3. Chapter Three: Phenotypic characterization of sweetpotato genotypes grown in
3.4 Discussion
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80 had remarkably high yields of storage roots (Table 3.6). Thus, it is important not to rule out the influence of multiple factors which may impact crop yields when tests are carried out under different environmental conditions.
Test genotypes showed broad variation in yields of storage root and vine, total biomass, dry matter content and flowering rate (Table 3.4). Genetic diversity analysis of sweetpotato using morphological and molecular markers revealed that all the characters evaluated were significantly different between genotypes (Karuri et al. 2010). Fongod et al. (2012) observed significant differences among sweetpotato accessions in agronomic and morphological characters. Using molecular, morphological and agronomic characterization methods, Maquia et al. (2013) observed a high level of genetic diversity in Mozambican sweetpotato germplasm. Gasura et al. (2010) observed that some of their test sweetpotato genotypes had strong or weak flowering ability while others failed to flower. Extreme phenotypic variations observed in his study confirm the presence of considerable genetic variations among sweetpotato genotypes in Rwanda. Sweetpotato shows broad phenotypic and genotypic diversity because of its inherent cross pollination owing to self- or cross- incompatibility, polyploidy and heterozygosity (Jones et al. 1986; Yoshida 2004). Lebot (2009) argued that the yield of sweetpotatoes could be determined by the length of the growing period. Across the present study sites growing periods were almost similar for all genotypes. Therefore, the broad variations observed among genotypes could be attributed to differences in genetic constitution.
The current study revealed that the effect of genotype and site were significantly different for flowering rate (Table 3.3). The within-plot variation for flowering rate indicates a high level of genetic variability among the test populations. Most sweetpotato genotypes flower naturally within the short day length of the tropics (Miller 1937; Jones et al. 1986). Sweetpotato flowers best during the cool season in tropical countries. The average daily temperature that favors seed set is between 20 and 25◦C while maximum seed set occurred when the mean daily temperature was about 23.9◦C (Lebot 2009). Several techniques such as physiological shocks, grafting, girdling, chemical treatment and use of controlled environmental conditions help improve flowering in sweetpotatoes. Flower and seed production is enhanced under tropical than temperate climates (Jones et al. 1976). In general, flowering ability and seed set have important implications on sweetpotato breeding.
81 3.4.2 Genetic relationships among sweetpotato genotypes
Knowledge on genetic distance between potential parents is important for breeding (Acquaah 2007). Plant breeding programs require sufficient genetic diversity for designed crosses and creating new genetic recombinants. This enables selection of segeregants with better quantitative or qualitative responses such as yield or resistance to abiotic and biotic stresses (Korzun 2003).
Fongod et al. (2012) showed that cluster analysis of 19 sweetpotato genotypes using 26 characters revealed the existence of three major groups with a similarity index ranging from 0.42 to 1.00 before maturity and 0.34 to 1.00 at maturity based on the Euclidean distance. In cluster analysis of Tanzanian elite sweetpotato genotypes for resistance to sweetpotato virus disease and high dry matter content, Tairo et al. (2008) found two major groups with a low genetic similarity of 0.52. Also, significant differences between genotypes and genetic distance ranging from 0.26 to 0.80 were reported during morphological characterization of eight genotypes of Solanum retroflexum (Jacoby et al. 2003). A cluster analysis using morphological and SSR Markers separated some Kenyan sweetpotato genotypes into two major groups (Karuri et al. 2010). In this study, genotypes were assorted into eight main clusters (Figure 3.1) and a dissimilarity distance ranging from 0 to 0.25.
Preliminary evaluation of sweetpotato based on horticultural traits may assist in identification of unrelated parents for specific breeding programmes. These unrelated genotypes may contribute distinctive alleles from different loci (Dhillon and Isiki 1999). Based on high yields, dry matter content and flowering rate, the current study identified genotypes such as Ukerewe and 2005-034 (cluster II), SPK004 and 2005-110 (cluster III), Otada 24 (cluster IV), Kwezikumwe and 2005-020 (cluster V) and K513261(cluster VII) as potential parents for sweetpoato breeding towards high yield and dry matter content.
3.4.3 Principal component analysis
Principal component analysis (PCA) was applied as a statistical approach to identify the major variance components, their contributions and correlated traits (Heberger et al. 2003).
This method assists in reducing the number of variables in the data collection in a breeding and selection process. Consequently, it saves time and resources and improves the selection responses in crop improvement programs (Johnson and Wichern 2007). Through this method, few variables explaining variations among individuals are identified among
82 various characters (Shimelis et al. 2013). Therefore, the PCA provides valuable information when there are several correlated traits, by reducing the costs of screening.
Principal component analysis assists in determining the relationships between traits, and the independent principal components that are effective on plant traits (Beheshtizadeh et al.
2013). In the evaluation of diversity among potato cultivars using agro-morphological and yield components, Ahmadizadeh and Felenji (2011), observed that three components explained 80.1% of the total variation among traits. The authors reported that the first PC was highly correlated with yield, tuber weight, dry matter content and harvest index. This PC was very important to select high yielding clones and parents for breeding programs. In the principal component analysis of 21 sweetpotato genotypes using 17 traits, Afuape et al.
(2011) found three PCs explaining 76% of total variance. They also observed that the number of marketable and unmarketable roots, total number of roots, weight of marketable and unmarketable roots, total root weight, incidence and severity of root Cylas spp, length of biggest, medium and smallest marketable roots and number of branches are traits that are important to differentiate sweetpotato genotypes (Afuape et al. 2011).
In an agro-morphological characterization of different accessions of sweetpotato, Fongod et al. (2012) identified four main components before maturity and five main components at maturity, explaining 78.2 and 76.4%, respectively, of the total genetic variability. The PCA revealed seven main principal components representing 77.83 % of total variance among the 54 genotypes of sweetpotato (Table 3.7). The first four principal components explained 17.51, 15.92, 13.95, and 10.00% of the total variance, respectively. These components represented 57.38 % of the total variance components (Table 3.7). Strong correlations were observed between PCs and phenotypic traits (Table 3.7). PC1 is well-correlated with leaf characteristics, such as leaf general outline, leaf lobe type, lobe number and shape of central lobe while PC2 correlated with flowering rate. PC3 has a strong relationship with storage root yields, weight of the biggest storage root and harvest index. PC4 correlated fairly well mainly with latex production and oxidation of storage root. Vine yield and total biomass had a stronger association with PC5. PC6 showed negative associations with vine and internode length. Finally PC7 showed positive correlations with skin color and storage root formation. Among the 26 phenotypic traits used in the current study, the PCA identified only 19 phenotypic traits with strong correlations with the seven principle components.
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