Molecular Plant Breeding 2016, Vol.7, No.02, 1
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genotypes (Shadpour et al., 2011).
The GGE biplot is a multi-faceted tool in quantitative
genetic analyses and plant breeding. In addition to
dissecting GEI, GGE Biplot helps analyze
genotype-by-trait data, genotype-by-marker data, and
diallel cross data (Yan et al., 2001; Yan, 2001; Yan and
Hunt, 2000, 2002; Yan and Sial et al., 2000). These
aspects make GGE biplot a most comprehensive tool
in quantitative genetics and plant breeding.
Farshadfar et al The objective of this study explored
the effect of genotype (G) and genotype ×
environment interaction (GEI) on grain yield of 16
bread wheat genotypes (Triticum aestivum L.) in five
different environments. Yield data were analyzed
using the GGE biplot method. Environment (E)
explained 59.39% of the total variation. Collective
analysis of the biplots suggested three bread wheat
mega-environments in Ilam Province. The first
mega-environment contained environments: E2 with
genotypes B2 and B7. Genotype B9 gave the highest
performance in environment E1 and E3 and genotypes
B8, B9, B3 and B2 revealed the highest performance
in environments E4 and E5. Genotypes B9, B8, B3
and B2 exhibited the highest mean yield and
genotypes B6 and B16 displayed the poorest mean
yield. The highest stability was attributed to genotypes
B8, B10, B16, B1 and B11 (Farshadfar et al., 2012).
Because each group of researchers have used one way
or combination of them in their studies to identify
high yielding and stable varieties, The study is
intended to determine the stability of genotypes is a
fusion of different ways.
1 Material and Methods
So as to study and probe on the stability of the
sunflower items, there we selected and prepared 16
genotypes of it (sunflower) ( these species were:
Alexandra, Joana, Fabiola, Euroflor, Brocar, Azargol,
Arena, Altesse, Almanzor, Alisson, Vidoc, Terra,
Pomar, Nkarmoni, Melody, Mas96a) from the
investigation on Eugenic of seeds and sapling in Karaj
and began a comparable procedure in the form of full
random design blocks in 4 iterated measure in 4
regions, Esfahan, Birjand, Sari and karaj and in the
cultivation year 2011-2012, the preparation operations
of lands included of cleaning of floor (ground),
plowing, Disk, tabulation and making gutters and
stacks. Every experimental Kurt was formed of 4 rows
of plant cultivation with 5 meters long and 80
centimeters width, the distance of bushes was
determined 20 centimeters. Amount of the applied
seeds was 6 Kilograms in hectare (60 bushes in every
squared meter). On the way to eradicate weeds, there
we used a mechanical method in all the cultivation
running periods until attaining harvest time, and then
we used cultivation caregiving support. Finally, all the
karts were harvested by hand. Generally, the attributes
of seed yield was noted in all time spared on the
experiment.
For the purpose of statistical analyses, first of all we
applied variance analyses for every region separately
and then, the final complex variance implemented.
These analyses were operated by means of SAS, GGE
Bi plot software and also to analyze pertaining
stability statistics gathered, we used NSTAB software.
2 Results and Discussion
After normal data control, pilot error variance
homogeneity test was performed using Bartlett. The
results of combined analysis of variance for grain
yield shows that the effect is significant difference in
the level of one percent. So the seeds were in different
places. It also suggests the usefulness of analysis for
grain yield is sustainable. The effect was significant at
the level of one percent. The significance of the effect
of the experimental data showed that the reaction was
not the same in different regions. In other words, there
is considerable variation. According to the results, the
effect of environment and genotype × environment
interaction was significant at the level of one percent.
Significant effects of genotype × environment
represent the environmental performance of different
genotypes for the other. With respect to the interaction
between genotype and environment, analysis of
variance unable to explain the stability of genotypes,
so using statistical methods, the effect of genotype ×
environment interaction analysis and review to be
stable genotypes identified (Table 1).
2.1 Results are based on analysis of variance
methods
The results of the coefficient of variation (CV
i
)
showed that the number of Brocar and Nkarmoni
cultivars of the lowest coefficient of variation was
accounted for. The biological stability and the
flexibility are high. Azargol and Nkarmoni cultivars
