GENOTYPE x ENVIRONMENT INTERACTION AND STABILITY OF
EARLY MATURING SORGHUM [Sorghum bicolor (L.) Moench]
GENOTYPES IN ETHIOPIA
M.Sc. THESIS
ABIY LEGESSE KIBEBE
OCTOBER 2015
HARAMAYA UNIVERSITY, HARAMAYA
GENOTYPE X ENVIRONMENT INTERACTION AND STABILITY OF EARLY MATURING
SORGHUM [Sorghum
bicolor (L.) Moench] GENOTYPES IN ETHIOPIA
A Thesis Submitted to the Postgraduate Program Directorate
(School of Plant Sciences)
HARAMAYA UNIVERSITY
In Partial Fulfilment of the Requirements for the Degree of
MASTER OF SCIENCE IN AGRICULTURE
(PLANT BREEDING)
By
Abiy Legesse Kibebe
October2015
Haramaya University
HARAMAYA UNIVERSITY
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Postgraduate Program Directorate
We hereby certify that we have read and evaluated this Thesis titled ̔ Genotype X Environment
Interaction and Stability of Early Maturing Sorghum (Sorghum bicolor (L.) Moench) Genotypes
in Ethiopia’ prepared under our guidance by Abiy Legesse. We recommend that it be submitted
as fulfilling the thesis requirement.
Firew Mekbib (PhD)
Major Advisor
Asfaw Adugna (PhD)
Co-Advisor
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the candidate. We recommend that the thesis be accepted as fulfilling the Thesis requirement for
the degree of Master of Science in Agriculture (Plant Breeding).
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STATEMENT OF AUTHOR
By my signature below, I declare and affirm that this thesis is my own work. I have followed all
ethical and technical principles of scholarship in the preparation, data collection, data analysis
and compilation of this thesis. Any scholarly matter that is included in the thesis has been given
recognition through citation.
This thesis is submitted in partial fulfillment of the requirements for the award of MSc degree in
Plant Breeding at Haramaya University. The thesis is deposited in the Haramaya University’s
Library and is made available to borrowers under the rules of the library. I solemnly declare that
this thesis has not been submitted to any other institution anywhere for the award of any
academic degree, diploma or certificate.
Brief quotations from this thesis may be used without special permission provided that accurate
and complete acknowledgement of the source is made. Requests for permission for extended
quotations from, or reproduction of, this thesis in whole or in part may be granted by the Head of
the School or Department when in his or her judgment the proposed use of the material is in the
interest of scholarship. In all other instances, however, permission must be obtained from the
author of the thesis.
Name: Abiy LegesseSignature: ––––––––––––––––––––
Date: October 2015
School: Plant Science, Haramaya University
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ACRONYMS AND ABBREVIATIONS
AMMI
Additive Main effect and Multiplicative Interaction
ANOVA
Analysis of Variance
ASV
Additive Main effect and Multiplicative Interaction Stability Value
CSA
Central Statistical Agency
DF
Degree of Freedom
EMSG
Early Maturing Sorghum Genotype
GE
Genotype by Environment
GEI
Genotype by Environment Interaction
ICRISAT
International Crop Research Institute for Sami-Arid Tropics
IPCA
Interaction Principal Component Analysis
MET
Multi-environment Trial
MS
Mean Squares
PCA
Principal Component Analysis
RCBD
Randomized Complete Block Design
SAS
Statistical Analysis System
SS
Sum of Squares
v
BIOGRAPHICAL SKETCH
The author, Abiy Legesse Kibebe, was born in West Shoa Zone, Ambo town on May 1983. He
attended his elementary education at Ambo Betekhnet Elementary School, and his junior and
secondary school education at Ambo Senior Secondary School. He joined the Ambo College of
Agriculture and graduated with diploma in General Agriculture in 2005. He began his first
degree study at Ambo University directly after completion of his diploma and completed with
BSc degree in Crop Production in March 2009.
Following his BSc Degree graduation, he was employed in Agricultural Office of North Shoa
Zone, as a seed science expert at Basonaworana Woreda for two months. Then, he joined
Amhara Agricultural Research Institute at Debre Brihan Agricultural Research Center as a Junior
Researcher I in June 2009, and worked there for four years and three months. In October 2006,
he joined Haramaya University, Postgraduate Program Directorate to follow MSc Degree in
Plant Breeding.
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ACKNOWLEDGMENTS
I thank Amhara Agriculture Research Institute (ARARI) for providing the opportunity and funds
for the study. I wish to express my deepest appreciation to my research advisors, Dr. Firew
Mekbib and Dr. Asfaw Adugna for their genuine guidance, provision of references and
constructive comments and encouragements to finalize the research work on time. I would like to
extend my special thanks to Dr. Ribka G/Tsadek and Mr. Daniel Admasu for their valuable
comments and edition of this manuscript.
I am highly indebted to all the sorghum improvement program staff members of Errer
Agricultural Research Sub-center, Sirinka Agricultural Research Center, Mieso Agricultural
Research Sub-center, Debre Brihan Agricultural Research Center and Melkassa Agricultural
Research Center for their assistance in field management, data collection and moral support. I
also extend my thanks to all staff members of Debre Brihan Agricultural Research Center who
directly or indirectly helped me during the implementation of the work, and especially to Mr.
Getaw Cheregn.
Last but not least, I would like to express my love and great thanks to my mother, father and
sisters, my wife Teklil Alemayehu including all her amazing families and all my friends who
gave courage and hospitality to me during the study period.
vii
TABLE OF CONTENT
STATEMENT OF AUTHOR
iii
ACRONYMS AND ABBREVIATIONS
iv
BIOGRAPHICAL SKETCH
v
ACKNOWLEDGMENTS
vi
TABLE OF CONTENT
vii
LIST OF TABLES
ixx
LIST OF TABLES IN THE APPENDICES
xx
ABSTRACT
xi
1. INTRODUCTION
1
2. LITERATURE REVIEW
4
2.1.Origin and Adaptability of Sorghum
4
2.2.Botany and Taxonomy of Sorghum
4
2.3.Constraints of Sorghum Growth and Development
5
2.4.Genotype x Environment Interaction
6
2.5.Concept of Stability
9
2.6.Genotype x Environment Interaction and Stability Analysis
10
2.6.1. Regression Coefficient and Deviation Mean Square
11
2.6.2. Additive Main Effect and Multiplicative Interaction (AMMI)
13
2.6.3. Additive Main Effect and Multiplicative Interaction Stability Value
16
3. MATERIALS AND METHODS
18
3.1.Description of the Study Area
18
3.2.Plant Materials
18
3.3.Experimental Design and Crop Management
19
3.4.Data Collection and Sampling Techniques
20
3.5.Data Analyses
21
3.5.1.Analysis of Variance for Each Location and Combined Over Locations
21
Continued
3.5.2.Stability Analysis
3.5.2.1.Eberhart and Russell’s joint regression model
22
22
3.5.2.2.The additive main effects and multiplicative interaction (AMMI) method 22
3.5.2.3.AMMI’s stability value (ASV)
4. RESULTS AND DISCUSSION
4.1.Analysis of Variance
23
24
24
4.1.1Single Location ANOVA
24
4.1.2Combined Analysis of Variance
25
4.2.Genotypes Mean Performance
28
4.3.Genotype x Environment Interaction Analysis of Variance
35
4.3.2. Genotype x Environment Interaction Analysis of Variance by AMMI Model
4.4.Stability Analysis
37
38
4.4.1. Stability Analysis by Eberhart and Russel's Model
38
4.4.2. Yield Stability Using ASV
40
5. SUMMARY AND CONCLUSIONS
43
6. REFERENCES
46
7. APPENDIX
58
ix
LIST OF TABLES
Table
Page
1
18
Agro-ecological features of the experimental locations
2
19
Description of sorghum genotypes tested at four locations during 2014 main
cropping season
3
Analysis of variance for days to emergence, flowering, and to maturity, plant
24
height, stand count at harvest, grain yield, grain filling period; and grain filling
rate of twenty two early maturing sorghum genotypes tested at four locations
during 2014 cropping season
4
Combined ANOVA for grain yield (ton/ha) and the percentage sum of squares
26
of the twenty two early maturing sorghum genotypes tested at four locations
5
Mean squares and coefficient of variations of yield, plant height; phenological
27
traits and grain filling rate of twenty two early maturing sorghum genotypes
tested at four locations during the 2014 main cropping season
6
Means for days to emergence, grain yield (ton/ha) and grain filling rate of
29
Early Maturing Sorghum Genotypes tested at four locations
7
Mean plant height, stand count at harvest and grain filling period of twenty
33
two early maturing sorghum genotypes tested at Errer, Kobo, Mieaso and
Shewa Robit during the 2014 main cropping season
8
Means for days to emergence, flowering and maturity, plant height, stand
34
count at harvest, grain yield, grain filling period and grain filling rate of twenty
two early maturing genotypes at Errer, Kobo, Mieso and Shewa Robit
9
Analysis of Variance by Eberhart and Russel's Model of early maturing
36
sorghum genotypes on mean grain yield (ton/ha) tested at four locations.
10
AMMI analysis of variance for grain yield (ton/ha) of early maturing sorghum
37
genotypes tested at four locations during the 2014 main cropping season.
11
Eberhart and Russell’s (1966) stability parameters of early maturing sorghum
39
genotypes tested at four locations.
12
IPCA1 and IPCA 2 scores; and ASV for the twenty two early maturing
sorghum genotypes sorted on mean yield (ton/ha) evaluated at four locations
47
x
LIST OF TABLES IN THE APPENDICES
Page
Appendix
1
Mean value of yield (ton/ha), phenological traits and grain filling rate of
56
early maturing sorghum genotypes for the data collected at Errer during the
2014 main cropping season.
2
Mean value of grain yield (ton/ha), phenological traits and grain filling rate
57
of early maturing sorghum genotypes for the data collected at Kobo during
the 2014 main cropping season.
3
Mean value of grain yield (ton/ha), phenological traits and grain filling rate
58
of early maturing sorghum genotypes for the data collected at Mieso during
the 2014 main cropping season.
4
Mean value of grain yield (ton/ha), phenological traits and plant height of
59
early maturing sorghum genotypes for the data collected at Shewa Robit the
during 2014 main cropping season.
5
Means for phenological traits and plant height and of early maturing
60
sorghum genotypes tested at four locations during the 2014 main cropping
season.
6
The interaction principal component analysis 1 and 2 scores for the four
61
sites, sorted on environmental mean yield
7
Total monthly rainfall (mm) and mean monthly temperature (0C) of the four
test locations during the main cropping season
61
xi
Genotype x Environment Interaction and Stability of Early Maturing Sorghum [Sorghum bicolor
(L.) Moench] Genotypes in Ethiopia
ABSTRACT
The yield performance of sorghum cultivars is highly influenced by environmental factors and
genotype x environment interaction; therefor interaction is the major concern to plant breeders
to develop improved cultivars. Twenty two early maturing sorghum genotypes were evaluated at
four locations using randomized complete block design with the objectives of estimating the
magnitude and nature of genotype x environment interaction for grain yield and other traits, and
to determine the stability of genotypes for grain yield in lowland areas of Ethiopia.
Phenological, plant height; grain yield, grain filling rate and stand count at harvest data were
recorded. The combined analysis of variance revealed that the significant effect of locations on
all the measured traits, while the interaction is significantly influences grain yield, grain filling
rate and days to emergence. This showed that genotypes were inconsistent for grain yield across
the testing locations. Joint linear regression analysis of variance revealed that the genotype x
environment interaction was non-linear type and the pooled deviations were highly significant
against pooled error. Genotype 2001 MS 7003, 2001 MS 7015, 2005 MI 5060 and 2005 MI 5066
were relatively stable and high yielders. The AMMI analysis of variance showed that the
environment, genotype and interaction sum squares contributed 74.19 %, 6.86 % and 18.98 % to
the treatment sum squares for grain yield respectively.In addition the first two IPCAs and
interaction residual are significant. The first two IPCAs accounted for a total of 78.60 % of the
interaction sum square. Due to the significant result of the interaction residual AMMI one and
two biplot are not necessary. In both the genotype x environment interaction ANOVA,the results
indicating the observed yield variation among genotypes were due to location and interaction
rather than differences of genetic potential of genotypes. Results ofASV parameter showed the
five most widely stable and high yielder genotypes are 2001 MS 7015, 2005 MI 5064, 2005 MI
5066, 2001 MS 7007 and 2001 MS 7003. Genotype 2001 MS 7003, 2001MS 7015 and 2005 MI
5060 are selected with both stability parameters as a high yielder and stable, and therefore, are
the promising materials. Generally, this study showed the importance of testing early maturing
sorghum genotypes for their yield and stability across diverse lowland areas of Ethiopia.
Key words: AMMI, ASV, early maturing, Joint linear regression, lowland, sorghum
1. INTRODUCTION
Sorghum [Sorghum bicolor (L.) Moench] belongs to the family Poaceae, and the genus
Sorghum. It is principally a self-pollinating short day cereal, grown mainly for its grain.
Depending on the genotype, panicle type, and wind direction and velocity the degree of out
crossing reaches up to 30 % (Perlman and Sleeper, 1995). Domesticated sorghum is a diploid (2n
= 2x = 20) C4 grass with a high photosynthetic efficiency and is a tropical origin. Sorghum
originated in Africa, more precisely in Ethiopia, between 5000 and 7000 years ago (ICRISAT,
2005). Ethiopia is rich in different races of wild and cultivated sorghums. Nowadays, it is widely
cultivated in different parts of Ethiopia. Firew (2009) states that Ethiopia is the primary center of
origin and hence, center of diversity for sorghum. Sorghum is now widely found in the dry areas
of Africa, Asia, Americas and Australia (Dickonet al., 2006).
Sorghum is an important staple food crop for millions of people and animal feed across the
world. Currently, large part of sorghum production areas in Ethiopia fall under the arid and semiarid regions that are characterized by high rainfall variability and low soil water storage capacity.
In these areas, sorghum is grown as one of the major food cereals. Sorghum grain has high
nutritive value, with 70-80 % carbohydrate, 11-13 % protein, 2-5 % fat, 1-3 % fiber, and 1-2 %
ash (Prasad and Staggenborg, 2009). It has been utilized in various forms such as for making
local bread (Injera) and for preparation of local alcoholic beverages (tela and areke). In addition,
sorghum stalks and leaves are an increasingly important source of dry season feed for livestock,
source of energy for cooking their daily foods, and as housing and fencing material
In Ethiopia, grain crops constitute the majority of the annual total agricultural crop production at
the country level. Total area coverage and production of grain crop in the country covers about
12.41 million hectares and 251, 536, 62.390 ton, respectively. Out of the total grain crop area,
79.38 % (9,848,745.96 hectares) was under cereals. From the total area of grain crops sorghum
(13.52 %) is the third widely cultivated cereal after tef (24.31 %) and maize (16.08 %). Sorghum
(15.22 %) is forth in its production after maize (25.81 %), tef (17.57 %), and wheat (15.60%).
Oromia, Amhara and Tigray regions are the major three sorghum producers in the country. Out
of the total sorghum area harvested in 2014 main cropping season, Oromia region accounts 39.92
% (669,575.97 hectares), Amhara and Tigray regions contributed 33.31 % (558,827.95 hectares)
2
and 12.82 % (215,111.82 hectares), respectively. The large share of national sorghum production
was from Oromia that is about 43.72 % (16, 739, 20.87 ton), Amhara 29.64 % (11, 350, 11.41
ton) and Tigray 14.27 % (5, 463, 22.53 ton). From Oromia region Eastern and Western Hararge,
and from Amhara region South Welo and North Shewa are among the major producers, that
covers 114,028.52, 123,897.29, 43,260.60 and 137,037.07 hectares of land; and 257,652.55,
310,036.50, 109,145.47 and 313,470.13 ton of sorghum production, respectively. The national
sorghum production is still low (2.28 ton/ha) and in major sorghum growing regions Oromia 2.5
ton/ha, Tigray 2.54 ton/ha and Amhara 2.03 ton/ha were obtained (CSA, 2014).
In Ethiopia sorghum is predominantly cultivated in dry areas that cover nearly 66% of the
total area of the country, sorghum production in this area is mainly dependent on seasonal
rainfall; its production is being limited mainly by water stress due to low and variable rainfall
between and with the seasons (Geremew et al.,2004). In these areas there is variability among
the growing environments and genotypes are performing differently in different environments
(Asfaw, 2007). Limitation of improved high yielding and stable varieties in these parts of the
country are considered as one of the major constraints of sorghum production.
In multi-environment trials the phenotype of an individual in each test environment is a measure
of an environment main effect, a genotype main effect, and the genotype by environment
interaction (GEI) (Yan and Tinker, 2005). GEI results from a change in the relative rank of
genotype performance or a change in the magnitude of differences between genotype
performances from one environment to another. GEI affects breeding progress because it
complicates the demonstration of superiority of any genotype across environments and the
selection of superior genotypes (Ebdon and Gauch, 2002).Genotype x environment interaction is
the major concern to plant breeders for developing improved cultivars. Traits that are of
economic relevance may be related to polygenic characteristics, and influenced by environment.
Typically, environment explains 80 % or higher of the total yield variation; however, it is
genotype and genotype x environment interaction that are relevant to cultivar evaluation (Yan et
al., 2002). The GE interaction reduces the correlation between phenotype and genotype and
selection progress.
The yield performance of sorghum genotypes in different lowland environments of Ethiopia is
not always the same; it is highly influenced by environmental factors. In sorghum breeding
3
programs it is difficult to select genotypes that produce high yields and stable in multi-location
trials (Asfaw, 2007). The occurrence of environmental causes of variation over the genetic
effects does not suggest that the importance of genotype should be minimized ( Faisal and
Aisha, 2011). So that a considerable attention should be given to the effect of GEI in the
plant breeding programs (Ghazy et al. 2012).
In the presence of GEI, one of the options open to the breeder is to use stability analyses to
identify the most high yielding and stable sorghum genotype. The stability of a cultivar refers to
its consistency in performance across environments and affected by the presence of GEI. For a
genotype to be released as a variety for cultivation, stability of performance is one of the most
desirable properties. Yield stability is a complex product of genetic yield potential and that
would mean minimum variation among environments for a particular genotype (Chahal and
Gosal, 2002). There are remarkable inconsistencies with the univariate stability estimates, which
create difficulty in recommendation of cultivars for production (Asfaw, 2007). However, the
multivariate approach, the additive main effect and multiplicative interaction (AMMI) model is
better for partitioning the GEI into the causes of variation (Asfaw, 2007).
Genotype x environment interaction for yield has been studied in several crops. Several research
institutions are actively working and able to screen out sorghum varieties for yield that resist
harsh environmental conditions and produce consistently better grain yield. Hence, the national
and regional sorghum improvement programs have been released a number of stable early
maturing sorghum genotypes for the moisture deficit lowland areas of Ethiopia. However,
information on the effect of GEI for the yield performance of early maturing sorghum genotypes
under different Ethiopian low land growing conditions is limited. Hence, the objectives of the
present study were to:
1. Assess the magnitude and nature of GEI for grain yield and related traits, and
2. Determine the stability of early maturing sorghum genotypes for grain yield at lowland
areas of Ethiopia.
4
2. LITERATURE REVIEW
2.1.
Origin and Adaptability of Sorghum
Sorghum is one of the crops for which Ethiopia has been credited as being a Vavilovian center of
origin (Vavilov, 1951) and/or diversity (Dillon et al., 2007). Ethiopia is one of the Vavilovian
centers of origin/diversity for sorghum (Vavilov, 1951). Sorghum originated in Africa, more
precisely in Ethiopia, between 5000 and 7000 years ago (ICRISAT, 2005). From there, it was
distributed along the trade and shipping routes around the African continent, and through the
Middle East to India at least 3000 years ago. It then journeyed along the Silk Route into China
(Dicko et al., 2006). It was first taken to North America in the 1700-1800's through the slave
trade from West Africa and was re-introduced in Africa in the late 19th century for commercial
cultivation and spread to South America and Australia.
Sorghum is now widely found in the dry areas of Africa, Asia (India and China), the Americas
and Australia (Dicko et al., 2006). It is an economically, socially and culturally important crop
grown over a wide range of ecological habitats in Ethiopia, in the range of 400-3000 m.a.s.l
(Teshome et al., 2007). Sorghum is the single most important cereal in the lowland areas because
of its drought tolerance (Kebede, 1991).
2.2.
Botany and Taxonomy of Sorghum
The genus Sorghum has been classified into five subgenera: Eu-sorghum,Chaetosorghum,
Heterosorghum, Para-sorghum and Stiposorghum. Although this classification is convenient,
however it does not stand for evolutionary relationships (Dillon et al., 2004). The Eu-sorghum
comprises the cultivated species S. Bicolor (L.) Moench and its subspecies are drummondii,
arundinaceum, and wild species includes S. xalum Parodi, S. halepense (L.) Pers. and S.
propinquum (deWet, 1978). The Eu-sorghum section is originated from Africa or Asia Doggett
(1976), DuVall and Doebley (1990). Sections Chaetosorghum and Heterosorghum consist of S.
macrospermum and S. Laxiflorum and both of these species are annuals and polyploids
(Lazarides et al., 1991). Section Stiposorghum includes ten species (Lazarides et al. 1991). Parasorghum Section is comprised seven African, Asian, Australian and Central American species.
The basic number of chromosome of species in each section is five. The species belong to Para
sorghum and Stiposorghum are mostly diploid (2n = 20), however a few species are tetraploid or
hexaploid.
5
Sorghum includes three species, S. halepense, S. propinquum and S. bicolor. Sorghumhalepense
is also known as Johnson grass, derived from a natural cross between S. arudinaceum and S.
propinquum (Doggett, 1976). Sorghum propinquum is a perennial species related to S. bicolor
(Sun et al., 1994). By using Harlan and deWet‘s system which is based on spikelet morphology,
Sorghum bicolor has been classified into five races. The five basic races of Sorghum bicolor are
bicolor, guinea, caudatum, kafir and durra; and ten intermediate races under S. bicolor. It is a
cereal of a remarkable genetic variability; with more than 30,000 selections present in the world
genetic collections (Assefa and Staggenborg, 2010). Most of the tropical sorghums are short day
plants and their response to day length is an important adaptation (Prasad and Staggenborg,
2009).
Grain sorghum belongs to the family of Poaceae, tribe Andopogoneae, sub-tribe Sorghinae, and
genus Sorghum. In 1794, Moench established the genus Sorghum and brought the sorghums
under the name S. bicolor. All cultivated sorghum belongs to Sorghum bicolor subsp. bicolor
(Dicko et al., 2006).
2.3.
Constraints of Sorghum Growth and Development
The productivity of grain sorghum is influenced by several abiotic and biotic constraints. Among
the abiotic yield constraints (water, temperature and nutritional stresses), water will likely the
primary yield constraint throughout the semi-arid tropics in the coming years (Ryan and Spencer,
2001). In addition, Assefa et al., 2010 reported, plant-available water, soil water content at
planting, growing–season rainfall amount and distribution, crop management practices, and other
climatic conditions highly affect the production of grain sorghum. Major biotic constraints to
sorghum production include shoot fly, stem borer, head bug and aphid insect pests; grain mold
anthracnosis, leaf blight and charcoal rot diseases; weed competition and the parasitic weed.
Jones and Johnson (1991) reported that effect of stress due to environmental factors on final
yield may depend upon the growth stage in which it occurs. Water stress has diverse effects on
physiology and development of sorghum that determines its final yield depending on the
development stage at which stress occurs.
Assefa et al. (2010) found a 36 % sorghum yield reduction when water stress occurred during
the vegetative stage. Prasad et al. (2008) reported lowered yield due to heat and drought stress
6
occurring during flowering and anthesis. More than 55 % yield reduction with water stress
occurring during the reproductive stage Assefa et al. (2010). At this stage the yield reduction was
caused by the failure of fertilization because of the impairment of pollen and ovule function.
Moisture stress early in the season will limit panicle size and delay maturity. If the stress occurs
later in the season, the seed size is greatly reduced Prasad et al. (2008). In addition, temperature
stress can delay flowering, reduce stem and root growth, plant height, pollen and ovule viability,
pollen number, stigma receptivity, seed number, seed filling duration, thus yield (Prasad and
Staggenborg, 2009).
2.4.
Genotype x Environment Interaction
Genotype x environment interactions (GEI) is of major concern to plant breeders for developing
improved cultivars. It refers to the differential responses of different genotypes across a range of
environments. The phenotype of an individual is determined by the effects of its genotypes (G),
the environment (E) surrounding it, and the interaction between the genotype of the individual
and the environment (Yan and Tinker, 2005).
Genotype x environment interactions is commonly observed by crop producers and breeders as
the differential ranking of cultivar yields among locations or years. Quantitative traits, those
which are controlled by several genes, are highly influenced by environmental factors. Most
agronomically and economically important traits, such as grain yield, are quantitative or
multigenic in nature. Experiments for such type of traits in single environments do not allow the
drawing of general conclusions regarding the tested genotypes; therefore yield trials should
typically be done on a number of varieties in a number of environments (reference).
With regard to the comparison of plant materials in a set of multi-environment trial, the term
genotypes refers to a cultivar with materials genetically homogeneous, such as pure lines or
clones, or heterogeneous, such as open-pollinated population rather than to an individual’s
genetic make-up. The term environment relates to the set of climatic, soil, biotic and
management conditions in an individual trial carried out at a given location in one year or over
several years. Purely the environmental effect, reflecting the different ecological potential of
locations and management conditions, are not of direct concern for the breeding or
recommendation of plant varieties. Genetic main effects provide the only information when GEI
effects are absent. However, differences between genotypes may vary widely among
7
environments in the presence of GEI effects. In general, GEIs are considered as a hindrance to
crop improvement in a target region (Kang, 1998).
Comstock and Moll (1963) classified environment into macro-environment and microenvironment. Micro-environment is environment of a single plant, which is made up of all the
things, other than genotype of a plant, which influence its development. Environments, that is
potential or experienced within a given area and period of time, are collectively known as macroenvironment. A macro-environment is a population of micro-environments. The environment
that organisms experience in one area as compared to the other in one period of time than in
another is not the same.
In order to distinguish among environmental source of variation that contributes to the GEI,
Allard and Bradshaw (1964) created two terms as predictable and unpredictable environmental
variation. Predictable variation includes all the permanent characters of environment such as
general features of climate and soil type as well as those characters of environment that fluctuate
in a systematic manner, such as the day-length. It also includes those aspects of environment
which are determined by man and can, therefore, be fixed more or less at will, like planting date,
and sowing density. The unpredictable variation includes fluctuations in weather, such as amount
and distribution of rain fall and temperature.
The phenotype of an individual is determined by both the genotype and the environment; these
two effects are not always additive which indicates that GEI, are present. The GEIs result in
inconsistent performances between the genotypes across environments. Significant GEI results
from the changes in the magnitude of differences between genotypes in different environments
or changes in the relative ranking of the genotypes Falconer (1952), Fernandez (1991).
According to Baker (1990) and Cornelius et al. (1996) GEIs have been grouped in to crossover
and non-crossover interactions.
The differential response of cultivars to diverse environments is referred to as a crossover
interaction when cultivar ranks change from one environment to another. A main feature of
crossover interaction is intersecting lines in a graphical representation. If the lines do not
intersect, there is no crossover interaction (Kang, 1998). Non-crossover (quantitative)
interactions represent changes in magnitude of genotype performance, but rank order of
genotypes across environments remains unchanged, i.e., genotypes that are superior in one
8
environment maintain their superiority in other environments. Non-crossover interactions may
mean that genotypes are genetically heterogeneous but test environments are more or less
homogeneous or that genotypes are genetically homogeneous but environments are
heterogeneous.
In crop breeding, the crossover interaction is more important than non-crossover interaction
(Baker, 1990). Since, the presence of a crossover interaction has strong implications for breeding
for specific adaptation, it is important to assess the frequency of crossover interactions (Singh et
al., 1999). Different agricultural researches have been conducted in multi-location experiments,
and in most of these experiments the mean square for GEI source of variation showed significant
differences and the rank order of genotypes tested are different from one environment to the
other.
Genotype x environment interaction is one of the main complications in the selection of broad
adaptation in most breeding programs. The phenotype of an organism is determined by the
combined effect of the environment and the genotype which interact with one another. Numerous
studies have shown that booth environmental and genetic factors are the cause for the interaction,
but in some studies the large difference of genotypes or environments has been the real cause of
the interaction Hagos and Fetien (2011), Mahnaz et al. (2013), Sewagegne et al. (2013).
Domitruk et al. (2001) indicated that the analysis of variance procedure is a useful tool for
estimating the existence and magnitude of GEI. In the multi environment trial, the combined
analysis of variance is useful for estimating variance components related to different sources of
variation, including genotypes, environment and GEI. In MET, environment explains 80 % or
higher of the total yield variation (Yan andHunt, 2002). The environment factors that are
contributing to the differences in mean grain yield across environments and years may include
soil types, sowing dates, sunshine hours and amount of rainfall during the crop cycle
(Dagnachew et al., 2014). Different authors have conducted their experiment on different crops
and as they have reported in a multi environment trial for yield, the total variation of the
contribution of environmental sum square takes the largest share Asfaw (2007) and Vangge et al.
(2014) on sorghum genotypes; Muez et al. (2014) on malt barely; Akcuraet al. (2006) on durum
wheat;Shrestha et al. (2012) on maize; Dagnachew et al. (2014) on triticale.
9
The effects of genotype and environment on sorghum grain were investigated using 15 sorghum
genotypes grown during three years (2003–2005) at three different locations (Melkasa, Kobo,
and Mieso) to investigate the effect of GEI on sorghum yield performance in the drought stressed
parts of Ethiopia (Asfaw, 2007). The study results revealed that the environments and genotypes
were diverse. The performance of genotypes in the various environments was different. The
contribution of genotypes, environments and GEI were 5.9 %, 73.8 % and 20.3 % of the total
sum of squares, respectively. The large sum of squares for environments indicated that the
environments were diverse, with large differences among environmental means causing most of
the variation in grain yield. The magnitude of the GEI sum of squares was 3.41 times larger than
that of the genotypes, indicating, that there were substantial differences in genotype response
across environments.
Similarly, different authors have also conducted a multi environment trial for yield in the world
to improve the yield potential and stability of crops. In most of the trials, the results revealed
that, the yield potential of genotypes have been significantly affected by their genetic capability,
environmental variation of the growing area and the interaction of the two Vangge et al. (2014);
Muez et al. (2014); Akcura et al. (2006); Yonas (2014). However, some of the others have got
significant effects the growing environments and GEI, but genetically genotypes variation was
insignificant Jipan (2013); Dagnachew et al.(2014).
2.5.
Concept of Stability
The term stability is sometimes used to characterize a genotype, which shows a relatively
constant yield, independent of change in environmental conditions. On the basis of this idea,
genotypes with a minimal variance for yield across different environments are considered to be
stable (Sabaghnia et al., 2006). The basic cause of differences between genotypes in their yield
stability is the wide occurrence of GEI, which means that the ranking of the genotype depends on
the particular environmental conditions where it is grown.
Different concepts and definitions of stability have been described over the years (Lin et al.,
1986; Becker and Leon, 1988). Two basic phenotypic stability concepts are distinguished as the
biological or static concept, and the agronomic or dynamic concept. The biological concept of
stability refers to the constant performance of a genotype over a wide range of environments.
According to Becker and Leon (1988), in biological stability,a genotype possesses unchanged
10
performance regardless of variation of the environments, thus, implying that its variance among
environments is zero. On the other hand, dynamic stability, also termed as agronomical concept
of stability, implies that a stable genotype should always give high yield expected at the level of
productivity of the respective environments, which means thata variety with GEI as small as
possible (Becker, 1981).
Becker and Leon (1988) stated that all stability procedures based on quantifying GEI effects
belong to the dynamic stability concept. This includes the procedures for partitioning the GEI of
Wricke’s (1962) ecovalence and Shukla’s (1972) stability of variance, procedures using the
regression approach such as proposed by Finlay and Wilkinson (1963), Eberhart and Russell
(1966) and Perkins and Jinks (1968), as well as non-parametric stability statistics.
Lin et al. (1986) identified three concepts of stability: Based on type one stability concept a
stable genotype possesses an unchanged performance regardless of any variation of the
environmental conditions. Parameters used for this type of stability are coefficient of variability
used by Francis and Kannenburg (1978) for each genotype as a stability parameter and the
genotypic variances across environments. Becker and Leon (1988) called this stability a
biological concept of stability. Type two stability concepts select a stable genotype, if a genotype
has no deviations from the general response to environments and thus permits a predictable
response to environments. A regression coefficient by Finlay and Wilkinson (1963) and Shukla
(1972) stability variance can be used to measure this type of stability. Becker and Leon (1988)
called this stability agronomic concept of stability. Type three stability concept refers to a
genotype that has a small mean deviation. Therefore a genotype is considered to be stable if the
residual mean square from the regression model on the environmental index is small. Breeding
for broad adaptability requires a different interpretation and approach to the stability analysis
procedure than breeding for specific adaptability. According to Becker and Leon (1988) this is
part of the agronomic stability concept. Methods to describe this type of stability are the methods
of Eberhart and Russell (1966) and Perkins and Jinks (1968).
2.6.
Genotype x Environment Interaction and Stability Analysis
Selection for superior genotypes based on yield per se at a single location in a year may not be
very effective (Shrestha et al., 2012). Thus, evaluation of genotypes for stability of performance
under varying environmental conditions for yield has become an essential part of any breeding
11
program. Several methods have been proposed to analyze GEI and phenotypic stability
including: Finlay and Wilkinson (1963) regression coefficient; Eberhart and Russell (1966)
regression coefficient and deviation from regression; Pinthus (1973) coefficient of determination;
Wricke (1962, 1964) ecovalence; Shukla (1972) stability variance parameter; Multivariate
analysis methods (Principal component analysis, Principal coordinate analysis , Factor analysis,
cluster analysis and Additive main effects and multiplicative interaction (AMMI).
2.6.1. Regression Coefficient and Deviation Mean Square
Joint linear regression is a model used for analyzing and interpreting the non-additive structure
(interaction) of two-way classification data. In the model proposed by Eberhart & Russell
(1966), sum of the mean square attributable to environments and GEI are partitioned into
environments (linear), GE (linear) and deviation from regression (pooled deviation over all the
genotypes). This model uses the marginal means of the environments as independent variables in
the regression analysis and restricts the interaction to additive form. The method divides the (g1) (e-1) degree of freedom for interaction into g-1 degree of freedom for heterogeneity among
genotype regressions and the remainder (g-1) (e-2) for deviation.
Eberhart and Russell’s defined a stable genotype as one with average response to the
environment. They further said that a large GEI limits progress from selection, and to reduce
this, the environments have to be stratified to make them more similar. In their study, they found
that GEI was large, and they decided to select stable genotypes that interact less with the
environments in which they were grown, and used only the more stable genotypes for the final
stages of testing.
Eberhart & Russell’s model portioned the interaction sum of squares into predictable (linear) and
unpredictable components to characterize adaptation of the different genotypes across different
environments. A regression coefficient approximating one coupled with deviation from
regression of zero indicates average stability (Eberhart and Russell, 1966). Regression
coefficient was considered as an indication of the response of the genotype to varying
environment. If the regression coefficient is not significantly different from unity, the genotype
is adapted to all environments. Regression coefficient values above one describe genotypes with
higher sensitivity to environmental change (below average stability) and greater specificity of
adaptability to high yielding environments. A regression coefficient below one (regression
12
coefficient values is negative) provides a measurement of greater resistant to environmental
change (above average stability), and thus increases the specificity of adaptability to poor
environments.
In Eberhart and Russell’s model, when the mean square for pooled deviation is significant but
mean square for GE (linear) is non-significant, variation in the performance of genotypes is
entirely unpredictable. On the other hand, significance of mean squares for pooled deviations,
when mean square for GE (linear) is also significant, implies that part of the variability is
unpredictable in nature. Generally, significance of pooled deviation from zero will invalidate the
linear prediction. But, if the pooled deviation is non-significant, the performance of a genotype
for a given environment may be predicted. Accordingly, a variety whose performance can be
predicted (𝑖. 𝑒. , Sd2𝑖 = 0) is said to be stable.
In another study, Kenga et al. (2003) conducted the multi environment trial on sorghum hybrids
and parental lines, and obtained significant mean square due to environment (linear), significant
G x E (linear) interaction, and also significant pooled deviations from regressions. Therefore, the
fluctuation in performance of genotypes grown in various environments is not fully predictable.
In addition to this, they observed that the large portion of the sum of squares of GEI effects was
accounted for by the deviations from regression than linear regression. Therefore, he noted that
the magnitudes of GEI effects in this set of materials are largely due to differential non-linear
responses of genotypes to varying environment; thus Sd2𝑖 parameters become important.
Contrary to the above, Akcura et al. (2006) tested the mean squares of linear and non- linear
against pooled error mean squares for grain yield of durum wheat. The result of the test
prompted him to say that the linear component was highly significant, indicating that the
predictable components shared GEI. Preponderance of linear GEI is of great practical
importance, implying that there are differences among linear regression coefficients for each
genotype. According to Eberhart and Russell (1966), mean squares of linear have to be tested
against the pooled deviation if and only if the pooled deviation against the pooled error is
significant. Otherwise, the mean squares of the linear part of the interaction are tested against the
pooled error as they did.
13
Becker and Leon (1988) the result of the analysis could be non-linear type of interaction, because
of insignificant GE (linear), reflecting lack of genetic differences among genotypes for their
response to varying environments. While pooled deviations were highly significant against
pooled error they show that the differences in stability were due to deviation from linear
regression only Khan et al.(1988) on sorghum; Ashraf et al. (2001) on wheat. In these situations,
the above method detect the most suitable and stable varieties over different environments based
on bi value of genotypes which is almost near to unity, non-significant deviation from regression
and above average grain yield of genotypes.
2.6.2. Additive Main Effect and Multiplicative Interaction (AMMI)
Analysis of variance (ANOVA) is merely an additive model in which the GEI is a source of
variation, but its key effects are not analyzed. In contrast, principal component analysis (PCA) is
a multiplicative model and, therefore, does not present additive main effects for the environment
nor genotype. However, the newly developed AMMI analysis includes ANOVA and PCA in a
unified approach that can be used to analyze multiple yield trials Kang and Gauch (1996);
Oliveira et al. (2014); Zobel et al. (1988). AMMI can treat both the additive main effect and
multiplicative interaction component employing the ANOVA and IPCA, respectively (Gauch
and Zobel, 1996). AMMI uses ANOVA to test the main effects of genotypes and environments,
and PCA to analyze the residual multiplicative interaction between genotypes and environments
to determine the sum of squares of the GEI, with a minimum number of degrees of freedom.
Because ANOVA and PCA are parts of the AMMI model, this model is likely more suitable for
characterizing the GEI (Zobel et al., 1988).
Furthermore, AMMI biplot analysis is considered as an effective tool to diagnose GEI patterns
graphically Gauch and Zobel (1996); Thillainathan and Fernandez (2001); Yuksel et al. (2002).
AMMI quantifies the contribution of each genotype and environment to the sum square of GEI,
and provides an easy graphical interpretation of the results, by the biplot technique that
simultaneously classifies genotypes and environments Kempton (1984); Zobel et al. (1988).
Therefore, with this technique, one can readily identify productive cultivars with wide
adaptability or mega environments, as well as delimit the agronomic zoning of cultivars with
specific adaptability and identify environments in which to conduct tests Kempton (1984).
14
The biplot display of PCA scores plotted against each other provides visual inspection and
interpretation of GEI components. Mixing biplot display and genotypic stability statistics enable
genotypes to be grouped based on similarity of performance across diverse environments
(Thillainathan and Femandez, 2001). It also clearly separates main and interaction effects that
present agricultural researchers with different kinds of opportunities, and the model provides
agronomically meaningful interpretation of the data (Ebdon and Gauch, 2002). The results of
AMMI analysis are useful in supporting breeding program decisions such as specific and broad
adaptation, and selection of environment (Gauch and Zobel, 1997).
There are several AMMI models characterized by number of significant PC axis ranging from
zero (AMMI-0, i.e. additive model) to a minimum between (g – 1) and (l – 1). The full model,
with the highest number of PC axes, provides a perfect fit between expected and observed data.
Models including one (AMMI-1) or two (AMMI-2) PC axes are usually the most appropriate
where there is significant GEI. Due to their simplicity, they provide a notable reduction of
dimensionality for the adaptation patterns relative to observed data.
Kampton (1984) pointed out that on the biplot results from the AMMI analysis, the following
points should be considered: the center of biplot shows the mean of a genotype or an
environment, a long distance of a genotype or an environment from the center of biplot indicates
a large interaction with that genotype or environment, the long length of a genotype on the
environmental vector reveals more deviation from the mean and vice versa and the angle
between the vectors of a genotype and an environments shows that the interaction is positive or
negative.
The AMMI1 biplot, showing main effects means on the abscissa and IPCA 1 values as the
ordinates, genotypes and/or environments that appear almost on a perpendicular line have similar
means and those that fall almost on a horizontal line have similar interaction patterns. Genotypes
and/or environments with large IPCA 1 scores (either positive or negative) have high
interactions, whereas genotypes and/or environments) with IPCA 1 scores near zero have small
interactions (Crossa et al., 1990).
The effect of GE on sorghum yield performance in the drought stressed parts of Ethiopia was
investigated using 14 sorghum hybrids and one released open-pollinated sorghum variety grown
at eight different environments; the environments being Melkasa and Mieso during 2003, 2004
15
and 2005 and Kobo during 2003 and 2004 (Asfaw, 2007). Because the GEI effect was significant
for grain yield, using five univariate stability models, he tried to analyze the yield data of
sorghum and compared for their effectiveness in partitioning the GEI into parameters that permit
a study of phenotypic stability of the sorghum genotypes. According to this work, the three types
of stability parameters declared different genotypes to be the most stable. This inconsistency in
ranking of genotypes was stated as a problem to reach a conclusion on producing genotype
recommendation. Because of this and the absence of considering the yield response of genotypes
across environments while using clustering genotypes (Flores et al., 1998), Asfaw (2007) tried to
solve all the problems using AMMI models. From the AMMI analysis of variance for grain
yield, observed, the environments were diverse withthis large difference among environmental
means causing most of the variation in grain yield of sorghum. In the AMMI analysis, he tried to
use the first three IPCA. However, as a reason of the large portion of the interaction was
explained by the first two IPCAs, GEI pattern is collected in the first principal components of
analysis (Sneller et al., 1997; Zobel et al., 1988), the first two IPCA axes best explain the GE
sum square and the remaining can be considered as noise, he used the first two IPCA.
Graphically, from the AMMI 1 biplot graph unfavorable and favorable environments were
identified based on their main effects. In his study, he identified the least interactive genotypes
and environments, and lastly depending on the yield potential of genotypes as well as stability,
he recommended four genotypes for the drought stressed sorghum growing areas (Asfaw, 2007).
Sixteen lowland rice genotypes were evaluated at three locations of eight environments in north
western Ethiopia from 2006 to 2008 to identify stable and high yielding genotypes for possible
release (Sewagegn et al., 2013). To achieve the objective of the study, he exploited the AMMI
model. AMMI analysis of variance indicated that the interaction was partitioned among the first
four IPCA, which cumulatively captured 91.13 % of the total GEI. AMMI 1 biplot and AMMI 2
biplot were used as the tools to classify genotypes and environments and recommend the most
stable genotypes. From AMMI 1 biplot, high yielder genotypes and favorable environments were
selected using the mean yield of genotypes over location and mean yield of environments,
respectively. The most interactive genotypes and environments were also identified from the
AMMI 1 graph using the IPCA sore of the respective genotypes and environments. Using the
IPCA 1 and IPCA 2 scores of both the additive factors graphically in the biplot two they selected
genotypes for both favorable and unfavorable environments. Lastly, they concluded that almost
16
all of the evaluated genotypes were affected by the GEI effects, so that no genotype had superior
performance in all environments. Therefore, they selected genotypes for specific adaptation
(Sewagegn et al., 2013).
Currently, plant breeders widely use AMMI stability model for the purpose of classifying
environments to be either favorable or unfavorable group for that specific crop to allocate
genotypes to either widely or specifically adaptation and to direct the countries breeding strategy.
Accordingly, Asfaw et al. (2011) and Molla et al. (2013) on finger millet ; Alemida et al. (2014),
and Human et al. (2011), on sorghum; Dagnachew et al. (2014) and Sunday et al. (2013) on
triticale were conducted multi environment yield trial and analyzed their yield data using AMMI
stability model.
2.6.3. Additive Main Effect and Multiplicative Interaction Stability Value
Purchase (1997) developed the AMMI stability value (ASV) based on the AMMI model’s
IPCA1 and IPCA2 (interaction principal components axes 1 and 2, respectively) scores for each
genotype. ASV is the distance from the coordinate point to the origin in a two dimensional plot
of IPCA1 scores against IPCA2 scores in the AMMI model. The ASV as described by Purchase
(1997) is comparable with the methods of Shukla(1972), Wricke, (1962) and Eberhart and
Russell (1966) in South African wheat (Purchase et al., 2000).
In effect, the ASV is the distance from zero in a two dimensional scatter-gram of IPCA 1 scores
against IPCA 2 scores. Since the IPCA 1 score contributes more to GEIsum square, to
compensate for the relative contribution of IPCA 1 and IPCA 2 to the total GEI sum square, it
has to be weighted by the proportional difference between IPCA 1 and IPCA 2 scores. The
distance from zero is then determined by using the theorem of Pythagoras. The larger the ASV
value, either negative or positive, the more specifically adapted a genotype is to certain
environments. Smaller ASV values indicate more stable genotypes across environments
(Purchase, 1997).
To identify stable high yielder triticale genotypes, Dagnachew et al. (2014) used Eberhart and
Russell’s model, AMMI, ASV mode and genotype selection index in one MET yield data of
triticale. The stability analysis result of this work supported Purchase’s (1997) ASV model; the
stable genotypes obtained from the above four stability models were almost similar. Based on
their yield and stability results, two triticale genotypes were selected as a candidate for wide
17
adaptation release. These genotypes were among five genotypes that had lower ASV values
(Dagnachew et al., 2014).
In Ethiopia, yield stability of malt barley genotypes was measured using ASV of genotypes and
other stability parameters. According to the ASV ranking of this trial, two malt barley genotypes
were identified as the most stable with their lowest ASV values and one genotype was found as
the most unstable with its high ASV value (Meuz et al., 2014).
Vange et al. (2014), conducted an field experiment on improved sorghum genotypes during the
2009 and 2010 cropping seasons in four locations within the Southern Guinea Savanna.
The result of the combined ANOVA for grain yield revealed significant differences with
reference to genotype and highly significant differences with respect to the Environment and
Environment X Genotype interactions. This indicating that there were tangible differences
among the environments as well as the genotypes. As the interaction is significant showing that
the relative performances of the genotypes were significantly affected by varying environmental
conditions. Based on Eberhart and Russell (1966) stability parameters three genotypes were
selected as stable and adapted to the test environments. From the result of this yield data
genotypes were also identified for poor environments Vange et al. (2014).
Almeida et al. (2014) evaluated grain yield of twenty five sorghum hybrid data collected from
experiments conducted across seven locations of Brazil during 2011for stability and adaptability.
To determine stability and adaptability Eberhart and Russell (1966) and AMMI (Zobel et al.,
1988) statistical parameters are implemented. Eberhart and Russell model identified three
sorghum hybrids as stable showed regression coefficients that were statistically higher than one
and were a great fit to the model. In addition, one cultivar shows adaptability to unfavorable
environments, although the fit to the model was low. The association between the AMMI and the
Eberhart and Russell (1966) methods was very useful in explaining the performance of hybrids
with adaptability to favorable environments. According to the AMMI model, the first two
principal components were used. In consequence, due to the fact that the first two IPCAs
explained 65.98% of the variance due to the GEI and the interaction residual is non-significant
the biplot is constructed (Almeida et al., 2014).
18
3. MATERIALS AND METHODS
3.1. Description of the Study Area
The field experiment was conducted during the 2014 main cropping season at four locations
representing the dry lowland areas of Ethiopia where sorghum is widely grown. The research
was conducted at Shewa Robit, Kobo, Mieso and Errer; which are found in North Shewa, North
Wello, Western Harerghe and Eastern Harerghe, respectively. The detailed agro-ecological
features of the locations are presented on Table 1.
Table 1
Agro-ecological features of the experimental locations
Locations
Altitude
(m.a.s.l)
Average
Rain fall Soil Type
(mm)
Geographic coordinates
Average
Temperature
(ºC)
Latitude
Longitude
Max.
Min.
Errer
1305
NA
NA
45o 05’ 20’’ N
09 o14’33’’E
NA
NA
Kobo
1450
673.4
Vertisol
12o 8’ 21’’ N
39o 18’ 21’’ E
34
13
Mieso
1470
856.8
Vertisol
16o 06N
37o 8E
35
8.3
Shewa Robit
1500
890.7
Vertisol
10o 35’ N
39o 93’E
36.23
12.05
Source:From annual reports ofMelkasa, Sirinka and Debre Brihan Agricultural Research Centers,
NA = Not available
3.2. Plant Materials
The experimental plant materials comprised of one released early maturing sorghum variety
Melkam (released from Melkasa Agricultural Research Center for low moisture stress areas of
the Ethiopian lowlands in 2009) as a standard check and twenty one advanced early maturing
sorghum genotypes screened in preliminary yield trials. The detailed information about the
materials is presented on Table 2.
19
Table 2
Description of sorghum genotypes tested at four locations during 2014 main cropping
season
Entry Code
Genotypes
Pedigree
Seed Source
1
2001 MS 7003
Local Bulk(white)/SRN-39
MARC
2
2001 MS 7013
PGRC/E#222880/ICSV-1/KAT369-1
MARC
3
2001 MS 7015
PGRC/E#222880/ICSV-1/KAT369-1
MARC
4
2001 MS 7037
PGRC/E#222878/ICSV708
MARC
5
IESV 92084-DL
IESV92084-DL
ICRISAT
6
IESV 92168-DL
IESV92168-DL
ICRISAT
7
IESV 92199-DL
IESV92199-DL
ICRISAT
8
IESV 92057-DL
IESV92057-DL
ICRISAT
9
IESV 9027-DL
IESV9027-DL
ICRISAT
10
2001 MS 7007
CR:35:5/DJ1195/N13
MARC
11
2005 MI 5060
WSV387/P9403
MARC
12
2005 MI 5064
WSV387/P9404
MARC
13
2005 MI 5065
WSV387/P9405
MARC
14
2005 MI 5066
M36121/P9401
MARC
15
2005 MI 5069
M36121/P9402
MARC
16
2005 MI 5070
M36121/P9403
MARC
17
2005 MI 5075
3443-2-OP/P9401
MARC
18
2005 MI 5079
3443-2-OP/P9401
MARC
19
2005 MI 5081
3443-2-OP/P9403
MARC
20
2005 MI 5082
3443-2-OP/P9403
MARC
21
ICSR 24005
ICSR24005
ICRISAT
22
Melkam (Check)
WSV387
MARC
MARC= Melkasa Agricultural Research Center, ICRISAT= International Crop Research
Institute for Sami-Arid Tropics.
3.3.
Experimental Design and Crop Management
The trial was laid out in randomized complete block design (RCBD) with three replications. The
experimental plots consisted of 5 rows, each 5 m in length with 75 cm row to row and 15 cm
20
plant-to-plant spacing. The total area of each plot and the three harvestable middle-rows had a
size of 18.75 m2 and 11.25 m2,respectively. Sowing was done by hand drilling. The seed rate for
each plot was calculated as per the recommendation for row planting (10 kg/ha). Seeds were
sown by hand drilling. Then, thinning was done two weeks after emergence to adjust plant to
plant space. Nitrogen and phosphorus fertilizer applications were practiced in the form of urea
(46 % N) and DAP (18 % N and 46 % P2O5) as the national sorghum improvement program have
done. During planting, 100 kg/ha of DAP was applied in the seed furrow. Urea was applied as
top dressing at the rate of 50 kg/ha at knee height stage. The field was kept free of weeds during
the period of the experiment. All of the other recommended agronomic management practices
such as land preparation and insect pest control were applied as required.
3.4. Data Collection and Sampling Techniques
Data were collected from the central three rows and five randomly sampled plants based on the
descriptors for sorghum (IBPGR/ICRISAT, 1993). Phenological data (emergence date, flowering
date, and maturity date), morphological data (plant height), and data on yield (g/plot) and stand
count at harvest were collected. The details of the data collection were as follow:
Days to 50 % seedling emergence:the number of days from the date of sowing to the date at
which 50 % of the seedlings in a plot were emerged.
Days to 50 % flowering: the number of days from 50 % seedling emergence to the date at which
50 % of the plants in a plot started flowering.
Days to maturity: the number of days from 50 % seedling emergence to the date at which 75 %
of the plants in a plot are physiologically matured.
Grain filling period:The numbers of days from days to 50 % flowering to days to 75 %
physiological maturity were counted, and it includes watery ripe stage, milk stage, soft dough
stage, hard dough stage and ripening stage..
Plant height (cm): Plant height was measured from five randomly sampled main plants from the
three central rows at 75 % physiological maturity. The mean height from the five plants was then
recorded for the plot.
21
Stand count at harvest:the total number of main plants in net plot area when 75 % of the total
population in a plot was physiologically mature.
Grain yield (kg/ha): after harvesting, the panicles from the three central rows of each plot were
threshed cleaned and weighed. The plot yield (g/plot) was converted to kg/ha and ton/hectare.
Grain filling rate (kg/ha/day): it is the ratio of grain yield (kg/ha) to grain filling period and
calculated as follows:
Grain yield (𝑘𝑔⁄ℎ𝑎)
Grain Filling Rate= Grain Filling Period (days)
3.5. Data Analyses
SAS, Spar 2.0 and Genestat statistical softwares were used to analyze the data. SAS 9.1 was
performed to analyze all the collected data from individual locations and the combined data over
locations.
3.5.1. Analysis of Variance for Each Location and Combined Over Locations
Using the raw data collected to eight characters of 22 genotypes, which were grown at four
locations, general analysis of variance (ANOVA) of RCBD was computed as outlined by Gomez
and Gomez (1984). Before pooling the data over locations, Bartlett’s test of homogeneity of
variance was adopted for the eight parameters to determine the validity of the combined analysis
of variance of the data. This analysis revealed the homogeneity of error variance. Therefore,
combined analysis of variance was done to determine the effects of the genotypes, locations and
their first order interactions using mixed linear model. Genotypes were assumed to be fixed and
environment effects random. Duncan’s multiple range test (DMRT) was used to determine the
significance of differences among the genotype means for each character.
The effects of genotypes, locations as well as their first order interaction were determined from
the ANOVA using the following model:
Yijk= µ + 𝐺𝑖 + 𝐸𝑗 + 𝐺𝐸𝑖𝑗 + 𝐵𝑗𝑘 + 𝑒𝑖𝑗𝑘
22
Where: µ is the grand mean, 𝐺𝑖 is the effect of the ith genotype, 𝐸𝑗 is the effect of the jth location,
𝐺𝐸𝑖𝑗 is the interaction of the ith genotype with the jth location, 𝐵𝑗𝑘 is the effect of the kth
replication in the jth location, and 𝑒𝑖𝑗𝑘 is the random error.
3.5.2. Stability Analysis
The following two analyses of the stability models were performed for grain yield (ton/ha) using
Spar 2.0 and Genestat softwares.
3.5.2.1. Eberhart and Russell’s joint regression model
Eberhart and Russell (1966) procedure involves the use of joint linear regression where the yield
of each genotype is regressed on the environmental mean yield. Then, the behavior of the
genotype was assessed by the model: 𝑌𝑖𝑗 = 𝜇𝑖 + 𝛽𝑖 𝐼𝑗 + δ𝑖𝑗 using Spar 2.0 statistical software.
Where: Yij = the mean performance of the ith genotype in the jth environment, µi = the grand
mean of the ith genotype over all the environments, βi = the regression coefficient which
measures the response of the ith genotype on environmental index, Ij = the environmental index
obtained by the difference between the mean of each environment and the grand mean and 𝛿𝑖𝑗 =
the deviation from regression of ithvariety in the jth environment
The pooled deviations mean square was tested against the pooled error mean square by the F-test
to evaluate the significance of the differences among the deviations of genotypes being evaluated
from their expected performances. As a result, in order to test the validity of the hypothesis that
whether there is significant difference among the 22 genotypes with respect to their mean grain
yields or not and whether there is significant difference among the regression coefficient or not,
genotypes mean square and regression mean square were tested against the pooled deviation
using the F-test.
3.5.2.2. The additive main effects and multiplicative interaction (AMMI) method
Additive main effects and multiplicative interaction(AMMI) model was performed for the mean
data of grain yield (ton/ha) from each location using Genestat statistical software. The AMMI
model equation is given as:
23
N
Y𝑖𝑗 = µ + α𝑖 + ß𝑗 + ∑ λ𝑛 γ𝑖𝑛 δ𝑗𝑛 + θij + εij
n=0
Where:Yij = the mean yield of genotype i in environment j,µ = the grand mean,αi = the deviation
of the genotype mean from the grand mean, βj = the deviation of the environment mean from the
grand mean, λ n = the singular value for the IPCA n, N = the number of PCA axis retained in the
model, γin = the PCA score of a genotype for PCA axis n,δjn = the environmental PCA score for
PCA axis n, θij = the AMMI residual and Eij = the residuals.
The degrees of freedom (DF) for the IPCA axis were calculated based on the following method
(Zobel et al., 1988). DF = G + E – 1 – 2n; Where: G = the number of genotypes, E = the number
of environments and n = the nth axis of IPCA.
3.5.2.3. AMMI’s stability value (ASV)
In order to quantify and rank genotypes according to their yield stability, the additive main effect
and multiplicative interaction effect stability value (ASV) was proposed by Purchase (1997). It
was calculated using Microsoft excel (2007) by employing the following formula:
√[𝐼𝑃𝐶𝐴1 𝑠𝑢𝑚𝑜𝑓𝑠𝑞𝑢𝑎𝑟𝑒𝑠(𝐼𝑃𝐶𝐴1 𝑠𝑐𝑜𝑟𝑒)]2
𝐴𝑆𝑉 =
+ (𝐼𝑃𝐶𝐴2 𝑠𝑐𝑜𝑟𝑒)2
𝐼𝑃𝐶𝐴2 𝑠𝑢𝑚𝑜𝑓𝑠𝑞𝑢𝑎𝑟𝑒
Where: ASV = AMMI’s stability value, IPCA1= interaction principal component analysis one,
and IPCA I= interaction principal component analysis II.
24
4. RESULTS AND DISCUSSION
4.1. Analysis of Variance
4.1.1 Single Location ANOVA
The separate ANOVA of the eight characters (grain yield, days to emergence, flowering and
maturity, plant height, stand count at harvest, grain filling period and grain filling rate) for
twenty two early maturing sorghum genotypes tested at Errer, Kobo, Mieso and Shewa Robit
was presented on Table 3. The results of ANOVA for grain yield at each location showed the
presence of genetic variation among the genotypes. The difference among the genotypes for
grain yield are highly significant (P≤ 0.01) at Mieso, and very highly significant (P≤ 0.001) at
Errer, Kobo and Shewa Robit (Table 3). This indicates that, at each location there are genetic
variability among genotypes for grain yield. Similar results of significant effect of genetic base
of genotypes on one of a multi environment yield trial growing environment for grain yield were
reported by the previous works of Abubakar and Bubuche (2013), Ahmed et al. (2012), Fahri
(2012), Mesfin et al. (2014), Tekle and Zemach (2014), Shrestha (2013).
Table 3
Analysis of variance for days to emergence, flowering, and to maturity, plant height,
stand count at harvest, grain yield, grain filling period; and grain filling rate of twenty two early
maturing sorghum genotypes tested at four locations during 2014 cropping season.
Loc.
Errer
Kobo
Mieso
Shewa
Robit
S.V.
Df
Gen.
21
DE
0ns
Rep.
Error
Gen.
Rep.
Error
Gen.
Rep.
Error
Gen.
Rep.
Error
2
42
21
2
42
21
2
42
21
2
42
0ns
0
0ns
0ns
0
0.3ns
0.2ns
0.21
2.7**
1.1ns
0.6
DF
2.5ns
DM
0.21ns
Traits
PH
SCH
553.2** 237.6ns
1.9ns
4.04
28.7*
52.6*
12.4
13.1ns
11.8ns
11.5
33.1ns
56.8ns
26.1
0.02ns
0.21
13.1*
13.9ns
5.78
32.5ns
32.2ns
36.8
6.5ns
1.0ns
4.7
386.9ns
201.69
361.4*
23.5ns
194.5
409.3ns
177.7ns
293.1
254.2ns
865.5*
182.8
1160.3*
161.5
108.4ns
68.0ns
73.9
69.4ns
81.5ns
50.0
288.8*
206.8ns
80.3
GY
1.15***
GFP
1.9ns
GFR
385.9***
1.35*
0.16
0.91***
0.10ns
0.13
0.52**
0.84*
0.16
1.60***
0.79*
0.22
1.8ns
3.7
8.7ns
13.7ns
5.2
7.8ns
5.9ns
3.1
21.0ns
70.4ns
21.9
373.1*
53.35
656.7***
203.16ns
108.07
192.17**
254.99*
64.78
414.45**
382.94ns
124.89
25
DE = Days to emergence (days), DF= Days to flowering (days), DM = Days to maturity (days),
PH = Plant height (Cm), SCH = Stand count at harvest (number), GY = Grain yield (ton/ha),
GFP = Grain filling period (days), GFR = Grain filling rate (%), *** = vary highly significant
(P≤ 0.0001), ** = highly significant (P≤ 0.001), * = significant (P≤ 0.0=01) and ns = insignificant
(P>0.05).
The ANOVA result for seven traits (days to emergence, days to flowering, days to maturity,
plant height, stand count at harvest, grain filling period and grain filling rate) of twenty two early
maturing sorghum genotypes tested at Errer, Kobo, Mieso and Shewa Robit showed that the
performances of genotypes for days to emergence, days to flowering, days to maturity, plant
height and stand count at harvest were not uniform in all the locations.
The observed numbers of days that genotypes spent to flower and mature were statistically
different at Kobo. At Kobo, genotypic differences were significant (P≤ 0.05) for days to
flowering, days to maturity, and plant height. Genotypes were also found to vary significantly
(P≤ 0.01) for plant height at Errer. At Shewa Robit the mean square of genotypes for days to
emergence and total number of sorghum stands at maturity revealed a highly significant (P≤
0.01) and significant (P≤ 0.05) variation among genotypes, respectively (Table 3). From the
seven measured traits genotypic differences were significant only for grain filling rate in all the
experimental areas. The differences of grain filling rate among genotypes are very highly
significant (P≤ 0.001) at Errer and Kobo, and highly significant (P≤ 0.01) at Mieso and Shewa
Robit. In all the four locations, genotypes took similar period to fill their grain (Table 3).
4.1.2 Combined Analysis of Variance
The combined ANOVA of the twenty two early maturing sorghum genotypes tested at four
locations during 2014 main cropping season is presented on Table 4. The result revealed that
there were significant (P≤ 0.001) differences among locations, but differences among the
genotypes were not significant. This indicates the diversity of the growing conditions in the four
locations and the lack of variability in the genotypes for grain yield performance. Significant
effect of location on yield of sorghum varieties was reported by Asfaw (2007), Almeida et al.
(2014), Maposa et al. (2010).The GEI was also very highly significant (p≤ 0.001), showing the
difference in the response of genotypes at different environments. This result is in agreement
with the findings of Almeida et al. (2014), Asfaw (2007), Kenga et al. (2003) differential
26
genotypic behavior in the environments. In the other study Dagnachew et al. (2014) obtained
insignificant genotypic effect and very highly significant environmental and GEI effect on the
yield of triticale. A significant GEI may be either a non-cross-over or cross-over type (Baker,
1990; Cornelius et al., 1996). In the present study, the interaction was of cross-over type as the
ranking of genotypes for grain yield changed at every location (Appendix 1-4).
Table 4
Combined ANOVA for grain yield (ton/ha) and the percentage sum of squares of the
twenty two early maturing sorghum genotypes tested at four locations during 2014 main
cropping season
Source of Variation
DF
SS
% SS
MS
Genotype
21
23.28
6.24
1.11ns
Location
3
252.09
67.34
84.03***
Replications in Environments
8
6.17
1.67
0.77***
Genotype x Location
63
64.48
17.22
1.02***
Error
168
28.37
7.58
0.17
Total
263
374.39
DF = Digress of freedom, SS = Sum of squares and MS = Means of squares, *** = vary highly
significant (P<0.0001), ** = highly significant (P<0.001), * = significant (P<0.0=01) and
ns
=
non-significant (P>0.05).
The result of the combined ANOVA showed that the total variation in yield was attributed to
environmental (67.34 %), genotypic (6.24 %) and GEI (17.22 %) effects (Table 6). This
indicates that the largest proportion of the variation was among the environments. Similar results
of large environmental effects were also reported for sorghum genotypes by Asfaw (2007, 2008),
Hagos and Fetien (2011), Mahnaz et al. (2013), Sewagegne et al. (2013). Therefore, high
percentage of the environment component of variation is an indication that environment is the
major factor that influence yield performance of sorghum genotypes in the dry lowlands of
Ethiopia. The sum of squares of GEI was 2.76 times higher than that of the genotypes. The
highest magnitude of the interaction as compared to the genotype component indicates that the
grain yield performance of sorghum genotypes across environments was different (Asfaw 2007).
27
The effect of interaction on the grain yield of sorghum genotypes was large, indicating the needs
for studying the nature of differential response of genotypes to environments up on selecting
genotypes for grain yield. Significant GEI indicates that the effects of genotypes and
environments are statistically nonadditive or the differences between genotypes depend on the
environment. Hence, superior genotypes across environments cannot be selected based on their
mean yield performance alone. As a result, there is a need to dissect the significant interaction
effect into the components that are responsible for the variation. Therefore, to test the
consistency of genotypes for grain yield performance across locations, the multi-location grain
yield data should be subjected to different stability analysis methods.
Combined ANOVA was made for days to emergence, flowering and maturity, plant height, stand
count at harvest, grain filling period and grain filling rate (Table 5). Statistically significant
differences (P≤ 0.05) among genotypes were found only for stand count (P≤ 0.01), plant height
(P≤ 0.001) and grain filling period. This indicates that the presence of the effect of genetic
differences for the above three traits. Differences among locations were highly significant for all
traits, indicating the wide variation among locations had high effect for these traits of early
maturing sorghum genotypes.
Table 5
Mean squares and coefficient of variations of yield, plant height; phenological traits
and grain filling rate of twenty two early maturing sorghum genotypes tested at four locations
during 2014 main cropping season.
Mean Squares
Pooled Errer
Traits
(Df = 168)
Genotype
Location
GLI
Replication
(Df=21)
(Df=3)
(Df= 63)
(Df= 2)
ns
DE
0.7736291
306.3977273*** 0.7495791*** 0.3219697ns
0.20689
ns
ns
DF
26.9761905
1150.676768*** 16.816979
30.746212*
13.512085
ns
ns
ns
DM
9.3881674
3809.11616***
14.30928
11.76894
11.88799
ns
ns
PH
903.75281*** 853.10186**
224.7872
360.89822
218.04163
SCH
324.05267** 16013.56566*** 126.70058ns
379.14394ns
91.42172
ns
GY
1.11186046
84.0271545***
1.0229794*** 0.7698375*** 0.1686208
GFP
14.7373737* 5136.13636***
8.19192ns
22.93182*
10.13023
ns
GFR
399.098695
25327.92576*** 416.61119*** 303.55752**
87.773
DE = Days to emergence (days), DF = Days to flowering (days), DM = Days to maturity (days),
PH = Plant height (Cm), SCH = Stand count at harvest (number), GY = Grain yield (tone/ha),
28
GFP = Grain filling period (days), GFR = Grain filling rate (%), *** = vary highly significant
(P<0.0001), ** = highly significant (P<0.001), * = significant (P<0.0=01) and
ns
= non-
significant (P>0.05).
The GEI was very highly significant for days to emergence and grain filling rate only. The result
of this indicates that genotypes took different days to emerge at different locations and also the
grain filling rate potential of genotypes varied from location to location. Zahra et al. (2013)
found that rate of 50 % germination was affected by temperature, genotype and their interaction.
4.2.
Genotypes Mean Performance
The performance of twenty two early maturing genotypes for days to emergence, grain yield and
grain filling rate are highly affected by the combined effect of both genotype and growing
conditions of locations. Therefore, the mean performance of genotypes for these three traits are
compared based on the average number of days that genotype took to emerge, mean yield and
average grain filling rate of genotypes obtained across the four tested locations. The mean
emergence day, grain yield and grain filling rate of genotypes across location are presented in
table 6.
According to Vanderlip (1979) emergence is the first stage (Stage 0) of grain sorghum
development; it is when the plant first breaks through the soil surface. Zahra et al. (2013)
observed that differences existed among sorghum genotypes in germination. The average
numbers of days that genotypes took to emerge are statistically similar. The average number of
days across location is 7.41. Above half of the tested genotypes were emerged before the mean
days of genotypes across the four locations. From the tested genotypes at four locations genotype
ICSR 24005 (8.17 days) had maximum number of days to emerge (Table 6). In contrary
genotype 2005 MI 5064 (7.08 days) had numerically minimum number of days to emerge. The
difference of the two marginal days of genotype emergence is small, this result agrees with the
ANOVA result that showed the statistical similarity of genotypes emergence date. Generally for
germination of sorghum genotypes it may occur 5 to 10 days after planting (table 6). The time
required for emergence depends on soil texture and temperature, moisture conditions, depth of
planting, vigor of the seed and genotypes Zahra et al. (2013).
29
Table 6
Means for days to emergence, grain yield (ton/ha) and grain filling rate of Early
Maturing Sorghum Genotypes tested at four locations during 2014 main cropping season.
Genotypes Code
Genotype
DE
GY (ton/ha)
GFR
1
2001 MS 7003
7.17
3.34
71.13
2
2001 MS 7013
7.58
3.33
72.02
3
2001 MS 7015
7.42
3.30
68.38
4
2001 MS 7037
7.33
2.57
55.81
5
IESV 92084-DL
7.42
3.71
77.59
6
IESV 92168-DL
7.17
3.33
67.22
7
IESV 92199-DL
7.17
2.88
59.15
8
IESV 92057-DL
7.42
3.12
60.70
9
IESV 9027-DL
7.67
2.86
58.90
10
2001 MS 7007
7.67
3.47
67.62
11
2005 MI 5060
7.33
3.37
69.29
12
2005 MI 5064
7.08
3.68
74.32
13
2005 MI 5065
7.25
3.67
72.21
14
2005 MI 5066
7.25
3.35
69.42
15
2005 MI 5069
7.67
3.09
66.83
16
2005 MI 5070
7.33
3.23
65.16
17
2005 MI 5075
7.25
3.09
62.49
18
2005 MI 5079
7.25
3.00
62.87
19
2005 MI 5081
7.75
2.67
56.59
20
2005 MI 5082
7.50
3.25
64.52
21
ICSR 24005
8.17
3.31
65.70
22
Melkam (Standard check
7.25
3.49
70.38
Mean
7.41
3.23
66.29
CV (%)
6.14
12.70
14.13
DE = days to emergence, GY = grain yield, GFR = grain filling rate
The average number of days that genotypes take to emerge at Errer, Kobo, Mieso and Shewa
Robit was 8, 6, 8.47 and 10.20 days, respectively (Table 8). The average number genotypes
30
emergence day at Shewa Robit was statistically larger than the three locations. This might be due
to the amount and occurrence of rain fain fall and temperature at the time of plantation. The
major environmental factors that affect germination of sorghum genotypes are temperature
(including soil temperature), moisture and soil texture. This author was also reported that, change
in temperature regime from 25/22 to 11/8 °C caused reduction in germination percentage, soil
with low temperature in few weeks after sowing reduces seed germination, emergence rate and
seedling establishment Zahra et al. (2013).
From the tested twenty two early maturing sorghum genotypes thirteen genotypes had higher
grain yield than the grand mean (3.23 ton/ha): 2001 MS 7003 (3.34 ton/ha), 2001 MS 7013 (3.33
ton/ha), 2001 MS 7015 (3.30 ton/ha), IESV 92084-DL (3.71 ton/ha), IESV 92168-DL (3.33
ton/ha), 2001 MS 7007 (3.47 ton/ha), 2005 MI 5060 (3.37 ton/ha), 2005 MI 5064 (3.68 ton/ha),
2005 MI 5065 (3.67 ton/ha), 2005 MI 5066 (3.35 ton/ha), 2005 MI 5082 (3.25 ton/ha), ICSR
24005 (3.31ton/ha) and Melkam (3.49 ton/ha). The standard check was also among the high
yielding genotypes. Maximum and minimum grain yield was obtained from genotype IESV
92084-DL (3.71 ton/ha) and 2001 MS 7037 (2.57 ton/ha), respectively (Table 6).
Melkam (WSV 387) was officially released as early maturing sorghum variety in 2009 from
Melkasa Agricultural Research Center for the dry lowland areas of Ethiopia (altitude having less
than 1600). The result of this study compares the relative yield potential of Melkam with the rest
21 early maturing sorghum genotypes; the grain yield performance of the standard check was
among the top high yielder genotypes at Kobo, Mieso and Shewa Robit; but at Errer it had
significantly low yield than all the tested genotypes, except, the five lowest yielder genotypes
(Table 6).
Sorghum growing conditions of the four locations were quite different. The result of this study
showed that the average yield of locations was 3.23 ton/ha. Except Shewa Robit (4.87 ton/ha)
none of the three locations had mean yield above the grand mean (3.23 ton/ha). The highest
mean of genotypes at this location might be due to variation in distribution of rain fall until the
end of the growing period. The mean yield of genotypes at Errer (3.02 ton/ha), Kobo (2.72
ton/ha) and Mieso (2.32 ton/ha) were also statistically different (Table 8). The large variation of
locations for grain yield might be due to the difference in total amount of rain fall at the growing
season and at different growing stage of sorghum genotypes, temperature, and soil conditions.
31
The performance of genotypes for grain filling rate differed from location to location. Twelve
genotypes (2001MS7003, 2001MS7013, 2001 MS 7015, IESV 92084-DL, IESV 92168-DL,
2001 MS 7007, 2005 MI 5060, 2005 MI 5064, 2005 MI 5065, 2005 MI 5066, 2005 MI 5069, and
Melkam) had above average grain filling rate (66.29 kg/day/ha). The highest grain filling rate
across locations was by genotype IESV 92084-DL (77.59 kg/day/ha). This genotype was among
the high yielder genotypes over locations and is preferred by its time consumption to fill the
grain (Table 6).
The average grain filling rates of genotypes were 54.63 kg/day/ha at Errer, 74.91 kg/day/ha at
Kobo, 46.17 kg/day/ha at Mieso and 89.45 kg/day/ha at Shewa Robit (Table 8). The variation
among means of grain filling rate of genotypes in the four locations was wide. The grand mean
grain filling rate of locations was 66.29 kg/day/ha, Shewa Robit and Kobo were the two
locations that had faster grain filling rate than the rest two locations. At Shewa Robit, genotypes
filled their grains at a faster rate than the genotypes in the other locations. At Mieaso, grain
filling rate was the poorest of all the locations.
As discussed above, plant height, stand count at harvest and grain filling rate of the tested
genotypes was not affected by the interaction effect. Hence, for these traitsgenotypes are
compared from their mean potential of single location. The mean plant height, stand count at
harvest and grain filling rate of genotypes tested at four sorghum growing locations are presented
on table 7.
Sorghum leaves are used as feed for animals, and the stalk as housing and fencing material, and
as energy source. Due to this reason, sorghum farmers in the lowlands of Ethiopia set plant
height (as component of biomass) as one of the selection criteria for sorghum varieties. As a
result, for better adoption of varieties, this parameter should be considered as one of the major
selection criteria during evaluation. Differences in the plant height of sorghum genotypes were
observed at Errer and Kobo only (Table 7). At Errer, genotypes 2005 MI 5065 (2.08 m), 2005
MI 5060 (1.94 m), IESV 92168-DL (1.93 m) and 2005 MI 5075 (1.93 m) were the tallest
genotypes. The tallest genotypes at Kobo were 2005 MI 5075 (1.97 m), 2005 MI 5060 (1.96 m),
2001 MS 7037 (1.95 m) and 2005MI 5069 (1.88 m). The standard check variety (Melkam) was
found to be the shortest of all the genotypes with height of 1.44 m and 1.5 m at Errer and at
Kobo, respectively (Appendix 2 and 3).
32
The mean plant height of all the genotypes at the tested locations was 183.00 m. Average height
of genotypes at Errer (179.21 m), Kobo (181.35 m) and Shewa Robit (183.87 m) were not
statistically different. The highest mean plant height of the genotypes was observed at Mieso, it
is significantly higher than that of Errer and Kobo, but statistically similar from the mean plant
height of genotypes at Shewa Robit (Table 7).
Statistically there was no significant difference among the genotypes for the average number of
days that the tested genotypes reached physiological maturity after flowering (grain filling
period) (Table 7). A genotype that has longer reproductive stage would have higher grain weight
and number of seeds per head. Moreover, due to the longer grain-filling period and increased
vegetative growth late maturity hybrids tend to yield higher than shorter season sorghum hybrids
(Baumhardt et al. 2005). Unfortunately, it was difficult to confirm these findings in the present
study as differences were not significant.
33
Table 7
Mean plant height, stand count at harvest and grain filling period of twenty two early maturing sorghum genotypes tested at
Errer, Kobo, Mieaso and Shewa Robit during 2014 main cropping season.
Entry
Genotypes
PH
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
2001 MS 7003
2001 MS 7013
2001 MS 7015
2001 MS 7037
IESV 92084-DL
IESV 92168-DL
IESV 92199-DL
IESV 92057-DL
IESV 9027-DL
2001 MS 7007
2005 MI 5060
2005 MI 5064
2005 MI 5065
2005 MI 5066
2005 MI 5069
2005 MI 5070
2005 MI 5075
2005 MI 5079
2005 MI 5081
2005 MI 5082
ICSR 24005
Melkam
Mean
CV (%)
bcd
174.33
164cde
170bcde
176.67bcd
182.33bcd
193.67ab
168.67bcde
189abc
160de
168bcde
194ab
187abcd
207.67a
180.33bcd
184.67abcd
183bcd
193ab
181.67bcd
174bcd
183bcd
183.33bcd
144.33e
179.21
7.92
Errer
SCH
40.67
24.33
32.33
30.67
41.33
24.00
33.67
44.00
23.67
28.33
45.00
51.67
35.33
32.33
24.33
51.67
32.67
35.33
21.67
37.33
28.67
25.00
33.82
37.58
GFP
55.00
55.00
55.67
55.67
54.67
55.33
53.67
56.33
56.00
55.33
54.67
55.33
55.33
56.67
55.67
53.67
55.67
55.00
54.67
56.33
56.33
56.00
55.63
3.46
PH
ab
187.33
175.67ab
180.67ab
195.33a
160.33bc
181.67ab
172.67abc
178.67ab
170.33abc
186.33ab
195.67a
184ab
187.33ab
187.33ab
188.33a
183.67ab
196.67a
187ab
178.67ab
180.67ab
181.33ab
150c
181.35
7.69
Experimental Locations
Kobo
Mieso
SCH GFP
PH
SCH
44.00
36.67
41.00
34.67
44.67
38.67
44.33
49.33
39.00
26.33
50.33
39.67
35.67
37.67
41.00
38.00
33.33
33.67
42.00
31.00
29.33
41.33
38.71
22.21
35.00
35.00
36.00
35.67
33.67
36.33
36.67
38.67
37.67
37.33
34.00
38.33
38.67
36.33
36.67
34.33
37.33
36.33
34.00
34.67
39.67
37.67
36.36
6.29
165.00
170.00
186.67
190.00
185.00
198.33
193.33
185.00
170.00
170.00
206.67
200.00
200.00
198.33
195.00
195.00
193.33
186.67
188.33
188.33
191.67
170.00
187.58
9.13
54.67
56.33
65.00
54.33
55.00
55.33
60.33
57.67
49.33
55.33
68.67
56.33
59.00
53.67
50.33
59.67
51.67
61.67
54.33
57.67
52.67
64.00
56.95
12.41
GFP
51.00
50.67
50.00
49.00
50.00
50.00
50.67
50.33
51.00
49.67
53.33
54.67
48.00
49.67
47.67
48.33
49.33
50.00
50.67
49.33
50.33
52.33
50.27
6.21
Shewa Robit
PH
SCH
GFP
181.47
188.80
181.60
179.40
191.80
205.73
178.87
187.67
186.73
170.33
167.60
179.27
197.00
198.03
177.73
189.40
185.07
183.27
174.67
179.40
187.73
173.67
183.87
7.35
84.33a
66bc
69abc
73abc
77.33ab
66.33bc
69.67abc
71.33abc
64.33bc
71.33abc
72.33abc
59cd
65.33bc
73.67abc
39.67e
68.33abc
66.67bc
61bc
64bc
72abc
45.33de
76.67abc
67.12
13.35
52.67
53.67
54.33
50.00
54.00
56.33
55.00
56.33
55.67
59.00
53.67
56.33
59.33
51.33
48.67
56.00
55.67
54.33
51.33
56.33
55.33
56.67
54.64
8.56
PH = plant height, SCH = stand count at harvest, GFR = grain filling rate. Means within the same column following by the same letter
are not significantly different at 5 %.
34
Grain filling periods of genotypes at Errer (55.63 days) and Shewa Robit (54.64 days) were
statistically equal. At these two locations grain filling period of sorghum genotypes was large
as compared to the rest locations. At Mieaso the average period of genotypes to fill their grain
was 50.27 days, this makes the grain filling period of genotypes at Mieso is faster than Errer
and Shewa Robit and late from that of Kobo. As compared to the three locations the smallest
period of time that genotypes required to fill their grain was observed at Kobo, it was 36.36
days. This variation might be due to the differences of locations in the amount of rain fall they
obtained (Table 8).
Table 8
Means for days to emergence, flowering and maturity, plant height, stand count at
harvest, grain yield, grain filling period and grain filling rate of twenty two early maturing
genotypes at Errer, Kobo, Mieso and Shewa Robit.
Location
Traits
DE
DF
DM
PH
SCH
GY
GFP
GFR
Errer
8.00b
80.65b
136.02a
179.02b
34.33d
3.02b
55.36a
55.26c
Kobo
6.00c
82.89a
119.26d
181.35b
38.71c
2.72c
36.36c
74.91b
Mieaso
5.45d
73.36d
123.64c
187.58a
56.96b
2.32c
50.27b
46.17d
77.09c
131.73b
183.87ab
67.12a
4.87a
54.64a
89.45a
Shewa Robit 10.20a
Mean
7.41
70.504
127.66
183.00
49.15
3.23
49.16
66.29
CV (%)
6.14
4.68
2.70
8.07
19.45
12.70
6.47
14.13
DE = days to emergence, DF = days to flowering. DM = days to maturity, PH = Plant height,
SCH = stand count at harvest, GY = grain yield, GFP = grain filling period and GFR = grain
filling rate. Means of locations within the same column following by the same letter are not
significantly different at 5 %.
The trial environment, agronomic practice and sorghum genotypes significantly affected the
required days for flowering and maturity Sally (2012). In the present study the effects of
location were very highly significant for both genotypes flowering and maturity day. Each of
the tested locations had significantly different among each other (Table 8). The mean days of
flowering that genotypes required are significantly early at Mieso (73 days) and late at Kobo
(83 days). Genotypes mean flowering date was 81 days at Errer and 77 days at Shewa Robit.
35
The mean maturity days of locations were not statistically similar. At Mieaso (124 days) the
required mean genotypes maturity days were significantly early than mean days of genotypes
maturity day at Shewa Robit (132 days) and Errer (136 days), but was significantly late from
that of at Kobo (119 days). Compared to the overall locations flowering and maturity date, it
was only at Errer and Kobo that had above the mean of the four locations flowering date (79
days). The overall maturity date of locations was 128 days. At Kobo and Mieaso the mean
maturity days were lower than the grand mean (Table 8).
4.3.
Genotype x Environment Interaction Analysis of Variance
4.3.1. Genotype x Environment Interaction Analysis of Variance by Eberhart and
Russel's Model
Genotype x environment interaction ANOVA of joint linear regression model is used for
estimation and partitioning of GE interaction in to components. The analysis of Variance by
Eberhart and Russel's Model of early maturing sorghum genotypes on mean grain yield
(ton/ha) tested at four locations is presented in table 9.
Eberhart and Russell (1966) procedure involves the use of joint linear regression where the
yield of each genotype is regressed on the environmental mean yield. In this model the SS due
to environments and GEI are partitioned into environments (linear), GE (linear) and
deviations from regression (pooled deviation over all the genotypes).
The genotype regressions term was tested for significance using an F-ratio by taking the
deviations from regressions mean square as the error term. The deviations from regressions
mean square were tested for significance using the error term for overall GEI in the ANOVA.
The result of Eberhart and Russell’s ANOVA revealed non-significant (P≤ 0.05) difference
among the genotypes for grain yield indicating the yield performance of genotypes was
similar. The GE (linear) interaction was not significant. Thus, the GE interaction was nonlinear type and shows the nonexistence of genetic differences among genotypes for their
response to varying locations, which is in agreement with earlier findings of Kenga et al.
(2003), Wedajo (2014), and Fekadu et al. (2009). Pooled deviations were highly significant
against pooled error.
36
Table 9
Analysis of Variance by Eberhart and Russel's Model of early maturing sorghum
genotypes on mean grain yield (ton/ha) tested at four locations.
Source of Variation
Df
Sum squares
Mean Squares
Total
87
113.2829
Genotype
21
7.7600
0.3695ns
Loc. + (Gen. x Loc.)
66
105.5229
1.599**
Location (Linear)
1
84.0310
84.031**
Genotype x Location (Linear)
21
6.9206
0.3296ns
Pooled Deviation
44
14.5713
0.3312**
Genotype 1
2
0.3371
0.169ns
Genotype 2
2
1.5867
0.793**
Genotype 3
2
0.0538
0.027ns
Genotype 4
2
0.1634
0.082ns
Genotype 5
2
0.1200
0.06ns
Genotype 6
2
1.0382
0.519**
Genotype 7
2
0.3339
0.167ns
Genotype 8
2
0.6477
0.324**
Genotype 9
2
0.9680
0.484**
Genotype 10
2
0.3151
0.158ns
Genotype 11
2
0.3954
0.198ns
Genotype 12
2
0.4511
0.226*
Genotype 13
2
0.0972
0.049ns
Genotype 14
2
0.1768
0.088ns
Genotype 15
2
0.1726
0.086ns
Genotype 16
2
1.7507
0.875**
Genotype 17
2
0.4997
0.250*
Genotype 18
2
0.3460
0.173ns
Genotype 19
2
0.5103
0.255*
Genotype 20
2
1.7500
0.875**
Genotype 21
2
0.3072
0.154ns
Genotype 22
2
2.5505
1.275**
Pooled Error
176
11.5122
0.0654
** = highly significant (P<0.001), * = significant (P<0.0=01) and ns = non-significant
(P>0.05).
In addition, only 32.176 % of the GEI sum of squares accounted by regression sum of square,
and the remaining 67.824 % was accounted for the SS of the regression deviation. This
indicates that the largest proportion of the interaction component of variation was explained
37
by the deviation from regression. Hence, according to Khan et al. (1988) and Ashraf et al.
(2001), such differences in stability were due to deviation from linear regression only. This
means the variation in the yield performance of genotypes are entirely unpredictable in nature.
4.3.2. Genotype x Environment Interaction Analysis of Variance by AMMI Model
The combined AMMI ANOVA of the twenty two early maturing sorghum genotypes over
four locations for grain yield (ton/ha) is presented in Table 10. The ANOVA indicated very
highly significant differences (p<0.01) for treatments (environments, genotypes and GEI).
Table 10 AMMI analysis of variance for grain yield (ton/ha) of early maturing sorghum
genotypes tested at four locations during 2014 main cropping season.
Source
Total
Treatments
Genotypes
Location
Block
GEI
IPCA 1
IPCA 2
Residuals
Error
DF = degree
DF
SS
%
Total
263 374.4
87
339.8 90.75
21
23.3
3
252.1
8
6.2
63
64.5
23
32.0
21
18.7
19
13.7
168 28.4
7.59
of freedom, SS =sum of
Sum of squares explained
%
%
%
Treatment
GxL
Cumulative
6.86
74.19
18.98
49.61
28.99
21.24
squares, MS = mean of
MS
1.424
3.906***
1.109
84.031
0.771
1.023
49.61
1.392***
78.60
0.892***
99.84
0.722***
0.169
squares and *** = vary highly
significant (P<0.0001)
The total variation explained (%) was 90.75 % for treatment and 7.59 % for error. The greater
contribution of the treatment than the error indicates the reliability of this multi-location
experiment. The treatment variation was largely due to among locations variation (74.19 %),
genotype and GEI accounted 6.86 % and 18.98 % for the treatment variation, respectively. As
mentioned earlier, the high percentage of the location is an indication that the major factor
that influence yield performance of sorghum in Ethiopia is the environment. In the
AMMI ANOVA the GEI was further partitioned by PCA. The Gollob F-test used to measure
significant of the GEI components. The number of PCA axis to be retained is determines by
38
testing the mean square of each axis with the estimate of residual through the F-statistics. The
result of ANOVA showed that the first two IPCA are significant at 0.001 probability level,
this result suggests the inclusion of the first two interactions PCA axes in the model. Hence,
the best fit AMMI model for this multi-location yield trial data was AMMI-2 (Table 10).
In particular, the first IPCA captured 49.61 % of the total interaction sum of squares while the
second IPCA explained 28.99 % of the interaction sum of squares. Gauch and Zobel (1996)
and Yan et al. (2002) also suggested that the most accurate model for AMMI can be predicted
by using the first two IPCAs. In the present study the first two IPCAs accounted for a total of
78.60 % of the interaction with 44 of the corresponding degrees of freedoms. This indicates
that the GEI of the twenty two sorghum genotypes with four locations was sufficiently
predicted by the first two principal components axes and therefore, most information may
well to graphically display in AMMI1 and AMMI2 biplot. However, due to the fact that as the
result of the ANOVA indicates the residual of the interaction was also significant; indicating,
the presence of unpredictable source of variation for the sum of squares of the interaction.
Therefore, that is impossible to express the GEI of the twenty two sorghum genotypes tested
at four locations using the first two principal components axes and no need of going further to
graphically display in AMMI1 and AMMI2 biplot.
4.4.
Stability Analysis
4.4.1. Stability Analysis by Eberhart and Russel's Model
The stability parameters of Eberhart and Russell’s (1966) model for the yield of early
maturing sorghum genotypes tested at four locations is presented in table 11. According to
this model the genotype’s performance is expressed in terms of three parameters, mean yield,
regression coefficient and the deviation from the regression. Therefore, a stable genotype is
one with high mean yield, bi=1, and S2di not significantly different from zero.
39
Table 11 Eberhart and Russell’s (1966) stability parameters of early maturing sorghum
genotypes tested at four locations.
Genotypes
Designation
Genotypes
𝑏𝑖
𝑆𝑑2𝑖
GY (ton/ha)
GY
Rank
1
Code
2
3
4
5
6
2001 MS 7003
2001 MS 7013
2001 MS 7015
2001 MS 7037
IESV 92084-DL
IESV 92168-DL
0.9456
1.0331
0.9754
0.9629
1.5200
1.5800
0.1031
0.7279**
-0.039
0.0163
-0.005
0.4537**
3.34
3.33
3.3
2.57
3.71
3.33
8
9
12
22
1
9
7
8
IESV 92199-DL
IESV 92057-DL
0.4188
0.9295
0.1016
0.2584**
2.88
3.12
19
15
9
10
IESV 9027-DL
2001 MS 7007
0.9663
1.3377
0.4186**
0.0921
2.86
3.47
20
5
11
12
2005 MI 5060
2005 MI 5064
0.8795
0.9718
0.1323
0.1601*
3.37
3.68
6
2
13
14
15
2005 MI 5065
2005 MI 5066
2005 MI 5069
1.4846
0.9501
0.7777
-0.0168
0.0230
0.0209
3.67
3.35
3.09
3
7
16
16
17
2005 MI 5070
2005 MI 5075
1.1498
1.1566
0.8099**
0.1844*
3.23
3.09
14
16
18
19
2005 MI 5079
2005 MI 5081
0.8263
0.5460
0.1076
0.1897*
3
2.67
18
21
20
21
2005 MI 5082
ICSR 24005
0.9471
0.6100
0.8096**
0.0882
3.25
3.31
13
11
22
Melkam
1.0311
1.2098**
3.49
4
The result of an individual genotypes deviation from linear regression (Table q) showed that
genotype 2001 MS 7003, 2001 MS 7037, 2001 MS 7015, IESV 92084-DL, IESV 92199-DL,
2001 MS 7007, 2005 MI 5060, 2005 MI 5065, 2005 MI 5066, 2005 MI 5069, 2005 MI 5079
and ICSR 24005 had non-significant deviation from regression. The 𝑏𝑖 estimate of these
genotypes ranged from 0.0895 to 1.5199, and the 𝑏𝑖 estimates of genotype 2001 MS 7003
(0.95), 2001 MS 7037 (0.96), 2001 MS 7015 (0.98), 2005 MI 5060 (0.89) and 2005 MI 5066
(0.95), are relatively near to unity. The average yield performance of genotype 2001 MS
40
7037, was below average (Table 11). Therefore, considering their above average mean grain
2
yield, 𝑏𝑖 value closest to unity and the𝑆𝑑𝑖
= 0, genotype 2001 MS 7003, 2001 MS 7015, 2005
MI 5060 and 2005 MI 5066 were the most stable genotypes based on Eberhart and Russell’s
2
model. In contrary, the 𝑆𝑑𝑖
value of genotype 2001 MS 7013, IESV 92168-DL, IESV 92057-
DL, IESV 9027-DL, 2005 MI 5064, 2005 MI 5070, 2005 MI 5075, 2005 MI 5081, 2005 MI
5082 and Melkam were significantly different from zero (Table 11). Hence, these genotypes
were unstable in a wide range of environments.
Wachira et al. (2002) categorized genotypes adaptability to specific environments based on
their estimate of 𝑏𝑖 as adaptable to high and low yielding environments. The 𝑏𝑖 values above
one describe genotypes with higher sensitivity to environmental change (below average
stability) and are suitable to high yielding environments, whereas 𝑏𝑖 below one provides a
measurement of greater resistance to environmental change (above average stability), and are
adaptable to low yielding environments. Based on this concept the present study indicates
(Table 11), genotype 2001 MS 7013, IESV 92084-DL, IESV 92168-DL, 2001 MS 5007, 2005
MI 5065, 2005 MI 5070, 2005 MI 5075 and Melkam had 𝑏𝑖 value of greater than one and
above mean performance. Therefore, these genotypes contributed a lot to the GEI and were
suitable for favorable environments. In contrary, genotype 2001 MS 7037, IESV 92199-DL,
IESV 92057-DL, IESV 9027-DL, 2005 MI 5064, 2005 MI 5069, 2005 MI 5079, 2005 MI
5081, 2005 MI 5082 and ICSR 24005 had 𝑏𝑖 value less than unity and these genotypes
contributed less to the GEI. Hence, these genotypes are suitable for unfavorable environments
(Table 11).
4.4.2. Yield Stability Using ASV
In additive main effect and multiplicative interaction effect stability analysis (ASV) method,
a genotype with least ASV score is the most stable across environments and the larger the
ASV value, either negative or positive, the more specifically adapted a genotype is to certain
environments (Purchase, 1997). Table 12 indicates ASV for each genotype and the ranks of
the genotypes according to their AS values.
41
Table 12 IPCA1 and IPCA 2 scores; and ASV for the twenty two early maturing sorghum
genotypes sorted on mean yield (ton/ha) evaluated at four locations during 2014 main
cropping season.
Genotypes
Designation
Genotypes
GY
(ton/ha)
GY
Rank
IPCA 1
Score
IPCA 2
Score
ASV
ASV
Rank
1
Code
2
3
4
5
6
2001 MS 7003
2001 MS 7013
2001 MS 7015
2001 MS 7037
IESV 92084-DL
IESV 92168-DL
3.34
3.33
3.3
2.57
3.71
3.33
8
9
12
22
1
9
-0.2281
-0.3431
-0.1032
-0.1547
-0.2701
-0.6682
-0.2507
-0.3776
-0.0934
-0.1755
0.6052
0.5449
0.4526
0.7288
0.1851
0.2951
0.8278
1.4387
9
14
3
6
15
22
7
8
IESV 92199-DL
IESV 92057-DL
2.88
3.12
19
15
0.5577
0.3956
-0.4817
0.192
1.1850
0.7128
18
13
9
10
IESV 9027-DL
2001 MS 7007
2.86
3.47
20
5
-0.4632
-0.0808
-0.3279
0.5136
0.8990
0.4019
17
8
11
12
2005 MI 5060
2005 MI 5064
3.37
3.68
6
2
0.3676
-0.0996
0.0499
-0.231
0.6306
0.2236
11
4
13
14
15
2005 MI 5065
2005 MI 5066
2005 MI 5069
3.67
3.35
3.09
3
7
16
-0.2797
0.1857
-0.0611
0.4079
0.0915
-0.262
0.6443
0.3257
0.1730
12
7
2
16
17
2005 MI 5070
2005 MI 5075
3.23
3.09
14
16
0.5881
0.0135
0.4797
0.0516
1.2350
0.0257
20
1
18
19
2005 MI 5079
2005 MI 5081
3.00
2.67
18
21
-0.0749
0.2851
-0.3747
-0.2725
0.2684
0.5614
5
10
20
21
2005 MI 5082
ICSR 24005
3.25
3.31
13
11
0.676
0.4591
0.3282
-0.2773
1.2628
0.8614
21
16
3.49
3.23
4
-0.7018
-0.1401
1.2188
19
22
Melkam
Mean
The results showed that from the tested early maturing sorghum genotypes seventeen of them
had ASV of below one. Accordingly 2005 MI 5075, 2005 MI 5069, 2001 MS 7015, 2005 MI
5064, 2005 MI 5079, 2001 MS 7073, 2005 MI 5066, 2001 MS 7007, 2001 MS 7003, 2005 MI
5081, 2005 MI 5060, 2005 MI 5065, IESV 92057-DL, 2001 MS 7013, IESV 92084-DL,
ICSR 24005 and IESV9027-DL were relatively widely stable (Table 12). In contrary, due to
42
their large ASV genotype IESV 92199-DL, Melkam, 2005 MI 5070, 2005 MI 5082 and IESV
92168-DL were the most unstable genotypes (Table 12). The mean yield of genotypes is also
considered for selection of genotypes as a high yielder and stable genotypes. From the
selected widely stable early maturing genotypes the mean yield of ten genotypes are above the
grand mean. Therefore, based on ASV, genotype 2001 MS 7015, 2005 MI 5064, 2005 MI
5066, 2001 MS 7007, 2001 MS 7003, 2005 MI 5060, 2005 MI 5065, 2001 MS 7013, IESV
92084-DL and ICSR 24005 are relatively high yielder and widely stable genotypes. The five
most stable and high yielder early maturing sorghum genotypes on this model were genotype
2001 MS 7015, 2005 MI 5064, 2005 MI 5066, 2001 MS 7007 and 2001 MS 7003 (Table 12).
43
5. SUMMARY AND CONCLUSIONS
A total of 22 EMSGs were evaluated at Errer (Errer Agricultural Research sub-center), Kobo
(Sirinka Agricultural Research Center), Mieso (Sub-center for Melkasa Agricultural Research
Center) and Shewa Robit (Debre Birhan Agricultural Research Center) during the 2014 main
cropping season with the objectives of estimating the magnitude of GEI for grain yield and
other traits and to determine stability effect on grain yield.
The ANOVA for each location showed that the genotypes were significantly different for
grain yield (ton/ha) and grain filling rate. In contrast, for each location, insignificant variation
among genotypes was obtained for grain filling period. The variation among genotypes for
days to emergence, days to flowering, days to maturity, plant height and stand count at
harvest was different from location to location. Genotypic differences were significant for
plant height, but non-significant for days to emergence, days to flowering, days to maturity
and stand count at harvest at Errer. Genotypic differences were non-significant for days to
emergence, days to flowering, days to maturity, plant height and stand count at harvest at
Mieso. However, days to flowering, days to maturity and plant height at Kobo and days to
emergence and stand count at harvest at Shewa Robit were found to be significant.
The combined ANOVA across the four locations revealed significant differences among the
sorghum genotypes for plant height (p≤ 0.001), stand count at harvest (p≤ 0.01) and grain
filling period (p≤ 0.05). There was also highly significant difference among the tested
locations for the entire measured parameters. The total variation in yield was attributed to
67.34 % to location, 6.24 % to genotype and 17.22 % to the interaction effects.
The grand mean yield of genotypes across location was 3.23 ton/ha. The highest mean grain
yield (4.87 ton/ha) was recorded at Shewa Robit followed by at Errer (3.22 ton/ha). ). From
the tested genotypes genotype 2001 MS 7003, 2001 MS 7013, 2001 MS 7015, IESV 92084DL, IESV 92168-DL, 2001 MS 7007, 2005 MI 5060, 2005 MI 5064, 2005 MI 5065, 2005 MI
5066, 2005 MI 5082, ICSR 24005 and Melkam had mean yield above the grand mean.
Genotype ICSR 24005 had maximum number of days to emerge and genotype 2005 MI 5064
had numerically minimum number of days to emerge. Genotypes emergence period was
44
minimum at Mieso and maximum at Shewa Robit. The Grain filling rate of genotypes was
significantly fast at Shewa Robit and slow at Mieso.
Due to insignificant effect of interaction on the performance of genotypes for days to
flowering and maturity, plant height, stand count at harvest, and grain filling period, selection
of genotypes for these traits could be carried out based on their genetic potential. The relative
performance of genotypes for days to emergence, grain yield and grain filling rate was
significantly affected by the varying environmental conditions. Hence, Eberhart and Russell’s
joint regression model, AMMI model and ASV models were used for grain yield to identify
superior, adaptable and relatively stable genotypes across location.
Eberhart and Russell’s joint regression ANOVA showed that the performance of genotypes
for grain yield was statistically similar. The GE (Linear) was insignificant and the pooled
deviation was significant. The interaction sum square was accounted largely by the pooled
deviation (67.82 %) and only 32.78 % by the GE (Linear). The mean square of pooled
deviation of genotype 2001 MS 7013, IESV 92168-DL, IESV 92057-DL, IESV 9027-DL,
2005 MI 5064, 2005 MI 5070, 2005 MI 5075, 2005 MI 5081, 2005 MI 5082 and Melkam was
significantly different from zero.
The combined AMMI ANOVA showed lack of genotype differences and significant
differences among genotypes and the presence of interaction effect. In this study including the
IPCA residual both the first two IPCAs were significant. For the total variation the treatment
variation accounted about 90.75 %, and for the treatment variation was attributed to genotype
variation 6.86 %, location variation 74.18 % and interaction 18.98 %. In addition 78.60 % of
the interaction effect was explained by the first two IPCAs.
Based on Eberhart and Russell’s stability analysis, considering their above average mean
2
grain yield, 𝑏𝑖 value closest to unity and the𝑆𝑑𝑖
= 0, genotype 2001 MS 7003, 2001 MS 7015,
2005 MI 5060 and 2005 MI 5066 were the most stable genotypes. Genotype 2001 MS 7013,
IESV 92084-DL, IESV 92168-DL, 2001 MS 5007, 2005 MI 5065, 2005 MI 5070, 2005 MI
5075 and Melkam had 𝑏𝑖 value of greater than one and above mean performance and are
selected for favorable locations. Genotype 2001 MS 7037, IESV 92199-DL, IESV 92057-DL,
IESV 9027-DL, 2005 MI 5064, 2005 MI 5069, 2005 MI 5079, 2005 MI 5081, 2005 MI 5082
45
and ICSR 24005 had 𝑏𝑖 value less than unity and these genotypes are suitable for unfavorable
environments.
Additive Main Effects and Multiplicative Interaction stability value (ASV) was one of the
stability models to identify the stable genotype for this study. Accordingly, The five most
stable and high yielder early maturing sorghum genotypes on this model were genotype 2001
MS 7015, 2005 MI 5064, 2005 MI 5066, 2001 MS 7007 and 2001 MS 7003.
The results of genotype x environment interaction and stability analysis indicated that, both
Eberhart and Russell’s stability analysis and ASV models identified threeearly maturing
sorghum genotypes(2001 MS 7003, 2001 MS 7015 and 2005 MI5066), that had a high mean
performance and high stability for yield. Therefore, genotype 2001 MS 7003, 2001 MS 7015
and 2005 MI5066 can be recommended as a candidate for releasing over a wide range of
locations of the lowland Ethiopia.
This study highlighted important points for future studies related to allocation of EMSGs to
different growing conditions in the lowlands of Ethiopia. The sorghum growing dry lowland
areas of Ethiopian were diverse and contributed largely to the changes of genotypes yield
performance over locations. Therefore, further study on the GEI effects and stability of early
maturing sorghum genotypes is needed in multi locations for a number of years and location
to identify the interaction effect of genotypes and select stable genotypes.
46
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58
7. APPENDIX
Appendix 1.
Mean value of yield (ton/ha), phenological traitsand grain filling rate of early maturing sorghum genotypes for the data
collected at Errer during 2014 main cropping season.
Entry
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Genotypes
2001 MS 7003
2001 MS 7013
2001 MS 7015
2001 MS 7037
IESV 92084-DL
IESV 92168-DL
IESV 92199-DL
IESV 92057-DL
IESV 9027-DL
2001 MS 7007
2005 MI 5060
2005 MI 5064
2005 MI 5065
2005 MI 5066
2005 MI 5069
2005 MI 5070
2005 MI 5075
2005 MI 5079
2005 MI 5081
2005 MI 5082
ICSR 24005
Melkam
Mean
CV (%)
DE
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
0
DF
81.00
81.33
80.67
80.33
81.67
80.67
82.33
79.67
80.00
81.00
81.33
80.67
80.67
79.33
80.33
82.33
80.33
81.00
81.33
79.67
78.67
80.00
80.65
2.49
DM
136.00
136.33
136.33
136.00
136.33
136.00
136.00
136.00
136.00
136.33
136.00
136.00
136.00
136.00
136.00
136.00
136.00
136.00
136.00
136.00
135.00
136.00
136.02
0.33
GY
2.84defg
2.98cdef
2.93cdef
2.17gh
3.23cde
2.15gh
3.28bcde
3.37abcd
1.97h
3.12cdef
3.65abc
3.52abcd
3.38abcd
3.31abcde
2.58efgh
4.05a
3.22cde
2.76defg
2.42fgh
4ab
3.64abc
1.91h
GFR
51.56defgh
54.37cdefg
52.51defgh
39.00hi
58.94bcdef
38.93hi
61.16bcde
60.03bcdef
35.11i
56.24cdefg
66.84abc
63.7abcde
61.36bcde
58.63bcdef
46.43fghi
75.43a
57.82bcdefg
49.96efgh
44.51ghi
70.99ab
64.43abcd
33.9i
3.02
13.11
54.63
13.37
DE = Days to emergence (days), DF = Days to flowering (days), DM = Days to maturity (days), GY = Grain yield (ton/ha) and GFR =
Grain filling rate (%). Any two or more means having a common letter in a column are not significantly different at 5 % level of
significant in DMRT.
59
Appendix 2.
Mean value of grain yield (ton/ha), phenological traitsand grain filling rate of early maturing sorghum genotypes for the
data collected at Kobo during 2014 main cropping season.
Entry
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Genotypes
2001 MS 7003
2001 MS 7013
2001 MS 7015
2001 MS 7037
IESV 92084-DL
IESV 92168-DL
IESV 92199-DL
IESV 92057-DL
IESV 9027-DL
2001 MS 7007
2005 MI 5060
2005 MI 5064
2005 MI 5065
2005 MI 5066
2005 MI 5069
2005 MI 5070
2005 MI 5075
2005 MI 5079
2005 MI 5081
2005 MI 5082
ICSR 24005
Melkam
Mean
CV (%)
DE = Days to emergence (days), DF
DE
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6.00
0.00
= Days to
DF
abcdefg
DM
abcdef
GY
abcd
GFR
abc
83
118
3.32
94.82
86.33abc
121.33bc
3.8a
108.89a
85abcde
121abcd
2.96cdef
82.47bcde
ab
a
fgh
87
122.67
2.4
67.76efghi
85.33abcde
119abcdef
2.77cdef
83.25bcde
84.33abcdef
120.67abcde
2.64defg
72.97defgh
abcdefg
abcdef
fgh
82.67
119.33
2.38
65.05efghi
77.33g
116ef
2.01gh
51.99i
fg
f
bcdef
77.67
115.33
3.06
81.27bcde
83.33abcdefg
120.67abcde
2.39fgh
63.92efghi
abcdefg
cdef
efgh
82.67
116.67
2.5
73.55defgh
79.33defg
117.67bcdef
3.67ab
96.58ab
80.33bcdefg
119abcdef
3.14bcde
81.43bcde
abcdefg
bcdef
efgh
81
117.33
2.52
69.26defghi
82abcdefg
118.67abcdef
2.76cdef
75.22cdef
a
ab
h
87.67
122
1.87
54.77ghi
84.33abcdef
121.67ab
2.77cdef
74.39defg
abcdefg
abcdef
bcdef
83.33
119.67
3.05
83.27bcde
85.67abcd
119.67abcdef
1.91h
55.8fghi
87ab
121.67ab
1.85h
53.72hi
cdefg
abcdef
cdef
79.67
119.33
2.7
68.43efghi
78.67efg
116.33def
3.36abc
89.12bcd
82.89
119.26
2.72
74.91
4.25
2.02
13.28
13.88
flowering (days), DM = Days to maturity (days), GY = Grain yield (ton/ha) and
GFR = Grain filling rate (%). Any two or more means having a common letter in a column are not significantly different at 5 %
level of significant in DMRT.
60
Appendix 3.
Mean value of grain yield (ton/ha), phenological traitsand grain filling rate of early maturing sorghum genotypes for the
data collected at Mieso during 2014 main cropping season.
Entry
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Genotypes
2001 MS 7003
2001 MS 7013
2001 MS 7015
2001 MS 7037
IESV 92084-DL
IESV 92168-DL
IESV 92199-DL
IESV 92057-DL
IESV 9027-DL
2001 MS 7007
2005 MI 5060
2005 MI 5064
2005 MI 5065
2005 MI 5066
2005 MI 5069
2005 MI 5070
2005 MI 5075
2005 MI 5079
2005 MI 5081
2005 MI 5082
ICSR 24005
Melkam
Mean
CV (%)
DE = Days to emergence (days), DF
DE
DF
5.00
74.33
5.67
74.67
5.67
72.00
5.67
71.00
5.67
74.00
5.67
74.67
5.00
72.00
5.33
76.33
6.00
72.67
6.00
71.67
5.33
75.67
5.00
76.67
5.33
72.00
5.33
72.33
5.33
71.67
5.00
69.67
5.67
75.00
6.00
73.67
5.33
78.00
5.00
72.00
5.67
71.67
5.33
72.33
5.45
73.36
8.47
4.63
= Days to flowering (days),
DM
GY
GFR
bcd
bcde
125.33
2.31
45.29
cde
125.33
1.65
32.58cde
bc
122.00
2.4
47.86abc
120.00
1.56de
31.87de
ab
124.00
2.58
51.50ab
124.67
2.4bc
47.79abc
bc
122.67
2.39
46.95abcd
126.67
2.48ab
49.1ab
123.67
1.88bcde
38.06bcde
ab
121.33
2.63
52.93ab
129.00
2.58ab
48.42ab
bc
131.33
2.34
42.81bcde
120.00
2.11bcde
43.92bcde
ab
122.00
2.66
53.49ab
119.33
2.58ab
53.95ab
118.00
2.05bcde
42.234bcde
e
124.33
1.52
30.88e
123.67
1.90bcde
38.04bcde
ab
128.67
2.67
52.69ab
121.33
2.46ab
50.33ab
ab
122.00
2.68
53.48ab
124.67
3.21a
61.54a
123.64
2.32
46.17
4.91
17.45
17.43
DM = Days to maturity (days), GY = Grain yield (ton/ha) and
GFR = Grain filling rate (%). Any two or more means having a common letter in a column are not significantly different at 5 %
level of significant in DMRT.
61
Appendix 4.
Mean value of grain yield (ton/ha), phenological traitsand plant height of early maturing sorghum genotypes for the
data collected at Shewa Robit during 2014 main cropping season.
Entry
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Genotypes
2001 MS 7003
2001 MS 7013
2001 MS 7015
2001 MS 7037
IESV 92084-DL
IESV 92168-DL
IESV 92199-DL
IESV 92057-DL
IESV 9027-DL
2001 MS 7007
2005 MI 5060
2005 MI 5064
2005 MI 5065
2005 MI 5066
2005 MI 5069
2005 MI 5070
2005 MI 5075
2005 MI 5079
2005 MI 5081
2005 MI 5082
ICSR 24005
Melkam
Mean
CV (%)
DE = Days to emergence (days), DF =
DE
DF
9.67
79.33
bcde
10.67
77.67
10cdef
77.67
def
9.67
83.67
10cdef
75.67
f
9
73.33
def
9.67
77.33
10.33bcdef
72.33
bcde
10.67
73.33
10.67bcde
73.33
cdef
10
80.00
9.33ef
75.67
def
9.67
72.00
def
9.67
79.67
11.33bc
84.00
bcdef
10.33
77.67
9.33ef
77.67
f
9
79.00
11.67b
80.33
bcd
11
76.00
a
13
76.00
9.67def
74.33
10.20
77.09
7.68
6.62
Days to flowering (days), DM =
def
DM
GY
GFR
bcde
bcde
132.00
4.90
92.85
bcde
131.33
4.91
92.26bcde
bcde
132.00
4.92
90.7bcde
133.67
4.15efg
84.6cdef
a
129.67
6.27
116.69a
129.67
6.13a
109.19ab
g
132.33
3.48
63.43f
128.67
4.62cde
81.67cdef
129.00
4.52def
81.15cdef
ab
132.33
5.74
97.37abcd
133.67
4.74cde
88.35bcde
bcd
132.00
5.18
94.2bcde
131.33
6.06a
102.16abc
bcde
131.00
4.91
96.31abcd
132.67
4.45def
91.72bcde
133.67
4.94bcde
88.22bcde
cde
133.33
4.83
86.86cde
133.33
4.30defg
80.19cdef
fg
131.67
3.68
73.36ef
132.33
4.68cde
83.07cdef
efg
131.33
4.23
76.48def
131.00
5.50abc
96.98abcd
131.73
4.87
89.45
1.65
9.70
12.49
Days to maturity (days), GY = Grain yield (ton/ha) and
GFR = Grain filling rate (%). Any two or more means having a common letter in a column are not significantly different at 5 %
level of significant in DMRT.
62
Appendix 5.
Means for phenological traits and plant height and of early maturing sorghum genotypes tested at four locations during
2014 main cropping season.
Entry
Genotypes
DH
DM
PH
SCH
GFP
2001 MS 7003
79.42
127.83
177.03def
55.92ab
48.42abc
2001 MS 7013
80.00
128.58
174.62ef
45.83cdef
48.58abc
cdef
abcd
2001 MS 7015
78.83
127.83
179.73
51.83
49abc
2001 MS 7037
80.50
128.08
185.35abcdef
48.17bcd
47.58bc
cdef
abc
IESV 92084-DL
79.17
127.25
179.87
54.58
48.08abc
IESV 92168-DL
78.25
127.75
194.85ab
46.08cdef
49.5abc
IESV 92199-DL
78.58
127.58
178.38cdef
52abcd
49abc
abcdef
ab
IESV 92057-DL
76.42
126.83
185.08
55.58
50.42ab
IESV 9027-DL
75.92
126.00
171.77f
44.08def
50.08abc
ef
cdef
2001 MS 7007
77.33
127.67
173.67
45.33
50.33ab
2005 MI 5060
79.92
128.83
190.98abcd
59.08a
48.92abc
abcde
abcd
2005 MI 5064
78.08
129.25
187.57
51.67
51.17a
2005 MI 5065
76.25
126.58
198a
48.83bcd
50.33ab
2005 MI 5066
78.08
126.58
191.01abcd
49.33bcd
48.5abc
abcde
f
2005 MI 5069
79.50
126.67
186.43
38.83
47.17c
2005 MI 5070
79.33
127.42
187.77abcde
54.42abc
48.08abc
abc
cdef
2005 MI 5075
79.33
128.83
192.02
46.08
49.5abc
2005 MI 5079
79.25
128.17
184.65abcdef
47.92bcde
48.92abc
cdef
cdef
2005 MI 5081
81.33
129.00
178.92
45.5
47.67bc
2005 MI 5082
78.67
127.83
182.85bcdef
49.5bcd
49.17abc
ICSR 24005
76.50
126.92
186.02abcdef
39ef
50.42ab
g
abcd
Melkam
76.33
127.00
159.5
51.75
50.67ab
Mean
78.50
127.66
183.00
49.15
49.16
CV (%)
4.68
2.70
8.07
19.45
6.47
DH = Days to heading (days), DM = Days to maturity (days), PH = Plant height (Cm), SCH = Stand count at harvest (number) and
GFP = Grain filling period (days). Any two or more means having a common letter in a column are not significantly different at 5%
level of significant in DMRT.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
63
Appendix 6.
The IPCA 1 and IPCA 2 scores for the four sites, sorted on environmental mean
yield, used in the study.
Location
Environment Mean
IPCA - 1
IPCA - 2
Errer
3.02
1.3587
Kobo
2.72
-0.7817
Mieso
2.32
0.2787
Shewa Robit
4.87
-0.8557
IPCA - 1 = Interaction Principal Component Analysis Score 1 and IPCA –
Principal Component Analysis Score 2.
Appendix 7.
0.159
-1.057
-0.2415
1.1395
2 = Interaction
Total monthly rainfall (mm) and mean monthly temperature (°C) of the four
tested locations during 2014 main cropping season.
Total Rain fall (mm)
Month
Errer
Kobo
Mieso
Jan.
NA
1.2
Feb.
NA
Mar.
Mean Temperature (°C)
Errer
Shewa
Kobo
Mieso
S/Robit
Robit
Min.
Max.
Min.
Max
Min
Max
0.0
2.9
NA
NA
13.0
33.8
10.5
29.5
13
30.02
7.7
1.1
12.5
NA
NA
15.1
28.6
15.0
30.7
16.75
30.48
NA
58.6
98.8
86.8
NA
NA
15.5
28.6
16.1
31.5
17.95
32.65
Apr.
NA
31.7
120.8
21.5
NA
NA
16.8
28.6
17.3
31.4
19.22
34.23
May
NA
0.0
64.2
76.5
NA
NA
16.8
30.2
17.2
32.5
19.05
34.19
June
NA
4.5
13.9
14.9
NA
NA
13.7
31.8
16.7
35.0
19.23
36.23
July
NA
154.2
154.2
244.9
NA
NA
0.0
33.9
18.8
33.2
19.29
34.25
Aug.
NA
255.8
90.1
188.1
NA
NA
0.0
34.0
18.0
31.2
18.61
31.82
Sept.
NA
159.7
158.7
98.2
NA
NA
0.0
30.9
16.7
30.6
18
31.95
Oct.
NA
xx
147.5
141.9
NA
NA
0.0
0.0
14.0
29.0
16.1
30.8
Nov.
NA
xx
7.5
25
NA
NA
0.0
0.0
11.8
29.8
14.68
30.33
Dec.
NA
0.0
0.0
0
NA
NA
0.0
0.0
8.3
28.5
12.05
29.18
Min
Max.