RGRA across genetic groups
Table 4 and Fig. 1 show variation in the relative effect of drought on
RGRA across genetic groups with genetic group Budongo
being the most strongly affected and genetic group Kibale being least
affected by drought (see also significant genetic group * treatment
effect Table 3). Under ample-water conditions, the absolute
RGRA did not differ significantly between genetic groups
while it did under restricted-water conditions. (Table 4; Fig. 1). Under
restricted-water conditions, genetic group Zoka had the highest
RGRA which was 12.0 % higher than the lowest
RGRA observed for genotypes from genetic group Budongo
(Table 4). Additionally, Fig. 1 and standard errors of means (Table 4)
suggest that there was wider genotypic variation in RGRAacross genetic groups under ample-water conditions than there was under
restricted-water conditions.
Fig. 1. Mean RGRA [d-1] as a function of treatment
(ample-water (AW) and restricted-water (RW) across genetic groups
(panels) and genotypes (coloured lines). Solid black line shows the mean
estimated response per genetic group.
RGRA across locations
There was a large variation in the relative effect of drought on
RGRA of genotypes collected from the different locations
(Table 4 and Appendix Fig. A.3.). The effect of drought on
RGRA was significant for all locations except for Kibale
and Itwara (Table 4; Appendix Fig. A.3.; Appendix Table A.5.). The mean
percentage change in performance was highest among genotypes collected
from Malabigambo, Budongo, Mabira and Kalangala, respectively, while the
effect of restricted-water supply was smallest for genotypes collected
from Kibale, Itwara, Zoka and Kituza, respectively (Table 4 and slope of
the black lines in Appendix Fig. A. 3.). In absolute terms, under
ample-water conditions, genotypes from Mabira had a significantly higher
mean RGRA, which was 27.4 % higher than the lowest mean
RGRA in location Kibale (Table 4 and Appendix Fig. S3).
Similarly, in restricted-water conditions, Mabira had the highest and
Kibale had the lowest RGRA but the difference was much
smaller (8.5%) (Table 4 and Appendix Fig. A.3.). Therefore, differences
between locations tended to converge in the restricted-water treatment.
Across the studied experimental factors (cultivation status, genetic
group and location), it is worth noting that results showed a tendency
of some genotypes to have higher RGRA under
restricted-water conditions than with ample-water although this effect
was not statistically significant in any of these cases (p
> 0.05) (Fig. 1 and Appendix Fig. A.3.). The effect
occurred in genotypes with both high and low RGRA values
in the ample-water treatment and therefore are very unlikely an
experimental artefact, whereby the genotypes could not have been
adequately watered under ample-water conditions. Additionally, for some
genotypes, the effect could be due to variations in sample size causing
the mean in restricted-water to be higher than that under ample-water
conditions.
Multivariate analysis of growth-related traits
The PCA analysis showed that TNL, TL and
TLDW were most loaded on the first PCA axis (explaining
46% of the variation), while SLA was mostly loaded on
the second PCA axis (explaining 20% of the variation). See Fig. 2. The
PCA on the individual replicates showed a similar pattern. Therefore,
SLA varied mostly independently of TL(correlation -0.002). The PERMANOVA showed that treatment, location and
cultivation status significantly affected the dissimilarities between
genotypes (p-values respectively <0.001, 0.03,
<0.001) see Appendix Table A.6. Treatment explained 20% of
the variation in the traits, location 10% and cultivation status only
1.8%.