| dc.description.abstract | Fourteen genotypes, (four hybrids, six
varieties,and four random mating populations) were
grown in a randomized complete block design and three
replications in five- locations for two seasons, i.e,
1979 long rains and 1979/80 short rains, in the
medium agricultural potential areas of Kenya.
Environment x Genotype interaction was studied for
grajn yield/plot, grain yield/head, days to 50%
flowering and mean plant height.
The combined analysis of variance for each
character indicated that genotype x location x season
interaction (G x L x S) variance was highly
significant and was also the most important component
of genotype x environment (G x E) interaction. Both
G x Land G x S interactions were not significant
for any character except days to 50% flowering,
although, the magnitude of G x S was higher than
G x L interaction component in all cases.
The nature of G x E interactions were
investigated by means of regression analysis
techniques of Finlay and Wilkinson (1963), Eberhart
and Russell (1966) and Perkins and Jinks Cl968a and b l,
I ( v )
It was concluded th~t a considerable portion of
G x E interaction sum of squares (SS) could be
attributed to the linear regressions in case of
grain yield/plot (67%), grain yield/head (70%),
plant height (83%) and days to 50% flowering (45%).
For all characters, the pooled deviations ·from
regressions were highly significant. In case of
grain yield/plot and days to 50% flowering, G x E
(linear) SS was not significant, showing more
environmental effect on genotypes to express
themselves in different environments. For grain
yield/head and mean plant height, this variance was
significant, showing that there are genetic
differences among the genotypes for their regression
on the environmental index. From the joint
regression analysis, it was shown that for grain yield/
plot, the predictions of GE interaction based on
linear regression are difficult to make. While for
grain yield/head and days to 50% flowering, reliable
predictions can be made only for some genotypes.
However, such predictions in case of plant height
were more practical.
A number of adaptability and stability
parameters were estimated. Adaptability was
referred to the response of a particular genotype to
environments and was determined by means of
regression coefficient value (bi). Genotypes with
bi = 1.00 were widely adapted; those with bi < L
were adapted specifically to the unfavourable
environments and those with bi >1 were adapted
specifically to the favourable environments. The
stability was referred to as the ability to show
minimum interaction with the environments. The
stability parameters taken into consideration were
'Phenotypic stability factor' (PS), 'Ecovalence'
(Wi), 'Co efficient 0 f de term ination' (R .2), and
1
'deviations from the regression' (S~). High
correlation was found between the ranks of genotypes
according to Wi, Ri
2 and S~ parameters. The defects
of PS parameter were pointed out. Hence, the
stability of the genotypes was based on the value of
S~ alone. The genotypes with the lowest S~ being
the most stable and vice versa.
The hybrids gave the highest t yield and were early at
each environment, although they were slightly taller
than the varieties. The hybrids, however, lacked
stability and in general were more specific in
adaptation. The populations tended to be taller,
and their adaptability and stability were better.
(vii)
The difference in adaptability and stability of the
different genotype groups was attributed to their
different buffering mechanisms. It was suggested
that the adaptability and stability of populations
be fully exploited to realize their full potential.
Considering the overall performance, the
hybrids were the most desirable genotypes. HYBRID,
which had wide adaptability, was most desirable
together with HIJACK, which performed better in
unfavorable environments and HIMIDI which performed
better in the favourable environments. Among the
varieties, MY 57 and 50 x 135/13/3/1, and among the
populations, SERERE ELITE an& RS/R appeared most
promising.
A comparison of standard error of genotype
means suggested that four locations, four seasons
and three replications were optimum for such
studies. The low intraclass correlations for all
characters indicated that the location and seasons
could be treated as random environments. Hence,
seasons and locations can be used interchangeably
when considering the allocation of plots for
evaluating the genotypes. | en |
