Genotype X environment interaction and stability analysis of cotton yield in Aegean region of Turkey.
ABSTRACT The objective of this study was to determine genotype X environment (GE) interaction and stability of cotton genotypes, and effect of different environments on seed cotton yield to understand its adaptation to varying environments. Fourteen cotton genotypes were evaluated at four locations across Aegean region of Turkey in 1997 and 1998. Genotypes were tested by two stability parameters as linear regression coefficient (b) and deviations from regression (S2d). Significant differences were observed for the mean yields in the 8 environments. Mean seed cotton yield ranged from 4.58 to 5.80 t ha(1). Genotypes showed significant interaction with environments. Regression coefficients ranged from 0.23 to 1.46, and deviations from regression were significant for only four genotypes. It was concluded that three high yielding cotton genotypes SG1001, SG125 and DLP5409 were found to be stable genotypes.
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 Crop Science  CROP SCI. 01/1966; 6(1).

Article: Genotype by Environment Interactions in Cotton — Their Nature and Related Environmental Variables1
Crop Science  CROP SCI. 01/1969; 9(3).  Crop Science  CROP SCI. 01/1982; 22(2).
Page 1
Journal of Environmental Biology
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Genotype X environment interaction and stability analysis of
cotton yield in Aegean region of Turkey
April 2006, 27(2) 427430 (2006)
For personal use only
Commercial distribution of this copy is illegal
Fatih Killi1 and Eyup Harem2
1University of Sutcu Imam, Faculty of Agriculture, Department of Field Crops, Kahramanmaras 46060, Turkey
2Nazilli Cotton Research Institute, Aydin 09900, Turkey
(Received: 18 October, 2004 ; Accepted: 17 May, 2005)
Abstract: The objective of this study was to determine genotype X environment (GE) interaction and stability of cotton genotypes,
and effect of different environments on seed cotton yield to understand its adaptation to varying environments. Fourteen cotton
genotypes were evaluated at four locations across Aegean region of Turkey in 1997 and 1998. Genotypes were tested by two
stability parameters as linear regression coefficient (b) and deviations from regression (S2d). Significant differences were observed
for the mean yields in the 8 environments. Mean seed cotton yield ranged from 4.58 to 5.80 t ha1. Genotypes showed significant
interaction with environments. Regression coefficients ranged from 0.23 to 1.46, and deviations from regression were significant for
only four genotypes. It was concluded that three high yielding cotton genotypes SG1001, SG125 and DLP5409 were found to be
stable genotypes.
Key words: GE interaction, Stability analysis, Adaptation, Cotton yield.
Introduction
Genotype X environment (GE) interactions are of
major concern to plant breeders for developing improved
cultivars. A cultivar, to be commercially successful, it must
perform well across the range of environments in which allowed
to grow. The presence of GE interactions reduces the
correlation between phenotype and genotype, and makes it
difficult to judge the genetic potential of a genotype (Sharma et
al., 1987). Further, the stability of a cultivar refers to its
consistency in performance across environments and is
affected by the presence of GE interactions. In the presence of
significant GE interactions, stability parameters are estimated
to determine the superiority of individual genotypes across the
range of environments. Genotype X location, genotype X year
and genotype X location X year interaction components were
found to be significant for seed cotton yield in many researches
(AbouElFittouh et al., 1960; Abd El Latif et al., 1975; Singh
and Gill, 1982; Killi and Gencer, 1995a; Unay et al., 2004)
In Turkey, cotton is grown in different environments
such as Aegean, Mediterranean and Southeastern Anatolian
regions, which differ in climate, soil, insect, disease and cultural
conditions spatially and temporally. Therefore, a search for best
suited cultivar requires evaluation of the comparative yield and
fiber properties of cotton varieties in replicated trails at different
locations over a period of years (Killi and Gencer, 1995b). The
present paper aims to determine the range of variability for
seed cotton yield of cotton cultivars and to estimate their
stability parameters for identifying superior cotton cultivars.
Materials and Methods
Fourteen cotton genotypes (SG1001, SG501, SG
125, SG404, Sicala 3/2, DLP5690, DLP5614, DLP5409,
DLP20, DLP50, S314, C1518, N84 and Cun S2) were
tested in 1997 and 1998 field trials at four locations: Manisa,
İzmir, Aydın and Denizli (Fig.1). The experimental design was a
randomized complete block with four replications at each
location. Plots were six rows of 12 m length spaced 0.7 m apart.
Each plot in an experiment had a plant population of 95 000
plants ha1. Accepted cultural practices and fertilization based
on individual soil test were applied at each location. All plots
were seeded between 30 April and 14 May 1997 and between
24 April and 28 May 1998. The plots were harvested between 7
and 17 October 1997 and between 2 September and 26
October 1998. At maturity, seed cotton yield were obtained from
an area 2.8 m wide and 10 m long (28 m2) of the central four
rows of each plot. Stability parameters were estimated by the
method described by Eberhart and Russell (1966).
Analysis of variance procedure (Comstock and Moll,
1963) was adopted to test the significance of location, year,
genotype, and first and second order interactions assuming the
year and location effects as random and genotype effect as
fixed. The stability analysis of variance and stability parameters:
linear regression coefficient (b) and deviation from regression
(S2d) of genotype means over environment index were
computed as suggested by Eberhart and Russell (1966). For the
regression analysis of variance, the residuals from the
combined analysis of variance were used as a pooled error to
test the S2d values. A significant F value would indicate that the
S2d was significantly different from zero. The hypothesis that
each regression coefficient equaled unity was tested by the t
test using the standard error of the corresponding b value.
Results and Discussion
According to the variance analysis, differences among
genotypes for seed cotton yield, and year, location and year X
location interaction were highly significant (Table 1). The first
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Fatih Killi and Eyup Harem
428
Fig. 1: Various locations (Manisa, İzmir, Aydın and Denizli) of the cotton field trials conducted in Aegean region of Turkey in 199798.
Table – 1 : Analysis of variance for seed cotton yield for 14
cotton genotypes based on 2 year data from four
locations in Aegean region of Turkey.
Source of variation df Mean squares
Year (Y) 1 10.18**
Location (L) 3 117.22**
Y x L 3 56.36**
Replication (Y, L) 24 3.04
Genotype (G) 13 20.30**
G x Y 13 5.57**
G x L 39 3.79**
G x Y x L 39 3.03**
Error 312 1.15
** Significant at the 0.01 probability levels.
order interactions (genotype X year and genotype X location)
and second order interaction (genotype X location X year)
components were also significant. AbouElFittouh et al.,
(1960), Abd El Latif et al., (1975), Singh and Gill (1982), Killi
and Gencer (1995a) and Unay et al., (2004) evaluated cotton
genotypes for seed cotton yield over years and locations and
found all main effects and interactions to be significant.
Average seed cotton yield for the 14 genotypes
ranged from 4.42 (C1518) to 5.53 (DLP5614) t ha1 (Table 2).
Seven of these genotypes (SG1001, SG501, SG125, SG
404, DLP5690, DLP5614 and DLP5409) gave higher seed
cotton yield than the grand mean yield (4.97 t ha1). According
to Eberhart and Russell (1966), an ideal cultivar would have
both a high average performance over a wide range of
environments plus stability. Becker et al., (1982) regarded
mean square for deviation from regression to be the most
appropriate criterion for measuring phenotypic stability in an
agronomic sense because this parameter measures the
predictability of genotypic reaction to environments. Langer et
al., (1979) suggested that the regression coefficient was a
measure of response to varying environments. The regression
coefficient (b) values of the 14 genotypes used in this study
ranged from 0.23 to 1.46 (Table 2). These variations in b values
suggested that the 14 cotton genotypes responded differently to
the different environments. Among the genotypes tested, the
regression coefficients of only 3 genotypes (DLP5690, DLP
5614 and C1518) significantly deviated from 1. Deviations from
regression were significant for 4 genotypes (DLP50, S314, C
1518 and N84). Relationship between the regression
coefficients and mean seed cotton yields for 14 cotton
genotypes is shown graphically in Fig 2. The diagrammatic
presentation (Fig. 2) of stability parameters showed that three
genotypes (SG1001, SG125 and DLP5409) excelled in yield
performance. Their regression coefficients and deviations from
regression were not significantly different from unity and zero,
respectively (Table 2). Therefore, these three genotypes formed
the group of the best adapted genotypes to all environments.
When the other genotypes were considered, SG501, SG404,
Sicala 3/2, DLP50 and N84 were defined as midadaptation to
all environments while DLP20, S314 and Cun S2 had poor
adaptation (Fig. 2). Three genotypes, DLP50, S314 and N84,
had high deviation from regression indicating sensitivity to
environmental changes. Due to high values of S2d, these
genotypes are expected to give good yield under favorable
environmental conditions. Two genotypes, DLP5690 and DLP
5614, produced above  average yield with a regression
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G E interaction and salability of cotton yield 429
Fig. 2 : Mean seed cotton yields and regression coefficients of 14 cotton genotypes.
Table – 2 : Mean seed cotton yields and estimates of stability parameters in 14 cotton genotypes based on 8 environments (4 locations, 2
years) in Aegean region of Turkey.
Code number Genotypes
(t ha1)
1 SG1001
2 SG501
3 SG125
4 SG404
5 Sicala 32
6 DLP5690
7 DLP5614
8 DLP5409
9 DLP20
10 DLP50
11 S314
12 C1518
13 N84
14 Cun S2
Mean
*,** Significantly different from 1.0 for regression coefficients and from 0.0 for the deviation mean squares at the 0.05 and 0.01 levels of probability, respectively.
Table – 3: Mean seed cotton yield and values of environmental index of various locations of the cotton field trials conducted in
Aegean region of Turkey in 199798.
Locations Mean seed cotton yield (t ha1)
Aydin1997 5.08
Denizli1997 4.69
Manisa1997 5.19
İzmir1997 5.12
Aydin1998 4.58
Denizli1998 4.60
Manisa1998 5.80
İzmir1998 4.70
Mean 4.97
Mean yield
b
0.97
1.06
1.20
1.00
1.03
0.23**
0.59*
0.86
0.91
1.13
1.23
1.46**
1.18
1.15
1.00
S2d
580
280
110
180
470
200
290
310
450
1490**
1590**
1170*
1020*
390
5.22
5.08
5.14
5.06
4.95
5.18
5.53
5.12
4.81
4.90
4.70
4.42
4.89
4.58
4.97
Values of environmental index
0.11
0.28
0.22
0.15
0.39
0.37
0.83
0.27
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Fatih Killi and Eyup Harem
430
coefficient significantly less than 1 (b= 0.23 and 0.59
respectively), but had small deviations from regression. The
genotype C1518 showed significant deviation from regression
with a regression coefficient significantly higher than 1 (b=
1.46). Therefore, it was defined specifically adaptation to
favorable environments. The average seed cotton yield
performances of locations over varieties were different. Mean
seed cotton yield for the 8 environments (4 locations, 2 years)
ranged from 4.58 to 5.80 t ha1 (Table 3). Location Manisa gave
the highest mean seed cotton yield in both years. It was the
best environment for cotton production in Aegean region.
It was concluded that the three high yielding cotton
genotypes SG1001, SG125 and DLP5409 were found as
stable and thus, these would be recommended for
environmental conditions of Aegean region.
References
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(1975).
AbouElFittouh, H.A., J.O. Rawlings and P.A. Miller: Genotype by
environment interactions in cotton, their nature and related
environmental variables. Crop Sci., 9, 377381 (1960).
Correspondence to :
Dr. Fatih Killi, Assoc. Prof.
Kahramanmaras Sutcu Imam University
Agricultural Faculty, Field Crops Department
46060 Kahramanmaras, Turkey
Email: fakilli@ksu.edu.tr
Tel.: +90344223 76 66
Fax: +90344223 00 48
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