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Thesis Proposal On: GENOTYPE BY ENVIRONMENT INTERACTIONS AND
STABILITY ANALYSIS IN COWPEA [Vigna unguiculata (L.) Walp] GENOTYPES
FOR YIELD IN ETHIOPIA MSc Research Proposal Tariku...
Experiment Findings · January 2016
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HARAMAYA UNIVERSITY
POST GRADUATE PROGRAM DIRECTORATE
GENOTYPE BY ENVIRONMENT INTERACTIONS AND STABILITY
ANALYSIS IN COWPEA [Vigna unguiculata (L.) Walp] GENOTYPES
FOR YIELD IN ETHIOPIA
MSc Research Proposal
Tariku Simion Dojamo
College:
Agriculture and Environment Sciences
Department:
Plant Sciences
Program:
Plant Breeding
Major Advisor:
Wassu Mohammed (PhD), Haramaya University
Co-Advisor:
Birhanu Amsalu (PhD), Melkasa Agriculture
Research Center, EIAR
Research Center, EIAR
JUNE 2016
Haramaya University, Haramaya
ABBREVIATIONS AND ACRONYMS
AMMI
Additive Main Effects and Multiplicative Interaction
ANOVA
Analysis of Variance
ASV
AMMI Stability Value
CSA
Central Statistical Agency
EIAR
Ethiopian Agricultural Research Institute
FAO
Food and Agriculture Organization
GEI
Genotype by Environment Interaction
GER
Genotypes in Environments each with Replicates
IITA
International Institute of Tropical Agriculture
IPCA
Interaction Principal Component Analysis
LDS
Least Significant Difference
US
United States
PCA
Principal Component Analysis
SAS
Statistical Analysis System
ii
TABLE OF CONTENTS
ABBREVIATIONS AND ACRONYMS
ii
TABLE OF CONTENTS
iii
LIST OF TABLES
v
1. INTRODUCTION
1
2. LITERATURE REVIEW
5
2.1. Centre of Origin and Importance of Cowpea
5
2.2. Cowpea Production and Utilization
7
2.3. Genotype by Environment Interactions
8
2.3.1. Significance of Genotype by Environment Interaction in Crop Variety Selection
9
2.4. The Concept of Stability
10
2.5. Methods to Measure Genotype by Environment Interaction
11
2.5.1. Analysis of Variance
12
2.5.2. Parametric Approach
13
2.5.2.1. Regression coefficient and deviation mean square
13
2.5.2.2. Coefficient of Determination
14
2.5.2.3. Multivariate analysis methods
15
2.6. Genotype x Environment Interaction on Yield and Yield Related Traits in Cowpea
16
2.7. Stability of Cowpea Genotypes Across Varied Environments
16
3. MATERIALS AND METHODS
17
3.1. Experimental Sites
17
3.2. Experimental Materials
18
3.3. Experimental Design
19
3.4. Data Collection
19
iii
Continue…
3.4.1. Crop Phenology and Growth
19
3.4.2. Yield and Yield Components
20
3.5. Data Analyses
20
3.5.1. Analyses of Variance
20
3.5.2. Stability Analysis
21
4. WORK PLAN
23
5. LOGISTICS
24
6. REFERENCES
28
iv
LIST OF TABLES
Tables
page
1. Description of test location
17
2. List of experimental materials
18
v
1. INTRODUCTION
Cowpea [Vigna unguiculata (L.) Walp] is an annual herbaceous legume that belongs to
Fabaceae family. It is diploid species with 2n=2x=22 chromosomes. It is a self-pollinated
crop, with natural cross-pollination of up to 1%. All cultivated cowpeas types are grouped
under the species Vignaunguiculata, which is subdivided into four cultivar groups such as
unguiculata (common cowpea used as food and fodder), sesquipedalis (the yard-long or
asparagus bean used as vegetable), biflora (catjang) and textiles (used for fibers). The cultivar
group of unguiculata is the most diverse of the four and is widely grown in Africa, Asia, and
Latin America (Singh et al., 2002).
Cowpea is originated in the center of Africa and is one of the oldest known crops in the
continent. There is some debate about the geographical origin of cowpea. Some authors feel
that cowpea is originated in either the southern Sahel of north-central Africa or in Ethiopia,
and then spread to Asia and the Mediterranean region by way of Egypt. Others have a view
that it is originated in India and it was introduced into Africa some 2,000 to 3,500 years ago.
From West Africa, they made their way to the Caribbean and then to North America with the
slave trade (Singh, 1997).
Cowpea is extremely drought resistant and adapted to poor soil, making it a useful staple crop
for farmers in areas that face increasingly moisture stress and hot temperatures due to climate
change. Cowpea is one of the widely cultivated and consumed grain legumes globally,
especially in the arid and semi-arid tropics (Noubissietchiagam et al.,2010). It is able to grow
in harsh environments under dry land condition, making it one of the most widely grown
legume crops in sub-Saharan Africa (Baidoo and Mochiah, 2014). World production of
cowpea was estimated as 5,249,571 tons in 2007, of which over 64% was produced in Africa
(Gbaguidi et al., 2013).
Pulse crops (Faba-bean, field pea, chickpea, common bean and lentil) occupy about 13% of
the croplands in Ethiopia, and they are the second most important element in the national diet,
1
providing principal protein sources and important dietary supplement to cereal consumption.
Pulses are used primarily for making wot, an Ethiopian stew, which is served as a main dish to
be eaten with injera. Faba bean, field pea, chickpea and lentil are widely grown in the
highlands, while common bean is grown in the low and intermediate altitudes. Pulses recently
have regained significance as export commodities (CSA, 2014/15). The Food and Agriculture
Organization FAO (2005-2006) estimated that 3.7 million tons of dry cowpea grains was
produced worldwide in 2003. The total area where cowpea is grown worldwide was 9.8
million ha, with about 91% of this in West Africa. Fatokun (2009) stated that the average
yields of cowpea in Africa increased to an average of 470 kg/ha. EIAR (2004) reported that
the production status of cowpea in Ethiopia was very low (20-22.5 kg/ha on research farm and
19.6 kg/ha on farmers’ land), which is very low compared to average yield in other African
countries.
The Cowpea grain is the most important part of the plant for human consumption. The grains
are most often harvested and dried for storage and consumption at a later time, either after
cooking whole or after being milled like a flour product and used in various recipes (Nielson
et al., 1997; Ahenkora et al., 1998). Cowpea plays a critical role in the lives of millions of
people in the developing world, providing them a major source of dietary protein that
nutritionally complements low protein staple cereal and tuber crops. Cowpea grain is a
relatively low fat content and total protein content is two to four fold higher than cereal and
tuber crops. Cowpea grains are rich in the amino acids lysine and tryptophan when compared
to cereal grains, but low in methionine and cystiene when compared to animal proteins. Total
grain protein content ranges from 23 to 32% of grain weight (Nielson et al., 1993; Hall et al.,
2003). Cowpea grains are also rich sources of minerals and vitamins (Hall et al., 2003) and
among plants that have highest contents of folic acid and vitamin B necessary during
pregnancy to prevent birth defects in the brain and spine (Singh et al., 2006).
Cowpea production and utilization in Ethiopia is very low as compared to other African
countries though the country is claimed to be center of diversity and/or origin and has a high
potential for the production of the crop. Ethiopia is the primary center of diversity of cowpea
and more than 66.5% of the arable land is very suitable for cowpea production (CCRP, 2015).
2
Significant long-term genetic improvement efforts of cowpea have taken place within national
laboratories and universities in India, Brazil, and the USA. Within the Consultative Group on
International Agricultural Research (CGIAR), the International Institute of Tropical
Agriculture (IITA) based in Ibadan, Nigeria, has the global mandate for improving cowpea
cultivars. International Institute of Tropical Agriculture (IITA) develops and distributes a
range of improved cowpea breeding lines to 65 countries. The accomplishments of some of
these programs have been described (Singh et al., 2002; Hall et al., 2003; Singh 2005).
However, Ethiopia has not been in a position to be benefited from international and
continental cowpea improvement programs or from the national pulse crops research. This is
because low attention is given in research for cowpea as compared to other pulse crops.
Drought is currently the most important abiotic stress limiting production of all crops
worldwide, even the most drought tolerant cowpea (Hall, 2004). More importantly, Ethiopia is
known as a victim with recurrent drought that causes for partial or total crop failure and,
subsequently, famine in the country. In such a situation, cowpea can be a potential crop to
reduce the consequences of drought because of its drought tolerant nature more than other
staple crops. In addition, the crop is suitable to intercropping and has a potential to improve
soil fertility through atmospheric nitrogen fixation. However, progress in cowpea breeding in
the country is limited either in exploiting the availability of genetic variability in the country
or from introduction of improved varieties elsewhere in the world. It is only very recently that
the improvement of the crop got attention at a national level. There are many opportunities for
breeders to develop cowpea cultivars possessing inherent tolerance to a wide range of abiotic
factors (e.g., drought, low soil fertility, high salinity), resistance to a variety of diseases and
parasites and agronomic characteristics (e.g., plant growth habits, flowering times, maturity
dates), specifically adapted to agro-ecological production zones and crop product utilizations
(i.e., dual purpose grain and hay production).
It is known that yield and other traits are influenced by genotype, environmental factors, and
their interaction. The relative magnitude of environment, genetic and their interaction effects
are a challenge that makes production difficult (Hall et al., 2003). Studies undertaken
3
elsewhere showed that cowpea is sensitive to environmental and geographic conditions such
as temperature, altitude, latitude photoperiod and others (Adeigbe et al., 2011; Adewale et al.,
2010; Patel and Jain, 2012). Therefore, in the process of developing cowpea varieties for
desirable traits, it is necessary to evaluate genotypes in contrasting environments in the
country. However, information on the effect of genotype, environment, and their interaction
on cowpea grain yield under diversified agro-climatic conditions of Ethiopia is limited.
Therefore, this research is initiated with the following objectives.
Objectives:
1. To estimate the magnitude of genotype, environment and genotype by environment interaction
for grain yield of cowpea;
2. To characterize yield stability of cowpea genotypes across different environments
4
2. LITERATURE REVIEW
2.1. Centre of Origin and Importance of Cowpea
Cowpea [Vignaunguiculata (L.) Walp] is one of the most ancient crops known to man, with
its centre of origin and subsequent domestication being closely associated with pearl millet
and sorghum in Africa. The precise location of the centre of origin of the species is, however,
difficult to determine. Previous speculation on the origin and domestication of cowpea has
been based on botanical and cytological evidence, as well as information on its geographical
distribution and cultural practices, and historical records (Faris, 1965; Steele and Mehra, 1980;
Ng and Marechal, 1985). Origin and domestication of cowpea occurred in Africa mainly
through the African Savannah (Duke, 1981). The probable centers of domestication are
thought to be mainly in West Africa, Central Africa and South Africa (Vavilov, 1951). The
most primitive wild V. unguiculata occurs in southern Africa in the region encompassing
Namibia from the west, across Botswana, Zambia, Zimbabwe, and Mozambique to the east,
and the Republic of South Africa in the south. Probably, the Limpopo region of the Republic
of South Africa was the centre of speciation of V. unguiculata due to the presence of the most
primitive wild varieties, i.e. var. rhomboidea, var. prottracta, var. tennis, and var. stenophylla
(Ng and Marechal,1985).
Cowpea is used as human food and livestock fodder. This dual purpose characteristic makes it
an attractive crop where land is limited. It is a drought tolerant warm weather crop, well
adapted to the semi-arid regions of the tropics where other food legumes do not perform well.
This trait is, in part, explained by the deep-rooting habit of some varieties. When grown in dry
environments desiccation of cells and tissue is easily avoided in the crop; thus neither its
biological activity nor
survival is threatened (Sinclair and Gardener, 1998). Cowpea is an important crop for the
nutrition and the livelihoods of millions of people in less developed countries. It is consumed
in many forms. Young leaves, green pods, and green grains are used as vegetables, where as
dry grains are used in a variety of food preparations (Nout, 1996; Nielselet al., 1997). The
green and dry haulms are fed to livestock, particularly in dry seasons when animal feed is
5
scarce. Trading of fresh produce and processed cowpea foods and snacks provides rural and
urban women with opportunity for earning cash income. Cowpea is also a major source of
protein, minerals and vitamins (Bressani, 1985).
Cowpea is very important as a nutritious fodder for livestock (Singh and Tarawali, 1997).
Tarawali et al. (1997) reviewed the use of cowpea haulms as fodder in different parts of the
world. In West Africa, the mature cowpea pods are harvested and the haulms are cut while still
green and rolled into bundles containing leaves and vines. The bundles are stored on rooftops
or on tree forks for use and for sale as harawa (feed supplement) in the dry season, making
cowpea haulms a key resource in crop-livestock systems (Singh and Tarawali, 1997). In West
Africa region on a dry weight basis, the price of cowpea haulms ranges between 50 to 80% of
the grain price. Haulms, therefore, constitute an important source of income.
The nutritive value of cowpea grain, leaves, and haulms is very high. The crude protein ranges
from 22 to 30% in the grain and leaves on a dry weight basis (Bressani, 1985; Nielsel et al.,
1997) and from 13 to 17% in the haulms with a high digestibility value and low fibre level
(Tarawali et al., 1997). With all this attributes, cowpea positively influences the nutrition and
health of poor people, particularly children.
The bulk of the diet of the rural and urban poor African consists of starchy foods, such as
cassava, yams, bananas, millet, sorghum and maize. The addition of even a small amount of
cowpea improves the nutritional balance of the diet and enhances protein quality by the
synergistic effect of high protein and lysine from cowpea and energy from starchy foods. The
nutritional quality of cowpea, particularly the protein, fat and iron content has been improved
through breeding (Singh and Ishiyaku, 2000). Cowpea provides an important source of protein
(20 to 40%), fat (1.3%), fibre (1.8%) and carbohydrates (76%), while cowpea grain protein is
high in lysine, it is deficient in sulphur containing amino acids and methionine. This is likely
to result in an insufficient supply of these amino acids, especially when cowpea is consumed
with sulfur amino acid deficient starch crops, such as cassava (Molvig et al., 1997).
6
Due to its unique ability to fix atmospheric nitrogen through its nodules, cowpea grows well in
poor soils having more than 85% sand, less than 0.2% organic matter, and low levels of
phosphorus (Kolawale et al., 2000; Sanginga et al., 2000). With these characteristics, the crop
does not deplete the naturally low reserves of soil nitrogen. Many experimental findings
illustrate that soil N levels increase, following a cowpea crop. Cowpea varies in sensitivity to
soil acidity, but it tolerates acid soils under conditions of adequate rainfall (Massey et al.,
1998). Cowpea is shade tolerant and, therefore, compatible as an intercrop with maize, millet,
sorghum, sugarcane and cotton as well as with several plantation crops (Singh and Emechebe,
1998). Due to its fast growth habit it covers the ground rapidly and, therefore, limits weed
competition and soil erosion.
2.2. Cowpea Production and Utilization
Reliable statistics on cowpea production and world’s planted area are not available as most
countries do not maintain separate records on cowpea. The Food and Agriculture Organization
(FAO), therefore, suspended formal publication of cowpea production data several years ago
(Singh et al., 1997). However, based on the information available from FAO, it is estimated
that cowpea is now cultivated on at least 12.5 million hectares, with an annual production of
over 3 million tons worldwide (Quin, 1997). Subsistence farmers in the semi-arid and subhumid regions of Africa are the major producers and consumers of cowpea. These farmers, not
only grow cowpea for human consumption and fodder for animal feed, but also utilize the
leaves and fruits as vegetables.
Cowpea is widely grown in eastern Africa and southeast Asia primarily as a leafy vegetable.
Steele et al. (1985) noted that the protein content of the leafy cowpea parts consumed annually
in Africa and Asia is equivalent to 5 million tons of dry cowpea grains, providing as much as
30% of the total food legume production in the lowland tropics. West and central Africa
regions are leading cowpea-producing area in the world. These part of Africa produce 64% of
the estimated 3 million tons of cowpea grain annually (Quin, 1997). Nigeria is the world’s
leading cowpea producing country while, Cameroon, Ghana, Niger and Senegal are significant
producers.
7
Outside Africa, the major production areas include Asia and Central and South America.
Brazil is the world’s second leading producer of cowpea grain, producing 600,000 tons
annually (Guazzelli, 1988). By the early 1980s, annual cowpea production in the USA was
estimated at 80,000 tons (Fery, 1981). The cowpea has long been valued in the southern USA
as a vegetable crop, and an extensive industry currently exists to supply fresh, canned, frozen,
and dry-pack products that are marketed nationwide. Additionally, the cowpea has long been a
popular item with home gardeners throughout the southern United States.
Presently the crop is not widely grown in Ethiopia. Inadequate transmission of research
results, community exposure, and lack of intensive research for improvement and introduction
are major constraints. Cowpea is one of the most important food leguminous crop plant of
great socio-economic, cultural, nutritional importance and a valuable component of the
traditional cropping systems in the semi-arid tropics, but in Ethiopia it has got less or no
consideration in all respective sectors.
2.3. Genotype by Environment Interactions
The improvement of varities which can be adapted to a wide range of diversified environments
is the ultimate goal of plant breeders in a crop improvement program. Genotype by
environment interactions are of major importance because they provide information about the
effect of different environments on varities’ performance and have a key role for assessment
of performance stability of the breeding materials (Moldovan et al., 2000). Increasing genetic
gains in yield is possible in part from narrowing the adaptation of varities, thus maximizing
yield in particular areas by exploiting genotype by environment interaction (Peterson et al.,
1989). Genotype by environment interaction is tremendously important in the development
and evaluation of plant varieties since it reduces the genotypic stability values under diverse
environments (Hebert et al., 1995). Genotype x environment interaction results in genotype
rank changes from an environment to another, a difference in scale among environments, or a
combination of these two situations (Aycicek and Yildirim, 2006). The adaptability of a
variety over diverse environments is usually tested by the degree of its interaction with
8
different environments under which it is planted. A variety is considered more adaptive or
stable if it has a high mean yield but a low degree of fluctuation in yielding ability when
grown over diverse environments (Eberhart and Russel (1966).
Genotype by environment interaction is a differential genotypic expression across
environments (Basford and Cooper, 1998). When productivity is extremely low, it is not even
possible to discriminate selectively among genotypes. Because of this, and the often observed
moderate-to-high correlation of genotype yield performance across a wide range of seasonal
water amounts, some researchers have recommended that breeding for stress tolerance should
be performed under optimal conditions. It also has been asserted that breeding for stress
tolerance under optimal conditions permits an efficient allocation of the resources available.
An individual's phenotype is the product of the genotype of the individual, the environment
that the individual is exposed to, and the interaction that occurs between the two.
2.3.1. Significance of Genotype by Environment Interaction in Crop Variety Selection
Genotype by environment interaction occurs when differences between genotypes are not the
same in all locations within and across years. It is the inconsistency of relative performance of
genotypes over environments (Hill et al., 1975). Crossover interaction (COI) is part of the
genotype by environment interaction (GEI) that is attributable to changes in genotype rank
among environments. Crossa (1990) recognized that COI is the most intricate type of GEI
with respect to identifying the best genotypes in a selection program. Gail and Simon (1985)
developed a statistical test for COI between two treatments evaluated in a number of
independent trials.
Significant achievement in crop production may be possible by breeding varieties for their
stability for yield and yield components (Singh et al., 2009; Lal et al., 2010). The inspection
of plant breeders is that environment is a general term that covers conditions under which
plants grow and may involve locations, years, management practices or a combination of these
factors. Every factor that is a part of the environment of a plant has the potential to cause
differential performance that is associated with genotype by environment interaction (Fehr,
9
1991). Allard and Bradshaw (1964) classified environmental variables as unpredictable and
predictable factors. The unpredictable variations include the fluctuating features of the
location, such as rainfall, relative humidity, temperature, etc., whereas the predictable
variations are those factors which are under human control and include planting date, row
spacing, plant population and rates of nutrient application. Both conditions provide a greater
range of environmental condition to test genotypes (Eberhart and Russell, 1966).
The relative grain yield of a set of varieties in a multi-environmental trial changes commonly
with respect to each other across location. This differential yield response of cultivars from
one environment to another is genotype by environment interaction (GEI) and can be studied,
described, and interpreted by statistical models (Crossa, 1990; Vergas et al., 1999). For plant
breeders, large genotype by environment interaction hinders progress from selection and has
important implications for testing and variety release. Identification of causal factors of the
genotype by environment interaction effect and quantification of unexplained variation are of
prime importance to recommend environmentally specific varieties. The ability of some crop
varieties to perform well over a wide range of environmental conditions has long been
appreciated by agronomists and plant breeders.
2.4. The Concept of Stability
The knowledge of phenotypic stability is important for the selection of crop varieties as well
as for breeding programs. Yield stability is an interesting feature of today’s plant breeding
programs, owing to the high annual or seasonal variation in mean yield, especially in the arid
and semi-arid areas (Mohammad et al., 2012). Plant breeders usually try a series of genotypes
in multi-environments, before a new improved variety is released for production to farmers
(Naghavi et al., 2010). The phenotypic performance of a genotype is not necessarily the same
under diverse agro-ecological conditions (Ali et al., 2003). Some genotypes may perform well
in certain environments than in others, but fail in several others. Genotype by environment
interactions is extremely important in the development and evaluation of plant varieties
because they reduce the genotypic-stability values under diverse environments (Hebert et al.,
1995). The varietal stability could be challenged not only due to the change in the test
10
environment but also due to change in growing season per environment (Dagnachew et al.,
2014).
The stability is adaptation of varieties to unpredictable environmental conditions and the
technique has been used to select stable genotypes unaffected by environmental changes
(Allard and Bradshaw, 1964). High yield stability usually refers to ability of variety to perform
consistently, whether at high or low yield levels across a wide range of environments
(Tarakanovas and Ruzgas, 2006). To identify the most stable and high yielding genotypes, it is
important to conduct multi-environment trials (Lu'quez et al., 2002). Stability across
environments is one of the most desirable properties of a genotype to be recommended for
wide cultivation (Benti et al., 1996). Multi environment testing will minimize the effect of
genotype by environmental interactions, but it has been shown that genotypes differ
significantly in the extent of their interactions (Setimela, 1996).
2.5. Methods to Measure Genotype by Environment Interaction
The statistical methods for measuring genotypic stability should partition the information from
a genotype by environment interaction data matrix into simpler components representing real
responses versus random variation (Gauch, 1992). These statistical methods can be classified
into two groups: univariate and multivariate. Univariate models ranged from parametric, such
as environmental variance, stability variance (Shukla, 1972), regression slope (Finlay and
Wilkinson, 1963), deviation from regression (Eberhart and Russell, 1966) and coefficient of
determination (Pinthus, 1973). Non-parametric models include Kang's yield stability statistic
(Kang, 1993). Multivariate models include a wide range of methods, such as principal
component analysis (PCA) (Gower, 1967), and additive main effects and multiplicative
interaction models (AMMI) (Gauch and Zobel, 1988). A wide range of methods is available
for the analysis of genotype by environment interaction and can be broadly classified into four
groups: the analysis of components of variance, stability analysis, multivariate methods and
qualitative methods.
11
2.5.1. Analysis of Variance
The conventional cultivar evaluation trial is one in which the yield of genotypes is measured
in environments each with replicates (GER). The classic model for analyzing the total yield
variation contained in GER observations is the analysis of variance (Fisher,1925). Within
environment, residual mean square measures the error in estimating the genotype means due to
differences in soil fertility and other factors, such as shading and competition from one plot to
another. After removing the replicate effect when combining the data, the genotype by
environment interaction observations is partitioned into two sources: (a) additive main effect
for genotypes and environments and (b) non-additive effects due to genotype by environment
interaction (Fisher, 1918, 1925). The analysis of variance of the combined data expresses to
the observed (Yij) mean yield of the ithgenotype at the jth environments as:
Yij= μ + Gi + Ej + GEij + eij
Where μ is the general mean, Gi, Ej and GEij represent the effects of the genotype,
environment and genotype by environment interaction respectively, and eij is the average
random error associated with the ith plot that receives the ith genotype in the jth environment.
The non-additive interaction (GEij) as defined in the above equation implies that an expected
value (Yij) depends not only on the level of genotype and environment separately, but also on
the particular combination of levels of genotype and environment interaction (Crossa, 1990).
The major restriction in this analysis is that the error variances over environments should be
homogeneous to test for genotypic differences. If error variances are heterogeneous, this
analysis is open to criticism as the F-test of the genotype and environment interaction mean
squares against the pooled error variances is biased towards significant results. One of the
main deficiencies of the combined analysis of variance of multi-location trials is that it does
not explore any underlying structure within the observed non-additively genotype and
environment interaction. Analysis of variance of multi-environment trials is useful for
estimating variance components related to different sources of variation, including genotypes
and genotype by environment interaction. Variance component methodology is important in
12
multi-environment trials, since errors in measuring the yield performance of a genotype arise
largely from genotype and environment interaction (Fisher, 1925).
2.5.2. Parametric Approach
Stability analysis provides a general summary of the response patterns of genotypes to
environmental changes. Freeman (1973) termed the main type of stability analysis, joint
regression analysis, or joint linear regression. It involves the regression of the genotypic
means on an environmental index. Joint regression analysis provides a means of testing
whether the genotypes have characteristic linear responses to changes in environments. Yates
and Cochran (1938) first proposed joint regression analysis.
2.5.2.1. Regression coefficient and deviation mean square
Joint linear regression is a model used for analyzing and interpreting the non-additive
interaction of two-way classification data. According to Ramagosa and Fox (1993) simple
linear regression provides a conceptual model for genotypic stability and is the most widely
used statistical technique in plant breeding. This model is also called the Finlay and Wilkinson
(1963) approach. It determines the regression coefficient by regressing variety mean on the
environmental mean, and plotting the obtained genotype regression coefficients against the
genotype mean yields.
Finlay and Wilkinson (1963) defined a genotype with regression coefficient zero as stable,
while Eberhart and Russell (1966) defined a genotype with regression coefficient with one to
be stable. Perkins and Jinks (1968) proposed an equivalent statistical analysis whereby the
observed values are adjusted to environmental effects before the regression. Eberhart and
Russell (1966) proposed pooling the sum of squares for environments and genotype by
environment interaction and subdividing it into a linear effect between environments (with 1
degree of freedom), a linear effect for genotype by environment interaction (with E-2 degree
of freedom).
13
In effect, the residual mean squares from the regression model across environments is used as
an index of stability, and a stable genotype is one in which the deviation from regression mean
squares is small. However, genotype by environment interaction becomes of practical
significance only when crossover interactions occur (Cornelius et al., 1996). Crossover
interactions occur in evaluation trials when ranks of cultivars change in different
environments. In varying environments, genotypes that provided high average yields with
minimum genotype by environment interaction have been gaining importance over increased
yields (Gauch and Zobel, 1997). The conventional method of partitioning total variation into
components due to genotype, environment, and genotype by environment interaction conveys
little information on the individual patterns of response (Zobel et al., 1988). To optimize
growers’ yields, the growing region must be subdivided into relatively homogenous megaenvironments and appropriate genotypes must be targeted for each of these megaenvironments (Gauch and Zobel, 1997). The usual analysis of variance (ANOVA) fails to
detect a significant interaction component, principal component analysis (PCA) fails to
identify and separate the significant genotype and environment main effects, and linear
regression (LR) accounts for only a small portion of the interaction sum of squares (Zobel et
al., 1988). Since ANOVA, PCA, and LR are sub-cases of the more complete AMMI model
(Zobel et al., 1988), AMMI offers a more appropriate first model of choice when main effects
and interaction are both important (Gauch and Zobel, 1997). AMMI increases the precision of
yield estimation and selection of higher yielding genotypes than treatment means (Crossa et
al., 1990).
2.5.2.2. Coefficient of Determination
Pinthus (1973) proposed the coefficient of determination (r2) instead of deviation mean
squares (S2d) to estimate stability of genotypes, because coefficient of determination is
strongly related to deviation mean squares. The effectiveness of the use of coefficient of
determination as an index of stability was demonstrated by the observation made by Eberhart
and Russell (1966). Coefficient of determination is used to estimate predictable performance
of genotypes (Pinthus, 1973).
14
2.5.2.3. Multivariate analysis methods
According to Crossa (1990), multivariate analysis has three main purposes: (i) to distinguish
systematic from non-systematic variation); (ii) to summarize the data; and (ii) to reveal a
structure in the data. Multivariate analysis is appropriate for analyzing two-way matrices of
genotypes and environments. Two groups of multivariate techniques have been used to
explain the internal structure of genotype by environment interaction. Ordination techniques,
such as principal component analysis, principal coordinate’s analysis, and factor analysis,.
These techniques attempt to represent genotype and environment relationships as faithfully as
possible in a low dimensional space. A graphical output displays similar genotypes or
environments near each other and dissimilar items are farther apart. Classification techniques,
such as cluster analysis and discriminate analysis, seek discontinuities in the data. These
methods involve grouping similar entities in clusters and are effective for summarizing
redundancy in the data (Crossa, 1990).
The development of high yielding varieties with wide adaptability is the ultimate aim of plant
breeders. However, attaining this goal is made more complicated by genotype by environment
interactions (Gauch and Zobel 1996). Combined analysis of variance can quantify genotype by
environment interactions and describe the main effects, but it does not explain the interaction
effect (Asnake et al., 2013). AMMI model, genotype, and genotype by environment
interactions biplot analysis are the most commonly used analytical and statistical tools to
determine the pattern of genotypic responses across environments (Yuksel et al., 2002).
Purchase et al. (2000) developed a quantitative stability value to rank genotypes through the
AMMI model, namely the AMMI Stability Value (ASV). Genotype and genotype by
environment interactions biplot analysis is based on environment-centered PCA, whereas
AMMI analysis refers to double-centered PCA. For the research purpose of describing megaenvironments, both AMMI and genotype and genotype by environment interactions are
suitable. The AMMI model combines the analysis of variance for the genotype and
environment main effects with principal components analysis of the genotype environment
interaction (Kaya et al., 2002). AMMI is the model of first choice when main effects and
interaction effects are both important, which is the most common cause with yield trials
15
(Mandel, 1971). If, for example, only main effects (additive structure) are present in the data,
then the AMMI can be reduced to an ANOVA model, whereas if non-additive structure is only
present then the PCA model is reflected.
2.6. Genotype by Environment Interaction on Yield and Yield Related Traits in Cowpea
Yield is a complex quantitative character governed by polygenic inheritance. In such traits, the
influence of the environment is high, and genotype x environment interaction effect is often
highly significant (Poehlman and Sleper, 1995). High seasonal variability of yield is common
in pulse crops due to pollination deficiency, water stress, competition from vegetative sinks,
and losses due to diseases, insect pest, and weeds. The occurrence of such variability makes it
difficult to predict ideal genotypes for both maximal yields in favorable seasons and for yield
stability under conditions of environmental stress (Williams, 1985). The great expenditure of
energy for nitrogen fixation in competition with grain filling is also cited as one factor for
reduced yield of pulses as compared to cereals (Simmonds, 1986). Genotype x environment
interaction, thus lack of stability in quantitatively inherited traits, is a challenging problem,
especially in areas with unpredictable environmental factors.
2.7. Stability of Cowpea Genotypes across Varied Environments
Many past studies on stability have often been on polygenic traits, most especially, the yield;
the genetic performances of other quantitative traits are likewise influenced by the
environment (Aremu et al., 2007). G x E cannot be avoided, in fact, it is an important limiting
factor for testing the efficiency of any breeding program. The occurrence of large genotype x
environment interaction affects the recommendations of the breeders in selecting genotypes
for specific environment. Genotype x environment analysis is used to provide unbiased
estimates of yield and other agronomic characteristics and to determine yield stability or the
ability to withstand both predictable and unpredictable environmental variation (Kamdi,
2001). Therefore, a good understanding of the genetic stability of those yield determining
traits would be prerequisite for any reliable prediction for grain yield in cowpea.
16
3. MATERIALS AND METHODS
3.1. Experimental Sites
The experiment will be conducted at six locations in cropping season of 2016. Description of
each test location is given here under (Table 1). Each location is different in soil type, altitude,
and mean annual rainfall, and each location is considered as one environment.
Table 1. Description of test location
Location
Soil type
Altitude
Average
Temperature(OC)
Geographical location (ON, O E)
(masl)
rainfall
Min
Max
Latitude (N)
Longitude (E)
(mm)
Arbaminch
Clay
1216
1000
16
37
06O 06' 841''
037O 35' 122''
1180
862.5
15.1
27.5
-
-
1650
671
15.5
28.1
9O 13' 09''
42O 19' 25''
loamy
Derashe
Babile
Sandy
loam
Fadis
Vertisol
1600
804
22.5
32.5
9O 07'
42O 04'
Melkasa
Andosol
1500
763.0
14.0
24.8
8O 30'
39O 21'
Miesso
Vertisol
1332
787.0
14.9
28.2
9O 28'
38O 08'
Source: Gamo Gofa and Segen Area People Zone Agricultural Department and Melkasa
Agricultural Research Center
17
3.2. Experimental Materials
Sixteen cowpea genotypes (14 advanced lines and two standard checks) will be used for this
study. Descriptions of the genotypes are given in Table 2.
Table 2. List of experimental materials
No. Genotype
Status
1
KENKETI
Standard check
2
86D-378
Advanced line
3
IT-89KD
Advanced line
4
MEL-NURL-96-3
Advanced line
5
IT-96D-610
Advanced line
6
IT-93K-556-4
Advanced line
7
IT-97K-568-18
Advanced line
8
IT-99K-1060
Advanced line
9
95K-1095-4A
Advanced line
10
IT-87D-1137
Advanced line
11
IT-96D-604
Advanced line
12
93K-619-1
Advanced line
13
IT-93K-293-2-2
Advanced line
14
IT-99K-1060
Advanced line
15
IT-960-604
Advanced line
16
TVU
Standard check
Source: Melkasa Agricultural Research Center
18
3.3. Experimental Design
In each location, 4 x 4 triple lattice experimental design with three replications will be used.
Each experimental plot will have six rows. The seeds of the experimental varieties will be
planted on 4 m x 2.4 m plots (9.6 m2) having six rows, with inter-row spacing of 60 cm and
20 cm within rows. All other agronomic managements will be applied based on national
recommendation.
3.4. Data Collection
Data will be collected on the basis of five sample plants randomly taken from the four central
rows, viz. plant height at maturity, number of pods per plant, pod length, and number of seeds
per pod, on the basis of entire plot, such as days to 50% flowering, days to 90% physiological
maturity, net plot which include yield per plant, grain yield per hectare, and 100 seed weight.
3.4.1. Crop Phenology and Growth
1. Days to 50% emergence (DE): Number of days from planting to when 50% of plants
emerged in a plot.
2. Days to 50% flowering (DF): Number of days from planting to when 50% of plants in a
plot have at least one open flower.
3. Days to 75% physiological maturity (DM): Number of days from planting to when 75%
of plants in a plot have at least 90% of their pods dried.
4. Plant height (PH) (cm): Length of the central axis of the stem, measured from the soil
surface up to the tip of the stem at physiological maturity.
19
3.4.2. Yield and Yield Components
5. Pod length (PL cm): Average length of pods, measured at physiological maturity on five
randomly taken plants, and five randomly taken pods per plant.
6. Number of seeds per pod (SPP): Average number of seeds per pod, counted at harvest on
five randomly taken plants, in five randomly taken pods per plant.
7. Number of pods per plant (NP): Average number of mature pods, counted at harvest on
five randomly taken plants.
8. Grain yield per plant (GY g): grain yield of all plants from net plot will be measured after
open air dried in grams and divided by the number of plants harvested to registered as grain
yield per plant.
9. Grain yield per hectare (GY kg ha-1): the net plot grain yield at adjusted 10% moisture
will be converted to yield per hectare in kilogram.
10. Hundred Seed weight (HSW g): 100 seeds will be randomly taken from the grain yield
of net plot and will be weighted in grams.
3.5. Data Analyses
3.5.1. Analyses of Variance
Analysis of variance for grain and related traits for each location will be analyzed with the
PROC Lattice procedure in SAS (2009) versions 9.00. The combined analysis of the variance
across locations will be analyzed by using PROC Lattice model of SAS software in order to
determine differences between genotypes across locations, among locations and also to
determine their interaction to check significance. Bartlett's test will be used to assess the
homogeneity of error variances prior to combine analysis over locations. Duncan’s Multiple
Range Test (DMRT) will be used for mean separation (Gomez and Gomez, 1984). The
20
comparison of mean performance of genotypes will be conducted depending on the test results
of homogeneity of error variances. The mean performance of genotypes will be compared on
the basis of pooled mean over locations for the traits that exhibited homogeneity of error
variances and mean performance of genotypes will be compared for each location for the traits
that exhibited heterogeneity of error variances.
Analyses of variances will be computed for six environments using Additive Main Effects and
Multiplicative Interaction (AMMI) (Zobel et al., 1988; Guach, 1988) and regression models
(Eberhart and Russell,1966) for grain yield and some yield components that exhibited
significant mean squares for genotype by location in combined analysis of variance. Genotype
by environment interaction
will be quantified using pooled analysis of variance, which
partitions the total variance into its component parts (genotype, environment, genotype by
environment interaction and pooled error). The genotype by environment interaction analysis
using Eberhart and Russell (1966) model will be computed using SPAR two statistical
software, while Gene Stat statistical software will be used for AMMI (Zobel et al., 1988;
Guach, 1988) model. In addition, environment index and ranking will be conducted using
Eberhart and Russell (1966) and AMMI (Gauch and Zobel, 1996) models, respectively.
3.5.2. Stability Analysis
The two models (Eberhart and Russell, 1966) and AMMI (Guach, 1988; Zobel et al., 1988)
stability parameters will be computed for the traits that exhibit significant GEI mean squares
and in both models GEI analyses of variance will be computed. Accordingly, regression
coefficient (bi) and deviation from linear regression (Sdi2) from Eberhart and Russell’s (1966)
model, and from AMMI model, interaction principal component axes (IPCA) scores of
genotype and environment, and AMMI bi-plots will be computed as per the established
standard procedures for each model.
Since AMMI model does not make provision for a quantitative stability measure, AMMI
stability value (ASV) (Purchase, 1997) measure will be computed in order to quantify and
rank genotypes according to their yield stability:
21
AMMI Stability Value (ASV) =
[IPCA1score] ²
The ASV is the distance from zero in a two-dimensional scatter gram of IPCA1 (Interaction
Principal Component Analysis axis 1) scores against IPCA2 score. Since the IPCA1 score
contributes more to G x E sum of squares; it has to be weighted by the proportional difference
between IPCA1 and IPCA2 scores to compensate for the relative contribution of IPCA1 and
IPCA2 to total G x E sum of squares.
22
4. WORK PLAN
Table 3. Description of work plan
No
Activity
1
Site selection
Period
June 01-20, 2016
2
Land preparation
June 21-July 20, 2016
3
Sowing
July 22-August 5, 2016
4
Weeding
August 06- August 29, 2016
5
Data collection
July 27- October 30, 2016
6
Data analysis
November 15- December 30, 2016
7
Thesis writing
January 1, 2017- March 20, 2017
8
Thesis submission
March 30, 2017
23
5. LOGISTICS
5.1. Supplies
Table 4. Supply description
No.
Item
Quantity
Unit
Unit Cost (Br.)
Total cost (Br.) Remark
1
Paper bags
400
Pcs.
2.00
800.00
2
Fertilizer (DAP)
33
Kg
12.00
400.00
3
Rope
700
M
1.400
1000.00
4
Meter
2
No.
50.00
100.00
5
Plastic bags
400
No.
2.00
800.00
6
Fuel and lubricants -----
-------
------
1087.00
Sub-total
4187
5.2. Personnel Expense
Table 5. Personal expense description
No. Item
Quantity Unit
No. of Labor Individual cost (Br.)
Total cost (Br.)
1
Land preparation
3
Days 35
20.00
2100.00
2
Sowing
1
Day
21
20.00
420.00
3
Weeding
3
Days 14
20.00
280.00
5
Data collection
5
Days 7
40.00
1,400.00
6
Harvesting
1
Day
14
20.00
280.00
7
Threshing
1
day
20
20.00
400.00
Sub-total
4,880
24
5.3. Stationery Costs
Table 6. Stationery costs description
No.
Item
Quantity Unit
Unit cost (Br.)
Total cost (Br.)
1
Notepad
7
No.
15.00
105.00
2
Printing paper
5
Reams
90.00
450.00
3
Pens
7
Pcs.
4.00
28.00
4
Photocopy
2
Pcs.
500.00
500.00
5
CD-RW
1
Pack
300.00
300.00
6
USB flash disk
2
No.
300
300.00
Sub-total
1683
5.4. Per Diem
Table 7. Per diem description
No.
Individual
Quantity
Unit
No. of days
Rate
Total cost (Br.)
1
Researcher
7
Days
2
70
980
2
Major
advisor
1
Days
10
171
1,710.00
3
Co-advisor
1
Days
10
70
1710.00
4
Driver
1
days
8
70
560.00
Sub-total
4,960
25
5.5. Transport Costs
Table 8. Transport cost description
No. Personnel
Departure
Arrival
No. of trips
Trip cost (Br.)
Total cost (Br.)
1
Researcher Haramaya
Trial stations
4
100.00
400.00
2
Haramaya
Trial stations
4
80.00
320.00
Melkesa
Trial stations
2
(Car rent)
970.00
3
Advisor
Sub-total
1690
5.6. Miscellaneous Costs
Table 9. Miscellaneous cost description
No.
Item
Unit cost (Br.)
200.00
Total cost (Br.)
1
Binding
2
Internet &phone costs
……..
500.00
3
Supervision fee
….
3,000.00
4
Thesis binding
…..
500.00
Sub-total
300.00
4,300
26
5.7. Budget Summary
Table 10. Budget summary description
No.
Item
Total cost (Br.)
1
Supplies
4187.00
2
Personnel
4880
3
Travel
1690.00
4
Stationery
1683
5
Miscellaneous
10,600.00
6
Per diem
4960.00
Grand total
28,000
Source: Southern Agricultural Research Institute (SARI) and Ethiopian Agricultural Research Institute (EIAR)
27
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