6.4 Results and discussion
6.4.3 GGE biplot
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The three IPCA axes can be taken as adequate dimensions for the data (Table 6.3 and 6.4). However, only the first two IPCA axes were plotted against one another to help investigate the G x E interactions pattern of each genotype (Figure 6.2). Among the test environments, Bako had the best yield potential and a good stability. Hybrids G68, G39 and G30 had the best association with the Bako and Jima, with the average yields of more than 7.5t/ha. The hybrids with low stability or associated with one or two sites would have a disadvantage of not adapting to other sites. It is therefore important to release for farmers these promising hybrids with good general stability that would also not only adapt, but also be productive in unstable environments. According to the AMMI analysis the hybrids G45, G2, G67, G5 and G11 were the most stable and they were more adapters to E2 (Jima) and E7 (Pawe) environments. While G30, G39, G14, G64 and G37 were most stable in E2 (Jima), G34, G21, G24, G83 and G16 were adapters to E4 (Arsi Negele).
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environment 2 (Hawassa and Areka) and mega environment 3 (Arsi Negelle). Different hybrids were identified as winning genotypes in different mega environments.
Accordingly hybrid G68 was the winning genotype in mega environment 1, G40 was winner in mega environment 2 and G52 was the winning hybrid in the third mega environments. Thus these three winning hybrids can be recommended for production in their respective mega environments.
The GGE biplot generated using the first two principal component scores showed a clear association between genotypes and environments (Figure 6.2). The biplot showed that Hawassa was the most discriminating environment for the genotypes as indicated by the longest distance between its marker and the origin. This environment provided adequate information on the performance of the hybrids. However, due to its high IPCA score, genotype variability at this environment may not exactly reflect the average genotypic performance across environments. Considering the environments closer relationships was observed between Haramaya and Ambo, which were both transitional highlands with similar production factors. Pawe was identified as stable environment as its IPCA2 score and its vector was near to the source (zero). Arsi Negele and Hawassa were projected in the opposite direction (Figure 6.2), indicating that hybrids better performed in Hawassa may not have the same trend at Arsi Negele, this was because the two locations are situated in different production zones, Hawassa in mid-altitude where as Arsi-Negele in the highland transitional areas.
Figure with PC
6.2 GGE C1 and PC2
biplot on g 2 showing
grain yield genotypes
169 of 84 maiz s, environm
ze hybrids ments and t
tested at 1 their vecto
10 location rs.
ns plotted
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The GGE biplot (Figure 6.2) also indicated the relationship among the maize hybrids.
Hybrid G74 was different from others as it is located far apart from the other hybrids in the biplot. This hybrid is also the most unstable. Hybrids which were positioned closer to the origin of the biplot (G67, G77, G3) indicate their stability in performance across environments, while those positioned far apart (G74, G40, G55) are unstable. Hybrid G19 was more adapted to low yielding environment (Areka) and hybrids G68 was more close to Bako, the high yielding environment. Generally hybrids which lies nearer to each other and those projecting in similar direction, designate their proximity in the grain yield performance (Figure 6.2).
Figure 6.3 Polygon views of the GGE biplot based on symmetrical scaling for which won where pattern of genotypes and environments.
31
34 44
61 20
35
21 83
22 2381
79
24
77 25
75 26
73 27
Scatter plot (Total - 49.72%)
28 70
29
68 30
66 64
1 32
3 33
5 63
7 9 36
11 37 13
38 15
39 17
40 19
41
82 78 42
43 74
62 71
6745
46 2
47 6
48 10
49 14
50 5118 80
52
72 53
65
54
8
55 16 56
76
57 4 84
69 12 58
60 59
7
2 8
5 4
3
6 9 10 1
PC2 - 19.55%
PC1 - 30.18%
Environment scores Sectors of convex hull Convex hull
Mega-Environments Genotype scores
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Mega environment classification and winning genotypes
Figure 6.3 presents schematic view of which hybrid won where. Accordingly, nine lines divide the biplot into nine sectors, out of these; environments fall into 3 of them. Seven environments (E1 (Bako), E2 (Jima), E6 (Asosa), E7 (Pawe), E8 (Ambo), E9 (Haromaya) and E10 (Finote Selam)) fell in one sector comprising one large mega- environment, and the vertex genotype for this sector was G68 implying that this genotype was the winning genotype for these environments. Sector 2 contained two environments (E3 and E5). The remaining environment (E4) was contained in the last smaller mega environment and G52 hybrid being the winner.
It appears that there exist three possible mega environments. The first mega environment was consisting of seven environments, with G68 as a winner genotype.
The second mega environment was smaller compared to the first and it comprises environments E3 (Hawasa), and E5 (Areka) with a winning genotype G40. The third and the smallest mega environment contained E4 (Arsi Negelle), and the winner genotype was G52.
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Figure 6.4 Ranking of genotypes relative to an ideal genotype. The ideal genotype can be used as a reference for genotype evaluation.
The ideal genotype can be used as a reference for genotype evaluation. In this study, G68, G3 and G39 were ideal genotypes (the center of concentric circles) and genotypes located closer to the ideal genotypes are more desirable than the others (Figure 6.4).
Genotypes grouped in the concentric circle next to ideal genotype were more desirable.
However, genotype G74 and G80 were undesirable (Figure 6.4). The present study used the AMMI and the GGE models and summarized patterns and relationships of genotypes and environments successfully. These models are reportedly useful to provide a valuable prediction assessment (Ezatollah et al., 2012). However, Becker and
52
10
71 73 11 12
75 13
77 79 14
81 15
16 83 17
70
18
68
19
66 20 64
21 62
22 60
51
23 59 58
24
Comparison biplot (Total - 50.45%)
25
55 26 1 27
3 28
29 5 7
30 9
31
74 32
78 33
82
34 69
35 65
36
61
37
57 38 54
39
4
40 8
41
76 42
84
43 63
44
56
45 6
46
80 47
53 72
2 67 48
49 50
4 6
9
7 8
5
10 2
3
1
PC2 - 19.38%
PC1 - 31.07%
AEC
Environment scores Genotype scores
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Léon (1988) stated that multivariate methods are too sophisticated to provide a simple measure of yield stability which allows a ranking of genotypes. In the present study the models have clearly demarcated the pattern of adaptation of hybrids to environments and can be used to identify the superior genotypes in relation with the environments.
Three hybrids namely G68, G40 and G52 were identifies as stable hybrids by both AMMI-3 and GGE biplot methods of stability analysis, and thus they can be recommended for further testing across years or be recommended for production in the mid-altitude sub-humid areas of Ethiopia and similar environments in Sub-Saharan Africa.