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CHARACTERIZATION, EVALUATION AND GENETIC DIVERGENCE OF OKRA [Abelmoschus esculentus (L.) Moench]
LANDRACES AT PAWE, NORTHWEST ETHIOPIA
MSc THESIS
FLAGOTE ALEMU BAYU
JUNE 2020
HARAMAYA UNIVERSITY, HARAMAYA
ii
Characterization, Evaluation and Genetic Divergence of Okra [Abelmoschus Esculentus (L.) Moench] Landraces at Pawe, northwest
Ethiopia
A Thesis Submitted to the School of Plant Sciences, Postgraduate Programs Directorate
HARAMAYA UNIVERSITY
In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCES IN AGRICULTURE (HORTICULTURE)
Flagote Alemu Bayu
June 2020
Haramaya University, Haramaya
iii
HARAMAYA UNIVERSITY POSTGRADUATE PROGRAM DIRECTORATE
As thesis research advisors, I hereby certify that I have read and evaluated the thesis entitled
“Characterization, Evaluation and Genetic Divergence of Okra [Abelmoschus
Esculentus (L.) Moench] Landraces at Pawe, northwest Ethiopia’’ prepared under my
guidance by Flagote Alemu. I recommend that it be submitted as fulfilling the thesis
requirement.
Wassu Mohammed (PhD)_____
_____________________
___________________
Major Advisor Signature Date
As members of the Board of Examiners of the MSc Thesis Open Defense Examination, we
certify that we have read and evaluated the thesis prepared by Flagote Alemu and examined
the candidate. We recommend that the thesis be accepted as fulfilling the requirements for
the Degree of Master Science in Horticulture.
________ ___________________ ___________________
Chairperson Signature Date
________
___________________
__________________
Internal Examiner Signature Date
_______
___________________
__________________
External Examiner Signature Date
Final approval and acceptance of the thesis is contingent upon the submission of the final
copy to the Council of Postgraduate Program (CPGP) through the Postgraduate Program
Directorate.
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STATEMENT OF THE AUTHOR
By my signature below, I declare and affirm that this thesis is my own result of 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 has been submitted in partial fulfillment of the requirements for a Master of
Science Degree at the Haramaya University. The thesis shall be deposited in the Haramaya
University’s Library and be 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 made 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 of Plant Sciences 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 thesis.
Name: Flagote Alemu Signature: ___________________
Date: ___________________
School: Plant Science
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BIOGRAPHICAL SKETCH
The author was born in Jijiga town, Fafan zone, Somala Regional State on April 28, 1984.
He attended his primary and secondary education in Jijiga. Following completion of his
Secondary education in 2002, he joined Jimma University College of Agriculture and
Veterinary Medicine in 2003 and graduated with BSc degree in Horticulture in 2006. Then,
he was employed at Ethiopian Institute of Agricultural Research in 2010 as a Junior
Researcher in Pawe Agricultural Research Center. He served the institute for two years until
he joined Haramaya University in 2013 to pursue his MSc degree in Horticulture.
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ACKNOWLEDGMENTS
First and for most, I would like to express my deepest gratitude to my major advisor Dr.
Wassu Mohammed for his valuable guidance and suggestions, for his insightful thoughts
and kindness in providing me valuable and constructive advises. His generous time devotion
from the early design of the research proposal to the final write-up of the thesis is crucial to
finalize the thesis timely.
I would like to thank the Ethiopian Institute of Agricultural Research for giving me the
chance to join the Master’s program. Besides, I would like to express my indebted thanks to
Pawe Agricultural Research Center and colleagues for their encouragement and
thoughtfulness.
I also greatly appreciate the moral and spiritual support I got from my wife Hiwote Teweled,
my son Markon Flagote, my father Alemu Bayu and my mother Roman Fanta.
Finally, I indebted to Almighty God, who allowed me to begin and conclude this research
thesis.
vii
ABBREVIATIONS AND ACRONYMS
AHC Agglomerative Hierarchical Clustering
DMRT Duncan’s Multiple Range Test
FAO Food and Agriculture Organization
FAOSTAT Food and Agriculture Organization Statistics
IPGRI International Plant Genetic Resource Institute
SAS Statistical Analysis System
SD Standard Deviation
viii
TABLE OF CONTENTS
STATEMENT OF THE AUTHOR iv
BIOGRAPHICAL SKETCH v
ACKNOWLEDGMENTS vi
ABBREVIATIONS AND ACRONYMS vii
LIST OF TABLES xi
LIST OF FIGURES xii
LIST OF TABLES IN THE APPENDIX xiii
LIST OF FIGURES IN THE APPENDIX xiv
ABSTRACT xv
1. INTRODUCTION 1
2. LITERATURE REVIEW 4
2.1. Origin, Distribution and Taxonomy 4
2.2. Nutritional Potential 5
2.2.1. Food Values 5
2.2.2. Health Value 6
2.3. Ecological Requirement 7
2.4. Phenology and Growth 7
2.4.1. Vegetative Growth 8
2.4.2. Reproductive Growth 8
2.5. Economic Importance of Okra 10
2.6. Germplasm Evaluation and Breeding 10
2.6.1. Characterization and Evaluation of Okra 10
2.6.2. Breeding Programmers and Methods 11
2.7. Phenotypic and Genotypic Variations 12
2.8. Heritability and Genetic Advance 14
2.9. Correlation Coefficient 16
2.10. Path Coefficient Analyses 17
2.11. Genetic Divergence 18
2.11.1. Cluster analysis 18
2.11.2. Genetic diversity 19
ix
CONTINUED
2.12. Principal Component Analysis 20
3. MATERIALS AND METHODS 21
3.1. Description of Experimental Site 21
3.2. Experimental Materials and Design 21
3.3. Field Management 21
3.4. Data Collection 24
3.4.1. Quantitative Traits 24
3.4.1.1. Crop phenology and growth traits 24
3.4.1.2. Fruit characteristics and yield 25
3.4.2. Qualitative Traits 27
3.5. Data Analysis 28
3.5.1. Analysis of Variance 28
3.5.2. Estimation of Variability 28
3.5.3. Heritability and Genetic Advance 29
3.5.3.1. Heritability in broad sense 29
3.5.3.2. Expected genetic advance under selection 29
3.5.4. Phenotypic and Genotypic Correlation Coefficient Analysis 30
3.5.5. Path Coefficient Analyses 30
3.5.6. Genetic Divergence and Clustering Analysis 31
3.5.7. Principal Component Analysis 32
4. RESULTS AND DISCUSSION 33
4.1. Analysis of Variance and Mean Performances of Landraces 33
4.1.1. Analysis of Variance 33
4.1.2. Mean Performances of Landraces 34
4.1.2.1. Phenological parameters 34
4.1.2.2. Growth traits 36
4.1.2.3. Fruit and seed characteristics 39
4.1.2.4. Fruit yield 41
4.1.3. Qualitative Characters 43
4.1.3.1. Plant growth habits 43
4.1.3.2. Leaf shape characteristics 43
x
CONTINUED
4.1.3.3. Pigmentation characteristics 43
4.2. Estimates of Genetic Parameters 45
4.2.1. Estimates of Phenotypic and Genotypic Variance Components 45
4.2.2. Estimates of Heritability and Expected Genetic Advance 46
4.3. Principal Component Analysis 49
4.4. Genetic Divergence Analysis 54
4.4.1. Genetic Distances among Okra Landraces 54
4.4.2. Clustering of Genotypes 56
4.4.2.1. Grouping of okra landraces into clusters 56
4.4.2.2. Cluster mean analysis 57
4.5. Association among Traits 60
4.5.1. Genotypic and Phenotypic Correlation Coefficients 60
4.5.1.1. Genotypic and phenotypic correlation of fruit yield with other traits 60
4.5.1.2. Phenotypic and genotypic correlations among other traits 61
4.5.2. Phenotypic and Genotypic Path Coefficient Analyses 66
5. SUMMARY AND CONCLUSION 69
6. REFERENCES 71
7. APPENDIX 87
LIST OF TABLES
Table Page
1. List of okra landraces and collection sites
2. Mean squares from analysis of variance for quantitative traits of okra landraces
3. Mean values of 35 okra landraces for phenological traits evaluated at Pawe in 2017
4. Mean values of 35 okra landraces for growth traits evaluated at Pawe in 2017
5. Mean values of 35 okra landraces for fruit and seed characteristics evaluated at
Pawe in 2017
6. Mean values of 35 okra landraces for fruit yield evaluated at Pawe in 2017
7. Distribution of 35 okra landraces into eight qualitative traits evaluated at Pawe in
2017
8. Phenotypic and genotypic variances, heritability and genetic advance for 22
quantitative traits of 35 okra landraces at Pawe in 2017
9. Factor loadings, contribution of traits and Eigen values of four principal component
axes in 35 okra landraces evaluated at Pawe in 2017
10. Mean genetic distances of 35 okra landraces as measured by Euclidean distance
11. Clusters of 35 okra landraces based on 22 quantitative traits
12. Cluster means for 22 quantitative traits of 35 okra landraces at Pawe in 2017
13. Genotypic (above diagonal) and Phenotypic (below diagonal) correlation
coefficient among 22 quantitative traits of 35 okra landrace
14. Genotypic direct (bold) and indirect effects of quantitative traits on okra yield
15. Phenotypic direct (bold) and indirect effects of quantitative traits on okra yield
22
33
35
38
40
42
44
48
51
55
57
59
64
67
68
xii
LIST OF FIGURES
Figure Page
1. Map of okra landrace collection sites 23
2. Scattered diagram 35 okra landraces by 22 quantitative traits using two dimensional
ordinations traits on PCA1 and PCA2. 52
3. Scattered diagram by using two dimensional ordinations of 35 okra landraces and 22
quantitative traits based on PC (principal component) axes 3 and 4 53
xiii
LIST OF TABLES IN THE APPENDIX
Appendix Table Page
1. Long-term average climatic data (1987-2018) for Pawe Station and the study
area 88
2. Description of qualitative traits according to IPGR, 1991 descriptors used to
characterize 35 okra landraces at Pawe in 2017 88
3. Qualitative traits of 35 okra landraces according to IPGR, 1991 descriptors
evaluated at Pawe in 2017 89
4. Euclidean distance matrix as estimates of genetic distances of 35 okra landraces
estimated from 22 quantitative traits 90
5. Genotypic direct (bold diagonal) and indirect (off diagonal) effects of
quantitative traits on okra yield 92
6. Phenotypic direct (bold diagonal) and indirect (off diagonal) effects of
quantitative traits on okra yield 94
xiv
LIST OF FIGURES IN THE APPENDIX
Appendix Figure
Page
1. Dendrogram showing clustering pattern among 35 okra landraces based on 22
quantitative traits evaluated 96
xv
CHARACTERIZATION, EVALUATION AND GENETIC DIVERGENCE OF OKRA [Abelmoschus Esculentus (L.) Moench]
LANDRACES AT PAWE, NORTHWEST ETHIOPIA
ABSTRACT
Okra is a traditional vegetable crop in northwestern Ethiopia but it has not given research
attention and considered a minor crop. Thus this research was conducted to characterize
and evaluate okra landraces and to estimate genotypic, phenotypic variability and genetic
divergence and to estimate heritability and genetic advance under selection, degree of
genotypic and phenotypic associations among yield and yield related traits. A total of 35
okra landraces from three districts (Guba, Mandura and Dangure) were evaluated for 23
agro-morphological and eight qualitative traits in 2017 at Pawe Agricultural Research
Center in randomized complete block design. Results of analysis of variance showed
significant differences among okra landraces for all traits and the landraces also distributed
in different categories of qualitative traits. The landraces had number of tender fruits per
plant ranged from 4.86 to 36.54 and 2.49 to 21.98 t ha-1 mean fruit yield per hectare. The
estimates of genotypic (GCV) and phenotypic coefficients of variation (PCV) for 22 traits of
35 okra landraces were in the range between 2.95 and 54.92 and 4.96 and 55.22%,
respectively. The broad sense heritability (H2) and genetic advance as percent of mean
(GAM) estimated in the range between 35.36 and 99.6% and 3.62 and 112.66%,
respectively. High GCV, PCV, H2 and GAM was estimated for plant height, number of
primary branches/stem, internodes length, number of matured fruits per plant, weight of
matured fruits per plant, dry weight of matured fruits/plant, number of seeds per fruit,
number of tender fruits per plant and yield per hectare indicated the high heritability was
due to the close correspondence between the genotypic and phenotypic variations as a result
of relatively small contribution of the environment to the phenotype expression of the traits.
Fruit yield per hectare had positive and significant genotypic and phenotypic correlation
coefficients with weight of matured fruits per plant, dry weight of matured fruits per plant,
hundred seed weight, number of tender fruits per plant and leaf length. Fruit yield per
hectare had positive and significant phenotypic correlation coefficients with number of ridge
and peduncle length. These traits also had positive direct effects on fruit yield at genotypic
and phenotypic levels except leaf length exerted negative direct effect on yield at phenotypic
level. This suggested direct and simultaneous selection of genotypes for yield and these traits
is possible. Results of principal component analysis indicated the first four principal
component axes (PCA1 to PCA4) accounted 65.59% of the total variation, of which PCA1
and PCA2 had larger contribution of 22.09 and 19.34%, respectively. The 35 okra landraces
were grouped into 12 distinct clusters from Euclidean distances matrix using Unweighted
Pair-group Methods with Arithmetic Means (UPGMA) of which Cluster IX consisted of 6
(17.14%), Cluster II, VI and X consisted each five and other clusters consisted of 1 and 3
landraces. The study results showed the presence of genetic variation among landraces for
all traits suggested that selection could be effective to develop okra varieties for high fruit
yield and other traits.
Keywords: Cluster, Direct effect, Euclidean distance, Genetic advance and Heritability
1. INTRODUCTION
Okra [Abelmoschus esculentus (L.) Moench] is one of the most popular vegetables in the
world and belongs to Malvaceae family. It requires a long, warm and humid growing period
(Abd El-Kader et al., 2010). The geographical origin of okra based on ancient cultivation in
East Africa, suggested that it is originated somewhere around Ethiopia, and it was cultivated
by the ancient Egyptians by the 12th century BC (Benchasri, 2012). It is the most ancient and
traditional vegetable crop grown in tropical and sub-tropical low land regions of Asia,
Africa, America and warmer parts of Mediterranean regions (Rambabu et al., 2019b). In
2017, the total world production of okra is 9.6 million-ton pods of which India contributes
62%, Nigeria 21%, Sudan 3%, Mali 2.11%, Côte d'Ivoire 1.64% and Niger 1.59%
(FAOSTAT, 2017).
Okra plays a significant role in human nutrition by providing carbohydrates, protein, fat,
minerals and vitamins that are generally deficient in basic foods. The composition of okra
pods per 100 g edible portion (81% of the product as purchased, ends trimmed) is: water
88.6 g, energy 144.00 kJ (36 kcal), protein 2.10 g, carbohydrate 8.20 g, fat 0.20 g, fibre 1.70
g, Ca 84.00 mg, P 90.00 mg, Fe 1.20 mg, β-carotene 185.00 μ g, riboflavin 0.08 mg, thiamin
0.04 mg, niacin 0.60 mg, ascorbic acid 47 mg (Benchasri, 2012). Mature okra seeds are good
sources of protein and oil (Habtamu et al., 2015). Okra seed oil is also a rich source of
antioxidants (Tian et al., 2015) and rich in unsaturated fatty acids, such as linoleic acid (Rao
et al., 1991; Sathish et al., 2013), which is essential for human nutrition.
Okra is a multipurpose crop due to its various uses of the fresh leaves, buds, flowers, pods,
seeds and stems (Mihretu et al., 2014). It is mainly grown for its young immature fruits,
which are consumed as a vegetable, raw, cooked or fried, soups and sauces (Habtamu et al.,
2014). The fruits can be conserved by drying or pickling. The leaves are sometimes used as
spinach or cattle feed, the fibers from the stem for cord, the mucilage’s for medical and
industrial purposes and the seeds as a substitute for coffee (Jayakumar, 2002; National
Research Council, 2006). Okra has been used medicinally in treatment of several disorders,
in lowering serum cholesterol, reducing heart disease and cancer, especially colorectal
cancer (Aminu et al., 2016).
2
In Africa, traditional vegetables are an important source of nutrients and vitamins for the
rural and urban population. Farmers have cultivated and collected these vegetables for
generations as an additional food source. Natural selection and farmer-based breeding
practices have developed the genetic base of the most important vegetables. Therefore,
improving the genetic potential of indigenous vegetables like okra is of paramount
importance (Kumar et al., 2010). In recent decades, there has been formal research by
national agricultural research programs and international research organizations on
cultivation methods of the vegetables to improve their yield. However, research on the
genetic base of these vegetables for breeding purposes has been scattered and the results of
studies have not always been published in scientific journals. Information on the results of
research and availability of this information for researchers has been a major obstacle for
stimulating further research on traditional vegetables (Mnzava et al. 1999).
In Ethiopia the economic importance of okra is negligible. The species is cultivated and
utilized as vegetable in some parts of the country while in other parts of the country, it is
grown as wild plant and its utilization is very limited. For generation, communities in
Gambella and Beneshangul Gumuz have been cultivating for its fruit and leaf to use as a
food and medicine of different diseases (Tesfa and Yosef, 2016). However, the crop has a
potential to be used for food security and tackling the crucial malnutrition problem. It has
also a potential to be export commodity to the neighboring and Arab countries which are
known as a heavy consumers and importers of the crop (Wassu et al., 2017).
As center of origin of okra Ethiopia is expected to have rich genetic diversity in the species.
The presence genetic diversity among okra landraces will play significant role in breeding
program as it helps to develop high yielding okra varieties. It is important for selection and
breeding to desired plant landraces (Prakash et al., 2017). However, recently in 2016 the first
improved variety (Bamia Humera) has been recommended for cultivation (MoANR, 2016).
Okra breeding activity in Ethiopia to exploit the rich genetic resource is very limited and
little works have been conducted in collection and characterization of landraces and wild
plants (Tesfa and Yosef, 2016; Muluken et al., 2016; Wassu et al., 2017). Therefore,
characterization and evaluation of the accessions for different morphological, agronomic and
quality traits is necessary to identify useful traits for either direct use or further improvement
programs. The value of a landraces collection depends not only on the number of accessions
it contains, but also upon the diversity present in those accessions (Ren et al., 1995).
3
Knowledge of the naturally occurring diversity in a population helps to identify groups of
genotypes that can be used for hybridization program as well as to screen desired genotypes
for the trait of interest. Similarly, information on the nature of interrelationship among traits
will help to formulate efficient scheme of multiple trait selection. Okra has multidirectional
importance and utilization, technology development regarding variety development and crop
management practices were very limited (Tesfa and Yosef, 2016). Characterization,
evaluation and diversity study of okra landraces collection was not attempted in Metekel
Zone where, the crop is a traditional vegetable and information is lacking on genetic
divergence, phenotypic and genotypic variance as well as heritability and interrelationships
of yield and yield related traits in okra landraces collected from Metekel Zone in particular
and from Ethiopia at large.
Therefore, characterization, evaluation, genetic variability and diversity study of Metekel
Zone's okra landraces would generate valuable information that can be exploited in breeding
programs to develop high yielding varieties and to improve the quality. In view of these, the
present study is initiated with the following objectives.
Specific objectives
to characterize and evaluate okra landraces and to estimate genotypic, phenotypic
variability, heritability and genetic advance under selection and genetic divergence;
to estimate the degree of genotypic and phenotypic associations among yield and yield
related traits, and
4
2. LITERATURE REVIEW
2.1. Origin, Distribution and Taxonomy
Okra [Abelmoschus esculentus (L.) Moench] is a warm-season annual herbaceous vegetable
crop grown primarily for immature fruits (Yildiz et al., 2015). Okra apparently originated in
what the geo-botanists call the Abyssinian center of origin of cultivated plants, an area that
includes present day Ethiopia, the mountainous or plateau area of Eritrea, and the eastern,
higher part of the Anglo-Egyptian and Sudan. It is one of the oldest cultivated crops and
presently grown in many countries and is widely distributed from Africa to Asia, southern
Europe and America (Kumar et al., 2013; Sathish et al., 2013). Although it has been
commonly cultivated in Egypt for many hundreds of years, no sign of it has ever been found
in any of the ancient monuments or relics of old Egypt (Aladele et al., 2008).
The word okra is borrowed from a West African language, probably from Igbo or Akan
(Weerasekar, 2006). The generic name Abelmoschus is derived from Arabic ‘abu-l-mosk’
(father of musk) in allusion to the smell of the seeds whereas the specific epithet means
‘musk smelling’. It is also known by different names in different countries. Latin binomial
names for okra are Abelmoschus esculentus (Kumar et al., 2013). Okra is known by many
local names in different parts of the world. It is called Kacang Bendi, qiukui, Okra, okura,
Okro, Quiabos, Ochro, Quiabo, Gumbo, Quimgombo, Bamieh, Bamya, Quingumbo, Bamia,
Ladies Fingers, Bendi, Bhindi and Kopi Arab (Kumar et al., 2013) In West and Central
Africa, okra is called Gombo (French), Miyan-gro (Hausa), La (Djerma), Layre (Fulani),
Gan (Bambara), Kandia (Manding), Nkruma (Akan), Fetri (Ewe) (Kumar et al., 2010) In
South East Asia okra is known as lady's fingers, Bhindi in India, Krajiabkheaw in Thailand,
However, in the Middle East it is known as Baima, Bamya or Bamieh and Gumbo in
Southern USA, and lady’s finger in England (Ndunguru and Rajabu, 2004). In Ethiopia, it
has also different names in different regions like ‘Amula’ in Gambella, ‘Qenqes’ or ‘Sharma’
in Asosa (Mihretu, 2013), ‘Weyeka’ in Agew (Anduale Zewedu; Personal communication)
‘Kenkase’ in Berta, ‘Andeha’ in Gumuz and ‘Bamia’ in Oromica/Amharic (Habtamu et al.,
2015).
5
Okra belongs to the family Malvaceae, genus Abelmoschus. It was previously included in
the genus Hibiscus. Later, it was designated to Abelmoschus, which is distinguished from
the genus Hibiscus by the characteristics of the calyx: spatulate, with five short teeth, connate
to the corolla and caduceus after flowering (Daniel, 2011).
There are about fifty species, both wild and cultivated. Some of these are A. esculentus, A.
caillei, A. moschatus, A. manihot, A. ficulneus and A. tetraphyllus. Among these, whereas,
the three species (A. esculentus, A. manihot L. and A. moschatus) are wild as well as
cultivated, the remaining is all wild in nature (IBPGR, 1991). There are significant variation
in the chromosome numbers and ploidy levels among Abelmoschus species. The lowest
chromosome number known is 2n = 56 for A. Angulosus (Ford, 1938) and the highest are
close to 200 for A. Caillei (Siemonsma, 1991). Even within A. esculentus, chromosome
numbers 2n = 72, 108, 120, 132 and 144 are in regular series of polyploids with n = 12
(Ikram et al., 2010).
2.2. Nutritional Potential
2.2.1. Food Values
Almost all parts of okra plant are consumed: fresh okra fruits are used as vegetable, roots
and stem are used for clearing the cane juice, and leaves and stems are used for making fiber
and ropes. Okra seeds containing good quality edible oil and high protein are used to
complement other protein sources. Okra has a flavor like asparagus and eggplant, and it is
popular in much of the world, including Africa, the Middle East, Greece, Turkey, India, the
Caribbean, South America and the Southern United States. It's an excellent source of fiber
and pods are commonly used as source of food. It is one of the plants that are known for
their mucilaginous quality. The extract of mucilage is often added to different recipes like
soups, stews, and sauces to increase the consistency (Biswal et al., 2014).
Okra is the only vegetable crop of importance in the Malvaceae family, cotton is the most
important economic crop in this family. Different people use okra in different ways. The
immature pods can be consumed as boiled, fried or cooked (Maurya et al., 2013). Avallone
et al. (2008) reported that they are dried and used as soup thickeners, or used in stews.
Further, he reported that the stem and mature pods produce fiber, which is used in
6
papermaking and for textile purposes. Potassium, sodium, magnesium and calcium are the
principal elements in the pods, which contain about 17% seeds. Presence of Fe, Zn, Mn and
Ni also has been reported (Moyin-Jesu, 2007). Fresh pods have low calories (20 per 100 g)
practically no fat, high in fiber, and have several valuable nutrients, including about 30% of
the recommended levels of vitamin C (16 to 29 mg), 10 to 20% of folate (46 to 88 mg) and
about 5% of vitamin A (14 to 20 µg) (Schneeman, 1998). Pods and seeds are rich in phenolic
compounds with important biological properties like quartering derivatives like catechin
oligomers and hydroxycinnamic derivatives (Brown et al., 1999). These properties, along
with the high content of carbohydrates, proteins, glycol protein, and other dietary elements,
enhance the importance of this food stuff in the human diet (Schneeman, 1998). Okra seed
is known to be rich in high quality protein especially with regards to its content of essential
amino acids relative to other plant protein sources (National Research Council, 2006).
2.2.2. Health Value
Vegetables are important protective foods for the maintenance of health and prevention of
diseases (Rai and Yadav, 2005). They contain valuable food ingredients, which can be
successfully utilized to build up and repair the body. Okra contains vegetable mucilage that
provides valuable service to the regeneration of small and large intestine flora. Okra
mucilage is used in Asian medicine as a protective food additive against irritating and
inflammatory gastric diseases (Lengsfeld, 2004). Its mucilage is suitable for medicinal
applications. The alkaline pH of okra could also contribute to its effect in gastro-intestinal
ulcers by neutralizing the digestive acids (Benchasri, 2012). It also strengthens the mucous
membrane and immune system. Culture of red blood cell and formation is activated and
sleeping memory cells are brought into speed (Habtamu et al., 2015). The high mineral
content keeps stable blood pressure and circulation. Okra is a repository of valuable nutrients
nearly half of which is soluble fiber in the form of gums and pectin’s. Soluble fiber helps to
lower serum cholesterol, reducing the risk of heart disease. The other half is insoluble fiber
that helps to keep the intestinal tract healthy, decreasing the risk of some forms of cancer,
especially colorectal cancer (Aminu et al., 2016).Okra mucilage binds cholesterol and bile
acid carrying toxins dumped into it by filtering liver, thus might act as a hepato protective
agent (Schneeman, 1998).
7
2.3. Ecological Requirement
Okra can be grown in a wide range of soils. Well-drained sandy to clay soils supplied with
enough organic matter are good for okra cultivation. However; loose, friable and well
manure loam soils having the pH range 6 to 7 are the best. Alkaline, saline soils and soils
with poor drainage are not good for this crop. Okra requires a moderate rainfall of 80-100
cm well distributed throughout the growing season to produce its young edible fruits
(Benchasri, 2012).
Being a warm season crop it is susceptible to cold and frost. It thrives well during warm,
moist season although it grows well in the hottest summer. Okra is a tropical crop and its
optimal temperature for germination, growth and fruit setting is between 20°C to 35°C.
Beyond this range the germination will be delayed and weak seeds may not germinate
(Tripathi et al., 2011) with 15°C and 42°C minimum and maximum temperature
respectively. For faster plant growth still higher temperature helps though it delays fruiting
but at temperatures beyond 40°C to 42°C, flowers may desiccate and drop, causing yield
losses (Amjad et al., 2001). Uniform day and night temperature levels are preferred by okra,
wide difference between day and night temperatures or fluctuation reduces the seed yield
considerably. It is a short day plant, but its wide geographical distribution indicates that
cultivars differ markedly in sensitivity. The shortest critical day length reported is 12.30
hours for flower initiation and flowerings are hardly affected by day length in popular
subtropical cultivars (Benchasri, 2012).
2.4. Phenology and Growth
Okra is an upright annual, herbaceous 91 to 244 cm tall plant with a hibiscus like flower. It
is mainly propagated by seeds and has life for 90-100 days (Lokesh, 2017). Different
genotypes have different growth habits, as a result of selection or a natural adaptation
mechanism. The common growth habit among all the landraces observed indeterminate with
erect growth appearance (Oppong-Sekyere et al. 2011). Flower buds are initiated at 22-26
days and the first flower opened 41-48 days after sowing. Once initiated, flowering continues
for 40-60 days (Tripathi et al. 2011). Growth and yield of okra depends upon many factors
including seed quality, soil nutrition, climatic conditions and cultural practices (Kusvuran,
2012).
8
Anthesis and stigma receptivity are influenced by genotype and climatic factors like
temperature and humidity. Anthesis is observed between 6 AM and 10 AM. Anthers dehisce
before flower opening and hence self-pollination may occur at anthesis. The dehiscence of
anthers is transverse and complete dehiscence occurs in 5-10 minutes. Pollen fertility is
maximized in the period between an hour before and an hour after opening of the flower.
The stigma is receptive on the day of (90-100%) flowering, day before (50-70%) and the
day after (1-15%) flowering. Flowers open only once in the morning and close after
pollination on the same day. Flowers are very attractive to bees and the plants are cross-
pollinated. Cross pollination up to 4-19% with maximum of 42.2% has been reported
(Tripathi et al., 2011). The extent of cross-pollination in a particular place will depend upon
the cultivar, competitive flora, insect population and season (Hamon and Koechlin, 1991).
2.4.1. Vegetative Growth
Okra’s stem is semi woody and sometimes pigmented with a green or reddish color. It is
erect, variable in branching, with many short branches that are attached to thick semi woody
stem. The stem attains height from 91 cm in dwarf varieties to 244 cm in height. The woody
stem bears leaves that are lobed and hairy. Leaf is heart-shaped, simple, usually palmately
3-7 lobed and veined. It is subtended by a pair of narrow stipules and alternate and usually
palmately five lobed, whereas the flower is axillary and solitary. The leaf is dark green in
color and resembles a maple leaf (Tripathi et al., 2011).
Okra has a strong deep taproot system which penetrates almost vertically downward up to
160 cm with a diameter of 0.5 cm. A total of 24 to 35 laterals ran horizontally from just
beneath the soil surface to a depth of 41 cm. A maximum spread of 46 cm is reached at the
13 cm level. A few of the deeper laterals pursued an obliquely down ward course (Tripathi
et al., 2011).
2.4.2. Reproductive Growth
Okra has perfect flowers (male and female reproductive parts in the same flower) and it is
self-pollinated. Its flowers are 4-8 cm in diameter, with five white to yellow petals, often
with a red or purple spot at the base of each petal and the flower withers within one day.
Flower structure is hermaphrodite and self-compatible. Flower bud appears in the axil of
each leaf, above 6th to 8th leaf depending upon the cultivar. The plant usually bears its first
9
flower one to two months after sowing (Tripathi et al., 2011). Flower bud initiation and
flowering are influenced by genotype and climatic factors like temperature and humidity.
Flowering is continuous but highly dependent upon biotic and a biotic stress. Flowering and
fruiting is continuing for an indefinite time, depending upon the variety, the season and soil
moisture and fertility (Tripathi et al., 2011). Flower initiation and flowerings are delayed as
temperatures increase (positive correlation between temperature and number of vegetative
nodes (Benchasri, 2012).
The fruit is an elongated, conical or cylindrical capsule and containing ovules. It is long pod
and generally ribbed, developing in the leaf axil and spineless in cultivated kinds. Its color
varies from normally yellowish green to green. Sometimes purple or whitish green colored
fruit exists. The fruit is a capsule and grows quickly after flowering. The greatest increase in
fruit length and diameter occurs during 4th to 6th day after pollination. At this stage fruit is
most often plucked for consumption. Its fruits are harvested when immature and high in
mucilage but before becoming highly fibrous. Generally the fiber production in the fruit
starts from 6th day onwards of fruit formation and a sudden increase in fiber content from 9th
day is observed (Tripathi et al., 2011). Pods older than 7 days are considered to be low in
quality mainly due to excessive increase in crude fiber, gradual reduction of moisture and
important components of the table quality. Crude protein and starch contents are also
reduced by late harvest while crude oil content increased (Duzyaman and Vural, 2003). Piloo
and Kabir (2011) in their study indicated that the optimum time of harvesting okra cultivars
is between 4 to 5 days after fruit set. Fruits harvested in this stage were of 9.85 to 11.33 cm
average fruit length, 11.45 to 13.69 mm average fruit diameter, 8.67 to 11.19 g average fresh
weight, 1.59 to 1.79 mm average pericarp thickness and 89.85 to 90.34% average moisture
content.
Okra fruit contains numerous ovals, smooth, striated and dark green to dark brown seeds
(Adeniji et al., 2005). The easiest way to keep the seed is to leave it in the pod. Seed weight
varies from 30 to 80 g 1000 seeds. Okra seeds contain about 20% protein and 20% oil. The
dried seeds are a nutritious material that can be used to prepare vegetable curds, or roasted
and ground to be used as coffee additive or substitute (Benchasri, 2012).
10
2.5. Economic Importance of Okra
Okra is the only vegetable crop of economic importance in the Malvaceae family and
cultivated throughout the tropic and sub-tropic regions (Sharma & Prasad, 2015). It is
tolerant to a wide range of climatic conditions (Akanbi et al., 2010). It provides good yields
and possibly more products than any other vegetables and economically speaking, its
products are within almost everyone’s reach (National Research Council, 2006). Okra
production besides creating jobs on the farm, also generates off-farm employment, especially
for women. This is the case for export and value-added processing industries, which are
important sectors of the economy of Ethiopia (Hunde, 2017). It has also a potential to be
export commodity to the neighboring and Arab countries which are known as a heavy
consumers and importers of the crop (Wassu et al., 2017).
2.6. Germplasm Evaluation and Breeding
2.6.1. Characterization and Evaluation of Okra
Plants are living things that have morphological, structural, and functional characteristics
that enables them to adapt to the habitat where they are established, interacting with changing
environmental conditions. Important information is believed to exist in plant genome and to
express itself as morphological, structural, or functional attributes. It contained in landraces,
which therefore, becomes the holder of a species entire sum of hereditary characteristics.
However, it should be emphasized that, to use it the landraces should be understood in detail
that is the type of attributes it possesses should be clearly determined. The process of gaining
such understanding is known as landraces characterization (Upadhyaya et al., 2008).
Adequate characterization for agronomic and morphological traits is necessary to facilitate
utilization of landraces by breeders. To achieve this objective landraces accessions of all
crops are characterized for morphological and agronomic traits in batches over the years.
Germplasm characterization is the recording of distinctly identifiable characteristics, which
are heritable. This needs to be distinguished from preliminary evaluation, which is the
recording of a limited number of agronomic traits considered important in crop
improvement. Germplasm characterization is carried out in precision fields by spaced
planting under adequate agronomic conditions and plant protection. For each accession,
11
several morpho-agronomic traits are recorded using the descriptors developed in
collaboration with International Plant Genetic Resources Institute (IPGRI) (Upadhyaya et
al., 2008). Information about a landraces accession is essential if collections are to be
effectively conserved, catalogued and retrieved from gene banks (De Vicente et al., 2005).
The major objectives of landraces characterization are describing landraces, establish their
diagnostic characteristic and identify duplicates. In addition, it helps to classify groups of
landraces using sound criteria. On the other hand, landraces characterization enables
researchers to identify landraces with desired agronomic traits, select entries for more precise
evaluation, and develop interrelationships between, or among traits and between geographic
groups of cultivars by estimating the extent of variation in the collection.
Traits required for characterization are generally highly heritable ones, which are expressed
within acceptable limits of deviation over a range of agro-climatic conditions. This is
essential because these traits are expected to help us identify an accession and may be used
to monitor the identity of an accession over several regenerations. Traits such as leaf shape,
flower color and seed-coat (testa) color fall into this group. Despite the ease with which these
parameters could be recorded, there is a need to define the exact growth stage or time to see
and method of recording so that it can be easily understood by the user community and other
evaluators. Thus, characterization is primarily the responsibility of the gene bank curator
(Sloten, 1989), helps to describe the diversity in collections, and assists the curator to manage
these collections effectively.
2.6.2. Breeding Programmers and Methods
International breeding efforts have been oriented towards intensive cultivation with high
production in a short period (early maturity) and wide adaptation (photoperiod insensitivity,
resistance to insects and diseases). Crossing between promising parents combined with
pedigree selection or backcrossing remains the most common breeding procedure. Several
American and Indian attractive cultivars have found their way to commercial growers
throughout the tropics and subtropics, but there is still plenty of scope for cultivar
improvement in Africa for the commercial sector (where good alternatives for the introduced
cultivars are needed with better adaptation to local conditions) as well as for the traditional
sector (where hardy, robust, long lived types are required). Nevertheless, molecular markers
analyses have shown a rather low level of genetic diversity in cultivated okra despite large
12
phenotypic variability. There is little information on improvement using biotechnology apart
from in vitro DNA extraction and plant regeneration from various explants and callus tissue.
The characteristics of okra species open new opportunities for recombination. They cross
readily in both directions and crosses result in vigorous hybrids for sustainable okra product
in the future (Benchasri, 2012).
2.7. Phenotypic and Genotypic Variations
Crop diversity is the variance in genetic and phenotypic characteristics of plants used in
agriculture. Crops may vary in seed size, branching pattern, plant height, flower color,
fruiting time, or flavor. They may also vary in less obvious characteristics, such as their
response to heat, cold or drought, or their ability to resist specific diseases and pests (Smith
and Smith, 1992). It is possible to discover variation in almost every conceivable trait,
including nutritional qualities, preparation and cooking techniques, and of course how a crop
taste. If a trait cannot be found in the crop itself, it can often be found in a wild relative of
the crop; a plant that has similar species that have not been farmed or used in agriculture,
but exist in the wild (Bisht et al., 1996).
Phenotypic variation is the variation of the physical traits, or phenotypic characters of the
organism, such as differences in anatomical, physiological, biochemical, or behavioral
characteristics. Local environmental conditions can alter phenotypic characters (Thormann
et al., 1994). Diversity based on phenotypic and morphological characters usually varies
with environments and evaluation of traits requires growing the plants to full maturity prior
to identification (Adeoluwa, 2011). Omonhinmin and Osawaru (2005) have reported a wide
morphological variation among accessions of okra, particularly in A. caillei (West African)
types. There are reports of diversity studies in okra that used morphological markers (Karp
et al., 1997). Whenever the genotype only partly controls the phenotypic expression,
variation in the quality of the growing conditions induces variation in phenotypic expression.
The size of the phenotypic variation within genetically homogeneous plant material reflects
the balance between the strength of the genetic control of the expression and the size of the
effects of variation in the quality of the growing conditions. Different genotypes may, with
the same variation in the quality of the growing conditions, show different phenotypic
variation (Bos and Caligari, 2008). Mihretu (2013) in estimation of phenotypic coefficients
of variation showed the presence of variability among the 25 accessions for most of the
13
characters: days to maturity, number of primary branches and fruit length have high
phenotypic coefficients.
Genetic diversity is defined as genetic variation within species. It is the precious heritage
and is essential for the survival of all organisms on earth. Genetic diversity in crop plants is
mainly preserved in landraces and wild relatives, and they are called plant genetic resources
(PGR). Characterization and quantification of genetic diversity and information on the
genetic diversity within and among closely related crop varieties is essential for a rational
use of plant genetic resources (Adeoluwa and Kehinde, 2011). The existing relationships
between traits are generally determined by the genotypic, phenotypic and environmental
correlations. The genotypic and phenotypic coefficients of variations (GCV and PCV) are
the measures of variability among the genotypes under study. The genotypic coefficient of
variation (GCV) measuring the range of genetic variability for different plant characters
helps to compare this variability and phenotypic coefficient of variation (PCV) indicates the
interaction effect of environment on these traits (Weerasekar, 2006). Adeoluwa and Kehinde
(2011) evaluated 35 accessions of West African Okra (Abelmoschus caillei) and reported as:
The accessions showed a wide variability for all characters evaluated; variation was
expressed in all qualitative trait studies except in leaf and petal color. Phenotypic variances
were generally higher than their respective genotypic variances thus revealing the role of
environmental factors; and high PCV and High GCV were observed for pod yield per plant
and peduncle length, respectively.
Adewusi and Adeweso (2018) studied genetic variability and heritability studies in west
African okra (Abelmoschus caillei (A. Chev. Stevels). They stated that the values of PCV
were higher than that of GCV values for all the 12 characters indicating influence of
environmental effects in expression of these characters. Kumar et al. (2019) also observed
the phenotypic and genotypic coefficient of variance was maximum for pod yield per plant
followed by plant height. On the other hand, Amandeep et al. (2019) observed high to
moderate PCV as well as GCV for mucilage followed by pods per plant, pod yield per plant,
average pod weight, ridges per pod, node at which the first pod set, nodes per plant, plant
height and inter nodal length depicting the presence of substantial variability and would
respond better to selection.
14
2.8. Heritability and Genetic Advance
Falconer and Mackay (1996) defined heritability as the measure of the correspondence
between breeding values and phenotypic values. In general, the degree to which the
variability of a character is transmitted to the progeny is referred to as its heritability.
Heritability estimates provide an indication of the expected response to selection in a
segregating population. Heritability is interest to the plant breeders, mainly as a measure of
the value of selection for characters and as index of transmissibility.
The main aim of breeding program is to increase the yield (Singh, 2007). Yield is the product
of action and interaction of the vital activities of the plants throughout the life cycle.
Therefore, the improvement of crop yields by breeding is determined by the amount of
heritable or genetic variation to that of non-heritable portion. Together with heritability,
genetic advance gives estimates of realizable gain at a specific intensity of selection which
is an important tool in plant breeding (Haq et al., 2008). Therefore, heritability estimates
along with genetic advance are normally more helpful in laying emphasis on selection for
yield and yield components.
Heritability is the transmissibility of characters from parents to offspring. In broad sense, it
is the ratio of genotypic variance to total phenotypic variance in percentage (Asish et al.,
2008). Heritability estimate provides information about the extent to which a character can
be transmitted to the successive generations. Knowledge of heritability of a trait thus guides
a plant breeder to predict behavior of succeeding generations and helps to predict the
response to selection (Shahid et al., 2002). It is a parameter of a population. Regarding the
population, it is a constant not a variable. It is not observable but can be estimated from data.
Because data are generated by a random process, the estimated heritability is a variable not
a constant. In other words, different samples will generate different estimates of the
heritability (Weerasekar, 2006).
Genetic advance (GA) is the improvement over the base population that can be potentially
achieved from selection. It is a function of the heritability of the trait, the amount of
phenotypic variation and the selection differential that the breeder uses. When high
heritability is accompanied by high genetic advance, it indicates additive gene effects and
selection may be effective. When low heritability is accompanied by low genetic advance,
15
it indicates predominance of environmental effects and the selection would be ineffective.
High heritability with low genetic advance indicates the importance of non-additive gene
effects, while low heritability with high genetic advance indicates the importance of additive
gene effects (Haq et al., 2008). Mehta et al. (2006) reported from his study that the GCV,
heritability and genetic advance as percentage of mean were high for fruit yield, average
fruit weight, plant height and fruit length, which might be attributed to additive gene action
resulting from their inheritance. Genetic variability and correlation studies in Okra
(Abelmoschus esculentus (L) Moench.) by Krushna et al. (2007) indicated that the estimate
of high heritability (broad sense) accompanied by high expected genetic advance for fruit
yield per plant and plant height indicates the presence of additive gene action in the
expression of these traits. The estimates of heritability (broad sense) were of high magnitude
for green fruit yield per plant, plant height at harvest, days to maturity and number of
internodes per plant, indicating the major role of genotype and ultimately less environmental
influence.
For predicting the real resultant effects of selection, high heritability coupled with high
genetic advance would be a more reliable criterion than simple heritability value alone
(Johnson et al., 1955). Characters with high magnitude of heritability as well as genetic
advance are controlled by additive gene action and, therefore, readily amenable to genetic
improvement through selection. Shivaramegowda et al. (2016) conducted an experiment in
36 accessions of okra collection and estimated high heritability estimated for plant height,
fruit yield and its related characters like number of fruits per plant, fruit weight, fruit length,
and fruit girth. On the other study, Bello et al. (2015) recorded that high heritability coupled
with genetic gain as percent of the mean were observed for all the studied characters except
fresh pod diameter and days to 50% flowering. This indicated diverse genetic background
thereby providing a great scope for selection. Again, this suggested the predominance of
additive gene control for these characters of studied, rather than the environment. Rambabu
et al. (2019b) also reported that high heritability coupled with high genetic advance as per
cent of mean for the characters viz., plant height, inter nodal length and fruit yield per plant.
It indicating that they were governed by additive genes and could be effectively improved
through selection.
16
2.9. Correlation Coefficient
Correlation coefficient analysis measures the mutual relationship between two characters
and determines component characters in which selection can be based for genetic
improvement in yield. Whether the association of these characters is due to their direct effect
on yield or is a consequence of their indirect effects via other component characters may be
answered through path coefficient analysis. Such information reveals the possibility of
simultaneous improvement of various attributes and helps in increasing the efficiency of
selection of complex traits (Gangashetty et al., 2011).
The correlation between two traits refers to a situation where the two traits vary with each
other, either positively or negatively, within a breeding population. Correlation could be due
to genetic or environmental causes. The result of correlation is of great value in determining
the most effective procedures for selection of superior genotypes for improvement (Mehta
et al., 2006). According to Simon et al. (2013), knowledge of the correlation among traits
serves as a guide to prevent the elimination of some useful traits at the expense of other
desirable traits during selection. For example, where desirable traits are known to be
negatively correlated, caution during selection would be needed to ensure a good balance of
desirable traits in improved cultivars. In addition to all this, knowing correlation among traits
in a breeding population serves in facilitating indirect selection for fruit yield through
selection for yield components. To evaluate the relationships, correlation analyses are used
such that the values of two characters are analyzed on a paired basis, results of which may
be either positive or negative. Ariyo (1989) has established that in developing a variety, it
would be difficult to exercise simultaneous selection for major yield characters when these
characters are negatively correlated, but when they are positively associated, component
breeding would be very effective.
The phenotypic correlation measures the degree of association of two variables and is
determined by genetic and environmental factors. The environmental correlation is mainly
responsible for the association of traits of low heritability. The genotypic correlation, which
represents the genetic portion of the phenotypic correlation, on the other hand is the only
one of inheritable nature and, therefore, used to orient breeding programs (Falconer and
Mackay, 1996). According to Bello et al. (2006), the genotypic correlation coefficients on
okra evaluation showed that more significant relationship between the pairs of characters
17
than the phenotypic correlations. This suggests that the characters are more related
genotypically than phenotypically. Rambabu et al. (2019a) reported that plant height,
number of branches, number of fruits per plant, inter nodal length, last harvest, fruit length,
fruit girth, fruit weight, number of fruits per plant, number of seeds per fruit, 100 seed
weight, number of picking and iodine content, were found to possess significant and positive
correlation with fruit yield per plant. Marketable yield per plant, fruit weight, fruit length,
number of fruits per plant and plant height were significantly and positively associated with
total yield per plant in okra.
2.10. Path Coefficient Analyses
Path analyses is an important analytical tool, which has been, or can be, used to quantify a
perceived biological relationship through partitioning of correlation coefficients into direct
and indirect effects. It is often observed that certain quantitative characters of economic
importance are associated with one another. Yield is dependent on several of its component
traits. In such cases, the knowledge on association between such characters is quite helpful
to plant breeders to formulate their selection strategies (Pradip et al., 2010). The study of
simple correlation does not provide an exact picture of relative importance of direct and
indirect influence of each of the component character towards the direct character. So, this
can be overcome by following path coefficient analysis technique by further partitioning the
correlation coefficient into direct and indirect effects. Assuming yield as a contribution of
several characters, which are correlated among themselves and to the yield, the concept of
path coefficient analysis, was originally developed by Wright in 1921, but Dewey and Lu
(1959) first used the technique for plant selection. Path analysis is simply standardized
partial regression coefficient, which splits the correlation coefficients into the measures of
direct and indirect effects of a set of independent variables on the dependent variable (Mehta
et al., 2006).
Character association and path are pre-requisites for improvement of any crop (Crawford,
1990). The estimate of path coefficient analysis is important for better understanding of the
crop. The major advantage of path analysis is that, it permits the partitioning of the
correlation coefficient into its components, one component being the path coefficient that
measures the direct effect of a predictor variable upon its response variable; the second
component being the indirect effect(s) of a predictor variable on the response variable
18
through another predictor variable. Path coefficient analysis at genotypic level revealed that
internodes number had highly positive direct effect on fruit yield followed by average fruit
weight, which had positively genotypic correlation with yield (Mihretu, 2013). Mehta et al.
(2006) shows Path coefficients on okra revealed that, fruit girth had the maximum direct
effect followed by fruit length towards fruit yield. Thus, the fruit yield in okra can be
improved by selecting for higher fruit length, fruit girth and average fruit weight
simultaneously. On the other study of Nasit et al. (2009) stated that number of fruits per
plant had maximum indirect contribution via fruit length and ten fruit weight in building
strong positive association with yield. Fruit shape index negatively correlated with fruit yield
but showed high direct effect towards yield. Reddy et al. (2013) shows Path coefficient
analyses revealed that fruit weight, total number of fruits per plant and number of marketable
fruits per plant had strong influence on marketable pod yield per plant and are the main
determiners of marketable pod yield per plant. According to Karri and Acharyya (2012),
Path analysis at phenotypic level revealed that total fresh yield per plant was positively
dependent on characters i.e. number of fruits per plant, average fruit weight, node at first
flowering, number of locules per fruit and inter nodal length.
2.11. Genetic Divergence
2.11.1. Cluster analysis
Cluster analysis refers to a group of multivariate techniques whose primary purpose is to
group individuals or objects based on the characteristics they possess, so that individuals
with similar descriptions are mathematically gathered into the same cluster. The resulting
clusters of individuals should then exhibit high internal (within cluster) homogeneity and
high external (between clusters) heterogeneity. Thus, if the classification is successful,
individuals within a cluster shall be closer when plotted geometrically and different clusters
shall be farther apart (Hair et al., 1995). There are broadly two types of clustering methods:
(i) distance-based methods, in which a pair-wise distance matrix is used as an input for
analysis by a specific clustering algorithm (Johnson and Wichern, 1992), leading to a
graphical representation (such a tree or dendrogram) in which clusters may be visually
identified; and (ii) model-based methods, in which observations from each cluster are
assumed to be random draws from some parametric model, and inferences about parameters
corresponding to each cluster and cluster membership of each individual are performed
19
jointly using standard statistical methods such as maximum-likelihood or Bayesian methods
(Aremu, 2012).
Distance-based clustering methods can be categorized into two groups: hierarchical and
nonhierarchical. Hierarchical clustering methods are more commonly employed in analysis
of genetic diversity in crop species. These methods proceed either by a series of successive
mergers or by a series of successive divisions of group of individuals. The former known as
agglomerative hierarchical methods, start with a single individual. Thus, there are initially
as many clusters as individuals. The most similar individuals are first grouped and these
initial groups are merged according to their similarities. Among various agglomerative
hierarchical methods, the UPGMA (Unweighted Paired Group Method using Arithmetic
averages) (Panchen, 1992) is the most commonly adopted clustering algorithm, followed by
the Ward’s minimum variance method. Although some studies indicated the relative
advantages of UPGMA clustering algorithm interims of consistency in grouping biological
materials with relationships computed from different types of data (Mumm et al., 1994). The
nonhierarchical clustering procedures do not involve construction of dendrogram or trees.
These procedures, also frequently referred to as “K-means clustering,” are based on
“sequential threshold,” “parallel threshold,” or “optimizing” approaches for assigning
individuals to specific clusters, once the number of clusters to be formed is specified (Everitt,
1980).
2.11.2. Genetic diversity
The diversity studies on these crops at their respective primitive levels (Landrace, wildtype,
accessions, lines etc.) led to the development of their widely distributed cultivars and
varieties with proven characteristics based on stability and adaptability of performance with
consistent tolerance to adverse weather conditions and resistant to diseases around the world.
Understanding the inter and intra specie genetic relationships as provided by diversity
studies has proven to increases hybrid vigor and reduce or avoid re-selection within existing
germsplasm. It is worthy of note that existing cultivar populations have narrow genetic bases,
hence need for creating variability within and among cultivars using genetic diversity
methods (Aremu, 2012).
20
Variations are recorded in the measurement of genetic diversity in genotype relationships
based on genetic distances and grouping populations from individual genotypes such as
accessions, lines, wild races etc. The recorded variations are primarily because of the
differences in the nature of genetic materials. Therefore, the basis or genetic variance
theories which identifies genotype relationships based on genetic distance estimating genetic
diversity depends largely on statistical genetic variance theories which identifies genotype
relationships based on genetic distance/variance. Beaumont et al. (1998) provided a more
comprehensive definition of genetic distance as any quantitative measure of genetic
difference at either sequence or allele frequency level calculated between genotype
individuals or populations. Euclidean or straight-line measure of distance is the most
commonly used statistic for estimating genetic distance between individuals (genotypes or
populations) by morphological data (Mohammadi and Prasanna, 2003). Euclidean distance
measurement allows the use of both qualitative and quantitative data several workers
identified genotype distances using Euclidean distance in okra. Wassu et al. (2017) used for
characterization and evaluation of okra [Abelmoschus esculentus (L.) Moench] collections
in Eastern Ethiopia and Ahiakpa (2017) used to estimate genetic distance among West
African and Asian germplasm of okra (Abelmoschus L.) under study.
2.12. Principal Component Analysis
Principal component analysis (PCA) is a mathematical algorithm that reduces the
dimensionality of the data while retaining most of the variation in the data set. It
accomplishes this reduction by identifying directions, called principal components, along
which the variation in the data is maximal. By using a few components, each sample can be
represented by relatively few numbers instead of by values for thousands of variables. The
main objective of this analysis was to reduce the observed variables in smaller number of
principal components that were accounted for most of the variance in the observed variables.
Finally, it defines the pattern of variation between the accessions by summarizing data in to
reduced number of traits (Markus, 2008). According to Prasad and Sharma (2010), the first
component was found heavily loaded with pod weight, pod diameter and days to first harvest
in a positive direction, and number of pods per plant, pod yield per plant, pod yield per plot,
plant height and number of branches in a negative direction.
21
3. MATERIALS AND METHODS
3.1. Description of Experimental Site
The experiment was conducted at Pawe district which is located 575 km from Addis Ababa.
The experimental site was located at Pawe Agricultural Research Center and has an elevation
of 1120 meters above sea level at latitude of 11°18′ N and longitude of 36°24′ E. Pawe was
situated in the hot humid agro-ecological zone that was well known for okra cultivation by
the farmers. The experiment was conducted on vertisol soil type with a pH of 6.5. The area
was characterized by a long season of rainfall which the main rainy season falls primarily
from early May to late October with mean annual rainfall of 980-1587mm per year and the
average annual minimum and maximum temperatures are 16°C and 32°C, respectively
(Appendix Table 1).
3.2. Experimental Materials and Design
Thirty-five okra landraces, which have been collected by Pawe Agricultural Research Center
from Metekel Zone, were used for this study (Table 1 and Figure 1) in 2017 rainy season.
The landraces were planted in a Randomized Complete Block Design (RCBD). Each
landraces was sown in single-row plot to minimize environmental variations associated with
large plots and in three replications. Each row was 6.3 m long with a row-row distance of 1
m and 45 cm plant-to-plant (Alam and Hossain, 2008). With 45 cm plant-to-plant, spacing
there were 14 plants per row.
3.3. Field Management
Ahead of sowing, seeds were soaked overnight in water to get uniform germination, good
stand, then three seeds per hole were placed at the depth of 3 to 5 cm. After germination the
seedlings were thinned to one plant per stand two weeks after germination. Weeding was
commenced at two weeks after sowing and subsequent weeding was carried out at 7 and 12
weeks. Fertilizer was used as local growers’ practice without using fertilizer. Regular plant
protection measures were carried out to safeguard the crop from pests and diseases. Okra
pods were harvested while still tender, which was usually 5 to 6 days after flowering. Two
22
plants were un-harvested plants next to these side plants at the beginning and end of rows,
which were left completely un-harvested until complete pod maturity (pods turn brown and
dry).
Table 1. List of okra landraces and collection sites
Landraces Source
District Zone
Year of
Collection Landraces
Source
District Zone
Year of
Collection
Gu-2 Guba Metekel 2011 Ma-24 Mandura Metekel 2012
Gu-3 Guba Metekel 2011 Ma-25 Mandura Metekel 2012
Gu-4 Guba Metekel 2011 Ma-29 Mandura Metekel 2012
Gu-5 Guba Metekel 2011 Ma-30 Mandura Metekel 2012
Gu-6 Guba Metekel 2011 Ma-31 Mandura Metekel 2012
Gu-7 Guba Metekel 2011 Ma-32 Mandura Metekel 2012
Gu-8 Guba Metekel 2011 Ma-33 Mandura Metekel 2012
Gu-9 Guba Metekel 2011 Ma-34 Mandura Metekel 2012
Gu-11 Guba Metekel 2011 Ma-35 Mandura Metekel 2012
Gu-12 Guba Metekel 2011 Ma-37 Mandura Metekel 2012
Gu-14 Guba Metekel 2011 Ma-39 Mandura Metekel 2012
Gu-17 Guba Metekel 2011 Da-40 Dangure Metekel 2012
Gu-18 Guba Metekel 2011 Da-41 Dangure Metekel 2012
Gu-20 Guba Metekel 2011 Da-42 Dangure Metekel 2012
Gu-21 Guba Metekel 2011 Da-43 Dangure Metekel 2012
Gu-22 Guba Metekel 2011 Da-45 Dangure Metekel 2012
Gu-23 Guba Metekel 2011
Gu-27 Guba Metekel 2011
Gu-47 Guba Metekel 2011
23
Figure 1. Map of okra landrace collection sites
24
3.4. Data Collection
Quantitative traits data were recorded from 12 plants per row left the two plants grown at
both end of the rows as border plants. The 10 plants, which were grown at the center of each
row, were used to record growth, phenology and tender fruits characters and to estimate
tender fruit yield. The two plants next to the border plants in each row were used to estimate
mature pods characters and 100 seeds weight. International Plant Genetic Resources Institute
(IPGRI, 1991) descriptor list for okra species was used to record the quantitative traits. Based
on the descriptor the following quantitative traits were recorded:
3.4.1. Quantitative Traits
3.4.1.1. Crop phenology and growth traits
Days to 50% flowering: The number of days taken from the date of sowing to the day on
which 50 percent of the plants in each row produce flower was recorded.
Days to maturity: The average number of days from sowing to the date of first harvest of
10 sample plants of the central rows was recorded.
Plant height (cm): The height of 10 plants from each plot from the ground level to the tip
will be measure at the time of final harvest and the average was considered for statistical
analysis.
Stem diameter (cm): Stem diameter at the basal region of plants was measured at the time
of final harvest.
Number of primary branches per stem: The total number of primary branches per plant
was counted at final picking and average of 10 plants will be calculated.
Number of internodes: The total number of internodes per plant was counted at final
picking and average of 10 plants will be calculated.
Internodes length (cm): The length of the internodes between the fifth and sixth node were
measured at time of maturity before the first tender fruit harvest.
25
Leaf length (cm): The length of 15 leaves on the main stem from each plot was sampled
randomly from ninth and eleventh node when the plants came into flowering. As the leaves
from seventh node onwards are representative of the shape and the size of the variety. Leaves
will be measured from the attachment of the base of the leaf and petiole to the tip of the leaf.
Leaf width (cm): The width of 15 leaves on the main stem from each plot was sampled
randomly from ninth and eleventh node when the plants came into flowering. Leaves were
measured from the widest part of the leaf.
Number of epicalyxes: The number of epicalyxes flowers of the five samples per plant at
flowering stage from each plot was counted.
3.4.1.2. Fruit characteristics and yield
Fruits were harvested three times per week, and number and weight of all tender fruits were
recorded in each harvest. Data from each harvest was summed at the end of the growing
season to estimate number of tender fruits/plant and tender fruits weight/plant. Average
tender fruits weight was calculated by dividing the total weight of tender fruits from all
harvest to the total number of tender fruits harvested. Five randomly chosen tender fruits
from each harvest in each plot and totally, not less than fifty tender fruits from each plot
were used to record average tender fruits diameter, length, pedicel length and number of
ridges. From two plants next to the side plants/border plants, all mature fruits that were
produced between the 6th and 20th nodes were harvested at the end of the growing season to
collect data on number and weight of matured fruits/plant, dry weight of matured fruits
/plant, seed number/ fruit and 100 seed weight.
Peduncle length (cm): Pedicel length of the five fruits per plant prior to picking was
measured at fully matured stage.
Fruit length (cm): The length of five tender fruits per plot in each harvest was measured
from the base of calyx to the tip of the fruit. The average was calculated by dividing the sum
of all tender fruit’s length by the total number of fruits measured.
26
Fruit diameter (cm): The five tender fruits per plot which fruit length was measured as
indicated above will be also used to measure tender fruits diameter at the center of the fruit
and the average was calculated like that of the fruit length.
Average fruit weight (g): Each of five tender fruits per plot that was used to measure fruit
length and width will be weighed using sensitive balance and the average weight of tender
fruit was calculated and recorded accordingly.
Number of tender fruits per plant: Fruits of ten plants in each plot at each harvest was
counted and summed at the end of the harvest and the average number of tender fruits per
plant was calculated that was considered for statistical analysis.
Number of ridges on fruit: The number of ridges was counted and the average was
calculated from five tender fruits per plot at each harvest that was used to measure fruit
length and width.
Yield per hectare (t ha-1): This was estimated from the 10 plants tender fruit yield in each
plot.
Number and weight of matured fruits per plant: Matured fruits of the two plants next to
the side plants/border plants in each plot was harvested, counted and weighted to estimate
and record number and weight (g/plant) of matured fruits per plant.
Dry weight of matured fruits per plant (g/plant): All the harvested matured fruits of the
two plants next to the side plants/border plants in each plot was dried, weighted and the
average dry weight of matured fruits per plant was calculated and recorded.
Number of seeds per fruit: Ten fully matured and dried fruits were collected randomly
from the two plants matured fruits in each plot as indicated above and seeds were extracted,
counted and average number of seeds per fruit was computed to be considered for statistical
analysis.
Hundred seed weight (g): Seeds extracted from ten matured fruits as indicated above were
kept in open air under sun for 24 hour and the dried 100 seeds were randomly counted and
weighted to estimate 100 seeds weight (Mihretu et al., 2014; Rambabu et al., 2019b).
27
3.4.2. Qualitative Traits
The qualitative traits were recorded on plot basis according to International Plant Genetic
Resources Institute (IPGRI, 1991) descriptor list for okra species as follows.
Plant habit: This was identified how the plants in each plot branched and described as:
1) Densely Branched at the Apex (DBA) 2) Densely Branched at the Base (DBB) and 3)
Densely Branched all Over (DBO).
Flower color: Red coloration of petals base was assessed at both side and described as:
1) Red color inside only or 2) Red color at both sides.
Leaf color: This was assessed from leaves lamina and ribs and described as:
1) Totally Green and 2) Green with Red vein.
Leaf petiole color: This was assessed from petioles color at both side and described as:
1) Green 2) Red above but green below and 3) Red on both sides.
Pod color: Main color of the pods was observed at harvesting stage and described as:
1) Green and 2) Red.
Stem color: Stems color of plants was assessed at first harvest stage and described as: 1)
Green 2) Green with red patch and 3) Red or purple. Color chart was used for all color
identification of pod, stem and leaf. http://w3schools.com/html/html_colorfull.asp
Shape of leaf: This was assessed from leaves of plants that was produced up to the first
harvest and described as: 1) oval undulate 2) heart-shaped 3) broadly ovate 4) star shaped
(palmately lobed) 5) palmately triangular lobes 6) palmately lobed with dentate margins 7)
palmately lobed with serrated margins and 8) linear-oblong or triangular lobes.
28
Position of fruits on main stem: The position of fruits on the main stem of the accessions
was observed and it was described in five distinct variations as: 1) Erect 2) Intermediate 3)
Horizontal 4) Slightly falling and 5) Totally falling.
3.5. Data Analysis
3.5.1. Analysis of Variance
Data on quantitative characters were subjected to analysis of variance (ANOVA) using SAS
version 9.4 (SAS, 2019) to test the presence of significant differences among accessions for
the traits measured. Traits, which exhibited significant mean squares, were considered to
further genetic analyses viz., Divergence analysis (D2), Path coefficient analysis, Phenotypic
Coefficient of Variation (PCV), Genotypic Coefficient of Variation (GCV), broad-sense
Heritability (H2) and Expected Genetic Advance as percentage to mean (GAM). Duncan’s
Multiple Range (DMRT) was employed to identify genotypes that were significantly
different from each other. Descriptive statistics was used for qualitative traits data.
3.5.2. Estimation of Variability
The variability of each quantitative trait was estimated by simple measures, such as, mean,
range, standard deviation, phenotypic and genotypic variances and coefficients of variation.
The phenotypic and genotypic coefficients of variation were computed using the formula
suggested by Burton and de Vane (1953) as follows.
r
g varianceGenotypiceMStMS
2
Where, δ2 g = genotypic variance
MSt = mean square of treatment
MSe = mean square of error
r = number of replications
Where, δ2g = Genotypic variance
δ2e = Environmental variance
22 variancePhenotipic eg
29
Phenotypic coefficient of variation (PCV) = √��
�
�� X 100
Genotypic coefficient of variation (GCV) = √��
�
�� X 100
X� = Population mean of the character being evaluated
3.5.3. Heritability and Genetic Advance
3.5.3.1. Heritability in broad sense
Broad sense heritability values were estimated based on the formula of Falconer and Mackay
(1996) as follows:
� =� �
� �∗ 100
Where, H2 = heritability in the broad sense.
σ2p = Phenotypic variance
σ2g = Genotypic variance
3.5.3.2. Expected genetic advance under selection
Genetic advance in absolute unit (GA) and percent of the mean (GAM), assuming selection
of superior 5% of the genotypes were estimated in accordance with the methods illustrated
by Johnson et al. (1955) as:
GA = K * SDp * H2
Where, GA = Genetic advance
SDp = the phenotypic standard deviation on mean basis;
H2 = heritability in the broad sense
k = the standardized selection differential at 5% selection intensity (K = 2.063)
Genetic advance as percent of mean
Genetic advance as percent of mean was estimated as follows:
30
GAM = GA
X�∗ 100
Where, GAM = Genetic advance as percent of mean
GA = Genetic advance
X� = Population mean of the character being evaluated
3.5.4. Phenotypic and Genotypic Correlation Coefficient Analysis
Phenotypic (rp) and genotypic (rg) correlations between two traits were estimated using the
formula suggested by Johnson et al. (1955), Singh and Chaudhury (1985).
Where, rp = Phenotypic correlation coefficient
rg = Genotypic correlation coefficient
Pcovxy = Phenotypic covariance between variables x and y
Gcovxy = Genotypic covariance between variables x and y
Vpx = Phenotypic variance of variable x
Vgx = Genotypic variance of variable x
Vpy = Phenotypic variance of variable y
Vgy = Genotypic variance of variable y
3.5.5. Path Coefficient Analyses
The direct and indirect effect of yield related traits on yield will be worked out through path
coefficient analysis. The analysis will be made following the method suggested by Dewey
and Lu (1959). The formula is as follows:
rij = Pij + ∑ rik Pkj
)yV.xV(
covGr
gg
xy
g
)yV.xV(
covPr
pp
xy
p
31
Where,
rij = Mutual association between the independent character (i) and dependent character, grain
yield (j) as measured by the correlation coefficients.
Pij = Components of direct effects of the independent character (i) as measured by the path
coefficients and ∑ rik Pkj = summation of components of indirect effect of a given
independent character (i) on a given dependent character (j) via all other independent
characters (k).
The contribution of the remaining unknown factor was measured as the residual factor (PR),
which is calculated as: PR = √ (1-∑rijPij). The magnitude of P indicates how best the causal
factors account for the variability of the dependent factor (Singh & Chaudhary, 1999). That
is, if PR value is small (for instance, nearly zero), the dependent character considered is fully
explained by the variability in the independent characters, whereas higher PR value indicates
that some other factors, which have not been considered, need to be included in the analysis
to account fully the variation in the dependent character (seed yield).
3.5.6. Genetic Divergence and Clustering Analysis
Frist the data was standardizes by subtracting the mean and dividing by the standard
deviation for each variable. Genetic dissimilarity matrix estimation and agglomerative
hierarchical clustering (AHC) analyses among landraces were performed using XLSTAT
2014 statistical package (XLSTAT, 2014). Un-weighted pair-group average linkage
agglomeration method was used to estimate the Euclidean distance and clustering operations
produced a binary clustering tree (dendrogram), whose root was the cluster that contained
all the landraces. This dendrogram represents a hierarchy of partitions truncated at 5.078
levels. The upper tail approach is a simple procedure in which the mean and the standard
deviation of distance values at the fusion points are used to calculate the optimal number of
clusters (Franco et al., 1997). An acceptable cluster is defined as a group of two or more
genotypes with a within-cluster genetic distance less than the overall mean genetic distance
and between cluster distances greater than their within cluster distance of the two clusters
involved (Brown et al., 2000)
32
3.5.7. Principal Component Analysis
Principal Component Analysis (PCA) was used to find out the characters, which accounted
more to the total variation. The data was standardized to mean zero and variance of one
before computing principal component analysis. Principal components based on correlation
matrix will be calculated using XLSTAT software (XLSTAT, 2014).
33
4. RESULTS AND DISCUSSION
4.1. Analysis of Variance and Mean Performances of Landraces
4.1.1. Analysis of Variance
Analysis of variance computed for 23 quantitative traits revealed the presence of highly
significant (P<0.01) differences among the 35 landraces (Table 2). The presence of
significant variations among landraces might give a good opportunity for breeders to identify
genotypes for high fruit yield and other desirable traits. Muluken et al. (2016), Mihretu et
al. (2014) and Tesfa and Yosef (2016) also observed highly significant differences among
okra genotypes for most of the traits.
Table 2. Mean squares from analysis of variance for quantitative traits of okra landraces
Traits Replication
(2)
Landraces
(34)
Error
(68)
CV
(%)
Days to 50% flowering 28.4 648.85** 10.92 3.83
Days to maturity 591.43 1008.64** 99.53 9.09
Plant height (m) 0.03 0.49** 0.02 9.54
Stem diameter (cm) 0.03 0.24** 0.05 10.12
Number of primary branches per stem 0.52 1.87** 0.06 9.79
Internodes number 31.01 13.62** 3.7 9.85
Internodes length (cm) 0.73 8.62** 0.25 8.52
Leaf length (cm) 60.32 12.26** 1.82 8.12
Leaf width (cm) 94.88 22.41** 2.33 7.2
Number of epicalyxes 0.08 0.85** 0.38 6.38
Number of ridges 0.24 1.94** 0.05 2.61
Peduncle length (cm) 0.05 0.29** 0.05 8.05
Fruit length (cm) 0.41 3.25** 0.38 8.35
Fruit diameter (cm) 0.03 0.03** 0.02 6.91
Average fruit weight (g) 2.15 25.69** 1.75 9.00
Number of matured fruits per plant 0.99 174.90** 2.83 10.81
Weight of matured fruits per plant (g) 42.18 17466.00** 206.54 8.34
Dry weight of matured fruits per plant (g) 12.97 3161.74** 12.64 4.24
Number of seeds per fruit 40.83 1020.69** 8.62 5.54
100 seed weight (g) 0.08 1.91** 0.24 7.61
Number of tender fruits per plant 3.26 253.14** 2.79 10.05
Yield per hectare (t/ha) 2.52 100.12** 0.94 10.28
**, significant at P<0.01; NS= Nonsignificant, Numbers in parenthesis represent degree of
freedom for the respective source of variation and CV (%) = Coefficient of variation in percent
34
4.1.2. Mean Performances of Landraces
4.1.2.1. Phenological parameters
The average days to 50% flowering and days to maturity of okra landraces ranged from 59.52
to 104.67 and 79.67 to 145.67, respectively (Table 3). The landrace Gu-22 was early
flowering and maturity; however, this landrace had nonsignificant difference with 5 and 10
landraces for days to 50% flowering and days to maturity, respectively. Whereas Da-45 and
Gu-17 attained 50% flowering late and maturity late, however, 7 and 5 landraces had
nonsignificant difference with Da-45 and Gu-17 to attain 50% flowering and maturity,
respectively. The landraces had a wide range of differences up to 45 and 66 days to attain
50% flowering and maturity, respectively. Days to 50% flowering and days to maturity
determine the earliness of a variety and consequently help in capturing early market which
fetches high price in markets.
Tesfa and Yosef (2016) studied 50 okra accessions collected from four major production
regions in Ethiopia and grouped accessions into early, medium and late for time of flowering
and time of commercial harvest. Mihretu et al. (2014) observed 37 to 65 and 50 to101 days
of first flowering and maturity, respectively, in 25 okra genotypes collected from
southwestern Ethiopia, while Muluken et al. (2016) observed 53 to 62 and 84 to 104 days of
first flowering and maturity, respectively, in genotypes collected from three geographic
regions of Ethiopia and two introduced okra varieties. Okra plant usually bears its first flower
one to two months after sowing and has maturity duration of 90 to100 days (Tripathi et al.,
2011). Singh et al. (2017) observed 32 to 49 days to 50% flowering among 108 okra
genotypes. Nwangburuka et al. (2012) and Olayiwola et al. (2015) studied okra collections
from Nigeria and reported 44 to 58 days to flowering.
35
Table 3. Mean values of 35 okra landraces for phenological traits evaluated at Pawe in 2017
Landraces Days to 50% flowering Days to maturity
Gu-2 63.33k 104.33i-o
Gu-3 72.00hi 89.33m-q
Gu-4 62.67k 87.00n-q
Gu-5 64.00k 85.67opq
Gu-6 70.67ij 81.33q
Gu-7 94.33def 104.00i-o
Gu-8 95.67def 117.22d-k
Gu-9 70.62ij 101.56j-p
Gu-11 93.00ef 105.33h-n
Gu-12 75.67hi 84.33pq
Gu-14 77.22h 94.00l-q
Gu-17 99.33a-e 145.67a
Gu-18 102.33abc 120.67c-j
Gu-20 65.11jk 84.77pq
Gu-21 65.33jk 85.33opq
Gu-22 59.52k 79.67q
Gu-23 90.00f 110.67f-l
Ma-24 91.00f 111.00e-l
Ma-25 97.67b-e 110.33g-l
Gu-27 95.00def 108.00g-m
Ma-29 97.44b-e 123.00b-i
Ma-30 95.57def 130.00a-f
Ma-31 99.67a-d 117.00d-k
Ma-32 97.33b-e 130.33a-e
Ma-33 103.33ab 138.33abc
Ma-34 97.67b-e 113.81d-k
Ma-35 99.33a-e 124.67b-h
Ma-37 96.00c-f 125.33b-g
Ma-39 99.33a-e 114.00d-k
Da-40 73.00hi 98.41k-q
Da-41 100.00a-d 124.33b-h
Da-42 95.00def 122.33c-i
Da-43 82.89g 141.67ab
Da-45 104.67a 131.00a-d
Gu-47 70.26ij 98.70k-q
Mean 86.17 109.80
CV (%) 3.80 9.10
LSD% 5.40 16.30
Mean values within column with similar letter(s) had nonsignificant differences with
DMRT, CV (%) = Coefficient of variation in percent and LSD (0.05) = List significant
difference at 5% probability level
36
4.1.2.2. Growth traits
The Da-40 had tallest plant (2.29 m) while Gu-4 had the shortest plant (0.67 cm). The mean
plant height of landraces was 1.52 m. A total of 16 landraces had plant height of lower than
the average plant height of landraces while 19 landraces had plant height of higher than the
average plant height of landraces. The landraces had main stems in the range between 1.54
(Gu-2) and 2.87 cm (Ma-34) with average main stems of 2.27 cm in which 18 and 19
landraces had lower and greater than average main stems of landraces, respectively. The
landraces had 2.59 average number of primary branches varied from 1.39 (Gu-5) to 4.45
(Ma-34), and a total of 21, 1 and 14 landraces had average number of primary branches
lower, equal and higher than average number of primary branches of landraces, respectively.
Okra is an annual plant with robust stem, erect growth and variable in branching that varying
from 0.5 to 4.0 meters in height (Tripathi et al., 2011). Muluken et al. (2016) reported
significant differences among 25 okra genotypes for plant height, stem diameter and
numbers of branches in the range between 110.5 and 302.13 cm, 19.78 and 37.19 mm and
2.6 and 13.61, respectively. Mihretu et al. (2014) and Tesfa and Yosef (2016) also observed
significant differences among 25 okra genotypes for plant height, stem diameter and
numbers of variations. Singh et al. (2017) evaluated 108 okra genotypes in India and reported
55.2 to 160 cm and 13.2 to 29 mm for plant height and stem diameter, respectively.
Olayiwola et al. (2015) reported plant height at flowering in the range between 40.1 and 99.6
cm and mean values ranged from 1.93 to 3.56 for number of branches in 10 okra accessions
evaluated over two years in southwestern Nigeria.
The landraces had 9.69 average number of epicalyx per flower ranged from 8.73 (Ma-25)
and 10.93 (Gu-12). Number of internodes and internodes length of landraces varied from
15.66 (Gu-4) to 23.72 (Gu-6) and 3.09 (Gu-27) to 9.50 (Da-45), respectively, whereas the
variation of landraces for leaf length and width ranged from 12.67 (Da-43) to 21.03 cm (Gu-
5) and 15.45 (Da-40) to 26.60 cm (Gu-11), respectively. The average number of epicalyx,
number of internodes and internodes length of landraces were 9.69, 19.53 cm and 5.81 cm,
respectively, whereas the landraces had 16.64 and 21.22 cm average leaf length and width,
respectively. A total of 18, 17 and 17 landraces had higher than the average number of
epicalyx, number of internodes and internodes length, respectively, while 14 and 16
landraces had higher than the average leaf length and width of landraces, respectively. The
37
landraces with large and broad leaves, and many number of internodes and long internodes
length might help for higher photosynthesis activities, increased plant height and thereby
contributed to produce higher fruit yield.
Muluken et al. (2016) observed significant differences among 25 okra genotypes for
internodes length, leaf length and leaf width in the range between 4.16 to 6.64, 18.27 to
26.91 and 23.32 to 36.54 cm, respectively, and 24.55 to 51.36 and 9.1 to 11.32 numbers of
internodes and number of epicalyx per flower, respectively. Singh et al. (2017) observed
4.18 to 19.24 cm internodes length of 108 okra genotypes in India. Badiger et al. (2017)
reported 3.13 to 6.23 cm of internodes length for 12 okra genotypes evaluated in Bangalore,
India.
38
Table 4. Mean values of 35 okra landraces for growth traits evaluated at Pawe in 2017
Landraces PH STD NPBS NIN INL LL LW NE
Gu-2 1.19h-k 1.54k 2.59ghi 16.81g-j 8.56bc 12.81kl 17.11jkl 9.93a-f
Gu-3 2.07ab 2.46a-e 2.74efg 20.23a-g 6.44ghi 15.58d-j 21.19e-h 10.53abc
Gu-4 0.67n 2.36b-h 2.42g-k 15.66j 3.64pqr 14.00jkl 17.71i-l 10.71ab
Gu-5 1.19h-k 2.25c-i 1.39p 16.67g-j 4.38nop 21.03a 22.63b-e 9.80a-f
Gu-6 1.92bcd 2.08e-i 1.98k-o 23.72a 5.62i-l 14.58h-l 19.58f-j 8.87f
Gu-7 1.46e-h 2.38b-h 1.53op 19.93a-h 4.68mno 17.00c-i 20.63e-i 9.40c-f
Gu-8 1.65def 2.19d-i 1.67m-p 17.77f-j 7.97cde 15.00f-l 21.10e-h 8.93ef
Gu-9 1.98bc 2.25c-i 3.16de 18.43d-j 8.30bcd 16.64c-j 22.63b-e 9.93a-f
Gu-11 2.03b 2.2c-i 2.06j-n 19.37c-j 8.94ab 19.81ab 26.60a 9.73a-f
Gu-12 1.73cde 2.21c-i 2.27g-l 17.83f-j 7.47def 16.37d-j 24.25a-d 10.93a
Gu-14 1.73cde 1.97g-j 2.68fgh 22.13a-d 5.93ij 16.96c-i 22.00c-f 9.73a-f
Gu-17 1.14i-l 2.78ab 3.50cd 20.30a-g 4.74l-o 16.49d-j 18.29h-k 9.80a-f
Gu-18 0.82mn 2.15d-i 2.51g-j 16.24hij 3.29qr 15.87d-j 20.91e-h 9.67b-f
Gu-20 1.80bcd 2.17d-i 2.02j-n 16.00ij 6.37hij 16.11d-j 20.00e-j 10.13a-e
Gu-21 1.12jkl 2.13d-i 2.08j-n 18.93c-j 4.03opq 20.99a 18.76h-k 9.00ef
Gu-22 0.94klm 2.21c-i 2.15i-m 16.23hij 4.32nop 17.35b-g 21.12e-h 9.60b-f
Gu-23 1.47efg 2.43a-f 2.71e-h 18.50d-j 6.47ghi 17.93b-e 25.03ab 9.38c-f
Ma-24 1.35g-j 2.24c-i 2.32g-k 18.83c-j 5.80ijk 16.58c-j 22.62b-e 9.27def
Ma-25 1.67de 2.35b-i 1.63nop 20.23a-g 4.73l-o 19.18abc 24.66a-d 8.73f
Gu-27 0.88lmn 2.66abc 3.64bc 18.00e-j 3.09r 16.60c-j 18.85g-k 9.60b-f
Ma-29 1.72cde 2.23c-i 3.83bc 21.97a-d 6.24hij 17.13c-h 24.80abc 9.93a-f
Ma-30 1.69de 2.58a-d 3.14de 21.73a-e 5.01k-n 16.51d-j 20.76e-h 9.93a-f
Ma-31 1.09jkl 2.50a-e 2.04j-n 19.73b-i 4.33nop 16.80c-i 22.73b-e 10.73ab
Ma-32 1.73cde 2.28c-i 3.13def 22.10a-d 5.49j-m 15.27e-k 20.29e-i 9.53b-f
Ma-33 1.89bcd 2.49a-e 1.79l-p 22.67abc 8.15b-e 19.73abc 24.53a-d 10.40a-d
Ma-34 1.31g-j 2.87a 4.54a 20.01a-h 4.46nop 18.24bcd 21.78d-g 9.33c-f
Ma-35 1.40f-i 2.42b-g 3.70bc 20.50a-g 6.15hij 16.42d-j 22.38b-f 9.87a-f
Ma-37 0.90lmn 2.28c-i 2.65gh 18.93c-j 3.36qr 15.69d-j 23.01b-e 9.47c-f
Ma-39 1.30g-j 2.56a-d 2.46g-k 19.50b-i 4.37nop 16.62c-j 18.94g-k 9.33c-f
Da-40 2.29a 1.90ijk 2.26g-l 21.53a-f 7.31efg 14.60h-l 15.45l 9.07ef
Da-41 1.72cde 2.36b-h 2.11i-n 20.43a-g 5.83ijk 19.20abc 26.00a 9.13ef
Da-42 1.82bcd 1.97f-j 4.05b 23.27ab 6.09hij 14.38i-l 18.44h-k 9.33c-f
Da-43 1.72cde 1.61jk 3.79bc 19.60b-i 6.97fgh 12.67l 16.27kl 9.73a-f
Da-45 1.99bc 2.51a-e 2.25h-l 18.40d-j 9.50a 17.49b-f 19.58f-j 9.80a-f
Gu-47 1.68de 1.95hij 1.74m-p 21.33a-f 5.42j-m 14.73g-l 21.96c-f 9.80a-f
Mean 1.52 2.27 2.59 19.53 5.81 16.64 21.22 9.69
CV (%) 9.50 10.10 9.80 9.90 8.50 8.10 7.20 6.40
LSD% 0.20 0.40 0.40 3.10 0.80 2.20 2.50 1.00
Mean values within column with similar letter(s) had nonsignificant differences with
DMRT, CV (%) = Coefficient of variation in percent and LSD (0.05) = List significant
difference at 5% probability level. PH= Plant height (m); STD= Stem diameter (cm);
NPBS= Number of primary branches per stem; NIN= Number of internodes; INL=
Internodes length (cm); LL= Leaf length (cm); LW= Leaf width (cm); NEP= Number of
epicalyxes.
39
4.1.2.3. Fruit and seed characteristics
The number of ridges on fruit ranged from 7.17 to 9.83 with the average fruit ridges of 8.8
while the peduncle length on fruits ranged from 2.27 to 3.46 with overall mean of 2.8 cm.
Fruit length ranged from 5.43 (Gu-3 & Gu-2) to 9.12 cm (Ma-33) with average fruit length
of 7.39 cm landraces. The minimum pod diameter was measured from Gu-5 (1.48 cm) and
the maximum fruit diameter was measured from Gu-2 (2.47 cm). The lowest (8.40g) and
highest (19.89 g) average fruit weight were obtained from Ma-34 and Da-40, respectively.
A total of 60% landraces had greater than the mean fruit weight (14.69g). The okra landraces
also had significant variations for number of matured fruits per plant; weight of matured
fruits per plant and dry weight of matured fruits per plant in the range between 3.23 to
34.61,47.26 to 323.16g and 23.18 to 138.12g, respectively (Table 5).
The longer and wider tender fruits with higher fruit weight are preferred by consumers
(Wassu et al., 2017). The research results showed the presence of higher chance for selection
of landraces for longer and wider tender fruits with higher fruit weight. The presence of
significant differences among okra genotypes for fruit characteristics collected from
different growing regions of Ethiopia were reported by other workers (Mihretu et al., 2014;
Muluken et al., 2016; Tesfa and Yosef, 2016). Salesh et al. (2010) and Kumar et al., (2013)
reported highly significant differences among okra genotypes for fruit diameter, average
fruit weight, average fruit length and number of fruits per plant.
The lowest number of seeds was obtained from Gu-14 (13.46) while the highest number of
seeds was obtained from Da-45 (83.29) and 19 landraces had higher number of seeds than
the mean number of seeds of 36 okra landraces. Hundred seeds weight of landraces ranged
from 4.89 (Da-43) to 8.13g (Gu-12) with mean hundred seeds weight of 6.38g. Muluken et
al. (2016) found that 48.9 to 122.3 number of seeds per pod with overall mean of 99.99 and
4.55 to 7.9 with overall mean of 6.49g. Mihretu et al. (2014a) and Amandeep and Sonia
(2019) also reported wide range of number of seeds per pod and hundred seed weight of okra
genotypes. Therefore, the observed high number of mature fruits, number of seeds per fruit
and hundred seed weight in okra landraces suggested the higher chance of improving seeds
yield of okra to produce high amount of edible oil per unit area. The oil content of the seed
is quite high at about 40% and seeds also used as a substitute for coffee (Anwar et al.,
2011; Tripathi et al., 2011).
40
Table 5. Mean values of 35 okra landraces for fruit and seed characteristics evaluated at
Pawe in 2017
Landrace NRG PDL FL FD AFW NMFP WMPF DWMF NSF HSW
Gu-2 9.08e-j 2.63e-k 5.43o 2.47a 17.54bcd 8.217mn 84.99q 36.37n 50.48j-m 6.13g-l
Gu-3 9.14d-j 2.96b-g 5.43o 2.01c-f 13.82h-k 14.67ij 199.02gh 109.77cd 61.85cde 5.38j-n
Gu-4 8.79ijk 2.64e-k 8.35a-e 1.79f-l 17.21b-e 24.67cde 323.16a 116.48c 57.85d-h 7.17b-f
Gu-5 8.99g-j 3.04a-f 8.93ab 1.48m 15.74d-h 10.17lm 133.83lmn 37.17n 36.19n 6.00g-m
Gu-6 7.47mno 2.48h-k 5.98mno 1.60klm 10.02mn 26.50bcd 184.83hi 91.55fg 35.25n 7.27a-e
Gu-7 9.34b-h 3.14abc 6.69i-n 1.75g-l 12.65ijkl 20.50f 269.62bc 105.68de 33.02no 7.53abc
Gu-8 7.61mn 2.33jk 6.30k-o 1.76g-l 10.48lmn 23.67de 236.97de 102.73e 15.72p 7.47a-d
Gu-9 8.97ghij 2.91c-h 7.64c-j 2.04cde 15.07d-i 9.83lm 159.07jkl 83.60h 51.70ijkl 7.73ab
Gu-11 9.32b-h 2.99b-g 7.46d-k 1.94d-h 15.47d-h 13.50jk 258.37cd 89.95fg 77.88ab 6.54d-i
Gu-12 9.34b-h 2.76c-j 5.57no 2.42ab 17.23b-e 29.17b 310.21a 115.15c 62.57cd 8.13a
Gu-14 7.40mno 2.60f-k 6.50j-o 1.63i-m 10.65lmn 27.33bc 146.02j-m 113.18c 13.46p 6.27f-j
Gu-17 8.12l 3.39ab 8.08a-g 1.91d-h 16.60c-g 3.23p 108.15opq 75.88ij 34.50n 5.73i-n
Gu-18 8.42kl 3.06a-e 8.16a-g 1.69h-m 15.23d-h 9.00mn 134.22lmn 94.68f 56.47e-i 6.60d-i
Gu-20 8.18l 2.97b-g 8.92ab 1.54lm 16.54c-g 11.00klm 120.03mno 82.97h 54.43g-k 5.93g-m
Gu-21 9.05f-j 3.36ab 7.49c-k 1.61j-m 16.02d-h 23.33e 267.68bc 116.47c 62.22cd 7.70ab
Gu-22 8.81ijk 3.10a-d 8.24a-f 1.61klm 15.49d-h 8.50mn 86.83q 23.18o 52.50h-l 6.33e-i
Gu-23 9.21c-i 2.81c-i 7.51c-k 1.73g-m 14.84e-j 17.00ghi 236.62de 113.77c 56.13f-i 6.47e-i
Ma-24 9.39a-g 2.63e-k 6.47j-o 1.97d-g 13.84h-k 18.83fg 286.73b 132.10b 49.45klm 6.60d-i
Ma-25 9.46a-f 3.13abc 6.76i-m 1.96d-g 14.17g-k 15.50hij 220.08efg 110.38cd 48.19lm 6.87b-g
Gu-27 9.83a 2.86c-h 8.25a-f 1.86d-j 16.91c-f 13.44jk 240.34de 109.82cd 74.35b 6.33e-i
Ma-29 9.47a-f 2.85c-h 7.16e-m 1.87d-i 12.47jkl 18.00fgh 224.55ef 86.63gh 77.92ab 5.80h-n
Ma-30 9.54a-d 2.58g-k 7.99a-h 2.11cd 16.79c-f 16.72ghi 163.79ijk 92.34fg 58.07d-g 6.28f-j
Ma-31 9.41a-g 2.92c-h 7.55c-j 1.82e-k 14.05g-k 12.61jkl 117.57nop 81.53hi 60.95c-f 6.53d-i
Ma-32 7.17o 2.29k 6.83h-m 1.58klm 9.05n 10.83klm 91.53pq 67.90k 30.87no 5.11mn
Ma-33 9.49a-f 2.33jk 9.12a 2.37ab 19.52ab 6.67no 141.28k-n 86.57gh 80.66a 6.53d-i
Ma-34 9.45a-f 2.73c-j 8.70abc 2.05cde 19.89a 19.39fg 204.45fgh 80.68hi 45.43m 6.20g-k
Ma-35 9.13d-j 2.60f-k 7.52c-k 1.94d-h 15.08d-i 24.67cde 278.78bc 138.12a 80.64a 5.27lmn
Ma-37 9.76ab 2.68d-k 7.86b-i 2.05cde 18.67abc 10.39klm 91.03pq 24.22o 55.03g-j 6.73c-h
Ma-39 8.75jk 3.46a 8.47a-d 1.70h-m 14.48f-j 12.50jkl 93.00pq 44.78m 59.73d-g 4.98n
Da-40 7.39mno 2.54g-k 6.82h-m 1.61klm 8.55n 18.22fgh 105.62opq 47.80m 34.13n 5.30k-n
Da-41 8.90hij 2.81c-i 9.06a 1.75g-l 17.13b-e 9.67lmn 106.98opq 56.48l 65.23c 6.60d-i
Da-42 7.20no 2.39ijk 6.14l-o 1.73g-m 9.32n 34.61a 168.50ij 112.15c 33.47no 6.42e-i
Da-43 7.74m 2.27k 6.96g-m 1.64i-m 11.85klm 12.83jkl 47.26r 6.68p 28.67o 4.89n
Da-45 9.63abc 2.91c-h 7.20e-l 2.11cd 16.37c-h 4.00op 85.90q 73.567jk 83.29a 6.43e-i
Gu-47 9.51a-e 2.96b-g 7.04f-m 2.22bc 15.49d-h 5.33op 101.23opq 73.05jk 77.93ab 6.07g-l
Mean 8.81 2.80 7.39 1.87 14.69 15.56 172.24 83.81 53.04 6.38
CV (%) 2.60 8.10 8.40 6.90 9.00 10.80 8.30 4.20 5.50 7.60
LSD% 0.40 0.40 1.00 0.20 2.20 2.70 23.40 5.80 4.80 0.80
Mean values within column with similar letter(s) had nonsignificant differences with DMRT, CV (%)
= Coefficient of variation in percent and LSD (0.05) = List significant difference at 5% probability
level. NRG= Number of ridge; PDL= Peduncle length (cm); FL= Fruit length (cm); FD= Fruit
diameter (cm); AFW= Average fruit weight (g); NMFP= Number of matured fruits per plant; WMFP=
Weight of matured fruits per plant; DWMFP= Dry weight of matured fruits per plant; NSF= Seed per
fruit; HSW=hundred seed weight (g ).
41
4.1.2.4. Fruit yield
Mean values of landraces were in the range of 4.86 and 36.54 for number of tender fruits per
plant and 2.49 and 21.98 t ha-1 Yield per hectare (Table 6). The landrace Gu-12 and Gu-23
were exhibited the highest number of tender fruits per plant and fruit yield per hectare;
however, these landraces had nonsignificant difference with Gu-6 for number of tender fruits
per plant. Whereas Gu-47 attained low number of tender fruits per plant and fruit yield per
hectare; however 7 and 5 landraces had nonsignificant difference with Gu-47 to attain
number of tender fruits per plant and fruit yield per hectare, respectively. The landraces had
a wide range of differences up to 31.65 and 19.49 to attain number of tender fruits per plant
and fruit yield per hectare, respectively. This higher fruit yield per hectare maybe due to
higher fruit length and more number of fruits per plant showing genetic response of landraces
to environmental conditions.
Muluken et al. (2016) reported the highest number of fruit per plant and tender fruit yield
per hectare exhibited for okra accessions obtained from Oromia (Wellega) and Benishangul
(Metekel) regions respectively. Tesfa and Yosef (2016) observed the greatest variability
among the quantitative traits measured for number of fruits per plant, mean fruit weight and
total fruit production. These results were in agreement with the findings of Saleem et al.
(2018) okra genotypes also exhibited significant variation in fruit and yield traits viz., fruit
length, girth, average fruit weight, fruit number plant -1 and total yield plant-1.
42
Table 6. Mean values of 35 okra landraces for fruit yield evaluated at Pawe in 2017
Landraces Number of tender fruits per plant Yield per hectare (t ha-1)
Gu-2 10.33kl 6.70opq
Gu-3 14.67hij 8.38m-p
Gu-4 25.17d 15.96ef
Gu-5 16.17f-i 17.19de
Gu-6 34.00ab 15.89efg
Gu-7 17.50e-h 19.68bc
Gu-8 7.83lm 2.80tu
Gu-9 14.50hij 8.55mno
Gu-11 13.72ij 6.45pqr
Gu-12 36.54a 21.98a
Gu-14 32.83b 15.15fgh
Gu-17 4.86m 3.37stu
Gu-18 15.33ghi 9.96klm
Gu-20 14.67hij 11.06kl
Gu-21 20.00e 17.41de
Gu-22 23.53d 13.99f-i
Gu-23 36.07a 21.42ab
Ma-24 23.33d 15.56e-h
Ma-25 24.50d 13.76hi
Gu-27 28.94c 19.08cd
Ma-29 7.50lm 5.17qrs
Ma-30 5.02m 3.46stu
Ma-31 11.89jk 7.06opq
Ma-32 5.94m 3.17tu
Ma-33 6.00m 4.57rst
Ma-34 16.56f-i 13.05ij
Ma-35 19.00ef 11.67jk
Ma-37 9.33kl 9.94klm
Ma-39 12.06jk 11.66jk
Da-40 19.00ef 7.00opq
Da-41 16.50f-i 13.90ghi
Da-42 18.36efg 7.90nop
Da-43 5.33m 2.77tu
Da-45 10.33kl 9.12lmn
Gu-47 4.89m 2.49u
Mean 16.63 10.78
CV (%) 10.00 10.30
LDS% 2.70 1.80
Mean values within column with similar letter(s) had nonsignificant differences with
DMRT, CV (%) = Coefficient of variation in percent and LSD (0.05) = List significant
difference at 5% probability level.
43
4.1.3. Qualitative Characters
4.1.3.1. Plant growth habits
The branching position at main stem was densely branched at the base (DBB) for 91% of
okra landraces and 9% landraces had densely branched all over the main stem (DBO) (Table
7). Comparable result was reported by Muluken et al. (2016) that 92% of okra genotypes
had densely branched base (DBB) and only 4% of genotypes had non-branched growth habit.
The 44, 46 and 10% of okra landraces had erect, intermediate and horizontal arrangement of
position of fruit on main stem, respectively. Oppong-Sekyere et al. (2011) reported that 60%
intermediate, 20% erect position, 12% horizontally for okra genotypes while only 4% had
slightly falling. Adeoluwa and Kehinde (2011) reported two types of fruit position viz. 40%
and 60% of genotypes fruit position was horizontal and erect, respectively. Muluken et al.
(2016) found that 68% of okra genotypes fruits positioned erect and 32% of genotypes fruits
positioned intermediate on main stem. This showed that okra genotypes had different
branching position at main stem and arrangement of position of fruit on main stem that could
be as morphological markers for varieties.
4.1.3.2. Leaf shape characteristics
The leaf shape for okra landraces were 18% of oval undulate, 25% of heart-shaped, 25% star
shaped (palmately lobed) and 31% of okra landraces palmately lobed with serrated margins
(Table 7). Comparable result was reported by Muluken et al. (2016) for 48% heart shapes,
28% broadly ovate shape, 16% star shaped and 8% palmately lobed with serrated margins.
These results indicate that there may a possibility of using these characters as a
morphological marker.
4.1.3.3. Pigmentation characteristics
Flower color was showed red color at inside only for 26% of okra landraces and 74%
landraces had showed red color at both sides (Table 7). This finding supported by Mihretu,
(2013) with 36% of red coloration at both sides and 64% only inside. The 87 and 13% of
okra landraces had green and green with red veins leaf colors, respectively. Leaf petiole
color for okra landraces was showed green of 29%, red above but green below of 41% and
30% landraces had red on both side. Muluken et al. (2016) reported that for leaf color with
80% green and 20% green with red veins and for petiole color 16% green, 76% red above
44
only and 8% red on both sides. The pods color was green for 94% of okra landraces and 6%
landraces had red color. Different result reported Daniel (2011) who reported that fruits color
with 72% of the landraces produced green fruits while 8% displayed green-with-red-spotted
fruits, dark green to black fruits and green to yellow-fruits respectively and 4% of the
landraces had fruits tinged purple. 27, 41 and 32% of okra landraces had green, green with
red patch and red or purple stem colors, respectively. Tesfa and Yosef (2016) reported that
for stem color 31.5% green, 17.8% red and 50.7% green with red patches. Furthermore, the
variability in qualitative characters exhibited by okra collection point out that, in the studied
landraces, a good possibility exists of finding a range of desirable traits to meet demands for
specific attributes requested by researchers, farmers and consumers.
Table 7. Distribution of 35 okra landraces into eight qualitative traits evaluated at Pawe in
2017
Qualitative traits Code Distribution (%)
Plant habit 2- Densely branched base 91
3- Densely branched all over 9
Flower color 1- Red color inside only 26
2- Red color at both side 74
Leaf color 1- Totally green 87
2- Green with red vein 13
Leaf petiole color 1- Green 29
2- Red above but green below 41
3- Red on both sides 30
Pod color 1- Green 94
2- Red 6
Stem color 1- Green 27
2- Green with red patch 41
3- Red or Purple 32
Shape of leaf 1- Oval undulate 18
2- Heart-shaped 25
4- Star shaped (palmately lobed) 26
7- Palmately lobed with serrated margins 31
Position of fruits on main stem 1- Erect 44
2- Intermediate 46
3- Horizontal 10
45
4.2. Estimates of Genetic Parameters
4.2.1. Estimates of Phenotypic and Genotypic Variance Components
The estimates of phenotypic (σ2p) and genotypic (σ2g) variances, and phenotypic
coefficients of variation (PCV) and genotypic coefficients of variation (GCV) are given in
Table 8. The estimates of genotypic and phenotypic coefficients of variation for 22 traits of
35 okra landraces were in the range between 2.95 and 54.92 and 4.96 and 55.22 %,
respectively. The lowest and highest estimates of GCV and PCV were for fruit diameter and
number of tender fruits per plant, respectively.
Deshmukh et al. (1986) and Sivasubramaniah and Meron (1973) regarded PCV and GCV as
high, medium and low if values >20, 10-20 and <10%, respectively. According to this rating,
plant height, number of primary branches/stem, internodes length, number of matured fruits
per plant, weight of matured fruits per plant, dry weight of matured fruits/plant, number of
seeds per fruit, number of tender fruits per plant and yield per hectare (t ha-1) had high
estimates of PCV and GCV. This showed that the correspondence of genotypic and
phenotypic expression of the okra landraces for these traits. This suggested that these traits
were less influenced by environmental factors and selection based on phenotypic expression
of the landraces could be applied as breeding method to improve the traits. Salesh et al.
(2010), Bharathiveeramani et al. (2012), Nwangburuka et al. (2012) and Swati et al. (2014)
suggested that the high phenotypic and genotypic coefficients of variation is an indication
of the less influence of environmental factors in the expression of traits and the higher chance
to improve the traits through selection breeding.
Moderate GCV and PCV values were estimated for days to 50% flowering, days to maturity,
leaf length, leaf width, peduncle length, fruit length, average fruit weight and 100 seed
weight. The estimates of PCV and GCV were low for stem diameter, internodes number,
number of epicalyxes, number of ridges and fruit diameter. The lower GCV than PCV values
for the traits reflects the apparent variation is not only due to genotypic effect but it is also
the involvement of environmental effects (Das et al., 2012; Ehab et al., 2013; Kishor et al.,
2016).
This findings of this study was in agreement with the observation of Vrunda et al. (2018)
that high magnitude of PCV and GCV were estimated for number of branches per plant, fruit
46
yield/plant, internodes length, number of fruits/plant and plant height. Sibsankar et al. (2012)
reported high PCV and GCV values for fruit yield per plant, numbers of fruit per plant and
plant height. Rambabu et al. (2019b) for days to 50% flowering, days to first harvest and
100 seed weight and Khalid et al. (2018) for average fruit weight estimated high PCV and
GCV values. Muluken et al. (2016) observed high GCV and PCV for number of branches
per plant, fruit length, fruit yield ha-1, number of mature fruits per plant and fresh weight of
mature fruits per plant as well as moderate GCV and PCV for days to 50% flowering, days
to maturity, stem diameter, number of internodes per plant, peduncle length, fruit diameter,
fruit ridges, number of seeds per fruit and 100 seed weight. Mihretu (2013) findings showed
that medium GCV values were estimated for days to 50% flowering, leaf length and leaf
width.
4.2.2. Estimates of Heritability and Expected Genetic Advance
The broad sense heritability (H2) and genetic advance as percent of mean (GAM) for 22
traits of 35 okra landraces estimated in the range between 35.36 and 99.6% and 3.62 and
112.66%, respectively. The lowest H2 and GAM values were estimated for fruit diameter,
whereas the highest H2 and GAM values were estimated for dry weight of matured
fruits/plant and number of tender fruits per plant, respectively (Table 8).
As suggested by Johnson et al. (1955), heritability values are categorized as low (<30%),
moderate (30-60%) and high (>60%), and genetic advance as percent of mean categorized
as low (< 10%), moderate (10–20%) and high (>20%). Accordingly estimates of High H2
and GAM were high for most of the traits except high H2 coupled with moderate GAM for
internodes number, number of ridges and peduncle length and moderate H2 accompanied
with low GAM for number of epicalyxes and fruit diameter. The high heritability would be
a close correspondence between the genotypic and phenotypic variations due to relatively
small contribution of the environment to the phenotype expression of the trait (Singh et al.,
1990). PhaniKrishna et al. (2015) suggested that selection based on phenotypic performance
of genotypes would be effective to improve the traits for which high genetic advance as per
cent of mean coupled with high heritability estimates. The low estimates of GAM for traits
indicated performances of genotypes for the traits were influenced by environmental factors
and hence, genetic improvement through selection is difficult due to masking effects of the
environment on the genotypic effects (Johnson et al., 1955).
47
Muluken et al. (2015) and Mihretu et al. (2014) reported high values both for heritability
and genetic advance for most of growth, tender fruit and fruit yield related traits in okra
genotypes collected from different parts of the country. High heritability along with
moderate GAM for the character internode number, number of ridges and peduncle length,
hence, selection based on the phenotype performances of genotypes for these traits was
suggested as rewarding. In agreement with this study results Adewusi and Adeweso (2018)
also reported for edible pod length, dry fruit weight, number of seeds per fruit, number of
fruits per branch and seed yield per plant.
The importance of considering both the genetic advance and heritability of traits was
suggested than considering parameters separately to estimate how much progress can be
made through selection (Johnson et al., 1955; Sibsankar et al., 2012). High GCV along with
high heritability and high genetic advance will provide better information than single
parameters alone (Sahao et al., 1990). In this study plant height, number of primary branches,
internodes length, number of seeds per fruit, number of matured fruits per plant, weight of
matured fruits per plant, dry weight of matured fruits per plant, number of tender fruits per
plant and yield per hectare exhibited high genotypic coefficients of variation, high
heritability coupled with high genetic advance as percent of means. This suggested
improvement through selection of genotypes for high performance could be effective.
48
Table 8. Phenotypic and genotypic variances, heritability and genetic advance for 22
quantitative traits of 35 okra landraces at Pawe in 2017
Quantitative trait σ2g σ2p GCV
(%)
PVC
(%) H2 GA
GA
(%)
Days to 50% flowering 212.64 216.28 16.92 17.07 98.32 29.83 34.62
Days to maturity 303.04 336.21 15.85 16.70 90.13 34.09 31.05
Plant height 0.16 0.16 26.21 26.78 95.77 0.80 52.91
Stem diameter 0.06 0.08 11.01 12.47 78.06 0.46 20.07
Number of primary branches/stem 0.60 0.62 30.00 30.53 96.57 1.57 60.81
Internodes number 3.31 4.54 9.31 10.91 72.84 3.20 16.40
Internodes length 2.79 2.87 28.75 29.16 97.16 3.40 58.45
Leaf length 3.48 4.09 11.21 12.15 85.13 3.55 21.34
Leaf width 6.69 7.47 12.19 12.88 89.58 5.05 23.80
Number of epicalyxes 0.15 0.28 4.06 5.48 54.79 0.60 6.19
Number of ridges 0.63 0.65 9.02 9.14 97.40 1.62 18.36
Peduncle length 0.08 0.10 10.00 11.02 82.22 0.52 18.70
Fruit length 0.96 1.08 13.23 14.08 88.29 1.89 25.64
Fruit diameter 0.00 0.01 2.95 4.96 35.36 0.07 3.62
Average fruit weight 7.98 8.56 19.23 19.92 93.19 5.63 38.29
Number of matured fruits/plant 57.36 58.30 48.68 49.08 98.38 15.50 99.62
Weight of matured fruits/plant 5753.2 5822.0 44.04 44.30 98.82 155.55 90.31
Dry weight of matured fruits/plant 1051.0 1055.24 38.68 38.76 99.60 66.75 79.64
Number of seeds per fruit 337.36 340.23 34.63 34.78 99.16 37.73 71.14
100 seed weight 0.56 0.64 11.72 12.51 87.67 1.44 22.63
Number of tender fruits/plant 83.45 84.38 54.92 55.22 98.90 18.74 112.66
Yield per hectare (t ha-1) 33.06 33.37 53.14 53.39 99.06 11.81 109.11
σ2g and σ2p= genotypic and phenotypic variances, respectively, GCV and PCV = percentage of
genotypic and phenotypic coefficients of variation, respectively. H2 = heritability in a broad sense
in percent and GA = expected genetic advance and GAM = expected genetic advance as percent
of mean at 5% selection intensity.
49
4.3. PrincipalComponent Analysis
The results of principal component analysis for 22 traits of 35 okra landraces are presented
in Table 9. The four principal component axes; PCA1, PCA2, PCA3 and PCA4 accounted
65.59% of the total variation with Eigenvalue of 4.86, 4.26, 2.93 and 2.39 for PCA1, PCA2,
PCA3 and PCA4, respectively. The four PCAs were retained in analysis because each PCA
had Eigen values are >1 and >10% contribution to total variability. The others factors having
Eigen value < 1 were ignored. These were ignored due to Gutten’s lower bound principle
that Eigen values <1 should be ignored (Kumar et al., 2011). Principal component analysis
(PCA) is one of the multivariate statistical techniques which are a powerful tool for
investigating and summarizing underlying trends in complex data structures (Legendre and
Legendre 1998). This analysis reflects the importance of the largest contributor to the total
variation at each axis for differentiation (Sharma, 1998). Holland (2008) suggested standard
criteria permit to ignore components whose variance explained are less than 1 when a
correlation matrix is used.
The first three principal components PCA1, PCA2 and PCA3 with values of 22.09%, 19.34%
and 13.31% respectively, contributed more to the total of 54.74% variation. Similar result
was reported by Amoatey et al. (2015), Shivaramegowda et al. (2016) and Khalid (2017).
According to Chahal and Gosal (2002), characters with largest absolute values closer to unity
with in the first principal component influence the clustering more than those with lower
absolute values closer to zero. Therefore, in this study, differentiation of the landraces in to
different cluster was because of a cumulative effect of several traits rather than the
contribution of specific few traits (±0.001 - 0.816).
The two-dimensional ordinations of 35 okra landraces and 22 quantitative traits on biplot
axes PC1 and PC2 (Figure 2) and biplot axes PC3 and PC4 (Figure 3), revealed scattered
diagram of landraces and quantitative traits distribution pattern on axes with cumulative
variations 41.44% and 24.15% respectively. Both the scattered diagram showed that 65.59%
of cumulative total variations were contributed by first 4 principal components, collectively.
Landraces Gu-23 and other five landraces with traits stem diameter, leaf length, leaf width,
number of ridge, peduncle length, fruit length, average fruit weight and seed per fruit were
contributed substantially to genetic variance in axis PCA1. Major yield components such as
date of maturity, number of matured fruits per plant, weight of matured fruits per plant,
50
hundred seed weight, number of tender fruits per plant and yield per hectare with landraces
Gu-12 and other twelve landrace were made the greatest contribution to variation in the
principal component axis PCA 2 (figure 2). Traits which is plant height, number of
internodes, fruit diameter and dry weight of matured fruits per plant with landraces Gu-5 and
other six landraces were contributed to genetic variance in axis PCA 3. Whereas, the
variation in the principal component axis PCA 4 was made by landraces Ma-34, Gu-9 and
Gu-2 with traits days to 50% flowering, internodes length and number of epicalyxes (figure
3).
Traits having relatively higher value in the first PCA were number of ridges (13.71%),
average fruit weight (12.13%), leaf length (8.84%), number of seeds per fruit (7.67%), stem
diameter (7.39%), peduncle length (6.58), and leaf width (5.83%) and had more contribution
to the total differentiation of okra landraces into twelve clusters. Traits like days to maturity
(12.11%), number and weight of matured fruits per plant (14.35% and 9.06% respectively),
hundred seed weight (6.32%), number of tender fruits per plant (15.80%) and yield per
hectare (10.77%) had contributed a lot for PCA2. Plant height (10.35%), number of
internodes (10.22%), fruit diameter (10.31%) and dry weight of matured fruits per plant
(10.90%) had contributed in the third PCA3 while days to 50% flowering (14.74%),
internodes length (12.86%) and number of epicalyxes (10.17%) in the fourth PCA4. The
above results are in conformity with the works done by Kumari et al. (2019).
Nwangburuka et al. (2012) reported that days to flowering, branches per plant, fruit diameter
and seeds per pod had relatively high in the principal axes. Ahiakpa (2012) also reported that
plant height, 50% germination and number of pods per plant were relatively high in the
principal axes. Mudhalvan and Senthilkumar (2018) reported number of fruits per plant
followed by number of branches per plant, fruit girth, days to fruit maturity and days to first
flowering contributed maximum towards the genetic divergence.
51
Table 9. Factor loadings, contribution of traits and Eigen values of four principal component
axes in 35 okra landraces evaluated at Pawe in 2017
Quantitative trait PCA1 PCA2 PCA3 PCA4
Days to 50% flowering 0.098 (0.14%) -0.47 (5.19%) 0.48 (7.92%) -0.59 (14.74%)
Days to maturity -0.21 (0.89%) -0.72 (12.11%) 0.314 (4.05%) -0.39 (6.36%)
Plant height -0.45 (4.22%) 0.01 (0.00%) 0.55 (10.35%) 0.22 (1.99%)
Stem diameter 0.60 (7.39%) -0.26 (1.65%) 0.16 (0.88%) -0.51 (10.83%)
Number of primary branches/stem -0.22 (1.02%) -0.23 (1.22%) 0.21 (1.51%) -0.37 (5.78%)
Internodes number -0.43 (3.88%) -0.08 (0.15%) 0.55 (10.22%) -0.35 (5.21%)
Internodes length -0.31 (1.99%) -0.09 (0.20%) 0.53 (9.44%) 0.55 (12.86%)
Leaf length 0.66 (8.84%) -0.04 (0.03%) 0.06 (0.11%) -0.24 (2.51%)
Leaf width 0.53 (5.83%) -0.05 (0.05%) 0.45 (6.85%) -0.02 (0.02%)
Number of epicalyxes 0.25 (1.29%) -0.18 (0.79%) 0.13 (0.55%) 0.49 (10.17%)
Number of ridges 0.82 (13.71%) -0.28 (1.80%) 0.19 (1.27%) 0.20 (1.70%)
Peduncle length 0.57 (6.58%) -0.06 (0.08%) -0.40 (5.54%) -0.07 (0.19%)
Fruit length 0.47 (4.56%) -0.45 (4.85%) -0.38 (5.05%) -0.32 (4.31%)
Fruit diameter 0.29 (1.71%) -0.31 (2.33%) 0.55 (10.31%) 0.52 (11.12%)
Average fruit weight 0.77 (12.13%) -0.39 (3.57%) -0.07 (0.17%) 0.23 (2.26%)
Number of matured fruits per plant -0.16 (0.54%) 0.78 (14.35%) 0.33 (3.81%) -0.24 (2.32%)
Weight of matured fruits per plant 0.45 (4.25%) 0.62 (9.06%) 0.43 (6.26%) -0.12 (0.56%)
Dry weight of matured fruits/plant 0.31 (1.95%) 0.52 (6.41%) 0.56 (10.90%) -0.23 (2.21%)
Number of seeds per fruit 0.61 (7.67%) -0.37 (3.26%) 0.28 (2.75%) 0.25 (2.54%)
100 seeds weight 0.43 (3.84%) 0.52 (6.32%) 0.18 (1.09%) 0.19 (1.47%)
Number of tender fruits/plant 0.26 (1.42% 0.82 (15.80%) 0.00 (0.00%) -0.07 (0.23%)
Yield per hectare (t/ha) 0.55 (6.13%) 0.68 (10.77%) -0.17 (0.97%) -0.13 (0.69%)
Eigenvalue 4.86 4.26 2.93 2.39
Contribution to variability (%) 22.09 19.34 13.31 10.85
Cumulative contribution (%) 22.09 41.44 54.74 65.59
PCA = Principal Component Axes
The numbers in bracket indicated contribution of the traits in percent in building the principal components
52
Figure 2. Scattered diagram 35 okra landraces by 22 quantitative traits using two
dimensional ordinations traits on PCA1 and PCA2.
PCA = Principal Component Axes; DFF= Days to 50% flowering; NDM=Days maturity;
PH= Plant height (m); STD= Stem diameter; NPBS= Number of primary branches per stem;
NIN= Number of internodes; INL= Internodes length; LL= Leaf length; LW= Leaf width;
NEP= Number of epicalyxes; NRG= Number of ridge; PDL= Peduncle length; FL= Fruit
length; FD= Fruit diameter; AFW= Average fruit weight; NMFP= Number of matured fruits
per plant; WMFP= Weight of matured fruits per plant; DWMFP= Dry weight of matured
fruits per plant; NSF= Number of seeds per fruit; HSW=hundred seed weight; NTFP=
Number of tender fruits per plant and YLPH= Yield in ton per hectare.
Gu-2
Gu-3
Gu-4
Gu-5
Gu-6
Gu-7
Gu-8
Gu-9
Gu-11
Gu-12
Gu-14
Gu-17
Gu-18
Gu-20
Gu-21
Gu-22
Gu-23Ma-24
Ma-25
Gu-27
Ma-29
Ma-30
Ma-31Ma-32
Ma-33
Ma-34
Ma-35
Ma-37
Ma-39
Da-40
Da-41
Da-42
Da-43
Da-45
Gu-47 NDF
NDM
PH
STDNPBS
NIN INLLLLW
NE
NRF
PL
FL
FD
AFW
NMFP
WMFP
DWFP
NSF
HSW
NTFP
YLDH
-3
-2
-1
0
1
2
3
-4 -3 -2 -1 0 1 2 3 4
PC
A2
(1
9.3
4 %
)
PCA1 (22.09 %)
Biplot (axes PCA1 and PCA2: 41.44 %)
53
Figure 3. Scattered diagram by using two dimensional ordinations of 35 okra landraces and
22 quantitative traits based on PC (principal component) axes 3 and 4.
PCA = Principal Component Axes; DFF= Days to 50% flowering; NDM=Days maturity;
PH= Plant height (m); STD= Stem diameter; NPBS= Number of primary branches per stem;
NIN= Number of internodes; INL= Internodes length; LL= Leaf length; LW= Leaf width;
NEP= Number of epicalyxes; NRG= Number of ridge; PDL= Peduncle length; FL= Fruit
length; FD= Fruit diameter; AFW= Average fruit weight; NMFP= Number of matured fruits
per plant; WMFP= Weight of matured fruits per plant; DWMFP= Dry weight of matured
fruits per plant; NSF= Number of seeds per fruit; HSW=hundred seed weight; NTFP=
Number of tender fruits per plant and YLPH= Yield per hectare.
Gu-2
Gu-3
Gu-4
Gu-5
Gu-6
Gu-7
Gu-8
Gu-9
Gu-11
Gu-12
Gu-14
Gu-17
Gu-18
Gu-20
Gu-21
Gu-22
Gu-23
Ma-24
Ma-25
Gu-27
Ma-29Ma-30
Ma-31
Ma-32
Ma-33
Ma-34
Ma-35
Ma-37
Ma-39
Da-40
Da-41
Da-42
Da-43
Da-45
Gu-47
NDF
NDM
PH
STD
NPBS NIN
INL
LL
LW
NE
NRF
PL
FL
FD
AFW
NMPP
WMFP
DWMF
NSFHSW
NTFPYLDH
-3
-2
-1
0
1
2
3
4
-4 -3 -2 -1 0 1 2 3 4 5
PC
A 4
(1
0.8
5 %
)
PCA3 (13.31 %)
Biplot (axes PCA3 and PCA4: 24.15 %)
54
4.4. Genetic Divergence Analysis
4.4.1. Genetic Distances among Okra Landraces
Genetic distances of all possible pairs of 35 okra landraces were estimated by Euclidean
distance from 22 traits and the results are presented (Table 10, Appendix Table 2). The
genetic distances for all possible pairs of 35 okra landraces ranged from 2.92 to 11.28 with
mean, standard deviation and CV% of 6.48, 0.16 and 2.51 respectively. The most distant
landraces were Gu-12 and Da-43 (11.28) followed by Gu-12 and Ma-32 (10.46), Gu-21 and
Da-43 (10.37), Gu-12 and Gu-17 (10.28). The lowest genetic distance was exhibited between
G-23 and Ma-24 (2.92) followed by Gu-5 and Gu-22 (2.93), Gu-7 and Ma-25 (3.21), Gu-7
and Ma-24 (3.23). This finding was supported by Muluken et al. (2015) who reported that
Ethiopia okra collections exhibited wide genetic distances in the range between 5.16 and
11.14. This suggested that the higher chance of improving the crop production through
collection, characterization, evaluation and selection of okra landraces from different regions
of Ethiopia.
The mean genetic distances of landraces (Table 10) showed that Da-43 (7.99) were the most
distant to others and followed by Gu-12 and Gu-2 with mean Euclidean distance of 7.92 and
7.64 respectively. In contrast, Gu-23, Ma-29, Ma-25, Gu-7, Da-41, Ma-35, Gu-3 and Gu-20
with mean Euclidean distance in between 5.85 to 6.06 were closest to others. Seven okra
landraces from Guba district and two from Mandura and Dangure districts each had mean
Euclidean distances above average distances (6.64 to 7.45) and other four landraces from
Guba district, three from Mandura and one from Dangure districts had near to average
distances of 6.10 and 6.46. Also, minimum mean Euclidean distances were observed two
from Guba and three from Mandura districts.
The extent of diversity present between landraces determines the extent of improvement
gained through selection and hybridization. The more divergent the two landraces are the
more will be the probability of improving through selection and hybridization. This result is
supported by Wassu et al. (2017) who reported that the presence of diverse okra genotypes
with wide range of genetic distances which enables the researchers to improve the okra
tender fruit yield and other desirable traits either through direct selection of genotypes or
crossing of okra genotypes having different desirable traits.
55
Thus, selection of genotypes for hybridization between the genetically diverse parents in
further breeding programs may produce large variability and better recombinants in the
segregating generations. High degree of divergence among the genotypes within a cluster
would produce more segregating breeding material and selection within such cluster might
be executed based on maximum mean value for the desirable characters (Prakash, 2017).
Table 10. Mean genetic distances of 35 okra landraces as measured by Euclidean distance
Accession Minimum Maximum Mean SD CV (%)
Gu-2 5.19 8.92 7.64 0.90 11.81
Gu-3 4.49 7.78 6.04 0.87 14.45
Gu-4 5.04 9.75 7.30 1.19 16.34
Gu-5 2.93 9.42 6.76 1.39 20.61
Gu-6 3.38 9.99 7.24 1.36 18.72
Gu-7 3.21 9.09 5.92 1.23 20.79
Gu-8 5.14 8.48 6.92 0.96 13.88
Gu-9 4.03 7.55 5.66 0.85 14.94
Gu-11 4.03 9.03 6.15 1.32 21.45
Gu-12 5.53 11.28 7.92 1.34 16.98
Gu-14 3.38 9.21 6.64 1.21 18.29
Gu-17 4.05 10.28 6.81 1.33 19.53
Gu-18 3.68 8.34 5.81 1.17 20.06
Gu-20 3.79 7.88 6.06 0.97 16.03
Gu-21 4.24 10.37 6.87 1.36 19.79
Gu-22 2.93 8.87 6.46 1.32 20.47
Gu-23 2.92 9.19 5.85 1.34 22.99
Ma-24 2.92 8.42 5.49 1.20 21.80
Ma-25 3.19 9.42 5.89 1.35 23.00
Gu-27 3.99 9.42 6.37 1.44 22.68
Ma-29 3.38 7.86 5.86 1.22 20.89
Ma-30 3.50 7.96 5.69 1.27 22.26
Ma-31 3.68 7.81 5.62 1.17 20.86
Ma-32 4.39 10.46 6.85 1.31 19.09
Ma-33 4.45 9.99 7.45 1.53 20.57
Ma-34 3.99 8.81 6.40 1.32 20.68
Ma-35 3.38 8.22 6.04 1.23 20.36
Ma-37 4.02 8.45 6.12 1.25 20.37
Ma-39 4.05 9.52 6.10 1.26 20.60
Da-40 4.55 9.80 7.18 1.31 18.24
Da-41 4.21 8.40 5.94 1.32 22.15
Da-42 4.28 9.42 7.22 1.35 18.64
Da-43 4.39 11.28 7.99 1.41 17.58
Da-45 4.51 8.81 6.41 1.29 20.05
Gu-47 4.53 8.08 6.25 1.06 16.91
Overall 2.92 11.28 6.48 0.16 2.51
SD = standard deviation and CV (%) = coefficient of variation in percent.
56
4.4.2. Clustering of Genotypes
4.4.2.1. Grouping of okra landraces into clusters
The Euclidean distance matrix for all possible pair of okra landraces estimated from
quantitative traits was used to construct dendrograms based on the Unweighted Pair-group
methods with Arithmetic Means (UPGMA). The 35 okra landraces were grouped into 12
distinct clusters at cut point 5.08 (overall mean Euclidean distance of landraces minus
standard deviation) with 45.41 and 54.59% variation within and between clusters,
respectively, as variance decomposition for the optimal classification (Table 11, Appendix
Figure 1).
Cluster IX consisted of six (17.14%), Cluster II, VI and X consisted each five (14.29%) and
Cluster IV, V and XI consisted each 3 (8.57%) landraces. Whereas Cluster I, III, VII and
VIII were solitary each represented one growing area of Guba district and cluster XII also
solitary from Mandura district (Table 11, Appendix Figure 1). Cluster IX comprised of six
landraces from Guba, Mandura and Dangure districts of three, two and one, respectively.
Cluster II comprised of four and one landraces from Guba and Dangure districts,
respectively, and Cluster VI comprised from Guba and Mandura districts of three and two
landraces, respectively, while Cluster X comprised of four and one landraces from Mandura
and Guba districts, respectively. Cluster V comprised three landraces (two landraces from
Guba and one from Dangure districts), while Cluster XI comprised of two and one landraces
from Dangure and Mandura districts, respectively, Cluster IV comprised of landraces from
Guba district.
The result suggested that the landraces grouped under same cluster had similarity for many
traits but dissimilarity to other landraces in other clusters with one or more traits. The
distribution pattern of landraces into twelve clusters confirmed the existence of diversity
among the landraces. This result agrees with Pradip et al. (2010) and Muluken et al. (2015)
that considerable number of clusters consisted of one okra genotype each and was solitary
clusters. Cluster analysis sequestrates genotypes into clusters which exhibit high
homogeneity within a cluster and high heterogeneity between clusters (Jaynes et al., 2003).
57
Table 11. Clusters of 35 okra landraces based on 22 quantitative traits
Cluster Number of landraces Landraces name
I 1 Gu-2
II 5 Gu-3, Gu-9, Gu-11, Da-45 and Gu-47
III 1 Gu-4
IV 3 Gu-5, Gu-20 and Gu-22
V 3 Gu-6, Gu-14 and Da-42
VI 5 Gu-7, Gu-21, Gu-23, Ma-24 and Ma-25
VII 1 Gu-8
VIII 1 Gu-12
IX 6 Gu-17, Gu-18, Ma-31, Ma-37, Ma-39 and Da-41
X 5 Gu-27, Ma-29, Ma-30, Ma-34 and Ma-35
XI 3 Ma-32, Da-40 and Da-43
XII 1 Ma-33
4.4.2.2. Cluster mean analysis
The mean value of the 22 quantitative traits in each cluster is presented in (Table 12). Cluster
I was showed longest internodes length (8.56 cm) and wider fruit diameter (2.47 cm), narrow
stem diameter (1.54 cm) and leaf width (17.11 cm), short leaf length (12.81 cm), short fruit
length (5.43 cm) and minimum dry weight of matured friuts per plant (36.37 g). Cluster II
had the tallest plant height (1.95 cm). Cluster III had shown maximum weight of matured
friuts per plant (323.16 g) and dry weight of matured friuts per plant (116.48 g) early for
days to 50% flowering (62.67), short plant height (0.67 cm) and few number of internodes
(15.66). Cluster IV had shown earliness for days to maturity (83.37) with narrow fruit
diameter (1.54 cm). Cluster V was showed large number of internodes (23.04) and number
of mature friuts per plant (29.48) and the least number of ridges per fruit (7.36). Cluster VII
had small number of primary branches per plant (1.67), number of epicalyxes (8.93), number
of seeds per fruit (15.72) and Yield per hectare (2.80 t h-1).
Cluster VIII showed the largest number of epicalyxes (10.93) and number of tender fruits
per plant (36.54), the highest hundred seed weight (8.13 g) and Yield per hectare (21.98 t h-
1). Cluster IX had the smallest peduncle length (3.05 cm). Cluster X had shown wider stem
diameter (2.55 cm), large number primary branches per plant (3.77). Cluster XI had shown
small average fruit weight (9.82), weight of matured friuts per plant (81.47 g) and hundred
seed weight (5.10 g). Among all the clusters, in cluster XII was observed smaller peduncle
length (2.33 cm), little number of matured friuts per plant (6.67) and number of tender fruits
per plant (6.0), late for days to 50% flowering (103.33) and days to maturity (138.33), large
58
leaf length (19.73 cm), fruit length (9.12 cm) and number of ridges per fruit (9.49), wide leaf
width (24.53 cm), high average fruit weight (19.52 g) and much number of seeds per fruit
(80.66).
Based on cluster means, it is evident from the data (Table 12) that landraces falling in cluster
I, VIII, X and XII showed higher performance for the traits of interest viz., internodes length,
fruit diameter, number of epicalyxes, hundred seed weight, number of tender fruits per plant,
Yield per hectare, stem diameter, number of primary branches, fruit length and average fruit
weight. On the other hand, cluster VII, which is consisted of one landrace was the least in
performance for most quantitative traits studied (Table 12). For example, the landraces
grouped under this cluster gave the least number of primary branches, number of epicalyxes,
number of seeds per friut and Yield per hectare. The result also pointed out that the
importance of landraces in cluster VII for their exploitation in fruit yield improvement
appeared limited in view of their poor performance for many of the traits of interest. This
indicated that different clusters have different breeding values that enable breeders to
improve different traits and parental selection should be made based on the relative merits
of each cluster for each trait depending on the objective of the breeding program.
59
Table 12. Cluster means for 22 quantitative traits of 35 okra landraces at Pawe in 2017
Traits Cluster
1 2 3 4 5 6 7 8 9 10 11 12
DFF 63.33 82.11 62.67 62.88 80.96 87.67 95.67 75.67 99.44 97.00 84.41 103.33
NDM 104.33 105.19 87.00 83.37 99.22 104.27 117.22 84.33 124.50 119.90 123.47 138.33
PH 1.19 1.95 0.67 1.31 1.82 1.41 1.65 1.73 1.16 1.40 1.91 1.89
STD 1.54 2.27 2.36 2.21 2.01 2.31 2.19 2.21 2.44 2.55 1.93 2.49
NPBS 2.59 2.39 2.42 1.85 2.90 2.05 1.67 2.27 2.54 3.77 3.06 1.79
NIN 16.81 19.55 15.66 16.30 23.04 19.29 17.77 17.83 19.19 20.44 21.08 22.67
INL 8.56 7.72 3.64 5.03 5.88 5.14 7.97 7.47 4.32 4.99 6.59 8.15
LL 12.81 16.85 14.00 18.16 15.31 18.34 15.00 16.37 16.78 16.98 14.18 19.73
LW 17.11 22.39 17.71 21.25 20.01 22.34 21.10 24.25 21.65 21.71 17.34 24.53
NE 9.93 9.96 10.71 9.84 9.31 9.16 8.93 10.93 9.69 9.73 9.44 10.40
NRG 9.08 9.32 8.79 8.66 7.36 9.29 7.61 9.34 8.89 9.48 7.43 9.49
PDL 2.63 2.95 2.64 3.04 2.49 3.01 2.33 2.76 3.05 2.73 2.37 2.33
FL 5.43 6.96 8.35 8.70 6.21 6.98 6.30 5.57 8.20 7.92 6.87 9.12
FD 2.47 2.07 1.79 1.54 1.65 1.81 1.76 2.42 1.82 1.97 1.61 2.37
AFW 17.54 15.24 17.21 15.92 10.00 14.30 10.48 17.23 16.03 16.23 9.82 19.52
NMFP 8.22 9.47 24.67 9.89 29.48 19.03 23.67 29.17 9.57 18.44 13.96 6.67
WMFP 84.99 160.72 323.16 113.57 166.45 256.15 236.97 310.21 108.49 222.38 81.47 141.28
DWMFP 36.37 85.99 116.48 47.77 105.63 115.68 102.73 115.15 62.93 101.52 40.79 86.57
NSF 50.48 70.53 57.85 47.71 27.39 49.80 15.72 62.57 55.32 67.28 31.22 80.66
HSW 6.13 6.43 7.17 6.09 6.65 7.03 7.47 8.13 6.20 5.98 5.10 6.53
NTFP 10.33 11.62 25.17 18.12 28.40 24.28 7.83 36.54 11.66 15.40 10.09 6.00
YLDH 6.70 7.00 15.96 14.08 12.98 17.57 2.80 21.98 9.32 10.49 4.31 4.57 DFF= Days to 50% flowering; NDM=Days maturity; PH= Plant height (m); STD= Stem diameter (mm); NPBS= Number of primary branches per stem;
NIN= Number of internodes; INL= Internodes length (cm); LL= Leaf length (cm); LW= Leaf width (cm); NEP= Number of epicalyxes; NRG= Number of
ridge; PDL= Peduncle length (cm); FL= Fruit length (cm); FD= Fruit diameter (cm); AFW= Average fruit weight (g); NMFP= Number of matured fruits per
plant; WMFP= Weight of matured fruits per plant (g); DWMFP= Dry weight of matured fruits per plant (g); NSF= Number of seeds per fruit; HSW=hundred
seed weight (g ); NTFP= Number of tender fruits per plant; YLPH= Yield in ton per hectare
60
4.5. Association among Traits
4.5.1. Genotypic and Phenotypic Correlation Coefficients
4.5.1.1. Genotypic and phenotypic correlation of fruit yield with other traits
The results of genotypic and phenotypic coefficients of correlations of yield with other traits
were presented in Table 13. Fruit yield per hectare with leaf length, number of matured pods
per plant, weight of matured pods per plant, hundred seed weight and number of pod per
plant had positive and significant correlations both at genotypic and phenotypic levels. Yield
per hectare had positive and significant phenotypic correlation with number of ridge and
peduncle length. Significant genotypic association between the different pairs of characters
indicates strong association between those characters genetically and changes brought about
by a natural or artificial selection among correlated traits, (Singh, 1993; Falconer et al., 1996;
Sharma, 1998). Therefore, the traits that showed positive and significant correlations with
fruit yield per hectare suggested the simultaneous selection of the traits with yield is possible
and correlated traits could be used for indirect selection of okra landraces for fruit yield.
The presence of positive and significant genotypic and phenotypic correlations of fruit yield
per hectare of okra with number of pod per plant, number of ridge and peduncle length has
been reported by Muluken et al. (2015). The authors also reported that fruit yield ha-1 had
positive and highly significant phenotypic correlation with stem diameter; plant height and
number of inter nodes. In agreement with the results of this study, Ahamed et al. (2015),
Tesfa and Yosef (2016) and Rambabu et al. (2019a) reported positive and highly significant
correlation for yield per plant with number of fruits per plant and 100-seed weight. Mehta et
al. (2006) and Pradip et al. (2010) also reported the positive and significant inherent or
genetic association fruit yield of okra with number of pod per plant, peduncle length, stem
diameter; plant height and number of inter nodes. This suggested that genes governing two
positive and significantly correlated traits were similar and environmental factors played a
small part in the expression of these traits that justified the possibility of correlated response
to selection.
Yield per hectare with days to maturity and internodes length showed negative and
significant correlations both at genotypic and phenotypic levels. Days to 50% flowering,
plant height, number of primary branches per stem and number of internodes also had
61
negative and significant phenotypic correlation with yield per hectare. Similar finding
were reported by Kumari et al. (2019) plant height, days to 50% flowering and intermodal
length were negatively and significantly correlated with yield per plant at genotypic level.
Das et al. (2012) reported that days to first flowering and node at first flowering exhibited
significantly negative correlation with fruit yield per plant. This suggests that selection of
traits negatively correlated will have negative effects on fruit yield. Negative association
of traits indicated that it is difficult or practically impossible to improve through
simultaneous selection of those traits (Akinyele and Osekita, 2006; Nwangburuka et al.,
2012; Ahiakpa et al., 2013).
4.5.1.2. Phenotypic and genotypic correlations among other traits
Positive and significant correlations among phenology parameters (days to 50% flowering
and days to maturity) were observed at genotypic and phenotypic levels (Table 13). Days to
50% flowering and days maturity with number of internodes had positive and significant
correlation both genotypic and phenotypic levels and days to maturity with days to 50%
flowering had positive and significant correlation. Stem diameter and number of primary
branches per stem had positive and significant association phenotypic with both days to 50%
flowering and days of maturity, respectively. Days to 50% flowering with stem diameter and
days of maturity with number of primary branches per stem had positive and significant
association genotypic levels; also days to 50% flowering with leaf width had positive and
significant correlation phenotypic level. Sharma and Prasad (2015) and Rambabu et al.
(2019a) reported that days to 50% flowering showed significant positive correlation with
days to first harvest.
Number of fruits per plant with days to 50% flowering and days of maturity had negative
and significant phonotypic and genotypic correlations, respectively. Days of maturity with
hundred seed weight showed negative and significant correlations both at genotypic and
phenotypic levels while days of maturity with peduncle length, number of matured fruits per
plant and weight of matured fruits per plant had negative and significant phonotypic levels.
Simon et al. (2013) and AI Patel et al. (2019) reported negative and highly significant
correlation for number of fruit /plant with days to first flowering and days to first picking,
respectively.
62
Plant height with number of internodes and internodes length, stem diameter with leaf length
and leaf length with leaf width had positive and significant correlation at genotypic and
phenotypic levels. Stem diameter had positive and significant genotypic and phenotypic
correlations with peduncle length, number of ridges on fruit, fruit length and average fruit
weight whereas stem diameter had positive and significant phenotypic correlations with leaf
width, dry weight of matured fruits per plant and number of seeds per fruit. The phenotypic
correlation between number of primary branches and number of internodes was positive and
significant.
In agreement with this study results, Muluken et al. (2015) reported the positive and
significant association of plant height with number of internodes and internodes length and
the positive. And also reported significant genotypic and phenotypic correlations of stem
diameter with number of branches per plant, number of internodes per plant and leaf width,
fruit diameter, number of fruit per plant and fruit ridges. Medagam et al. (2013) and
Rambabu et al. (2019a) also reported positive and significant correlation of plant height with
inter node length at genotypic and phenotypic levels. Mihretu (2013) and Deepak et al.
(2015) also observed significant and positive correlation of stem diameter with number of
branches per plant, number of internodes per plant and leaf width, fruit diameter and number
of fruit per plant. This indicated that the selection of genotypes for any of the significantly
positive inter-related traits is expected to give a desired correlated response in other traits
(Das et al. 2012; Ehab et al., 2013).
Average fruit weight with number of ridge, peduncle length, fruit length, fruit width and
number of seeds per fruit had positive and significant association genotypic and phenotypic
levels, respectively. Number of seeds per fruit had positive and significant association
genotypic and phenotypic levels with number of ridge and fruit diameter. Number of seeds
per fruit also had positive and significant association phenotypic levels with fruit length and
peduncle length. Number of ridge had positive and significant association genotypic and
phenotypic levels with peduncle length and fruit diameter whereas number of ridge had
positive and significant phenotypic correlation with fruit length, hundred seed weight and
weight of matured pods per plant. Similar results were reported by Reddy et al. (2013) and
Rambabu et al. (2019a) fruit length and fruit width showed positive and significant
correlation with fruit weight and number of seeds per fruit.
63
In contrast, average fruit weight with (plant height and number of internodes), fruit length
with (plant height and internode length), peduncle length with (number of internodes and
internode length), internode length with (stem diameter) and hundred seed weight with
(number of primary branches per stem) had negative and significant genotypic and
phenotypic association. Negative and significant phenotypic correlation had for plant height
and number of internodes with (number of ridge), number of primary branches per stem and
plant height with (peduncle length) and number of primary branches per stem with leaf
length. Number of mature fruits per plant had negative and significant genotypic and
phenotypic correlation with peduncle length, fruit length and average fruit weight,
respectively. Number of mature fruits per plant also had negative and significant correlation
phenotypic with fruit length. Singh et al, (2007) recorded that negative and significant
correlation of plant height with fruit girth among 19 diverse okra genotypes. Kumari et al.
(2019) also observed fruit length, fruit width and average fruit weight were significantly and
negatively associated with internodal length and number of nodes.
64
Table 13. Genotypic (above diagonal) and Phenotypic (below diagonal) correlation coefficient among 22 quantitative traits of 35 okra landrace
Trait DFF NDM PH STD NPBS NIN INL LL LW NE NRG
DFF 0.82** 0.02 0.51** 0.27 0.35* -0.03 0.18 0.31 -0.17 0.17
NDM 0.72** 0.04 0.25 0.43** 0.34* 0.11 -0.08 0.03 -0.07 0.02
PH 0.02 0.05 -0.22 -0.06 0.55** 0.74** -0.02 0.14 -0.08 -0.27
STD 0.38** 0.19* -0.14 0.17 0.02 -0.33* 0.46** 0.29 0.11 0.44**
NPBS 0.25** 0.35** -0.04 0.14 0.24 -0.08 -0.3 -0.22 0.06 -0.1
NIN 0.23* 0.25** 0.49** 0.14 0.24* 0.12 -0.03 0.08 -0.24 -0.26
INL -0.03 0.12 0.71** -0.23* -0.07 0.11 -0.09 0.12 0.14 -0.10
LL 0.13 -0.08 0.00 0.31** -0.20* 0.04 -0.05 0.61** -0.13 0.44**
LW 0.23* 0.03 0.13 0.23* -0.16 0.13 0.12 0.64** 0.09 0.48**
NE -0.11 -0.06 -0.06 0.03 0.03 -0.15 0.08 0.00 0.05 0.28
NRG 0.17 0.04 -0.26** 0.36** -0.1 -0.23* -0.09 0.32** 0.37** 0.17
PDL -0.08 -0.28** -0.26** 0.27** -0.20* -0.21* -0.29* 0.42** 0.13 0.05 0.31**
FL 0.13 0.11 -0.32* 0.38** 0.03 -0.13 -0.34* 0.38** 0.16 0.01 0.22*
FD 0.11 0.18 0.08 0.12 0.08 0.12 0.33** -0.05 0.24** 0.25** 0.52**
AFW 0.01 0.01 -0.37** 0.37** 0.03 -0.25** -0.13 0.32** 0.28** 0.24* 0.67**
NMFP -0.10 -0.29** 0.09 -0.14 0.17 0.20* -0.05 -0.09 -0.05 -0.09 -0.29
WMFP 0.00 -0.29** -0.09 0.18 -0.01 -0.09 -0.06 0.19 0.25** 0.04 0.28**
DWMFP 0.17 -0.14 0.05 0.24** 0.04 0.09 -0.02 0.13 0.21* 0.05 0.11
NSF 0.18 0.06 -0.04 0.26** 0.00 -0.07 0.09 0.23* 0.29** 0.26** 0.72**
HSW -0.14 -0.31* -0.10 0.03 -0.33* -0.13 0.01 0.17 0.25** -0.01 0.21*
NTFP -0.29** -0.56** -0.11 -0.03 -0.10 -0.13 -0.16 0.10 0.10 -0.07 -0.03
YLDH -0.23* -0.53** -0.31** 0.12 -0.19* -0.28** -0.33* 0.28** 0.13 -0.06 0.23*
65
Table 13. Continued
Trait PDL FL FD AFW NMFP WMFP DWMFP NSF HSW NTFP YLDH
DFF -0.07 0.18 0.15 0.03 -0.11 -0.01 0.17 0.18 -0.14 -0.3 -0.24
NDM -0.25 0.18 0.21 0.05 -0.31 -0.32 -0.15 0.07 -0.36* -0.60** -0.57**
PH -0.32 -0.37* 0.10 -0.42** 0.10 -0.10 0.06 -0.05 -0.12 -0.10 -0.32
STD 0.35* 0.50** 0.10 0.44** -0.16 0.24 0.29 0.31 0.03 -0.01 0.17
NPBS -0.24 0.01 0.07 -0.01 0.17 -0.02 0.04 -0.01 -0.39* -0.11 -0.20
NIN -0.35* -0.25 0.04 -0.44** 0.25 -0.10 0.13 -0.10 -0.22 -0.13 -0.32
INL -0.36* -0.38* 0.38* -0.13 -0.05 -0.06 -0.02 0.09 0.02 -0.15 -0.33*
LL 0.47** 0.49** -0.07 0.38* -0.14 0.22 0.16 0.31 0.24 0.12 0.35*
LW 0.10 0.17 0.25 0.28 -0.06 0.29 0.25 0.38* 0.29 0.13 0.17
NE -0.03 0.05 0.41** 0.37* -0.12 0.06 0.07 0.38* -0.02 -0.10 -0.09
NRG 0.39* 0.26 0.59** 0.74** -0.32 0.28 0.11 0.74** 0.23 -0.04 0.24
PDL 0.29 -0.10 0.33* -0.33* 0.03 -0.01 0.27 0.08 0.04 0.29
FL 0.30** -0.22 0.55** -0.44** -0.20 -0.24 0.30 -0.13 -0.21 0.04
FD -0.07 -0.12 0.55** -0.19 0.11 0.11 0.49** 0.21 -0.19 -0.19
AFW 0.30** 0.55** 0.55** -0.42** 0.06 -0.07 0.59** 0.20 -0.10 0.18
NMFP -0.28* -0.40** -0.17 -0.39** 0.64** 0.58** -0.32 0.33* 0.57** 0.39*
WMFP 0.03 -0.17 0.10 0.06 0.64** 0.81** 0.16 0.52** 0.52** 0.53**
DWMFP 0.00 -0.22 0.10 -0.07 0.57** 0.81** 0.14 0.40* 0.46** 0.35*
NSF 0.23* 0.26** 0.44** 0.55** -0.31 0.16 0.14 0 -0.11 0.01
HSW 0.06 -0.09 0.19* 0.18 0.29** 0.47** 0.36** 0.01 0.41** 0.46**
NTFP 0.03 -0.19* -0.18 -0.09 0.56** 0.52** 0.45** -0.10 0.36** 0.86**
YLDH 0.26** 0.05 -0.17 0.17 0.39** 0.52** 0.35** 0.01 0.40** 0.85**
* = Significant 0.05 (rg = 0.34 and rp = 0.19) probability level; ** = highly significant at 0.01 (rg = 0.33 and rp = 0.24) level of probability level. DFF= Days
to 50% flowering; NDM=Days maturity; PH= Plant height (m); STD= Stem diameter (mm); NPBS= Number of primary branches per stem; NIN= Number
of internodes; INL= Internodes length (cm); LL= Leaf length (cm); LW= Leaf width (cm); NEP= Number of epicalyxes; NRG= Number of ridge; PDL=
Peduncle length (cm); FL= Fruit length (cm); FD= Fruit diameter (cm); AFW= Average fruit weight (g); NMFP= Number of matured fruits per plant; WMFP=
Weight of matured fruits per plant (g); DWMFP= Dry weight of matured fruits per plant (g); NSF= Number of seeds per fruit; HSW=hundred seed weight (g
); NTFP= Number of tender fruits per plant; YLPH= Yield in ton per hectare.
66
4.5.2. Phenotypic and Genotypic Path Coefficient Analyses
Weight of matured pods per plant, dry weight of matured pods per plant, hundred seed
weight and number of tender fruits per plant had positive and significant correlations with
fruit yield per hectare (Table 13) and also had positive direct effects on yield (Table 14 and
Table 15). Leaf length had positive direct effects on yield at genotypic level but negative
direct effect at phenotypic level though it had positive and significant genotypic and
phenotypic correlation with yield. Number of ridge and peduncle length had positive and
significant phenotypic correlations and also had positive direct effects on fruit yield at
phenotypic level (Table 15). This indicated that the positive and significant genotypic and
phenotypic correlations of these traits with fruit yield were due to the direct effects of the
traits on yield, therefore, it is possible to suggest that the traits could be used for indirect
selection of genotypes for high fruit yield. Prasath et al. (2017) and Rambabu et al. (2019a)
suggested that the direct effect of traits on fruit yields per hectare favors yield improvement
through the selection of these traits. These suggested that indirect selection based on these
traits will be effective in yield improvement.
Number of matured fruits per plant had negative direct effect on fruit yield at genotypic and
phenotypic levels (Table 14 and Table 15) though it had positive and significant genotypic
and phenotypic correlation with yield (Table 13). This indicated that the positive correlation
of number of matured fruits per plant with fruit yield was due to the positive indirect effect
of the trait through other traits on yield. If the variable or trait has positive correlation and
the direct effect of the variable or trait is negative or negligible, the positive correlation of
the trait is because of the indirect effects through other traits. In such situation, it was
suggested that the importance of considering indirect causal factors/traits for simultaneously
selection is necessary (Singh and Chaudhary, 1977).
Days to maturity had negative and significant phenotypic and genotypic correlations with
yield and had also negative direct effects on fruit yield per hectare at phenotypic and
genotypic levels (Table 14 and Table 15). Internodes length had negative and significant
phenotypic and genotypic correlations with yield and had negative direct effects on fruit
yield per hectare at genotypic level (Table 14) but positive direct effects on yield at
phenotypic level (Table 15). Number of internode had positive direct effect on yield at
phenotypic level though it had negative and significant phenotypic correlation with fruit
yield per hectare. Plant height and number of primary branches had negative and significant
67
phenotypic correlations with yield and had also negative direct effects on fruit yield per
hectare at phenotypic level (Table 15). The correlation coefficient indicates the association
of variables which is the total effect that does not show the direct effect and indirect effects
of variables. The path analysis is the portioning of the total correlation into direct and indirect
effects of independent variable(s) on dependent variable (Singh and Chaudhary, 1977;
Dabholkar, 1992; Nadaranjan and Gunasekaran, 2005). Therefore, the traits that had
negative correlation and negative direct effects on yield not necessary to consider for indirect
selection for yield or selection of genotypes simultaneously for these traits and fruit is hardly
possible.
The residual effect was 0.2525 in genotypic path analysis (Table 14) and it was 0.0854 in
phenotypic path analysis (Table 15). This indicated that 75% and 91% of the variability in
fruit yield per hectare was explained by the component factors at genotypic and phenotypic
levels, respectively. The remaining 25 and 5% could be explained by other traits genotypic
and phenotypic levels, respectively, not considered in this study. The residual effect
determines how much best the causal factors or dependent variables account for the
variability of dependent variable (Dabholkar, 1992; Singh and Chaudhary, 1977).
Table 14. Genotypic direct (bold diagonal) and indirect (off diagonal) effects of quantitative
traits on okra fruit yield per hectare at Pawe in 2017
Trait FFD NDM INL LL NMFP WMFP DWMFP HSW NTFP rg (YLDH)
NDM 0.342 -0.22 -0.002 -0.008 0.016 -0.022 -0.001 -0.011 -0.489 -0.57**
INL -0.011 -0.025 -0.02 -0.009 0.002 -0.004 0.00 0.001 -0.124 -0.33*
LL 0.075 0.017 0.002 0.10 0.007 0.015 0.002 0.007 0.098 0.35*
NMFP -0.045 0.069 0.001 -0.014 -0.05 0.045 0.006 0.01 0.46 0.39*
WMFP -0.003 0.07 0.001 0.022 -0.032 0.07 0.008 0.016 0.422 0.53**
DWMFP 0.07 0.032 0.00 0.016 -0.029 0.057 0.01 0.012 0.369 0.35*
HSW -0.06 0.08 0.00 0.024 -0.016 0.036 0.004 0.03 0.332 0.46**
NTFP -0.126 0.133 0.003 0.012 -0.028 0.036 0.005 0.012 0.81 0.86**
Residual effect= 0.2525. FFD= Days to 50% flowering; NDM=Days maturity; INL=
Internodes length (cm); LL= Leaf length (cm); NMFP= Number of matured fruits per plant;
WMFP= Weight of matured fruits per plant (g); DWMFP= Dry weight of matured fruits per
plant (g); HSW=hundred seed weight (g); NTFP = Number of tender fruits per plant and
YLPH= Yield in ton per hectare
68
Table 15. Phenotypic direct (bold diagonal) and indirect (off diagonal) effects of quantitative traits on okra fruit yield per hectare at Pawe in 2017
Trait FFD NDM PH NPBS NIN INL LL NRG PDL NMFP WMFP DWMF HSW NTFP rp
(YLDH)
FFD 0.49 -0.072 0.00 -0.033 0.095 -0.003 -0.023 0.093 -0.02 0.019 -0.001 0.018 0.01 -0.289 -0.23*
NDM 0.353 -0.1 0.00 -0.045 0.103 0.013 0.015 0.02 -0.072 0.055 -0.073 -0.016 0.022 -0.557 -0.53**
PH 0.009 -0.005 -0.01 0.006 0.205 0.078 0.001 -0.143 -0.067 -0.018 -0.023 0.006 0.007 -0.106 -0.31**
NPBS 0.123 -0.035 0.00 -0.13 0.099 -0.008 0.037 -0.056 -0.051 -0.032 -0.002 0.004 0.023 -0.104 -0.19*
NIN 0.111 -0.025 -0.005 -0.031 0.42 0.012 -0.008 -0.128 -0.055 -0.038 -0.023 0.01 0.009 -0.13 -0.28**
INL -0.013 -0.012 -0.007 0.009 0.045 0.11 0.009 -0.052 -0.076 0.009 -0.015 -0.002 0.00 -0.157 -0.33*
LL 0.062 0.008 0.00 0.027 0.018 -0.006 -0.18 0.175 0.109 0.016 0.047 0.015 -0.012 0.102 0.28**
NRG 0.083 -0.004 0.003 0.013 -0.098 -0.01 -0.057 0.55 0.081 0.056 0.069 0.012 -0.015 -0.035 0.23*
PDL -0.037 0.028 0.003 0.026 -0.088 -0.032 -0.075 0.172 0.26 0.052 0.007 0.00 -0.004 0.026 0.26**
NMFP -0.049 0.029 -0.001 -0.022 0.084 -0.005 0.015 -0.161 -0.072 -0.19 0.159 0.063 -0.021 0.563 0.39**
WMFP -0.001 0.029 0.001 0.001 -0.038 -0.007 -0.034 0.152 0.007 -0.121 0.25 0.089 -0.033 0.516 0.52**
DWMF 0.082 0.014 -0.001 -0.005 0.04 -0.002 -0.024 0.062 -0.001 -0.109 0.202 0.11 -0.025 0.453 0.35**
HSW -0.067 0.031 0.001 0.043 -0.055 0.001 -0.03 0.115 0.014 -0.056 0.118 0.039 -0.07 0.364 0.40**
NTFP -0.142 0.056 0.001 0.014 -0.055 -0.017 -0.018 -0.019 0.007 -0.107 0.129 0.05 -0.025 1 0.85**
Residual effect= 0.0854. FFD= Days to 50% flowering; NDM=Days maturity; PH= Plant height (m); NPBS = Number of primary branches per
stem; NIN= Number of internodes; INL= Internodes length (cm); LL= Leaf length (cm); NRG= Number of ridge; PDL= Peduncle length (cm);
NMFP= Number of matured fruits per plant; WMFP= Weight of matured fruits per plant (g); DWMPP= Dry weight of matured pods per plant (g);
HSW=hundred seed weight (g ); NTFP = Number of tender fruits per plant and YLPH= Yield in ton per hectare.
69
5. SUMMARY AND CONCLUSION
Okra [Abelmoschus esculentus (L.) Moench] is believed to have originated in Ethiopia. It is
a traditional vegetable crop in northwestern Ethiopia, Metekel Zone of Benshanguel Gumuz
Regional state. The crop is considered a minor crop and it has not given research attention
in Ethiopia. Therefore, the study was conducted to characterize and evaluate okra landraces
and to estimate genotypic, phenotypic variability and genetic divergence and to estimate
heritability and genetic advance under selection, degree of genotypic and phenotypic
associations among yield and yield related traits. A total of 35 okra landraces collected from
three districts (Guba, Mandura and Dangure) of Metekel Administration Zone were
evaluated for 23 agro-morphological and eight qualitative traits in 2017 at Pawe Agricultural
Research Center in randomized complete block design.
The analysis of variance revealed that the okra landraces had significant differences for all
quantitative traits. The okra landraces also distributed into different categories of plant habit,
leaf color, leaf petiole color, pod color, stem color, shape of leaf, flower color and position
of fruit on the main stem. Moreover, the okra landraces had number of tender fruits per plant
ranged from 4.86 to 36.54 and 2.49 to 21.98 t ha-1 mean fruit yield per hectare. The results
showed that the presence of significant variations among landraces that indicated the higher
chance of developing okra varieties for high fruit yield and other desirable traits through
selection.
The estimates of phenotypic (PCV) and genotypic (GCV) coefficient of variations for 22
traits of 35 okra landraces were in the range between 4.96 and 55.22% and 2.95 and 54.92%,
respectively. The broad sense heritability (H2) and genetic advance as percent of mean
(GAM) estimated in the range between 35.36 and 99.6% and 3.62 and 112.66%, respectively.
High GCV, PCV, H2 and GAM was estimated for plant height, number of primary
branches/stem, internodes length, number of matured fruits per plant, weight of matured
fruits per plant, dry weight of matured fruits/plant, number of seeds per fruit, number of
tender fruits per plant and yield per hectare indicated the high heritability was due to the
close correspondence between the genotypic and phenotypic variations as a result of
relatively small contribution of the environment to the phenotype expression of the traits.
This suggested that selection based on phenotypic performances of genotypes could be
effective to identify high performing genotypes to be developed as varieties.
70
Number of tender fruits per plant, hundred seed weight, weight of matured fruits per plant,
leaf length had positively phenotypic and genotypic highly significant correlation with fruit
yield per hectare. Negative phenotypic correlation was recorded for days to 50% flowering,
days to maturity, leaf length, internodes number, internodes length, number of primary
branch and plant height insignificantly. On the other hand, fruit yield per hectare had
significantly negative genotypic correlation with days to maturity and internodes length. The
interdependency of other traits on each other’s was also recorded for both genotypic and
phenotypic correlation. Generally, fruit yield per hectare had positive and significant
genotypic and phenotypic correlation coefficients with weight of matured pods per plant, dry
weight of matured pods per plant, hundred seed weight, number of tender fruits per plant and
leaf length. Fruit yield per hectare had positive and significant phenotypic correlation
coefficients with number of ridge and peduncle length. These traits also had positive direct
effects on fruit yield at genotypic and phenotypic levels except leaf length exerted negative
direct effect on yield at phenotypic level. This suggested that direct and simultaneous
selection of okra landraces for yield and these traits is possible.
The first four principal component axes (PCA1 to PCA4) accounted 65.59% of the total
variation of which PCA1 and PCA2 had larger contribution of 22.09 and 19.34%,
respectively. Genetic distances estimated by Euclidean distances from 23 traits and the
genetic distances for all possible pairs of 35 okra landraces ranged from 2.92 to 11.28 with
mean Euclidean distance, standard deviation and CV% of 6.48, 0.16 and 2.51 respectively.
The 35 okra landraces were grouped into 12 distinct clusters from Euclidean distances matrix
using of which Cluster IX consisted of 6 (17.14%), Cluster II , VI and X consisted each five
and other clusters consisted of 1 and 3 landraces. The study results showed the presence of
genetic variation among landraces for all traits suggested that selection could be effective to
develop okra varieties for high fruit yield and other traits.
However, the study was conducted for one season at one location and it is suggested to
conduct further research across locations and seasons and followed by molecular
characterization to evaluate the landraces for fruit yield and other fruit related traits.
71
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87
7. APPENDIX
88
Appendix Table 1. Long-term average wheather data (1987-2018) for Pawe Station
Source: Meteorological Station of Pawe Agricultural Research Station, 2019.
Appendix Table 2. Description of qualitative traits according to IPGR, 1991 descriptors used
to characterize 35 okra landraces at Pawe in 2017
Parameter Character codes
Plant habit 1. Densely branched at apex (DBA), 2. Densely Branched Base
(DBB) and 3. Densely branched all over (DBO)
Flower color 1. Red color inside only and 2. Red color at both sides.
Leaf color 1. Totally green and 2. Green with red vein.
Leaf petiole color 1. Green, 2. Red above but green below and 3.Red on both sides.
Pod color 1. Green, 2. Red, 3. Green yellow and 4. Yellow
Stem color 1. Green, 2. Green with red patch, 3. Red and 4. Purple
Leaf Shape 1. Oval undulate, 2. Heart-shaped, 3. Broadly ovate, 4. Star shaped
(palmately lobed), 5. Palmately triangular lobes, 6. Palmately lobed
with dentate margins, 7. Palmately lobed with serrated margins and
8. Linear-oblong or tri angular lobes.
Fruit Position 1. Erect, 2. Intermediate, 3. Horizontal, 4. Slightly falling and 5.
Totally falling
Month Max.T.
(oC)
Min.T.
(oC)
R.F
(ml)
R.H.
(%)
W.S.
(Km/day)
ETo
(mm/day)
January 34.29 12.01 0.70 48.97 39.85 3.83
February 36.36 14.84 0.60 49.20 53.58 4.46
March 37.68 18.15 7.80 52.08 65.49 5.04
April 37.49 19.57 27.80 53.95 75.89 5.42
May 34.69 19.39 93.20 63.67 78.53 4.96
June 30.25 18.28 289.80 72.01 78.70 4.13
July 27.81 17.93 361.40 73.91 58.69 3.41
August 27.80 17.66 396.30 73.87 51.06 3.41
September 28.16 17.45 261.10 70.67 46.71 3.67
October 30.46 17.05 132.60 66.92 29.68 3.72
November 32.28 14.37 14.40 56.59 27.69 3.72
December 33.25 12.37 0.70 51.20 41.35 3.76
Average 32.80 16.59 132.20 61.09 53.93 4.13
Max. T. (oC) = Maximum temperature in degree celsius; Min. T. (oC) = Minimum
temperature in degree celsius; R.F (ml) = Rainfall in milliliter; R.H. (%) = Relative
humidity in percent; W.S. (km/day) = Wind speed in kilometric per day and ETo (mm/d)
= Reference evapotranspiration in millimeter per day
89
Appendix Table 3. Qualitative traits of 35 okra landraces according to IPGR, 1991
descriptors evaluated at Pawe in 2017
Landrace Plha FlCo Lcol LPCol PodCol StCol ShaLef PFruMS
Gu-2 2 1 2 3 2 3 4 2
Gu-3 2 2 1 2 1 3 4 2
Gu-4 2 2 1 2 1 2 1 1
Gu-5 2 1 1 1 1 1 7 1
Gu-6 2 2 1 1 1 2 7 1
Gu-7 2 2 1 1 1 1 4 1
Gu-8 2 2 1 2 1 3 7 1
Gu-9 2 2 1 3 1 3 4 3
Gu-11 2 2 1 1 1 2 7 1
Gu-12 2 2 1 3 1 3 4 2
Gu-14 2 2 1 2 1 1 7 1
Gu-17 2 2 1 3 1 3 1 2
Gu-18 2 2 1 1 1 1 1 2
Gu-20 2 1 2 3 2 3 4 2
Gu-21 2 2 1 1 1 1 7 2
Gu-22 2 1 2 2 1 2 7 1
Gu-23 2 2 1 2 1 3 1 1
Ma-24 2 2 1 2 1 3 2 2
Ma-25 2 2 1 1 1 1 2 2
Gu-27 3 2 1 3 1 2 1 1
Ma-29 2 2 1 2 1 2 2 2
Ma-30 2 2 1 2 1 2 2 1
Ma-31 2 2 1 3 1 2 1 2
Ma-32 2 2 1 2 1 2 7 2
Ma-33 2 1 1 2 1 3 7 2
Ma-34 3 2 1 2 1 2 2 2
Ma-35 2 2 1 3 1 2 2 2
Ma-37 2 2 1 3 1 2 2 2
Ma-39 2 2 1 2 1 2 4 1
Da-40 2 1 1 1 1 1 7 1
Da-41 2 1 2 3 1 3 2 2
Da-42 2 2 1 1 1 1 4 1
Da-43 2 1 1 2 1 1 7 1
Da-45 2 1 1 2 1 2 2 2
Gu-47 2 2 1 2 1 2 4 3
Plha= Plant habit, FlCo =Flower color, Lcol = Leaf color, LPCol= Leaf petiole color,
PodCol= Pod color, StCol= Stem color, ShaLef = Shape of leaf and PFruMS = Position of
fruits on main stem
90
Appendix Table 4. Euclidean distance matrix as estimates of genetic distances of 35 okra landraces estimated from 22 quantitative traits
Landraces Gu-
3
Gu-
4
Gu-
5
Gu-
6
Gu-
7
Gu-
8
Gu-
9
Gu-
11
Gu-
12
Gu-
14
Gu-
17
Gu-
18
Gu-
20
Gu-
21
Gu-
22
Gu-
23
Ma-
24
Ma-
25
Gu-
27
Gu-2 6.44 8.07 8.36 8.56 8.22 7.62 5.95 7.81 8.24 8.25 8.10 7.20 6.74 8.92 6.92 8.43 7.19 8.42 8.51
Gu-3 6.80 7.04 6.66 5.67 6.87 4.49 5.06 6.24 5.90 6.69 6.42 5.22 6.96 6.57 5.68 4.85 5.75 6.39
Gu-4 6.80 7.78 6.04 7.95 6.69 8.05 6.31 7.35 8.39 5.81 5.75 5.95 6.06 6.19 5.66 7.57 5.04
Gu-5 7.97 5.82 8.08 6.09 6.44 8.65 6.95 7.31 5.39 4.13 5.33 2.93 5.76 6.47 5.93 6.51
Gu-6 5.51 5.79 6.71 7.81 7.69 3.38 9.26 7.73 7.09 6.72 7.35 6.20 5.66 6.22 8.01
Gu-7 5.56 5.33 5.71 6.29 5.35 6.92 5.06 6.02 4.24 6.05 4.15 3.23 3.21 5.30
Gu-8 5.87 6.60 8.40 5.62 7.70 6.48 6.87 7.67 7.96 6.76 5.14 6.14 8.38
Gu-9 4.03 6.10 6.11 6.32 5.53 4.41 6.15 5.54 5.03 4.80 5.23 6.27
Gu-11 6.67 7.09 7.33 6.26 5.71 6.41 6.84 4.82 4.78 4.41 6.87
Gu-12 7.25 10.28 8.34 7.84 7.23 8.31 5.78 5.53 6.84 7.32
Gu-14 8.16 6.79 6.23 6.66 6.85 5.31 5.12 5.86 7.50
Gu-17 4.79 6.55 8.09 6.94 7.33 7.02 6.83 6.34
Gu-18 4.79 5.86 4.67 5.22 4.88 5.22 4.46
Gu-20 6.00 3.79 5.69 5.94 6.39 6.28
Gu-21 5.49 5.28 5.12 4.80 5.59
Gu-22 5.78 6.32 6.22 5.82
Gu-23 2.92 3.54 4.24
Ma-24 3.19 4.52
Ma-25 5.49
Gu-27
91
Appendix Table 4. Continued
Landraces Ma-29 Ma-30 Ma-31 Ma-32 Ma-33 Ma-34 Ma-35 Ma-37 Ma-39 Da-40 Da-41 Da-42 Da-43 Da-45 Gu-47
Gu-2 7.66 7.19 7.30 7.94 8.62 8.68 8.15 6.46 7.88 7.12 8.40 8.39 6.29 6.66 5.19
Gu-3 4.51 4.93 4.90 6.39 7.13 6.52 4.93 6.77 6.04 6.31 6.71 6.69 7.78 5.54 4.53
Gu-4 7.86 7.18 6.24 9.17 9.41 6.96 6.43 7.11 7.62 8.95 8.16 8.47 9.75 8.67 7.77
Gu-5 7.41 7.21 5.64 8.00 8.30 6.85 7.80 5.94 5.31 7.78 5.17 9.42 8.96 7.42 6.77
Gu-6 7.57 7.81 7.81 6.69 9.99 8.39 7.45 8.37 7.93 4.78 7.59 4.89 8.26 8.81 7.64
Gu-7 5.98 5.86 5.02 7.29 8.10 6.12 5.83 6.04 5.69 7.14 5.61 6.73 9.09 6.64 6.56
Gu-8 6.73 6.45 6.80 5.49 8.48 7.90 6.97 7.34 7.68 5.78 7.09 5.25 7.11 7.29 7.31
Gu-9 4.83 4.71 4.97 6.77 6.05 5.74 5.81 5.57 6.19 6.61 4.98 6.96 7.55 4.54 4.65
Gu-11 4.05 5.21 5.21 7.50 5.07 6.38 5.16 6.38 6.39 7.77 4.31 8.00 9.03 4.51 5.24
Gu-12 7.76 7.96 7.41 10.46 8.75 8.07 6.95 8.45 9.52 9.80 8.37 8.84 11.28 8.10 8.08
Gu-14 6.63 7.07 6.74 5.77 9.21 7.44 6.42 7.86 7.23 5.02 6.92 4.28 7.49 8.36 7.52
Gu-17 5.95 4.61 4.94 5.82 7.17 5.35 6.53 5.34 4.05 7.94 6.02 7.85 7.26 5.84 6.49
Gu-18 5.65 5.23 3.68 6.29 7.49 5.62 5.59 4.02 4.08 7.53 4.86 7.36 7.44 5.98 5.76
Gu-20 6.41 6.10 5.23 6.78 7.60 6.72 6.54 6.18 5.05 6.08 5.46 7.88 7.31 6.03 5.36
Gu-21 7.18 7.28 6.63 9.11 9.17 6.74 6.84 7.14 6.54 8.29 6.46 8.25 10.37 7.94 7.26
Gu-22 7.22 7.06 5.46 7.72 8.77 6.72 7.60 5.18 4.88 7.16 5.61 8.87 8.10 7.12 5.92
Gu-23 5.40 5.95 5.39 7.49 7.33 5.10 4.38 6.08 5.95 7.67 4.49 7.13 9.19 6.17 6.99
Ma-24 4.82 5.00 4.99 6.73 7.10 5.34 3.81 5.63 6.15 7.00 5.35 6.05 8.42 5.98 5.94
Ma-25 5.31 5.49 5.19 7.30 7.00 5.67 5.47 5.65 5.53 7.51 4.21 7.29 9.42 5.93 5.95
Gu-27 5.98 5.44 5.13 8.16 7.88 3.99 4.49 5.27 5.29 8.82 5.87 8.09 9.42 6.63 7.12
Ma-29 3.50 4.35 5.59 5.72 5.25 3.38 5.50 5.49 7.10 4.99 6.03 7.25 5.20 5.01
Ma-30 3.77 5.55 4.45 4.00 4.09 4.27 5.16 7.24 4.80 6.43 7.13 4.62 4.73
Ma-31 6.09 5.64 5.52 5.07 4.02 4.49 7.68 4.69 7.45 7.79 5.02 4.77
Ma-32 7.98 7.36 6.54 6.77 6.04 4.55 6.58 4.77 4.39 7.06 6.69
Ma-33 6.63 6.38 6.23 7.53 9.44 5.07 9.38 9.41 4.67 6.03
Ma-34 4.61 4.82 5.39 8.69 5.07 7.53 8.81 6.36 7.20
Ma-35 6.20 5.95 7.71 5.75 5.97 8.22 5.93 6.42
Ma-37 4.81 8.14 4.34 8.13 7.30 5.63 5.02
Ma-39 6.68 4.69 7.70 7.30 5.53 5.69
Da-40 7.57 5.04 4.93 7.61 6.70
Da-41 7.94 8.27 4.87 5.73
Da-42 6.21 8.37 7.95
Da-43 7.89 7.28
Da-45 5.12
92
Appendix Table 5. Genotypic direct (bold diagonal) and indirect (off diagonal) effects of quantitative traits on okra yield
Trait DFF NDM PH STD NPBS NIN INL LL LW NE rg (YLDH)
DFF 0.420 -0.179 0.000 -0.128 -0.005 0.017 0.001 0.018 -0.052 -0.021 -0.24
NDM 0.342 -0.220 0.001 -0.061 -0.009 0.017 -0.002 -0.008 -0.005 -0.008 -0.57**
PH 0.009 -0.009 0.020 0.054 0.001 0.028 -0.015 -0.002 -0.023 -0.010 -0.32
STD 0.215 -0.054 -0.004 -0.250 -0.003 0.001 0.007 0.046 -0.050 0.013 0.17
NPBS 0.114 -0.095 -0.001 -0.042 -0.020 0.012 0.002 -0.030 0.037 0.007 -0.20
NIN 0.147 -0.075 0.011 -0.004 -0.005 0.050 -0.002 -0.003 -0.014 -0.029 -0.32
INL -0.011 -0.025 0.015 0.083 0.002 0.006 -0.020 -0.009 -0.020 0.016 -0.33*
LL 0.075 0.017 0.000 -0.116 0.006 -0.001 0.002 0.100 -0.104 -0.016 0.35*
LW 0.129 -0.007 0.003 -0.073 0.004 0.004 -00.002 0.061 -0.170 0.011 0.17
NE -0.072 0.015 -0.002 -0.027 -0.001 -0.012 -0.003 -0.013 -0.015 0.120 -0.09
NRG 0.073 -0.003 -0.005 -0.111 0.002 -0.013 0.002 0.044 -0.082 0.034 0.24
PDL -0.030 0.056 -0.006 -0.088 0.005 -0.017 0.007 0.047 -0.017 -0.003 0.29
FL 0.075 -0.040 -0.007 -0.124 0.000 -0.013 0.008 0.049 -0.029 0.006 0.04
FD 0.062 -0.046 0.002 -0.025 -0.001 0.002 -0.008 -0.007 -0.042 0.050 -0.19
AFW 0.014 -0.010 -0.008 -0.109 0.000 -0.022 0.003 0.038 -0.048 0.044 0.18
NMFP -0.045 0.069 0.002 0.039 -0.003 0.013 0.001 -0.014 0.010 -0.015 0.39*
WMFP -0.003 0.070 -0.002 -0.060 0.000 -0.005 0.001 0.022 -0.049 0.007 0.53**
DWMFP 0.070 0.032 0.001 -0.073 -0.001 0.007 0.000 0.016 -0.042 0.008 0.35*
NSF 0.076 -0.016 -0.001 -0.077 0.000 -0.005 -0.002 0.031 -0.065 0.045 0.01
HSW -0.060 0.080 -0.002 -0.008 0.008 -0.011 0.000 0.024 -0.049 -0.002 0.46**
NTFP -0.126 0.133 -0.002 0.004 0.002 -0.006 0.003 0.012 -0.022 -0.012 0.86**
93
Appendix Table 5. Continued
Trait NRG PDL FL FD AFW NMFP WMFP DWMFP NSF HSW NTFP rg(YLDH)
DFF 0.054 -0.006 -0.013 -0.056 0.014 0.005 0.000 0.002 -0.047 -0.004 -0.243 -0.24
NDM 0.005 -0.020 -0.013 -0.079 0.020 0.016 -0.022 -0.001 -0.019 -0.011 -0.489 -0.57**
PH -0.085 -0.026 0.027 -0.037 -0.187 -0.005 -0.007 0.001 0.012 -0.003 -0.077 -0.32
STD 0.137 0.028 -0.037 -0.039 0.192 0.008 0.017 0.003 -0.080 0.001 -0.012 0.17
NPBS -0.032 -0.019 -0.001 -0.026 -0.003 -0.009 -0.001 0.000 0.003 -0.012 -0.088 -0.20
NIN -0.081 -0.028 0.019 -0.015 -0.194 -0.013 -0.007 0.001 0.026 -0.007 -0.101 -0.32
INL -0.032 -0.029 0.028 -0.146 -0.059 0.002 -0.004 0.000 -0.025 0.001 -0.124 -0.33*
LL 0.136 0.037 -0.036 0.026 0.165 0.007 0.015 0.002 -0.081 0.007 0.098 0.35*
LW 0.149 0.008 -0.012 -0.093 0.124 0.003 0.020 0.002 -0.099 0.009 0.105 0.17
NE 0.088 -0.002 -0.003 -0.157 0.163 0.006 0.004 0.001 -0.098 -0.001 -0.082 -0.09
NRG 0.310 0.031 -0.019 -0.224 0.326 0.016 0.020 0.001 -0.192 0.007 -0.031 0.24
PDL 0.121 0.080 -0.021 0.038 0.144 0.017 0.002 0.000 -0.071 0.002 0.029 0.29
FL 0.079 0.023 -0.074 0.083 0.242 0.022 -0.014 -0.002 -0.078 -0.004 -0.167 0.04
FD 0.182 -0.008 0.016 -0.380 0.241 0.009 0.008 0.001 -0.126 0.006 -0.151 -0.19
AFW 0.230 0.026 -0.041 -0.208 0.440 0.021 0.004 -0.001 -0.152 0.006 -0.077 0.18
NMFP -0.098 -0.027 0.033 0.071 -0.185 -0.050 0.045 0.006 0.084 0.010 0.460 0.39*
WMFP 0.087 0.002 0.015 -0.042 0.026 -0.032 0.070 0.008 -0.041 0.016 0.422 0.53**
DWMFP 0.036 -0.001 0.018 -0.041 -0.033 -0.029 0.057 0.010 -0.037 0.012 0.369 0.35*
NSF 0.229 0.022 -0.022 -0.184 0.258 0.016 0.011 0.001 -0.260 0.000 -0.087 0.01
HSW 0.072 0.007 0.010 -0.079 0.087 -0.016 0.036 0.004 0.000 0.030 0.332 0.46**
NTFP -0.012 0.003 0.015 0.071 -0.042 -0.028 0.036 0.005 0.028 0.012 0.810 0.86** Residual effect= 0.2525
* Significant 0.05 (r = 0.34) probability level; **= highly significant at 0.01 (r = 0.35) level of probability level
DFF= Days to 50% flowering; NDM=Days maturity; PH= Plant height (m); STD= Stem diameter (mm); NPBS= Number of primary branches per stem; NIN= Number of
internodes; INL= Internodes length (cm); LL= Leaf length (cm); LW= Leaf width (cm); NEP= Number of epicalyxes; NRG= Number of ridge; PDL= Peduncle length (cm);
FL= Fruit length (cm); FD= Fruit diameter (cm); AFW= Average fruit weight (g); NMFP= Number of matured fruits per plant; WMFP= Weight of matured fruits per plant
(g); DWMFP= Dry weight of matured fruits per plant (g); NSF= Number of seeds per fruit; HSW=hundred seed weight (g ); NTFP= Number of tender fruits per plant;
YLPH= Yield in ton per hectare
94
Appendix Table 6. Phenotypic direct (bold diagonal) and indirect (off diagonal) effects of quantitative traits on okra yield
Trait DFF NDM PH STD NPBS NIN INL LL LW NE rp (YLDH)
DFF 0.490 -0.072 0.000 -0.223 -0.033 0.095 -0.003 -0.023 -0.050 -0.026 -0.23*
NDM 0.353 -0.100 0.000 -0.109 -0.045 0.103 0.013 0.015 -0.007 -0.014 -0.53**
PH 0.009 -0.005 -0.010 0.083 0.006 0.205 0.078 0.001 -0.029 -0.013 -0.31**
STD 0.188 -0.019 0.001 -0.580 -0.018 0.060 -0.025 -0.055 -0.050 0.007 0.12
NPBS 0.123 -0.035 0.000 -0.082 -0.130 0.099 -0.008 0.037 0.034 0.006 -0.19*
NIN 0.111 -0.025 -0.005 -0.083 -0.031 0.420 0.012 -0.008 -0.029 -0.035 -0.28**
INL -0.013 -0.012 -0.007 0.134 0.009 0.045 0.110 0.009 -0.026 0.020 -0.33*
LL 0.062 0.008 0.000 -0.178 0.027 0.018 -0.006 -0.180 -0.142 0.000 0.28**
LW 0.112 -0.003 -0.001 -0.133 0.020 0.056 0.013 -0.116 -0.220 0.013 0.13
NE -0.053 0.006 0.001 -0.018 -0.003 -0.061 0.009 0.000 -0.012 0.240 -0.06
NRG 0.083 -0.004 0.003 -0.206 0.013 -0.098 -0.010 -0.057 -0.081 0.041 0.23*
PDL -0.037 0.028 0.003 -0.157 0.026 -0.088 -0.032 -0.075 -0.029 0.013 0.26**
FL 0.064 -0.011 0.003 -0.220 -0.004 -0.054 -0.037 -0.068 -0.036 0.003 0.05
FD 0.056 -0.018 -0.001 -0.068 -0.010 0.051 0.037 0.009 -0.052 0.060 -0.17
AFW 0.007 -0.001 0.004 -0.213 -0.004 -0.103 -0.014 -0.057 -0.062 0.056 0.17
NMFP -0.049 0.029 -0.001 0.078 -0.022 0.084 -0.005 0.015 0.010 -0.021 0.39**
WMFP -0.001 0.029 0.001 -0.107 0.001 -0.038 -0.007 -0.034 -0.054 0.010 0.52**
DWMFP 0.082 0.014 -0.001 -0.140 -0.005 0.040 -0.002 -0.024 -0.045 0.013 0.35**
NSF 0.089 -0.006 0.000 -0.153 0.001 -0.031 0.009 -0.042 -0.065 0.063 0.01
HSW -0.067 0.031 0.001 -0.019 0.043 -0.055 0.001 -0.030 -0.055 -0.002 0.40**
NTFP -0.142 0.056 0.001 0.018 0.014 -0.055 -0.017 -0.018 -0.022 -0.016 0.85**
95
Appendix Table 6. Continued
Trait NRG PDL FL FD AFW NMFP WMFP DWMFP NSF HSW NTFP rp (YLDH)
DFF 0.093 -0.020 0.013 -0.071 0.010 0.019 -0.001 0.018 -0.089 0.010 -0.289 -0.23*
NDM 0.020 -0.072 0.011 -0.110 0.007 0.055 -0.073 -0.016 -0.030 0.022 -0.557 -0.53**
PH -0.143 -0.067 -0.031 -0.051 -0.263 -0.018 -0.023 0.006 0.021 0.007 -0.106 -0.31**
STD 0.196 0.071 0.036 -0.072 0.260 0.026 0.046 0.027 -0.129 -0.002 -0.031 0.12
NPBS -0.056 -0.051 0.003 -0.050 0.022 -0.032 -0.002 0.004 0.002 0.023 -0.104 -0.19*
NIN -0.128 -0.055 -0.012 -0.075 -0.174 -0.038 -0.023 0.010 0.036 0.009 -0.130 -0.28**
INL -0.052 -0.076 -0.032 -0.206 -0.089 0.009 -0.015 -0.002 -0.042 0.000 -0.157 -0.33*
LL 0.175 0.109 0.036 0.032 0.224 0.016 0.047 0.015 -0.113 -0.012 0.102 0.28**
LW 0.202 0.034 0.016 -0.147 0.200 0.009 0.062 0.023 -0.144 -0.018 0.102 0.13
NE 0.095 0.014 0.001 -0.155 0.167 0.017 0.011 0.006 -0.128 0.000 -0.068 -0.06
NRG 0.550 0.081 0.021 -0.322 0.479 0.056 0.069 0.012 -0.353 -0.015 -0.035 0.23*
PDL 0.172 0.260 0.029 0.044 0.214 0.052 0.007 0.000 -0.112 -0.004 0.026 0.26**
FL 0.121 0.077 0.096 0.076 0.387 0.076 -0.044 -0.024 -0.126 0.007 -0.189 0.05
FD 0.286 -0.018 -0.012 -0.620 0.388 0.032 0.025 0.011 -0.214 -0.013 -0.177 -0.17
AFW 0.371 0.078 0.052 -0.339 0.710 0.073 0.015 -0.007 -0.270 -0.013 -0.093 0.17
NMFP -0.161 -0.072 -0.038 0.104 -0.274 -0.190 0.159 0.063 0.152 -0.021 0.563 0.39**
WMFP 0.152 0.007 -0.017 -0.062 0.043 -0.121 0.250 0.089 -0.077 -0.033 0.516 0.52**
DWMFP 0.062 -0.001 -0.021 -0.061 -0.046 -0.109 0.202 0.110 -0.070 -0.025 0.453 0.35**
NSF 0.396 0.059 0.025 -0.271 0.391 0.059 0.040 0.016 -0.490 -0.001 -0.104 0.01
HSW 0.115 0.014 -0.009 -0.118 0.129 -0.056 0.118 0.039 -0.004 -0.070 0.364 0.40**
NTFP -0.019 0.007 -0.018 0.109 -0.066 -0.107 0.129 0.050 0.051 -0.025 1.000 0.85**
Residual effect= 0.0854
* Significant 0.05 (r = 0.19) probability level; **= highly significant at 0.01 (r = 0.24) level of probability level
DFF= Days to 50% flowering; NDM=Days maturity; PH= Plant height (m); STD= Stem diameter (mm); NPBS= Number of primary branches per stem; NIN= Number of
internodes; INL= Internodes length (cm); LL= Leaf length (cm); LW= Leaf width (cm); NEP= Number of epicalyxes; NRG= Number of ridge; PDL= Peduncle length (cm);
FL= Fruit length (cm); FD= Fruit diameter (cm); AFW= Average fruit weight (g); NMFP= Number of matured fruits per plant; WMFP= Weight of matured fruits per plant
(g); DWMFP= Dry weight of matured fruits per plant (g); NSF= Number of seeds per fruit; HSW=hundred seed weight (g ); NTFP= Number of tender fruits per plant; YLPH=
Yield in ton per hectare
96
Appendix Figure 1. Dendrogram showing clustering pattern among 35 okra landraces based on 22 quantitative traits evaluated
Gu-2
Da-
40
Ma-
32
Da-
43
Gu-8
Da-
42
Gu-6
Gu-1
4
Gu-4
Gu-1
2
Ma-
33
Gu-2
0
Gu-5
Gu-2
2
Gu-2
1
Gu-2
3
Ma-
24
Gu-7
Ma-
25
Da-
45
Gu-9
Gu-1
1
Gu-3
Gu-4
7
Gu-1
7
Ma-
39
Da-
41
Ma-
37
Gu-1
8
Ma-
31
Ma-
30
Ma-
29
Ma-
35
Gu-2
7
Ma-
34
0
1
2
3
4
5
6
7
8
9E
ucl
idea
nd d
icta
nce
Okra landraces
