6.3 Results and discussion
6.3.5 GGE biplot analysis
Relationships among test environments and their discriminating ability
The GGE biplot (Figure 6.4) accounted 67.695 of the total phenotypic variation. The first principal component explained 40.97% while the second explained 26.72%. Tonk et al. (2011) found that 61.2% of total variation resulting from the two principal components in their GGE studies. The length of vectors of each environment and their cosine angles among them were analyzed. Environments, E1 and E2 had relatively long vectors and their cosine angle between them was significantly small indicating that they are positively strongly correlated and had high discriminating ability about the genotypes (Figure 6.4). These two environments can be used in
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evaluation studies because they have ability to discriminate the genotype and they give more information about them. Similar finding were reported by (Dagnachew et al., 2014). But the same environments showed had negative relationships with the remaining environments (E3, E5, D6 and E4) as reflected by the obtuse cosine angle between them. Yan and Tinker (2006) studied that strong negative correlation of this type causes significant crossover in performance of genotypes and therefore affect areas of recommendation for cultivation and production of the developed genotypes. E3 and E5 had moderately long vectors 450 angle between them. This implies that their correlation was 0.707 because cosine of 450 is approximately to 0.707. E6 was considered to have low discriminating ability because of its shortest vectors (Figure 6.4). This environment gives little information about the performance of the genotypes under study thus should not be used in evaluation studies. A similar pattern of environments was reported in Ethiopia when Rezene et al.(2014) studying GGE biplot anlaysis on grain yield of pea.
Figure 6.4: GGE Biplot showing relationships of the test environments and their discriminating ability. See code descriptions of environments and genotypes in Tables 6.1 and 6.5,
respectively.
Stability and representativeness of environment s and genotypes
The concentric circles drawn on the biplot assisted breeders to visualize the stability of environments and genotypes in yield performance (Asnake et al., 2013, Dagnachew et al., 2014). Environments or genotypes that fall onto the centre of the innermost concentric smallest circle are considered ideal while those located closer to it (innermost circle) are considered
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desirable and discriminating (Naroui et al., 2013). In the present study, E1 was most stable and representative because it was found in the innermost concentric small circle (Figure 6.5). E2 located on the second circle next to this smallest circle suggests that it was relatively most desirable. In contrast, E3, E4, E5 and E6 located far away from the concentric innermost circle hence were considered undesirable with E4 and E5 being the most undesirable. These environments were neither representative nor discriminating (Figure 6.5). On the other hand, hybrids G44, G12, G19, G12, G1, G13 and G42 were found within the innermost concentric smallest circle and had performance above average suggesting that they are most ideal hybrids (Figure 6.6). Such genotypes are considered to be stable and can be used as reference genotypes or hybrids for evaluating (Karimizadeh et al., 2013; Mohamed et al., 2013). G14, G4, G46 and G25 were located in the concentric circle next to the inner most concentric smallest circle therefore were considered more desirable. Other genotypes such as G31 were undesirable with G34 and G11 being the most undesirable (Figure 6.6). Several studies have reported similar phenomenon (Jandong et al., 2011; Asnake et al., 2013).
Figure 6.5: GGE Biplot showing ranking of environments based on ideal test environments or representativeness. See code descriptions of environments and genotypes in Tables 6.1 and 6.5, respectively
G12 G18 G33G2G32
Comparison biplot (Total - 65.72%)
G31
G30G8 G6
G3 G5 G29 G48
G46 G28
G27 G44
G26 G42G25
G40 G24 G39
G23 G37
G22
G35 G21 G7G20
G17 G50
G49 G45
G1
G41
G10
G38 G11
G34
G19 G47
G13
G4
G9
G43
G36 G14
G15
G16 E6 E3
E2 E1 E4E5
PC2 - 21.03%
PC1 - 44.69%
AEC
Environment scores Genotype scores
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Figure 6.6: GGE biplot showing genotypes based on ideal genotype. See code descriptions of environments and genotypes in Tables 6.1 and 6.5, respectively.
Identification of superior genotypes in each mega environments
Figure 6.7 presents the which won where pattern view of the GGE biplot. This biplot accounted 65.72% of the total variation of the data with each component 44.69% for the first and 21.03%
for the second component. This biplot is important it is used to indicate the most performing genotypes (superior) in each of the possible mega environments identified. The vertices of the irregular polygon drawn on the GGE biplot represent the yield potential of the wining genotypes (Yan et al., 2007). Hybrids G43 and G10 were considered superior because they were located at the vertices of the polygon. These hybrids were also very close to E5, E3 and E6 suggesting that they adapted well to these environments (Figure 6.7). G31 and G34 were also among the superior hybrids but in lower yielding environments.
G12
G18 G33G2G32
Comparison biplot (Total - 65.72%)
G31
G30G8 G6
G3 G5 G29 G48
G46 G28
G27 G44
G26 G42G25
G40 G24 G39
G23G37 G22
G35 G20G21 G7
G17 G50
G49 G45
G1
G41
G10
G38 G11
G34
G19 G47
G13
G4
G9
G43
G36 G14
G15
G16 E6 E3
E2 E1 E4E5
PC1 - 44.69%
PC2 - 21.03%
AEC
Environment scores Genotype scores
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Figure 6.7: The polygon view of the GGE biplot analysis showing the which won where pattern for selecting superior genotypes. See code descriptions of environments and genotypes in Tables 6.1 and 6.5, respectively.
Ranking of genotypes and environments based on yields and stability
Figure 6.8 presents the average environment coordination (AEC) or average environment axis (AEA) view of the GGE biplot showing stability and mean performance ranking of genotypes.
The biplot view consisted of two principal lines. The single arrowed line is the AEC or AEA abscissa points to higher mean yield across environments and the crossing line that points to greater variability (poor stability) in either direction. This biplot explained 64.19% of the total variation with the first and second PCs contributing to 43.43 and 20.77%, respectively. G10 had highest mean yield, followed by G28 and G43 but were unstable. G31 appeared to have the same variability levels with G10 but differed considerably in yield performance (Figure 6.8).
These two hybrids had grain yields of 1.95 and 6.70 t/ha, respectively (Table 6.5). The hybrid G43 demonstrated highest level of variability in yield performance because it had lower (2.80 t/ha) than expected yield in E3 but produced highest yield in E5 and E6 (Table 6.5). Tonk et al.
(2011) and Nzuve et al. (2013) reported similar finding when analyzing multi-environment trial data using GGE biplot.
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Figure 6.8: The average environment coordination (AEC) view showing mean performance and stability of 45 F1 hybrids tested across six environments (E1-E6). See code descriptions of environments and genotypes in Tables 6.1 and 6.5, respectively.
6.3.6 Means of MSV disease reaction of F1 maize hybrids and standard checks evaluated