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Rice Genomics and Genetics 2013, Vol. 4, No. 5, 22-27
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the data (Gauch, 1992). AMMI is a combination of
ANOVA for the main effects of the genotypes and the
environment together with principal components
analysis (PCA) of the genotype-environment
interaction (Zobel et al., 1998; Gauch, 1988). AMMI
models are usually called AMMI (1), AMMI (2), to
AMMI (n), depending on the number of principal
components used to study the interaction. Graphic
representations are obtained using bi-plots (Gabriel,
1971) that allow (1) the observation, in the same
graph, of the genotypes (points) and the environments
(vectors), and (2) the exploration of patterns
attributable to the effects of G × E interaction. In the
bi-plot, the angles between the vectors that represent
genotypes and environments show the interaction, and
the distances from the origin indicate the degree of
interaction that the genotypes show throughout the
environments or vice versa.
Performance of improved, high yielding varieties of
rice over different agro ecological regions of India has
been well documented by several workers
(Vijayakumar et al., 2001). The occurrence of G x E
interaction within target environments necessitates
conduct of multi-environment trials to evaluate
genotype adaptation. A lot of work has been done in
rice for phenotypic stability and adaptability of
varieties as far back as early 1970. Tang et al. (1975)
tested eleven
japonica
lines in sixteen environments
for one set and fourteen environments for second set
and found that, average yields of the lines over
environments were highly correlated between two
environments. No linear response was observed for
the stability performance of lines between sets. Naidu
et al. (1980) identified IET-2730, a red grained variety
stable in Karnataka state and in other 29 locations
throughout India. In another study, RD 3 and IR 8
were recognized as satisfactorily stable varieties for
yield among 14 lines tested over 22 localities by
Poonyarth et al. (1980). Sudin (1985) observed that
shorter the plant, the lower was the stability in his
investigations on adaptability and stability.
In recent years, AMMI analysis has been applied to
interpret GEI in rice (Wade et al., 1999; Vijayakumar
et al., 2001; Lafitte et al., 2002; Stanley et al., 2005;
Mall et al., 2005; Ouk et al., 2007). Vijayakumar et al.,
(2001) studied G × E interaction effects on yield of 16
rice hybrids evaluated over 11 locations in different
agro ecological regions of India. They reported
presence of significant GEI that influenced the relative
ranking of hybrids across the locations. It was evident
from AMMI analysis that, genotypes, environment
and the first principal component of interaction effect
accounts for 86.96% of treatment sum of squares and
that the first five principal components of interaction
effect were found to be significant. The usefulness of
the AMMI in selecting genotypes for general or
specific adaptation was depicted by these authors. Das
et al., (2010) conducted multi location yield trials of
11 mid-early (110-125 days) rice genotypes at four
locations in Odisha state, India, over 3 years-
2003~2005, during kharif season. According to Das
and colleagues, AMMI-predicted yield showed that
Lalat and OR 2006-12 were high yielders and
possessed broad adaptation to most locations.
Genotypes showing good adaptation to specific
locations were OR 2200-5 for Ranital, OR 2172-7 and
OR 1916-19 for Bhubaneswar, OR 1976-11 for
Chiplima and Konark for Ranital.
The main objectives of the present investigation is to
determine the GEl effects on grain yield of Superior
BILs derived from Swarna × WAB 450 inter cross and
to select genotypes that are widely adapted across
upland rice growing rainfed areas in North Karnataka,
India.
1 Results and Discussion
Table 1 presents result of Additive Main Effects and
Multiplicative Interaction (AMMI) analysis of
variance for grain yield (kg/ha) of 19 BILs with 3
check varieties (Swarna, Pasanna and MGD 101)
tested at 3 locations in six environments. Table 2
presents mean grain yield (kg/ha) of 19 BILs with
three checks grown in 6 environments and the PCA
scores for the GE Interaction effect as derived from
AMMI analysis. The means of the genotypes and the
environments along with the first principal component
(PCAI) scores of corresponding genotypes are also
presented. The genotype mean yields ranged from
3935.79 kg/ha to 5917.02 kg/ha (Table 2).
Result showed that genotypes, environments and GEI