability.

2.5.1 GEI for yield traits in groundnut

In groundnut, yield and its major contributing traits, biotic and abiotic stress resistance/tolerance and nutritional quality traits are governed by a pool of major and minor genes along with environmental influence (Hardwick and

25

Wood 1972; Janila et al., 2013a). Significant genotype × location, genotype × year and genotype × location × year interactions for yield and its components in groundnut have been reported in several studies (Punto and Lantinan, 1982, Senapathi and Roy, 1991; Ntare and Williams, 1998; Iwo et al., 2002; Mekontchou et al., 2006; Bucheyeki et al., 2008; Khan et al., 2009; Mothilal et al., 2010a; Makinde and Ariyo, 2010; Dolinassou et al., 2016; Patra et al., 1995). Mathur et al. (1997) reported that shelling out-turn is a most stable character in groundnut and can be used as selection criteria. Similar findings for identification of genotypes for their stability for different traits under varying environmental conditions were also reported by Chunilal et al. (2006); Hariprasana et al. (2008)and Pradhan et al. (2010).

2.5.2 GEI for foliar disease resistance

In literature, significant differences among genotypes, environments, and genotype × environment interaction for resistance LLS and rust was reported (Singh and Sinha 1993; Reddy et al., 1995; Thaware, 2009; Mothilal et al., 2010b). Genotypes with stable expression of resistance to LLS across the eight environments were reported in different maturity groups (Iwo and Olorunju, 2009). Stable source of resistance to LLS and rust has been reported by Singh et al. (1997). Significant genotype, environment, and G × E interaction were reported for days to maturity, number of mature pods per plant, shelling percentage, 100 kernel weight and LLS severity (Chavan et al., 2009; Godfrey and Olorunju, 2009). A lower area under the disease progress curve for percent defoliation was reported across years and locations (Gremillion et al., 2011).

2.5.3 GEI for nutritional quality traits

Information about the influence of various factors on oil quality may be useful to guide the choice of location, sowing date, and crop management according to the purpose of the crop production. Significant genotypic differences and interactions with growing season and geographic location have been reported for oil, protein and fatty acid composition (Fore et al., 1953; Worthington et al., 1972; Holaday and Pearson, 1974; Mohamed-Som, 1974;

26

Mozingo and Steele, 1982; Norden et al., 1987; Dwivedi et al., 1993; Wang et al., 2008; Sarvamangala 2009; Upadhyaya et al., 2012; Azharudheen et al., 2013; Dolinassou et al., 2016). They suggested that temperatures after pegging could be the factor that affects oil composition to a greater extent. Considering proportion of variance due to genotype × environment (G × E) interaction to the total phenotypic variance, oil content was least stable followed by oleic acid whereas the protein and O/L ratio were the most stable nutritional traits (Upadhyaya et al., 2005). The stability analysis resulted in the identification of a high oleic acid (>73%) containing genotype (ICG 2381).

Environmental factors such as soil and climatic variations and temperature are the most important factor affecting fatty acid composition (Cobb and Johnson, 1973; Sanders, 1982; Slack and Browse, 1984; Bansal, et al., 1993). According to the report of Holaday and Pearson (1974), monounsaturated fatty acid content increases and polyunsaturated fatty acid content decreases with the increase of the soil temperature. This can be attributed to higher metabolic rate at elevated temperatures and decreased availability of oxygen that reoxidizes the desaturase enzyme system required to synthesize linoleate and linoleneate.

Seed maturity can also influence the fatty acid composition of groundnut. The actual impact of seed maturity was depended on genotype, climatic conditions, and genotype/climatic interactions. In general, oleic acid increases and linoleic acid decreases with seed maturity (Cobb and Johnson, 1973; Hinds, 1995; Young and Waller, 1972). In contrast to this, a reduction in oleic acid and an increase in linoleic acid with seed maturity was also reported by Hashim et al. (1993); Lynd and Ansman, (1989), whereas Knauft et al. (1987) observed no influence of maturity on oil chemistry. Besides fatty acid composition, early harvests decrease oil and protein yields and impaired oil quality (Nagaraj et al., 1989).

The mean oleic acid concentration was observed high in Virginia runner followed by Virginia bunch and Spanish bunch genotypes (Picket and Holley, 1951; Worthington and Hammons, 1971; Taira, 1985; Raheja et al., 1987; Lopez et al., 2001). Very slight reduction in the variance components observed

27

across the environments a lesser role of G × E interaction for fatty acid profile and O/L ratio (Azharudheen et al., 2013). Similarly, the least influence of environment on oil content was reported by Prakash et al. (2000) and Venkataramana et al. (2001).