CHAPTER 2..................................................................................................................................... 42
3.5 Discussion
Figure 3. 4 Dendrogram of the 36 QPM and non-QPM maize inbred lines revealed by UPGMA cluster analysis based on 18 phenotypic traits when evaluated at two locations
Prasanna, 2003; Has et al., 2009). According to FAO’s prediction, an additional 60 million tonnes of maize grain will be needed in the tropics from the annual global harvest by 2030 (Paliwal et al., 2000). To meet such requirements, conservation and utilization of useful genetic diversity is crucial. Because it is fundamental for sustainable genetic gain of economically desirable traits, and can help prevent losses due to biotic and abiotic stress (Pollak and Scott, 2005; Osorno and Carena, 2008; Has et al., 2010).
The broad range of phenotypic variation detected in the current study implied great potential for the development of improved open-pollinating varieties, inbred lines and hybrids of QPM adapted to the highland agro-ecologies. The ranges in days to anthesis at Ambo (96.6–118.7) and Kulumsa (92.0–109.5), for example, suggests the possibility to develop cultivars with different maturity groups for the diverse highland environments. The combined analysis of variance showed highly significant location x line interaction for most of the traits indicating rank differences in performance of the lines across the two locations. Luxurious vegetative growth and better yield performances of the lines were observed at Ambo than Kulumsa site which could be attributed to inadequate rainfall during the growing season at Kulumsa. It was interesting to note that out of the six non-QPM inbreds used in this study three had better grain yield, one provided equivalent and the rest two had lower grain yield than their QPM converted counterparts. The result conforms to previous reports by Prasanna et al. (2001), Sofi et al. (2009), and Atlin et al. (2011) that it could be possible to develop a QPM genotype which is comparable or even better in agronomic performances than the normal maize genotypes. The implication is that QPM adoption, especially in Africa, could be facilitated if the QPM cultivar is agronomically better than the non-QPM for farmers’ acceptance and marketing (Krivanek et al., 2007).
Understanding of the relationships among traits is important in designing effective selection programs for crop improvement. Genetic correlations are of interest to determine degree of association between traits and how they may enhance selection (Hallauer et al., 2010). In this study, grain yield was highly correlated with ear length, number of kernels per row, ear aspect, and ear height. All these traits are ear traits,
Hallauer et al. (2010), average genetic correlations with yield observed from several experimental results were larger for ear traits than for plant and ear height. The positive correlation of grain yield with its components such as ear diameter and length, thousand kernel weight, and number of kernel per row was also reported by other workers (Bolaños and Edmeades, 1996; Edmeades et al., 1997). Unlike the reports of Dagne (2008) and Tollenaar et al. (2004), weak correlations of grain yield with foliar traits such as leaf length and width, leaf number and area, and foliar rating were observed in the present study. In general, the traits with greater heritability value and strong association with yield can be considered as secondary traits during indirect selection for grain yield. Indirect selection is the selection for a secondary trait with the purpose to obtain a positive response in the desirable or primary trait (Hallauer et al., 2010).
Variance components, coefficients of variability, heritability and genetic advance parameters provide estimates of genetic variation of quantitative traits. The variance component derived from further partitioning of genotypic differences into phenotypic, genotypic, and environmental coefficient of variation and heritability is a good index of transmission of characters from parents to their offsprings (Falconer, 1960). The proportions of genotypic variances (σ2g) were higher than both error variances (σ2e) and genotype x environment interactions (σ2gxe) for most of the traits. It means that the proportion of the heritable component of the variances is overwhelming, indicating potential of the lines that can be exploited through selection. However, the proportion of σ2gxe component of the phenotypic variance for anthesis date and anthesis silking interval traits is greater than the σ2g. This, in turn, contributed to the small genotypic coefficients of variations and genetic advance values of these two traits. The result is also in agreement with the report of Assefa et al. (1999) that genetic coefficients of variation together with heritability estimates would give the best picture of genetic advance to be expected from selection.
On the other hand, traits such as thousand kernel weight, ear height, and tassel size exhibited relatively high estimates of genotypic coefficient of variation coupled with high heritability and genetic advance as percent of the mean. High heritability estimates along with expected genetic advance are more useful in predicting the response to selection. Johnson et al. (1955) also suggested that the estimate of
heritability and genetic advance should always be considered simultaneously. The genetic variance for the three traits could be attributed to their high additive gene effects (Johnson et al., 1955) and thus there is better scope for improvement of these traits through direct selection. For example, based on the result of this study selection of the top 5% with higher thousand kernel weight may lead to expected increase of thousand kernel weight by 40.3% after one cycle of selection. Dagne (2008) reported that QPM line development should focus on thousand kernel weight as selection criteria to increase grain yield because this trait had also positive correlation with grain yield. However, heritability and selection response expressed as a percentage of the mean were very low for anthesis date, anthesis silking interval, and ear diameter. This implied the predominant role of non-additive gene action and environmental effect in governing these traits. Shanthi et al. (2011) also reported low values of heritability in broad sense and genetic advance as percent of the mean for days to 50% tasseling and silking, protein and oil contents. The authors further pointed out the major role of non-additive gene action for these traits and thus improvement of the traits may be possible through hybrid breeding.
The presence of broad phenotypic diversity among the highland maize inbred lines was further substantiated by principal component analysis, which indicated that the total variation was fairly distributed across all the 12 morpho-agronomic traits. It was also quite conceivable that these traits were adequately represented by four principal components to measure underlying ‘dimensions’ in the data. Accordingly, the first principal component is just a weighted average of standardized measurements of four traits with equal positive (for grain yield and ear height) and negative (for plant and ear aspects) signs, and showing weights that are more or less similar.
Furthermore, these four traits contributed more than the other traits for the 32.2%
variation accounted for by the first principal component (PC1). The variations in PC2 and PC3 were contributed more by foliar and ear traits, respectively, while PC4 was dominated by a mixture of traits that contributed more variation in PC2 and PC3. The cumulative variation explained by the four PCs (80.7%) in the current study is greater than the finding’s of Beyene et al. (2006). The authors reported 71.8% of the total variation in 62 traditional Ethiopian highland maize accessions was represented by the first four PCs. Similarly, Dagne (2008) found a comparable result of 78% total
of Alika et al. (1993) also supports the major role of morphological traits in phenotypic variation observed in this study.
Genetic distance estimates and cluster analysis based on the phenotypic data further revealed the existence of considerable variability among the inbred lines. The dendrogram presented the resolution power of the phenotypic traits for grouping the inbred lines following more or less similar patterns of previous classifications using combining ability study. In agreement to this finding, Beyene et al. (2006) classified 62 traditional highland maize accessions into three groups using 15 morphological traits. Lucchin et al. (2003) clustered 20 Italian flint maize landraces using 34 morphological and agronomic traits. Wietholter et al. (2008), on the other hand, emphasized the contribution of traits viz., plant height, ear insertion, female flowering, male flowering and kernel row number per ear in the classification of Brazilian corn landraces. Besides, Abu-Alrub et al. (2006) reported that tassel traits were found to be less reliable descriptors unlike kernel and ear traits for classifying Peruvian highland maize germplasm.