Principles of Plant Genetics and Breeding

Table 23.2 Stability analysis.
PERFORMANCE EVALUATION FOR CROP CULTIVAR RELEASE 425
ANOVA
Source df SS MS F Probability
Environment (E) 4 27,103.87 6,775.96 104.03 0.0001
Reps (R) × E 15 22,580.65 1,505.37 23.11 0.0001
Genotype (G) 9 4,595.65 510.65 7.84 0.0001
E × G 36 6,068.63 168.57 2.59 0.0001
Pooled error 135 8,792.85 65.13
Regression analysis
Genotype Mean Regression coefficient
N1 105.2 0.906
N2 103.3 0.759
N3 108.3 1.741
N4 99.9 0.972
N5 101.9 0.559
N6 108.5 1.141
N7 106.7 0.926
N8 103.7 0.999
N9 94.8 0.968
N10 112.9 1.028
Genotypes N2 and N5 are stable performers; N3 and N6 are
responsive to the environment and unstable in performance;
N8 and N10 are average performers.
Mean yield
Mean CV
1 2
3 4
Coefficient of variation (CV)
Group 1 = high mean, low CV; this is most desirable
Group 2 = high mean, high CV
Group 3 = low mean, low CV
Group 4 = low mean, high CV; this is least desirable
Grand
mean
yield
Figure 23.2 G × E interaction based on the coefficient of
variation (CV).
Environmental index
Genotype Index
N1 93.47
N2 96.3
N3 112.4
N4 123.6
N5 96.7
their associated statistical error – something often
ignored by many breeders. Limitations not withstanding,
the regression technique is simple and has biological
significance. Complex interactions are reduced to linear
responses.
Plot of means versus coefficient of variation
Proposed by Francis and Kannenburg in 1978, this
method entails calculating for each variety, the overall
mean and the coefficient of variations (CVs) across
the environments. A plot of means versus CVs yields
a scattergram that can be divided into four sections by
transecting the average CV and the grand mean yield
(Figure 23.2). The most desirable genotype will be
found in group 1 (high yield, low CV) while the least
desirable (low yield, high CV) will occur in group 4.
Non-parametric methods
Multivariate procedures used to analyze G × E interactions
include clustering, principal component analysis
(PCA), and factor analysis. These procedures perform
uniformly across environments. A recent addition to these
techniques is the additive mean effects multiplicative