4.3 Results
4.3.1 Phenotypic variation in disease and agronomic traits
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Minimum and maximum values for each trait in the population were estimated. The agronomic and disease traits in each population were subjected to principal component analysis using correlation matrix-method to explore the relative contribution of each trait in the overall variation in each population. To estimate the relationship between agronomic and disease parameters within each population, Pearson correlation coefficients were generated and tested at P = 0.05. To find out how yield (bunch weight) in (kg) was affected by agronomic and disease parameters, a stepwise regression analysis was carried out for the tetraploid and the diploid populations.
Amplified fragments were scored for either present (1) or absent (0). Jaccard’s similarity coefficients were computed as follows:
GS = a/(a + b + c) (Nei and Li, 1979) where,
a is the number of fragments present in both samples,
b and c are the number of fragments present only in sample 1 and in sample 2, respectively.
The similarity coefficients were then converted into distance coefficients using the formula, distance matrices = 1− GS (Nei and Li, 1979).
Principal component analysis using average distance method was carried out to group the tetraploids into different clusters.
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Table 4.2. Mean squares of analysis of variance of agronomic and disease traits of diploid bananas evaluated at Kawanda Agricultural Research Institute during 2005-2007
Source of variation df
Youngest leaf spotted
Total leaves at flowering
Height (cm)
Girth (cm)
No. of leaves at harvest
Bunch weight (kg)
No. of hands
Block(replication) 9 5.5 11.7 900 20.6 3.4 11.2 2.1
Genotype 17 13.2 24.1 19669 324 36.7 69.4 11.3
Error 116-170 5.3 3 667 8.9 1.9 6.4 1.2
CV(%) 23 16 13 9 55 61 16
R2(%) 54 59 82 85 77 69 66
Table 4.2 continued
Source of variation df
Days to harvest
Area under disease progress curve
No. of suckers
Block(replication) 9 2899 58365 4.1
Genotype 17 8886 1632411 16.9
Error 116-170 1005 50325 3.4
CV(%) 19 55 53
R2(%) 71 77 22
All variances for genotypes were highly significant at P=0.0001; Presence of missing plots caused variation in error degrees of freedom
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Table 4.3. Mean squares of analysis of variance of agronomic and disease traits of tetraploid bananas evaluated at Kawanda Agricultural Research Institute during 2005-2007
Source of
variation df
Youngest
leaf spotted
Total leaves at flowering
Height
(cm) Girth (cm)
Bunch weight (kg)
No. of hands
Days to harvest
Area under disease progress curve
No. of suckers
Replication 1 0.8 1.5 1009 9 1.2 0.2 370 53715 1.1
Genotype 8 1.4ns 4.1ns 4600* 40* 2.6ns 0.8ns 311ns 44869ns 4.2*
Plant(genotype) 25 1.2ns 3.0ns 1203ns 23ns 1.6ns 0.5ns 172ns 33513ns 3.0ns
Error 15 1.7 2.0 1965 18 3.3 1.2 228 23879 1.3
CV(%) 26 16 16 11 31 21 33 24 22
R 2(%) 0.68 0.82 0.73 0.80 0.73 0.71 0.81 0.84 0.71
* Significant at P= 0.05; ns = non significant
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The diploid accession had higher maximum values for youngest leaf spotted, total leaves at flowering, plant height, number of functional leaves at harvest, bunch weight, days to harvest, and area under disease progress curve than the tetraploid clones. The range (maximum-minimum) values were higher in diploids than in the tetraploids (Table 4.4).
Table 4.4. Maximum and minimum values of different traits in diploid and tetraploid populations evaluated at Kawanda Agricultural Research Institute during 2005-2007
Diploids Tetraploids
Trait Mean Minimum Maximum Mean Minimum Maximum
Youngest leaf spotted 6.6 2.0 11 4.9 2 7
Total leaves at flowering 10.4 6.0 20 8.7 4 11
Plant height (cm) 253.0 113.0 360 227.0 117 308
Plant girth (cm) 37.0 18.0 51 39.0 30 56
NSL at harvest 2.2 0.0 14 0.4 0 3
Bunch weight (kg) 5.4 0.3 20 5.8 2 9
Number of hands 7.3 4.0 12 5.0 3 8
Days to harvest 158.0 55.0 328 112.0 79 142
AUDPC 478.0 0.0 2439 588.0 0 1111
Number of suckers 3.4 0.0 10 1.8 0 6
The broad sense heritability estimates were higher in the diploids than in the tetraploid accessions (Table 4.5). The heritability estimates ranged between 0.16-0.82 in the diploid population. The area under disease progress curve had the highest broad sense heritability estimate of 0.82 in the diploids while youngest leaf spotted had the least estimate of 0.16. The heritability estimates in the tetraploid population were very low.
The highest broad sense heritability estimate was 0.22 for the number of suckers in the tetraploid population.
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Table 4.5. Broad sense heritability estimates of different traits in diploid and tetraploid populations evaluated at Kawanda Agricultural Research Institute during 2005-2007
Trait
Broad sense heritability (H2)
diploids
Broad sense heritability (H2) tetraploids
Youngest leaf spotted 0.16(0.31) -
Total leaves at flowering 0.47(0.58) 0.12(0.45)
Plant height (cm) 0.78(0.03) 0.14(0.02)
Plant girth (cm) 0.82(0.26) 0.13(0.16)
Number of leaves at harvest 0.73(0.70) -
Bunch weight (kg) 0.55(0.39) -
Number of hands 0.51(0.71) -
Days to harvest 0.50(0.03) 0.04(0.03)
Area under disease curve 0.80(0.03) 0.09(0.04)
Number of suckers 0.33(0.51) 0.22(0.32)
- the estimates were very close to zero; Values in parentheses are standard errors of heritability
After principal component analysis of agronomic and disease parameters in the diploid and tetraploid populations four principal components were retained in each population.
These principal components explained 85% and 78% of the total variation in the data for diploids and tetraploids, respectively (Tables 4.6 and 4.7). The agronomic parameters (plant height, girth, number of clusters and bunch weight) contributed higher to principal component 1 than disease resistance parameters in the diploid population.
In the diploid population disease resistance traits (total leaves at flowering, number of functional leaves at harvest, and youngest leaf spotted) had high loadings on principal component 2 (Table 4.7). In the tetraploid population, apart from bunch weight, disease parameters accounted for most of the variation under principal component 1 and agronomic parameters accounted for most variation in the principal component 2.
Generally, bunch weight and AUDPC had high loadings on principal component 1 for both populations (Tables 4.6 and 4.7).
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Table 4.6. Latent vector loadings for agronomic and disease traits in the diploid population evaluated at Kawanda Agricultural Research institute during 2005- 2007
Prin1 Prin2 Prin3 Prin4
Area under disease progress curve 0.43931 -0.1645 -0.27393 -0.1978
Bunch weight (kg) 0.42537 0.10873 0.12071 -0.1166
Number of clusters 0.3621 -0.1327 0.48597 0.37473
Days to harvest -0.0833 -0.3075 0.71079 -0.0362
Plant girth (cm) 0.48333 0.15041 -0.00127 0.31973
Plant height (cm) 0.49009 -0.0009 -0.17156 -0.1225
No. functional leaves at harvest -0.07074 0.53053 -0.10003 0.50405 Total leaves at flowering 0.02374 0.55777 0.25244 0.00928 Youngest leaf spotted 0.09296 0.47502 0.25657 -0.6589 Table 4.7. Latent vector loadings for agronomic and disease traits in the tetraploid population evaluated at Kawanda Agricultural Research institute during 2005-2007
Prin1 Prin2 Prin3 Prin4
Area under disease progress curve -0.32197 0.30575 0.24531 0.47896
Bunch weight (kg) 0.41206 0.38147 -0.21248 0.15221
Number of clusters 0.37528 -0.1879 -0.34588 -0.3191
Days to harvest 0.20846 0.21052 -0.49105 0.5533
Plant girth (cm) -0.00449 0.63959 0.35359 -0.0074
Plant height (cm) -0.24769 0.51259 -0.04239 -0.4861
No. functional leaves at harvest 0.39471 -0.0174 0.10493 -0.2812 Total leaves at flowering 0.39123 0.00846 0.52902 0.0802 Youngest leaf spotted 0.41477 -0.0958 0.34092 0.1328