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3. MORPHOLOGICAL VARIATION...
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Principal component analysis (PCA) was found to explain 87% of the variation between
populations in the six components (Table 3.9). The first two principal components (PC1,
PC2) explained 53% of the variation and they are concerned with generative characters
(height at ear emergence, spike length, spikelet number and flag leaf) and plant weight (dry
and green). This analysis showed that two characters commonly regarded as highly discriminant
for L. multiflorum and L. perenne i.e., winterhardiness and the 1 st year ear emergence
explained only 6% of the variation between populations (PC6). Following PCA, as in clustering
based on the Mahalanobis distance, the separation of L. multiflorum and L. perenne was
not possible. Plots of the first two principal components separated well only six cultivars of
L. multiflorum (Figure 3.3A) that had longer spike, greater number of spikelets and bigger
flag leaf (longer, wider and bigger area). The ecotypes of L. multiflorum were placed among
L. perenne populations. Furthermore, the overlapping was even higher when additional generative
characters were added (spikelet length and floret number) as it was shown by the
scatterplot based on PC1 and PC3. These two functions separated Crema, Bartissimo in
addition to Nagamamikari that occupied different position probably because of higher florets
number than other L. multiflorum populations (Figure 3.3B). A slightly better separation was
received by PC2 and PC4 concerned with vegetative characters (Figure 3.3C). In this plot
most L. multiflorum populations formed a fairly separate cluster and high yielding L. perenne
(Rela, Solen, Argona) were grouped together. Moreover, low yielding L. multiflorum populations
(Atalja, Limulta) were placed apart from L. perenne populations. However, these two
variables were only responsible for 24% of variation and there was still some overlapping
in the middle of the scatterplot. Because of difficulties in separation of L. multiflorum and
L. perenne, a multidimensional scaling based on distance matrix was performed (Figure 3.4).
When tree dimensions were defined the better degree of separation was obtained. Follow-