The document discusses the interaction between genotype and environment affecting phenotype, emphasizing the importance of genotype x environmental interaction (GxE) in crop performance. It details various models for analyzing GxE interactions, notably the AMMI model, which combines ANOVA and principal components analysis to assess genotype stability across different environments. The findings include experiments conducted on rice genotypes in varying agro-ecological zones and reveal significant GxE interaction, underscoring the need for stable genotypes adaptable to diverse conditions.

1. Submitted to
DR.K.B.ESWARI,
Associate
professor,
Dept of GPBR

2. The interaction between the genotype
and environment that produces the
phenotype is called as Genotype x
Environmental Interaction.
P = G + E + GE
 Genotypes respond differently across
a range of environments i.e., the
relative performance of varieties
depends on the environment.

3.  Environmental variable can be divided into 2
groups (Allard and Bradshaw , 1964)
1.Predictable environment factor
2.Unpredictable factor
Predictable factors include permanent features
of environment which are under human control
such as soil type, planting date, row spacing,
rows of new trend application.
Unpredictable factors are those which
fluctuate inconsistently like rainfall,
temperature, relative humidity not under
control are called unpredictable environment
conditions.

4.  The environment refers to the external
conditions that affect expression of
genes of an individual genotype.
 Environment can be classified into two
groups ( Comstock and Moll , 1963)
a) macro environment
b) micro environment

5. Refers to the environment with variables having
large and easily recognisable effect.
 Main features:
1. The environment affects are easily
detectable such as fertilizer doses, planting
dates, spacing, irrigation schedules.
2. Macro environment is controlled by
Predictable factors such as soil type, planting
dates and close spacing.
3. It is under human control

6. The environment of a single organism
/genotype as opposed to that of another
growing at the same time in almost the same
place is referred to as micro environment.
Main features:
1.The environment effects are not easily
recognizable such as differences in humidity
, temperature, etc.. at the same place.
2.Micro environment is governed by
unpredictable factors like rain fall,
temperature, relative humidity which
fluctuate inconsistently.
3.It is not under human control.

7. It refers to those changes in structure or
function of an individual/population which lead to
better survival in a given environment is known
as adaptation.
 Main features:
₭ Adaptation favours those characters which are
advantageous for survival and through which an
individual acquires adaptive value or fitness.
₭ In the process of adaptation survival is the main
concern.
₭ Natural selection plays an important role in the
process of adaptation.
ADAPTATION

8.  TYPES OF ADAPTATION
There are four types of adaptation
1.Specific genotypic adaptation
2.General genotypic adaptation
3.Specific population adaptation
4.General population adaptation
Factors affecting adaptability:
 Heterogeneity.
Heterozygosity.
Genetic polymorphism.
Mode of pollination.

9.  Stability refers to the performance with
respective changing environmental factors
overtime within given location.
 selection for stability is not possible until
a biometrical model with suitable
parameters is available to provide criteria
necessary to rank varieties / breeds for
stability.
 Low magnitude of G.E interaction involves
the consistent performance of a population
over variable environments.

10. It consists of following steps:
Location / environment wise analysis of
variance.
 pooled analysis of variance for all the
locations/ environments.
If G.E interaction is found significant
,stability analysis can be carried out using
one of the four methods:
1.Finlay and Wilkinson model (1963)
2.Eberhat and Russell model(1966)
3.Perkins and Jinks model(1968)
4.Freeman and Perkins model (1971)

11. Used two parameters
1)Mean performance over environments.
2)Regression performance in different
environments.
The following inferences can be drawn:
1)The regression coefficient of unity indicates
average stability
2)If the regression coefficient is >1,it means
below average stability
3) If the regression coefficient is <1,it means
above average stability.
4) Regression coefficient of 0 would express
absolute stability.

12. MERITS
 Analysis of this model is simple.
 2 parameters- mean yield over locations and
regression coefficient are used to asses the
phenotypic stability.
DEMERITS
 The deviations from the regression line are not
estimated which are important for the stability
analysis.
 Greater emphasis is given on mean performance
over environments than regression coefficients.

13.  It is the most popular and useful model.
 In 1966 both made further improvement in stability
analysis by partitioning the G.E interaction of each
variety into 2 parts. one is slope of the regression line ,
second is deviation from regression line.
 In this model total variance is first divided into 2
components:
-genotypes
-environment plus interaction (E+G*E)
The second component is further divided in to 3
components.
I. Environment linear
II. G.E linear
III. Pooled deviations
Sum of squares due to pooled deviations are further
divided into sum of squares due to individual genotype.

14.  This model consists of three parameters
a) mean yield over locations
b)regression coefficient =bi
C)Deviation from regression =s²di
 Analysis of stability parameters is simple as
compared to other models of stability analysis.
 The degree of freedom for environment is 1.
 It requires less area hence less expensive when
compared to other models.
 It does not provide independent estimation for
mean performance and environmental index

15. Source of variation Degrees of freedom
Genotypes g-1
E+ G*E interaction g(e-1)
environment (linear) 1
G.E linear g-1
pooled deviations g(e-2)
genotype-1 e-2
genotype-2
Pooled error
e-2
ge(r-1)

16. Merits:
 It measures three parameters of stability
A=mean yield over environments
B=regression coefficient
C=deviation from regression line
 It provides more reliable information on stability than
Finlay and Wilkinson model.
 Analysis is simple.
Demerits:
 estimation of mean performance and environment index is
not independent.
 There is a combined estimation of sum of squares of
environment and interactions which is not proper.
 Eberhart and Russell (1956) defined stable variety as
one with a regression coefficient of unity(b=1) and a
minimum deviation from the regression lines(s²d=0).

17.  In this model total variance is first divided into 3
components.
1)genotypes
2)environments
3)genotypes x environment
 G-E variance is sub divided into
a) heterogeneity due to regression
b) sum of square due to remainder
 This model is less expensive than Freeman and Perkins.
 It requires less area for experimentation.
 The degree of freedom for environment is e-2.
 Analysis is more difficult than Eberhart and Russell
model.
 It does not provide independent estimation of mean
performance and environmental index.

18. Source of variation Degrees of freedom
Genotypes g-1
Environment e-1
Genotype x environment (g-1)(e-1)
Heterogeneity among regressions g-1
Remainder (g-1)(e-2)
Error ge(r-1)

19.  In this model total variance is first divided into 3
components.
1)Genotypes 2)environment 3) G*E
 The environmental s.s is sub divided into 2 components
a) combined regression b) residual 1
 The interaction variance is also subdivided into two
parts
a)homogeneity of regression b) residual 2
 This model also includes 3 parameters like Eberhart
and Russell model and provides independent estimation
of mean performance and environmental index.
 The degree of freedom for environment is e-2 like
perkins and jinks model.
 Analysis of this model is more difficult and expensive
as compared to earlier two models.

20. Source of variation Degrees of freedom
Genotypes g-1
Environment e-1
Combined regression 1
residual (1) e-2
Interaction(GxE) (g-1)(e-2)
Heterogeneity of regressions g-1
residual (2)
error
(g-1)(e-2)
ge(r-1)

21.  AMMI is a combination of ANOVA for the main
effects of the genotypes and the environment
together with principal components analysis of
the genotype-environment interaction.
 Method for analyzing GEI to identify patterns of
interaction and reduce background noise.
 May provide more reliable estimates of genotype
performance than the mean across sites.
 Biplots help to visualize relationships among
genotypes and environments; show both main and
interaction effects.

22. Yijl =  + Gi + Ej + (kikjk) +
eijl
Where,
•Yij is the observed mean yield of the ith genotype in jth
environment
•μ is the general mean
•Gi and Ej represent the effects of the genotype and
environment
•λk is the singular value of the kth axis in the PCA
•αik is the eigenvector of the ith genotype for the kth axis
•γjk is the eigenvector of the jth environment for the kth
axis
•n is the number of principal components in the model
•eij is the average of the corresponding random errors
AMMI Model

23. source df SS MS F
TOTAL (ger- 1)
Treatment (ge -1)
Genotype (g -1)
Environment (e-1)
Interaction
IPCA
1
IPCA
2
Residual
(g-1) (e-1)
blocks (r-1)
error (r-1) (ge -1)
Analysis of variance for stability – AMMI
Model

24. PRINCIPAL COMPONENTS
usually the first principal component (CP1)
represents responses of the genotypes
that are proportional to the
environments, which are associated with
the GxE interaction without change of the
range.
The second principal component (CP2)
provides information about cultivation
locations that are not proportional to the
environments, indicating that those are
responsible of the GxE crossover
interaction.

25. Biplot allows 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 GxE interaction.
In the biplot, 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.
Graphical representation of numerical results
often allows a straight forward interpretation
of GEI.
BIPLOTS

27.  General interpretation
◦ genotypes that occur close to particular
environments on the IPCA2 vs IPCA1
biplot show specific adaptation to those
environments
◦ a genotype that falls near the center of
the biplot (small IPCA1 and IPCA2
values) may have broader adaptation

28.  How many IPCAs (interaction principal component
axes) are needed to adequately explain patterns
in the data?
◦ Rule of thumb - discard higher order IPCAs
until total SS due to discarded IPCA's ~ SSE.
◦ Usually need only the first 2 PC axes to
adequately explain the data (IPCA1 and
IPCA2). This model is referred to as AMMI2.
 Approach is most useful when G x location
effects are more important than G x year
effects

29.  Name of the journal – Journal of radiation
research
 Year of publishing – 2014
 Authors of the research paper -Anowara
Akter1*, Jamil Hassan M1, Umma Kulsum M1, Islam
MR1, Kamal Hossain1 and Mamunur Rahman M2*
 1Plant Breeding Division, Bangladesh Rice Research
Institute, Bangladesh
 2Senior Scientific Officer, Farm Management
Division, Bangladesh

30.  Genotype x environment interaction and stability
performance were investigated on grain yield
with 12 rice genotypes in five environments.
 The ANOVA for grain yield revealed highly
significant (P<0.01) for genotypes, environments
and their interactions.
 The significant interaction indicated that the
genotypes respond differently across the
different environments.

31.  The AMMI model is a hybrid model involving both
additive and multiplicative components of two way
data structure which enabled a breeder to get
precise prediction on genotypic potentiality and
environmental influences on it.
 AMMI uses ordinary ANOVA to analyze the main
effects (additive part) and PCA to analyze the non
additive residual left over by the ANOVA .
 The main objectives of the present study are to
identify more high yielding stable promising
hybrids and to determine the areas where rice
hybrids would be adapted by AMMI model.

32.  The experiments were conducted at five districts
namely Gazipur(E1), Comilla (E2), Barisal (E3),
Rangpur (E4) and Jessore (E5) representing five
different agro-ecological zones (AEZ) of
Bangladesh.
 Twelve genotypes consisting of 3 advanced lines
(BRRI 1A/ BRRI 827R(G1), IR58025A/ BRRI 10R
(G2) and BRRI 10A/ BRRI 10R (G3)), 6released
hybrids (BRRI hybrid dhan1(G4), Tea (G5), Mayna
(G6),Richer (G7), Heera-2 (G8) and Heeta 99-5
(G9)), and 3 inbred check varieties (BRRI dhan31
(G10), BRRI dhan33 (G11) and BRRI dhan39(G12))
were used as experimental materials.

33.  The experiments were carried out in a randomized
complete block design (RCBD), with 3 replications.
 21 days old seedlings were transplanted in 20
square meter plot using single seedling per hill at
a spacing of 20 cm×15cm.
 Fertilizers were applied @ 150:100:70:60:10
kg/ha Urea, TSP,MP, gypsum and ZnSO4,
respectively.
 Standard agronomic practices were followed and
plant protection measures were taken as required.
 The grain yield data for 12 genotypes in 5
environments were subjected to AMMI analysis of
variance using statistical analysis package
software Cropstat version 6.1

34. ANOVA

36. Figure 1: AMMI 1 Biplot for grain yield (tha-1) of 12
rice genotypes (G) and five environments (E) using
genotypic and environmental scores.

37. Figure 2: AMMI 2 Biplot for grain yield (tha-1) showing the interaction
of IPCA2 against IPCA1 scores of 12 rice genotypes (G) in five
environments (E).

38.  The mean grain yield value of genotypes averaged
over environments indicated that G3 had the
highest (5.99tha-1) and G12 the lowest yield
(3.19 tha-1), respectively.
 It is noted that the variety G3 showed higher
grain yield than all other varieties over all the
environments.
 The genotypes (G1), (G2), (G3) and (G4) were
hardly affected by the G x E interaction and thus
would perform well across a wide range of
environments.

39.  Name of the journal;-Advances in Biological
Research
 Year of publishing;-2009
 Authors of the research paper;-A. Anandan, R.
Eswaran, T. Sabesan and M. Prakash.
 Department of Agricultural Botany, Faculty of
Agriculture, Annamalai University, T.N.

40. ABSTRACT:
 The objective of the present investigation was
to analyse the pattern of Genotype x
Environment (G x E) interaction for grain yield
of 46 genotypes by Additive Main effects and
Multiplicative Interaction (AMMI) model using
the data generated from three saline stress
environments of east coastal region of Tamil
Nadu, India.
 The results showed highly significant genotypic
and G x E interaction.
 The G x E interaction influenced the relative
ranking of the genotypes across saline stress
environment condition.

41.  The developed cultivars should adapt to a
wide range of target environments, is the
eventual goal of plant breeders. Hence,
pattern of response of genotypes is studied
by testing genotypes in different
environments to study G X E interaction.
 AMMI offers on appropriate first statistical
analysis of yield trials that may have a G x E
interaction . The objectives of this study
were to assess the extent of G x E
interaction and to select the stable
genotypes of rice

42.  46 rice genotypes from different parts of India were
evaluated at Plant Breeding Research Farm, Faculty of
Agriculture, Annamalai University, Annamalai,East
coastal region of Tamil Nadu, India.
 With soil pH of 8 to 8.5 and EC of 2.51 to 2.8 dSm .
 The each genotype was evaluated in three seasons viz.,
E1 (Kharif, 2006), E2 (Kharif, 2007) and E3 (Rabi,
2007).
 For all trials, the design used was RCBD with three
replications.
 The plot had 10sq.m with spacing of 20 cm between
environments and rows and 20 cm between plants.
 Management practices were uniformly adapted to all
seasons as per the recommendation for rice in the
irrigated condition.

44. source df SS MS F
Treatments 137 905.60 6.61 216.78**
Genotypes 45 865.30 19.23 630.56**
Environments 2 36.50 18.24 5.43**
Interactions 90 3.90 0.04 1.42*
IPCA 1 46 2.90 0.06 2.04**
Residuals 44 1.00 0.02 0.77
Error 270 8.20 0.03

45. G
2
6

46.  The genotypes which had IPCA score
nearest to zero are G24, G26, G27, G32
G34, G35, G39 and G45.
 Among the above mentioned stable
genotypes, G45, G26, G27 G35 and G34
exhibited above average grain yield and
indicated that these genotypes were well
adaptable to saline environment condition

47. SUBMITTED BY
B.RACHANA
RAM/16-45
DEPARTMENT OF
GENETICS AND
PLANT BREEDING