physicochemical

PARIMALA and THANGAVEL
ability analysis of four methods of Griffing’s (1956)
numerical approach viz., method 1 (M1), method 2
(M2), method 3 (M3) and method 4 (M4) and by partial
diallel cross (PDC) design (Kempthorne and Curnow
1961). For partial diallel cross analysis, a sample
size of three crosses was used and the crosses (ns/
2=9) were sampled following circulant matrix design.
The error means for squares of respective RCBD were
used further in combining ability analysis.
RESULTS AND DISCUSSION
The magnitude of variances due to general
combing ability variance (GCA) and specific
combaining ability variance (SCA) were found to vary
with the method of analysis (Table-1). However, the
overall GCA / SCA variance ratio did not vary much
for all the characters studied. This indicated
preponderance of non-additive component of gene
action across the methods of estimation. Results of
all the four methods were complementary to each
other and not alternative.
The correlation coefficients between per se
performance and gca effects estimated through
different methods were computed and compared
(Table 2). The per se performance had the significant
and highest magnitude of correlation coefficients with
gca effects computed through M2 method followed
by M1. Further, correlation coefficients between the
gca effects estimated through M2 v/s M4 and M1
v/s M3 were of high magnitude and hence, were
comparable. In Partial Diallel Cross, correlation
coefficient showed significant gca effects in M4. The
rank correlation coefficients reflected that rankings
of the lines differed with respect to the method of
combining ability analysis. Strong correlations
between ranks of lines based on per se performance
v/s gca effects estimated through M2 method followed
by M4 method were observed for all the characters
studied. The rankings of lines based on gca effects
computed through M1 method were significant and
highly correlated compared with M3 method.
The correlation coefficients between per se
performance and sca effects estimated through four
approaches of Griffing were computed and compared
(Table 3). The per se performance had significant
and highest magnitude of correlation coefficients with
sca effects computed through M2 method followed
by M1 method. Further, correlation coefficients
among sca effects estimated through four approaches
revealed that the association between sca effects
estimated through M2 v/s M4 and M1 v/s M3 methods
were of high magnitude and hence, were comparable.
Correlation coefficients estimated through M1 and
M2 methods showed highly significant values
compared with M3 and M4. The rank correlations
computed between ranks based on sca effects
estimated through M2 method followed by M4
methods correlated with the ranks based on per se
performance of the direct crosses. Rank correlation
coefficients estimated through M1 and M2 methods
showed highly significant values compared with M3
and M4.
The association between per se performance of
reciprocal crosses and rca effects calculated (Table
4) through M1 or M3 methods of Griffing (1956) were
of low magnitude.The correlation coefficient
estimated through M1 method showed high
significant effects than M3 method. Further, the
mutual association between rca effects computed
through M1 and M3 methods, was of low magnitude.
This indicated significant differences for rca effects
computed through two methods. The rank correlation
coefficient analysis also depicted the similar results.
The results of present study clearly suggest that ; a
breeder is required to study full diallel. To know the
real genetic worthiness of the test material, the
sampling of crosses in partial diallel cross analysis
has given variable picture when compared to full or
other diallel approaches.
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