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ANALYSIS OF MORPHOLOGICAL AND GENETIC VARIATION AND CLONAL PROPAGATION OF GUAVA
By
Abdul Kareem M.Sc. (Hons.) Horticulture
A thesis submitted in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
IN
Horticulture
INSTITUTE OF HORTICULTURAL SCIENCES FACULTY OF AGRICULTURE
UNIVERSITY OF AGRICULTURE FAISALABAD, PAKISTAN
2013
ii
Declaration
I hereby declare that the contents of the thesis “Analysis of Morphological and
Genetic Variation and Clonal Propagation of Guava.” are the product of my own
research and no part has been occupied from any published sources (except the
references, standard mathematical or genetic models / equations / formula / protocol
etc.). I further declare that this work has not been submitted for the award of any other
diploma / degree. The university may take action if the information provided is found
inaccurate at any stage. In case of any default, the scholar will be proceeded against as
per HEC plagiarism policy.
Abdul Kareem 2000-ag-1476
iii
To The Controller of Examinations, University of Agriculture, Faisalabad.
We, the Supervisory Committee, certify that the contents and form of thesis submitted by Mr.
Abdul Kareem, 2000-ag-1476 has been found satisfactory and recommend that it be
processed for evaluation by the External Examiner (s) for the award of Ph.D degree.
SUPERVISORY COMMITTEE:
1. CHAIRMAN : ------------------------------------------------
(Dr. Muhammad Jafar Jaskani)
2. MEMBER : -----------------------------------------------
(Dr. Bilquees Fatima)
3. MEMBER : ------------------------------------------------- (Dr. Bushra Sadia)
iv
Dedicated
To
PROPHET MUHAMMAD (PBUH)
v
ACKNOWLEDGMENTS
All the praises and thanks for Almighty Allah, the most merciful and the most compassionate who gave me health, affectionate parents, talented teachers, helping friends and courage to present this piece of work. Special praises and respects for the Holy Prophet Hazrat Muhammad (PBUH) and Ahlbait, who enlightened the soul of mankind with the spirit of Islam and directed to acquire knowledge where ever it.
I feel a matter of great honor and pleasure to express deep sense of devotion and sincerest feeling of gratitude to my respected supervisor Dr. Muhammad Jafar Jaskani, Professor, Institute of Horticultural Sciences, University of Agriculture, Faisalabad for his kind supervision, keen interest, valuable suggestions and sympathetic attitude throughout my research. His kind behavior and efforts towards the completion of my study period will be rememberable throughout my life. My deepest gratefulness is due to Dr. Bilquees Fatima, Assistant Professor, Institute of Horticultural Sciences, University of Agriculture, Faisalabad, member of my supervisory committee, who enriched me with encouragement, confidence and optimism. I sincerely recognize the efforts of member of my supervisory committee Dr. Bushra Sadia, Assistant Professor, Centre of Agricultural Biochemistry and Biotechnology, University of Agriculture, Faisalabad, for making valuable improvements for the completion of this manuscript.
I gratefully acknowledge the nice and cooperative behavior of all the teachers and staff of the Institute of Horticultural Sciences, and CABB, especially Prof. Dr. Iqrar Ahmad Khan (S.I) Dr. Faisal Saeed Awan, Dr. Azeem Iqbal and Mr. Muhammad Usman for completing this research project in due time.
I would like to express my special gratitude and deepest acknowledgements to my teachers especially Prof. Dr. Muhammad Mumtaz khan, Institute of Horticultural Sciences, for scientific and technical support during my research work. I am also very much grateful to all my class fellows and friends Yasir Sajjad, Asim Mehmood, Dr. Saeed Ahmad Qaisrani, Muhammad Adnan Bukhari, Muhammad Rashid Abbasi and Syed Samar Abbas Naqvi, etc. for providing scientific and technical support during the PhD degree. There are many Institutes and people I want to thank for their help and support, Punjab Agricultural Research Board (PARB), Higher Education Commission (HEC) and Dr. Zhanao Deng for giving me the opportunity of six month (IRSP) training.
I extend my deepest gratitude to all of my chum friends, who supported me a lot during my studies, especially, Abdul Manan.
I express my deepest appreciation to my sincere parents, loving brothers Eng. Muhammad Ibrahim and Muhammad Hanif and caring sisters and wife, for their all kind of support and prayers that enabled me to achieve this milestone in my life.
Abdul Kareem
vi
CONTENTS
S.# Title Page#
1 DECLARATION ii
2 SIGNATURE OF SUPERVISORY COMMITTEE iii
3 DEDICATION iv
4 ACKNOWLEDGEMENT v
5 CONTENTS vi
6 LIST OF TABLES xi
7 LIST OF FIGURES xv
8 ABSTRACT xvii
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 REVIEW OF LITERATURE 7
2.1 Morphological analysis 7
2.2 Genetic analysis 14
2.3 Markers system used for detection of diversity 15
2.3.1 Biochemical markers 15
2 3.2 Protein-based markers 16
2.3.3 Isozymes 17
2.3.4 DNA-based markers 17
2.3.5 Amplified Fragment Length Polymorphisms (AFLPs) 19
2.3.6 Restriction Fragment Length Polymorphisms (RFLPs) 19
2.3.7 Random Amplified Polymorphic DNA (RAPD) 20
2.3.8 Inter Simple Sequence Repeats (ISSRs) 21
2.3.9 Simple Sequence Repeats (SSRs) 21
2.3.10 Single Nucleotide Polymorphisms (SNPs) 22
2.3.11 Cleaved Amplified Polymorphic Sequences (CAPS) 22
2.4 Achievements through molecular markers approaches in guava
23
2.5 Clonal propagation 33
vii
CHAPTER 3 MATERIALS AND METHODS 41
3.1 Experimental materials 42
3.2 Surveying of orchards for guava accession collection form district of Faisalabad and Sheikhupura
44
3.3 Mapping & Tagging of plant material 44
3.4 Morphological analysis 45
3.4.1 Tree parameters 45
3.4.2 Leaf parameters 45
3.4.3 Flower parameters 49
3.4.4 Fruit parameters 50
3.4.5 Seed parameters 54
3.5 Statistical analysis 54
3.6 Genetic analysis 54
3.6.1 Plant material 54
3.6.2 DNA extraction 55
3.6.3 DNA extraction procedure 55
3.6.4 DNA quantification 56
3.6.5 DNA amplification 56
3.6.6 Primer mix 57
3.6.7 Genomic DNA concentration 57
3.6.8 MgCl2 concentration 57
3.6.9 dNTPs 57
3.6.10 PCR temperature profile 57
3.6.11 SSR primers 58
3.6.12 Simple Sequence Repeat (SSR) Analysis 58
3.6.13 PCR reaction 59
3.6.14 Gel electrophoresis 59
3.6.15 Data analysis 59
3.7 Clonal propagation of guava. 60
viii
3.7.1 Preparation of cuttings 60
3.7.2 Preparation of Growth Regulator Treatments 60
3.7.3 Planting of cuttings 60
3.8 Data collection 61
3.9 Statistical analysis 61
CHAPTER 4 RESULTS AND DISCUSSIONS 68
4.1 Morphological characters 68
4.1.1 Descriptive statistical analysis for phenotypic parameters 68
4.1.1.1 Statistical summary of tree parameters in guava accessions 69
4.1.1.2 Statistical summary of leaf parameters in guava accessions 69
4.1.1.3 Statistical summary for flower parameters in guava accessions
71
4.1.1.4 Statistical summary for fruit parameters in guava accessions 72
4.1.1.5 Statistical summary of seed parameters in guava accessions 72
4.1.2 Principal Component Analysis (PCA) 74
4.1.3 PCs: 74
4.1.4 Principal Component Analysis for guava accessions of district Faisalabad
75
4.1.4.1 Variability position of accessions on the basis of phenotypic traits studied
75
4.1.4.2 The accession vector view of the biplot to show similarities among accessions
77
4.1.4.3 Variability position of guava accessions for Faisalabad on the basis of phenotypic traits studied
80
4.1.4.4 The phenotypic-vector view of the biplot to show phenotypic similarities among different traits
84
4.1.4.5 Group constellation and linkage distance based on phenotypic characters of guava for district of Faisalabad
84
4.2 Principal Component Analysis of guava accessions of district Sheikhupura
88
4.2.1 Variability position of guava accessions of district Sheikhupura on the basis of morphological traits studied
89
4.2.2 The guava accession-vector view of the biplot of district Sheikhupura
91
4.2.3 Variability position of accessions on the basis of phenotypic traits studied
91
4.2.4 The phenotypic-vector view of the biplot to show phenotypic similarities among different phenotypic traits of guava
98
ix
accessions of district Sheikhupura 4.2.5 Group constellation and linkage distance of guava accession
of district of Sheikhupura based on phenotypic characters 98
4.3 Principal component analysis for guava accession of district Faisalabad and Sheikhupura
102
4.3.1 Variability position of guava accessions of district Faisalabad and Sheikhupura on the basis of their traits
104
4.3.2 The accession vector view of the biplot to show similarities among 37 guava accessions of districts Faisalabad and Sheikhupura
106
4.3.3 Variability position of guava accessions in districts Faisalabad and Sheikhupura on the basis of phenotypic their different traits.
108
4.3.4 The phenotypic vector view of the biplot to show phenotypic similarities among different phenotypic traits of guava accessions of districts Faisalabad and Sheikhupura
113
4.3.5 Group constellation and linkage distance of guava accessions of districts Faisalabad and Sheikhupura based on phenotypic characters
113
4.4 Genetic variation analysis of guava 119
4.4.1 DNA extraction and quality estimation 119
4.4.2 Optimization of polymerase chain reactions (PCR) 120
4.4.3 Template DNA 120
4.4.4 MgCl2 concentration 121
4.4.5 Taq DNA polymerase concentration 121
4.4.6 Annealing temperature 121
4.4.7 dNTPs 122
4.4.8 Agarose gel electrophoresis 122
4.4.9 Scoring the amplified fragments 122
4.4.10 Primer screening 122
4.4.11 Group constellation 37 accessions of belonging to Faisalabad and Sheikhupura districts
123
4.4.12 Genetic diversity in 37 different accessions of guava 129
4.4.13 Estimating phenotypic data with genetic data for 37 accessions of guava
131
4.5 Clonal propagation of guava 132
4.5.1 Experiment: (A) Effect of IBA on rooting of soft wood cuttings of guava
133
x
4.5.1.1 Number of rooted cuttings 133
4.5.1.2 Percent of rooted cuttings 134
4.5.1.3 Average number of roots per cuttings in guava 135
4.5.1.4 Average root length (cm) 137
4.5.1.5 Survival percentage of plantlets 138
4.5.2 Experiment: (B) Effect of NAA on rooting of soft wood cuttings of guava
141
4.5.2.1 Number of rooted cuttings 141
4.5.2.2 Percent of rooted cuttings 142
4.5.2.3 Average number of roots per cuttings in guava 143
4.5.2.4 Average root length (cm) in guava 144
4.5.2.5 Survival percent of guava plantlets 145
4.5.3 Experiment: (C) Comparison of IBA and NAA on rooting of soft wood cuttings of guava
147
4.5.3.1 Number of rooted cuttings 147
4.5.3.2 Percent rooted cuttings 148
4.5.3.3 Average number of roots per cuttings 149
4.5.3.4 Average root length (cm) 151
4.5.3.5 Survival of guava plantlets 151
CHAPTER 5 SUMMARY 155
LITERATURE CITED 157
xi
LIST OF TABLES
Table
No. Title
Page
No.
2.1 Classification of different markers systems 16
2.2 Comparison of different molecular marker systems 20
2.3 Comparison of molecular markers for their advantages and disadvantages.
23
2.4 Achievements made in guava through molecular makers based approaches
25
3.1 List of accession collected from Faisalabad district of Punjab-Pakistan
42
3.2 List of accession collected from Sheikhupura district of Punjab-Pakistan
43
3.3 Orchards surveyed and accession collection from district of Faisalabad and Sheikhupura
44
3.4 Synopsis of characteristics of 23 nuclear SSR loci isolated from Psidium guajava (Risterucci et al., 2005)
58
4.1 Explainable statistical summary of tree parameters in guava accessions
69
4.2 Explainable statistical summary of leaf parameters in guava accessions
70
4.3 Explainable statistical summary for flower parameters in guava accessions
71
4.4 Explainable statistical summary for fruit parameters in guava accessions
73
4.5 Explainable statistical summary of seed and flowering to fruit maturity parameters in guava accessions
74
4.6 Eigen values of correlation matrix and related statistics for guava accessions of district Faisalabad
75
4.7 Factor coordinates of 17 accessions of guava based on correlations for Faisalabad district
76
4.8 Variability position of accessions on the basis of phenotypic traits studied
77
4.9 Factor coordinates of 57 variables of guava for Faisalabad district based on correlations
79
4.10 Variability position of accessions of guava for Faisalabad district on the basis of phenotypic traits studied
82
4.11 Phenotypic distances between all possible pairs of guava accessions of district Faisalabad calculated as linkage distances
86
xii
Table
No. Title
Page
No.
4. 12 Group constellation of 17 accessions of guava belonging to Faisalabad district based on 57 phenotypic characters
88
4.13 Eigen values of correlation matrix and related statistics for guava accessions of district Sheikhupura
89
4.14 Factor coordinates of guava accessions of district of Sheikhupura based on correlations
90
4.15 Variability position of guava accessions of district Sheikhupura on the basis of morphological traits studied
91
4.16 Factor coordinates of the variables of the guava accessions based on correlations for district Sheikhupura
94
4.17 Variability position of guava accessions of district Sheikhupura on the basis of trait studied
96
4.18 Phenotypic distances between all possible pairs of guava accessions of district Sheikhupura calculated as linkage distances
100
4. 19 Group constellation of 20 guava accessions of Sheikhupura district based on 57 phenotypic characters studied
102
4.20 Eigen values calculated by statistica software for 57 phenotypic variables of guava accession of district Faisalabad and Sheikhupura
103
4.21 PC. Factors coordinating for 37 of guava accessions of district Faisalabad and Sheikhupura
103
4.22 Variability position of guava accessions of district Faisalabad and Sheikhupura on the basis of their traits.
106
4. 23 Factor coordinates of the variables based on correlations of guava accession in district Faisalabad and Sheikhupura
109
4.24 Variability position of guava accessions in district of Faisalabad and Sheikhupura on the basis of their different traits.
110
4.25 Phenotypic distances between all possible pairs of guava accessions of district Faisalabad and Sheikhupura calculated as linkage distances
114
4. 26 Group constellation of 37 guava accessions of district Faisalabad and Sheikhupura belonging to Faisalabad district based on 57 phenotypic characters
116
4. 27 SSR primers showing amplification, polymorphism, and size among 37 accessions of Psidium guajava
124
4.28 Similarity matrix data of 37 Psidium genotypes obtained using SSR markers.
126
4. 29 Group constellation of 20 accessions of guava belonging to Faisalabad district based on 57 phenotypic characters studied
128
4.30 Analysis of variance for number of rooted cuttings in guava 134
4.31 Means number of rooted cuttings and percent rooted cuttings in guava 135
xiii
Table
No. Title
Page
No.
4.32 Analysis of variance for sprouting percentage 135
4.33 Analysis of variance for average number of roots per cuttings 136
4.34 Mean table for average number of roots per cuttings 136
4.35 Analysis of variance for average root length (cm) in guava 137
4.36 Means for average root length (cm) in guava 138
4.37 Analysis of variance for survival percentage of guava plantlets 139
4.38 Mean table for survival percentage of guava plantlets 139
4.39 Analysis of variance for number of rooted cuttings in guava 141
4.40 Mean table for number of rooted cuttings and percent rooted cuttings in guava
141
4.41 Analysis of variance for sprouting percent rooted cuttings in guava 142
4.42 Analysis of variance average number of roots per cuttings in guava 143
4.43 Mean table for average number of roots per cuttings in guava 143
4.44 Analysis of variance for average root length in guava 144
4.45 Means for average root length for guava in guava 144
4.46 Analysis of variance for survival percentage in guava 145
4.47 Means for survival percentage of guava 145
4.48 Analysis of variance table for number of rooted cuttings (IBA and NAA) in guava
147
4.49 Comparison of growth regulators and concentrations for number of rooted cuttings in guava
148
4.50 Analysis of variance for sprouting percentage (IBA and NAA) in guava
149
4. 51 Comparison of growth regulators and concentrations for sprouting percentage in guava
149
4.52 Analysis of variance for average number of roots/cutting (IBA and NAA) in guava
150
4.53 Comparison of growth regulators and concentrations for average number of roots/cutting of guava
150
4.54 Analysis of variance for average root length (IBA and NAA) in guava 152
4.55 Comparison of growth regulators and concentrations for average root 152
xiv
Table
No. Title
Page
No.
length 4.56 Analysis of variance for survival (IBA and NAA) of guava plantlets 153
4.57 Comparison of growth regulators and concentrations for survival percent of guava plantlets
153
xv
LIST OF FIGURES
Fig.
No. Title
Page
No.
3.1 Phenotypic characters of 37 accessions of guava collected from districts of Faisalabad and Sheikhupura, Punjab Pakistan.
62
3.2 Steps for preparation, treating of cuttings, insertion and providing mist conditions to softwood cuttings of guava
67
4.1 Diagram showing projection and the relationships among 17 accession of guava based on the first two principal component factors
78
4.2 Diagram showing projection and the relationships among 57 variable of guava based on the first two principal component factors for Faisalabad district
83
4.3 Dendrogram showing phenotypic diversity between 17 P. sidium accessions based on morphological characterization for Faisalabad district.
87
4.4 Diagram showing projection and the relationships among 20 guava accession based on the first two principal component factors
93
4.5 Diagram showing projection and the relationships among 57 variables of guava accessions of district Sheikhupura based on the first two principal component factors for
97
4.6 Dendrogram showing phenotypic diversity between 20 guava accessions of districts of Sheikhupura based on morphological characterization.
101
4.7 Diagram showing projection and the relationships among 37 guava accessions of guava based on the first two principal component factors
107
4.8 Diagram showing projection and the relationships among 57 variables of guava accessions of district Faisalabad and Sheikhupura based on the first two principal component factors.
112
4.9 Dendrogram showing phenotypic diversity among 37 guava accessions of district Faisalabad and Sheikhupura based on morphological characters
115
4.11 Quality of DNA for 37 accessions of guava extracted through CTAB method
120
4.12 The pattern of polymorphic bands for 37 accession of guava by primer mpgCIR-18
125
4.13 Dendrogram showing genetic relationship between 37 Psidium accessions based on SSR marker analysis.
127
4.14 Rooting behavior of softwood cuttings of guava treated with different concentrations of IBA
140
4.15 Rooting behavior of softwood cuttings of guava treated with different concentrations of NAA
146
4.16 Comparison of roots induction behavior of softwood cuttings of guava treated with growth regulator and non-growth
154
xvi
ABSTRACT
Guava (Psidium guajava L.) is a luscious and important tropical fruit crop. It belongs to the
family Myrtaceae; is ever green and hardiest among all the tropical fruit trees and exceeds
most other fruit crops in productivity and adaptability and is richest source of vitamin C. The
research studies were carried out at Institute of Horticultural Sciences, University of
Agricultural, Faisalabad and the objectives to determine guava genetic diversity using
morphological and DNA markers based approaches and to develop vegetative propagation
system to avoid clonal degradation of guava. Tree parameters like growth habit, leaf shape,
flowering, fruit and seed was recorded in two districts Faisalabad and Sheikhupura according
to the plant descriptor. Ten plants from each accession of guava were selected for tree, leaf,
fruit, flower and seed morphological analysis. Data was registered from 25
leaves/flowers/fruits/seed per accession. 15 to 20 young leaves per accession of guava were
collected for DNA extraction following CTAB protocol. The extracted DNA was subjected
to SSR analysis. For clonal propagation softwood cuttings from five year old trees were
prepared from the tips of current season growth. IBA (0, 2000, 4000, 6000 and 8000 ppm)
was used to treat cuttings for root induction. The data collected was subjected to different
statistical software.
During the survey 37 accessions of guava were collected from two major growing areas of
district Faisalabad and Sheikhupura for phenotypic genetic variation analysis. Multivariate
analysis principal component indicated high diversity for accessions Mota gola, Khata gola,
Rough gola, Bangladeshi gola and Surahi in district Faisalabad and Mota gola, Larkana gola,
Desi gola, Gola and Sadabahar gola were most diverse accessions in district Sheikhupura.
The combined analysis of accessions of both districts indicated Mota gola (SKP), Mota gola
(FSD) belonging two different orchards, Bangladeshi gola (FSD), Rough gola (FSD), Surahi
(FSD), Moti surahi (SKP), Gola (SKP) and Gola (SKP) (belonging two different orchards) as
most divergent accessions. Among tree parameters like, young shoot color, fully developed
leaf shape of tips, fully developed leaf shape of base, fully developed leaf color, fully
developed leaf curvature in cross section, thickness of outer flesh in relation to core diameter,
fruit length, fruit width, fruit juiciness, fruit length of stalk, fruit length/width ratio, fruit
relief of surface, fruit size of sepal, fruit sweetness, fruit diameter of calyx cavity in relation
xvii
to that of fruit and fruit ridged collar around calyx cavity and seed size was the most diverse
phenotypic markers for guava accessions identification. The phenotypic and genotypic based
dendrograms grouped guava accessions into two main groups and many sub groups and sub
groups. High diversity was found in all accessions especially Mota gola (SKP) was the most
diverse phenotype among all accessions of both districts and Gola (SKP) had most diverse
genotype on genetic basis. Sadabahar gola (SKP), Gola (SKP), Gola (FSD), Choti surahi
(SKP) and Surahi (FSD) were found quite phenotypic diverse accessions in morphological
analysis, and Bangladeshi gola (FSD), Lal gola (FSD), Larkana gola (FSD), Gola (SKP),
Moti surahi (SKP) and Sadabahar gola (SKP) were quite genetically diverse accessions on
the basis of genetic markers variation analysis. As for as clonal propagation is concerned
highly significant results were obtained for all the parameters (number of rooted cuttings,
sprouting % age, average number of roots per cuttings, average root length (cm) and survival
% age) and all concentrations (0, 2000, 4000, 6000 and 8000 ppm) for rooting of softwood
cuttings treated with IBA and NAA. Highly significant results were obtained with 4000 ppm
IBA and 2000 ppm of NAA and IBA performed the best for rooting of softwood cuttings in
comparison with NAA. This study revealed the great potential of morphological and genetic
variation and clonal propagation technique in guava. However, a comprehensive and detailed
inventory for documentation of all guava accessions, supported with gene banks and also an
annexed crop catalogue needs to be carried out.
1
Chapter 1
INTRODUCTION
Guava (Psidium guajava L.) often called ‘amrood’, ‘guayaba’, or ‘piyara’ belonging to the
family Myrtaceae (Thaipong and Booprakob, 2005) is ever green and is the hardiest among
tropical fruit trees and exceeds most other fruit crops in productivity and adaptability due to
its hardy nature and prolific bearing habit. The family Myrtaceae comprises 133 genera and
more than 3800 species of trees and shrubs, (Watson and Dallwitz, 2007) which are mainly
growing in Australia, Southeast Asia, tropics and sub tropics, and a smaller number of
species in Africa and South America. The Myrtaceae family is subdivided in two natural
subfamilies: Myrtoideae consists of indehiscent fleshy fruits; and Leptospermoideae,
constituted with dehiscent capsulated fruits (Wilson et al., 2001). The genus Psidium (2n =
2x = 22) includes about 100 species, which are all fruit-bearing trees or shrubs (Jaiswal and
Jaiswal, 2005). These include Strawberry guava Psidium cattleianum, Costa Rica guava
(Psidium friedrichsthalium), Apple guava (Psidium guajava), Guinea guava (Psidium
guineense), Cattley guava (Psidium littorale) and Mountain guava (Psidium montanum).
Along these Brazilian guava (Psidium guineense) and Mountain guava (Psidium montanum)
are wild relative of guava and are not commercially important.
Guava is an important fruit cultivated in several tropical and subtropical countries of the
world (Dias, 1983; Samson, 1986; Morton, 1987) despite its origin in tropical America
(Southern Mexico to Peru or through Central America) (Pathak and Ojha, 1993; Shigeura and
Bullock, 1983). The Spaniard and Portuguese are considered to be responsible for
distribution of guava to the other parts of the world. The Spaniard transported the guava of
Pacific to Malay Archipelago to India; from here it passed to South Africa. Presently guava
is cultivated in many tropical and subtropical countries of the world (Samson, 1986). Its
commercial cultivation, today, covers large areas in numerous countries (Brazil, 1972;
Marteleto, 1980) including Bangladesh, India, South Africa, Brazil, Cuba, Venezuela, New
Zealand, the Phillippines, Haiti, California, Florida, Thailand, Vietnam, Thailand, West
2
Indies and many other countries (Bailey, 2003; Yadava, 1996; Le et al., 1998; Tate, 2000). It
is probably impossible to establish with certainty the precise native range of guava. However,
Osman (1993) and Soepadmo (1978) suggested the Malay Archipelago and the Indo Chinese
Peninsula of continental Asia as the home of most tropical fruits including guava. (Singh,
1993).
Worldwide Pakistan is the 2nd largest producer of guava after India. Guava, along with
mango and mangos teens, reached a combined global production of 39.99 million tons (FAO,
2010-2011). The other largest guava producing countries are Mexico, Brazil and Thailand
(Padilla et al., 2003) Venezuela, Cuba, and Colombia are important Latin-American
countries cultivating guava (Morton, 2000). In Pakistan it ranks fifth with respect to area and
production, and is grown almost in all provinces. Large areas have been brought under this
fruit plant and were not cultivated as extensively as it is being cultivated now-a-days after
Independence. Major growing areas include Jhang, Kasur, Lahore, Gujranwala, Sheikhupura,
Sahiwal, Faisalabad and on a smaller scale throughout the plains of the Punjab province. In
Sindh province, an excellent pear-shaped guava with a smaller seed core is grown mostly in
the districts of Larkana, Dadu, Shikarpur and Hyderabad. In NWFP, the Mardan district and
Hazara Valley are famous for production of good quality guava.
The Guava fruit is consumed either fresh or processed into various products like juice jams
puree, nectars, paste, or marmalade (Morton, 2000). Guava has been traditionally used as
medicinal plant (Mata and Rodriguez, 1990; Medina and Pagano, 2003) for the treatment of
diarrhea, hypertension, diabetes, gastroenteritis, antibacterial and antifungal and analgesic
properties (Chah et al., 2006; Echemendia-Salis and Moron-Rodriguez, 2004; Ojewole,
2005, Santos, et al., 2000). Guava has a high nutritive value, a pleasant aroma, and a good
flavor (Thaipong and Boonprakob, 2005). The fruit contains high vitamins content, A and B
complex and being exceptionally rich in vitamin C which is source of antioxidants. The fruit
contains 2 to 3 times more ascorbic acid (Vitamin C) than fresh orange and minerals like
iron, calcium and magnesium and phosphorous. Ripe guava fruits emit sweet aroma and have
a pleasant sour sweet taste.
Guava cultivation is gaining importance in the recent years due to increase in international
trade for fresh fruits as well as value added products. It is grown on an area of 62,495
hectares with a total annual production of 516 thousands tones (MINFAL, 2009-2010) and
3
per hectare yield is 9320 kg. In Pakistan under guava during 2007 has been increased from
63,500 to 90,300 thousand hectares rise increased from 0.49 to 0.5 million tons, although
guava crop is a very productive and a highly profitable but yield of guava fruit is less than
potential yields. The gap between potential and actual yield is hampered by a number of
factors, i.e. poor quality fruit, poor management practices, traditional production system
domination among the growers, beating of trees and stopping water to discourage summer
crop, improper use of FYM and fertilizer, inter-culturing, untimely irrigation, diseases
(dieback), insect pests like fruit fly particularly for summer crop, post-harvest loses include
improper handling, immature fruit harvesting and inadequate transport and storage facilities.
Above all the varietal characteristics in guava are not as distinct as in other fruit crops.
Propagation through seeds reduces the distinctive characteristics of a commercial cultivation
and the seedlings have long juvenile phase, give lower yields and bear poor quality fruits.
The guava bears solitary flowers or in a cyme of two to three flowers on the current season's
growth which are 1 in (2.5 cm) wide with 4 or 5 white petals and a prominent tuft of perhaps
250 white stamens tipped with pale-yellow anthers in the axils of the leaves. Normally, the
bearing twigs grow a few centimeters long, putting over 4 or 5 leaves. Both self and cross-
pollinations occur. Pollination is usually carried by honeybees (Apis mellifera). The amount
of cross-pollination ranges from 25.7 to 41.3% (Purseglove, 1968). In guava pollens most
frequently deposit in stigmas of the own flower and result in high possibilities of selfing
(Singh and Seghal, 1968; Soubihe Sobrinho, 1951). This type of pollination is possible since
Boti (2001) states that guava has no problem of self-incompatibility. However, Seth (1960),
and Hirano and Nakasone (1969) found partial self-incompatibility in guava. No other form
of restriction on selfing in P. guajava found in literature.
Cross-pollination is considered the most frequent by some authors in guava (Dasarathy,
1951; Balasubrahmanyan, 1959) and is enhanced by two features. The first is that the flower
morphology of guava, points to a melittophily tendency for being white flowers, anthesis had
during the day; odors present sweet flowers and anthers with no depth enough pollen (Faegri
and Van Der Pijl, 1979). The second feature is that, in fact, the flowers of guava are visited
by solitary bees as reported by Medina (1988), Heard (1999), Alves (2000) and Boti (2001).
Thus seeds produced typically from sexual reproduction are genetically recombinant and
have different characteristics from its parents. This genetic variation occurs in seed from
4
cross-pollination, because they are heterozygous. This means that the plant grown from seed
may not exactly duplicate the characteristics of its parents and may possess undesirable
characteristics.
To explore the potentiality of the varieties it is believed that the identifying suitable
varieties, vegetative propagation of guava with superior horticultural characteristics and
adaptation ability for given locations and conditions, is useful and necessary for overcoming
drawbacks, and providing genetically uniform, cost-effective planting material (Perry, 1987).
Genetic variation assortment is important factor for the sustainable agricultural production
(Zhou et al., 2002). Also a diverse gene pool is essential for cultivars development with high
yield and little susceptibility to pest and diseases or biotic stresses and to evolve varieties
according to consumer preferences (Cui et al., 2001), is one of crucial bases of world agro
alimentary security (IPGRI, 1998).
An accurate knowledge of the genetic diversity in an available population can assist in the
selection of parents in a hybridization program (Torres and Moreno, 2001). A careful
selection of germplasm can also help to eliminate duplicates in the germplasm collection,
thus saving land; space time and also a complete genetic map are essential for proprietary
rights to avoid any controversy worldwide.
Traditionally germplasm has been characterized through morphological data (Paiva et al.,
1995; Lederman et al., 1997; Padilla et al., 2003; Rodríguez et al., 2003); these may be
changed with the cultivation and growth environment may not be reliable to discriminate
between closely related genotypes (Chandra et al., 2005). In order to characterize guava
landraces in systematic way, specific morphological and genetic markers have been
developed such as Restriction Fragment Length Polymorphism (RFLP) (Krishna and Singh,
2007), Sequence Characterized Amplified Regions (SCAR), Simple Sequence Repeats (SSR)
(Risterucci et al., 2005) and Random Amplified Polymorphism DNA (RAPD) (Dahiya et al.,
2002; Prakash et al., 2002; Chen et al., 2007; Feria-Romero et al., 2009; Chen et al., 2007)
for the estimation of genetic diversity (Valdes-Infante et al., 2003).
Up to now, only dominant polymerase chain breaction (PCR)-based marker technologies
such as random amplified polymorphic DNA (RAPD) (Prakash et al., 2002) were applied to
study the guava molecular genetic diversity. Amplify Fragment Length Polymorphism
(AFLP) (Hernandez-Delgado et al., 2007) analysis is a very useful molecular marker
5
technique, of genome-wide coverage, that allows detection of a high number of
polymorphisms. The SSR or micro satellite co dominant technique, which has proven its
advantages and suitability in a large range of applications in genetics, was developed in order
to improve the availability of best performing molecular tools for genetic studies and further
marker assisted breeding in guava and its close related species. Hence, characterization of
genotype at the genetic level supplemented with phenotypic character will be an important
step towards efficient conservation, maintenance and utilization of existing genetic diversity.
As we are aware that farming as an enterprise is exposed to various risks and uncertainties,
which can be broadly classified into production/product and market related risks. These risks
bring an uncertainty in the quantum of produce; arise on account of weather, pest and
diseases, area, non-market quality etc. Similarly marketing fruit under a brand name known
for quality is one way to wriggle out of the squeeze put on the market by larger exporters.
Guava growing is not a famously fast-moving industry. The liberalization and globalization
of the economy has widened the opportunities for guava both domestically and globally.
However, the opening up of the world market has also posed new challenges in terms of
quality, food safety and price to meet the global competitiveness as well as WTO regulations.
In order to meet the WTO regulations and proprietary rights, priority needs to be given to the
good fruit quality, disease resistance, increasing yields, longer shelf life of fruits, attractive
skin, flesh color, soft seeds, good aroma, high vitamin C and pectin content, (Dinesh et al.,
2005).
Several methods of propagation have been proposed for the guava both sexual (by seed)
(Zamir et al., 2003) and asexual methods (by cutting, layering, budding and grafting) (Singh,
1985; Mortan, 1987). During sexual propagation plants cannot maintain the genetic purity of
the variety due to the segregation and recombination of characters, owing to floral structure
(epigynous flower, with abundant incurred stamens of various sizes and extended juvenile
period. Self-incompatibility and heterozygous nature limited the scope of guava breeding
programs (Jaiswal et al., 1992) and has given rise to selection of several promising landraces
and “Sharakpur” guava is the result of such seedling selections in Pakistan. For these reasons
standardization of cultivars is not possible (Singh, 1996).
On the contrary, clonal propagation of guava can be considered to avoid the segregation of
genetic variety, maintain the quality of fruits and have considerable potential for the
6
improvement of economically important trees within a limited time frame (Giri et al., 2004;
Singh et al., 2004).
Conventionally guava is propagated through budding, grafting, stooling, or air layering but
these methods are time consuming (Chandra et al., 2005). Cuttings among the vegetative
methods of propagation is undoubtedly the most evolved and expanded method (Manica et
al., 2000) but the information regarding the rooting ability of the cuttings in guava is very
scarce. Propagation by cuttings has significant advantage, since, in addition to obtaining
plants with the same type of tree, will ensure production of economically important tree in
just one growing season (Tavares et al., 1995). To overcome some of these inherent
difficulty encountered by the cuttings to root, auxins are helpful (Reddy and Singh, 1998).
Keeping in view the gravity of the problems ascertained with guava fruit crop the present
research work was intended to cope with these inherent problems. These studies will be
helpful for fruit growers trying to control this devastating genetic variation and clonal fidelity
problem.
The main objectives of the investigation were:
o To evaluate guava genetic diversity through morphological and molecular markers-
based approach
o To develop an asexual propagation system to avoid clonal degradation ex-vitro.
7
Chapter 2
REVIEW OF LITERATURE
The research work conducted in different parts of the world on various morphological
aspects, genetic variation analysis and clonal propagation is reviewed as under:-
2.1 MORPHOLOGICAL ANALYSIS
Among organisms that add beauty to the nature, the main credit goes to the domain of plants.
The reasons for this are the enormous diversity that can be seen among plants. This diversity
has enabled plants to overcome not only terrestrial, but also aquatic environments, i.e.
marine and fresh-water habitats etc.
A great diversity can be seen in plant structures ranging from the smallest water plants,
bushes, herbs, creepers to large trees. In whatever environment plants live, most of these are
made up of the simple parts namely roots, leaves, stem, buds, flowers, fruits and seeds
Agarwal, (2002). Morphology has originally been developed as a descriptive science to
identify and differentiate the diversity of plants which is an aspect, obviously of great
practical importance. However, from our current perspective, this represents only a narrow
aspect of morphology.
The study of plant morphology in a wide sense, considers as relationships between plant
shapes (Morph) and other levels of the structural hierarchy, i.e. anatomy, histology, cytology.
Plant form is not limited to outer surfaces but flowers, leaves and fruit also have inner
surfaces as these are intricately synorganized schemes of original structural elements that
may fused in various ways.
Additionally, morphology encompasses development stages, i.e. the development of plant
parts, and development of whole plant, which in turn, helps us to better understanding of
mutual relations of the other individual parts (Singh et al., 2002). The knowledge of
developmental stages help us better understand established structures, which are mostly
complex. Development of plants is also important for better understanding the plasticity of
8
plant forms in an individual, also plants produces new organs throughout the life and the
structure and shape of these organs may vary depending upon the environment.
Thus morphology can be considered as a multidimensional key discipline in the plant
biology, with relationships to many other fields, such as:
a) - Development of structure and molecular developmental genetics
b) - Diversity of structure and evolution of plants
c) - Evo-Devo (which combines evolution and development)
d) - Structure and systematics
e) - Floral structure and reproductive biology
f) - Structure and ecology
g) - Structure and vegetation
h) - Structure and biomechanics
i) - Structure and systems biology.
It is to be expected that plant morphology based on many new tools and an integrative
approach will successfully grow in the near future.
Morphological markers are generally corresponding to the qualitative characters that can be
easily and visually scored and these have been found in nature as the result of
mutagenesis processes that occur in the different genotypes. There are many different
undesirable factors that are concerned about the morphological markers. The first one is
their high dependency on the environmental factors. Most often those conditions that a plant
is grown in can affect the expressions of these markers which lead to false determination of
these charters (Chawla, 2004). Secondly, these mutant characters results in undesirable
features such as dwarfism or albinism. And finally, these markers are time consuming when
performing breeding experiments, labour intensive and a large population of plants requires
large plots of land and greenhouse space in which these to be grown (Stuber et al., 1999).
Phenotypic characters can be used for the estimation of the variation within and between
species (Hammer, 1980). Morphological traits are the oldest and most widely used genetic
markers which are still useful for certain germplasm and cultivars management applications,
where the cultivars have been identified based on morphological characteristics. These
characters, however, may vary with environmental conditions. The main advantages of
9
morphological markers are its simplest process, inexpensive assays and the disadvantages are
that they change with environmental conditions; it’s a personally biased judgment.
The interaction of genotypes by environmental conditions is of fundamental importance in
phenotypic manifestation in order to verify the phenotypic stability and to identify groups of
accessions with alternative potentials for genetic improvement and for commercial
exploitation. Junior et al. (2008) collected and evaluated 63 accessions of guava from the
germplasm bank of Instituto Agronomico (IAC, Campinas, SP) in order to verify the
phenotypic stability. They continued evaluation for six harvests, from 1987 until 1992 by
using the methodology of simple linear regression to study phenotypic stability over time.
The evaluated parameters were fruit weight, crop duration and precocity. The results
indicated that the most outstanding accessions were Campos and Creme Arredondada, which
showed most desirable averages and phenotypic stability for fruit weight, crop duration and
precocity.
Morphological characterization of P. guajava was firstly reported by Rodriguez et al. (2004).
Raseira and Raseira, (1996) conducted an extensive work on guava phenotypic and genotypic
characterization. The guava accessions were assembled in the Cuban germplasm collection at
Aquinas (Havana Province) by phenotypic descriptors as well as by genotypic markers
(Rodriguez et al., 2004; Valdes-Infante et al., 2003). QTL analysis was performed for
vegetative characters identification. A total of fifteen QTL loci contributed for identification
of leaf length, leaf width, petiole length, height, and growth rates for diameter and height.
The QTL analysis for fruit characters resulted in the identification of loci such as fruit
weight, acidity, total soluble solids, vitamin C content, and pulp thickness.
Carlos et al. (2008) characterized 119 accessions of guava and 40 accessions of araca
sampled in 35 Brazilian ecoregions, following the International Union for the Protection of
New Varieties of Plants (UPOV) descriptors. The results regarding leaf veins indicated that
majority of araca accessions possess wide spacing of leaf veins, while guava accessions
presented medium to close spacing. Most fruits of araca accessions were small and classified
as small, contrasting with medium to large fruits of guava accessions. White flesh fruit color
was found in 91% of araca, while 58% of guava accessions presented pale pink, pink and
10
dark pink colors. Similarly fruit differences among wild and cultivated Psidium species
indicated fruit as the most altered trait under artificial selection.
Sanjuana et al. (2007) studied fifty morphological traits of guava by using UPOV descriptors
to investigate a set of 48 guava (Psidium guajava L.) accessions cultivated in Mexico, for
their genetic relationships. The study mainly included two P. cattleianum (Sabine) and two
P. friedrichsthalianum (Berg-Niedenzu) accessions from Costa Rica as outgroups. Principal
component analysis (PCA) was used to analyze the differences in different morphological
parameters which explained less than 30% of total variation among 14 characteristics of trees
showed 1 parameter, leaves (2) and fruits (11) parameters variation which were the most
informative. Significant differences were found in fruit yield, detected among accessions and
years, where P. guajava produced give more yields as compared to P. cattleianum and P.
friedrichsthalianum. The fruit yield in broad sense showed 0.25% heritability.
Morphological characterization of guava was also performed by Hilsy et al. (2005) in Cauca
Valley, Colombia. Fifty three accessions of Psidium guajava, in 9 transects were collected
according to the qualitative descriptors. The accessions were included in three groups which
were differentiated according to fruit form descriptors. Regarding quantitative descriptors
75% of the guava accessions showed greater variation of coefficient to 24%. The 72.41% of
the total variation of these quantitative and qualitative descriptors was explained in three
principle components that allowed discrimination for variables of performance of the fruit, as
well as structure of the tree and quality of the fruit. Sanchez-Urdaneta et al. (2008)
characterized the fruits of guava on morphological basis of several variants In Blanca, Criolla
Roja, Blanca and Montalban of Cuba they evaluated fruit shape, apex, base, cavity, diameter,
mass and longitude of the fruit, the insertion point of the peduncle, absence, presence and
shape of the sinus, color and texture of the epicarp, mesocarp and endocarp mass and color,
number and mass of seeds. The results indicated that the spherical shaped fruits of Criolla
Roja were predominating where as in Montalban pyriform shape. Shape of fruit base in
Montalban with neck whereas in Blanca and Cuban was concave and in Criolla Roja was
convex with neck. In variants of epicarp Criolla Roja, Cuban and Montalban the color was
yellow, while in Blanca dominated the green-yellow epicarp. A statistical difference for the
mass of the fruit, mesocarp, endocarp, and epicarp was found among these regions. Blanca
11
variant showed a higher mass of the fruit, mesocarp, endocarp, and epicarp. Regarding the
seed mass and the number a statistically difference was found in Blanca, Criolla Roja, Cuban
and Montalban, while Blanca and Criolla Roja had the higher number of seeds in fruits.
A large germplasm prospecting expedition in different eco-regions in ten Brazilian
States in order to collect and characterize Psidium guajava L. and another Psidium
species, known as areca were carried out by Fernandez-Santos et al. (2010). He collected
eco geographic sampling areas, defined based on eco-geographical zoning and vegetation
maps. These accessions samples were characterized for 40 descriptors, according to the
International Union for the Protection of New Varieties of Plants guidelines (UPOV). 119
guava accessions and 40 areca accessions were sampled and characterized in 35 different
Brazilian Eco regions. The results for most invariable descriptors of guava and areca
accessions were color of young shoots, leaf pubescence on lower side, leaf blade length and
width, leaf variegation, fruit surface relief, fruit longitudinal ridges and grooves and evenness
of fruit flesh color. The most of areca accession presented a character of widely spaced leaf
veins, contrasting with the guava accessions that represented medium to close spacing in leaf
veins. Most of areca accessions fruits were classified as small, while most fruits of the
guava accessions were grouped the class of medium size. The results for the fruit flesh color,
91% areca fruits were grouped as cream and white color, while 58% of guava accessions
represented pale pink, pink and dark pink coloring. The differences among wild Psidium
species and guava indicated that the fruit traits have been changed trait by the artificial
selection.
Aranguren et al. (2010) described numerous types of guava, which varied in color, form, and
flavor. Commercial guava orchards located mainly in the Venezuelan plains, were sampled
and characterized following UPOV descriptors. The 100 collected samples were observed
for phenotypic markers, which revealed an elevated degree of polymorphism. The
most important markers, based on the degree of variability, were related to fruit
characters such as number of seeds and color of the skin and the flesh. These analyses
also indicated distributive patterns for apparently related phenotypes within certain regions of
the country that showed a great quantity and/or more diversity of phenotypes.
12
Variability in tree growth, shape and horticultural performance is common in guava
(Psidium guajava L.). Viloria et al. (2010) in consequence, performed the morphological
characterization of elite genotypes of guava seedlings. They chose guava with white or red
mesocarp and endocarp variants. The recorded variables were quantitative and qualitative
i.e. stem surface (SS), phyllotaxy (F), leaf shape (LS), leaf margin (ML), foliar area (AF),
apex (FA) shapes, leaf base (FB), and flowers (flower diameter (DF). For evaluation
qualitative and quantitative variables frequency tables and analysis of variance were utilized.
Multivariate analysis through principal components analysis (PCA) was performed to
determine the typical traits of each guava variant. The acute FB, sinuate ML, opposite F and
smooth with few flaky SS predominated in white and red guavas regarding the qualitative
variables. The oblong LS and obtuse FA was present in white guavas and absent in red
guava while elliptical LS and acute FA descriptors were dominated in red guavas.
Regarding the quantitative variables LI, AF and PS were significantly different among
all orchards and AF among guava variants. No significant differences for all variance
were determined for DF. The first three principle components analysis showed 78% of
the variability among guava variants. The variables AF, LI, FB, SS and PS were variant
and allowed differentiation among different guava variants.
The origin of guava (Psidium guajava L.) is supposed to be in the tropical areas of America
and from here, it was reached to Europe and Asia where it is currently playing an important
role among all other tropical fruits (Padilla-Ramirez and Gonzalez-Gaona, 2003). In Mexico,
the principal guava cultivar “Media China” is very similar among the main producing areas,
for this reason, it was important to collect and characterize. They collected fruit from all over
the country and established seedling population during the growth season and characterized
according to UPOV descriptors. The results from collected material showed a great
variability for fruit parameters with respect to form, size, pulp and pulp thickness, skin color,
seed number etc. All these findings indicate a richness of guava in Mexico and provide great
prospects for the establishment of future guava cultivars.
Rodriguez-Medina et al. (2010) work on Genetic resources of guava in Cuba, and reported
that guava (Psidium guajava) was not cultured in Cuba in the first half of the last
century. A principal Cuban guava collection was established with the foundation of the
13
Germplasm Bank of Tropical and Subtropical Fruit Trees in Havana Province. During
this period, an initial breeding program was started, which resulted in the identification of
high yielding cultivars like 'E.E.A. 1-23', ‘E.E.A. 18-40', ‘Belic L-207', ‘Belic L-215', ‘Belic
L-97’ and ‘E.E.A. 28-44'. A breeding program by using controlled crosses produced a total
of 354 hybrid plants from which 25 dwarf genotypes were produced which were selected on
the basis of quantitative and qualitative traits to establish new commercial cultivars. Then
three populations resulted from these crosses were further employed QTLs maps using
morph-agronomic characters and molecular markers for developing guava genetic
linkage maps. This combination of molecular marker and phenotypic description helped
greatly for identification of cultivar, estimation of diversity, and for the elite genotypes
recommendation in guava germplasm.
Fernandes-Santos et al. (2008) established a relationship between eco-geographic origin
and phenotypic variation for 143 Psidium accessions includeding “areca” and guava (P.
guajava) species, collected 31 different eco-geographic regions of ten Brazilian States. These
accessions were characterized for 35 categorical traits by using the International Union
for the Protection of New Varieties of Plants (UPOV) descriptors for P. guajava. A
correlation between the phenotypic values and the simple matching matrices was established
which resulted in 0.55, and a multidimensional scaling for the badness-of-fit of was 0.30.
88% of the areca accessions were grouped together and cultured in accordance with the
eco-geographic regions.
Hernandez-Delgado et al. (2007) while analyzing phynotypic relationship studied fifty
morphological characteristics and fruit production over 3 years (from 1999 to 2002) guava
(Psidium guajava L.) accessions cultivated in Mexico, in order to characterize their
phenotypic relationships. Germplasm was collected from the Calvillo-Can region and planted
in Huanusco, Mexico. The study included two P. cattleianum (Sabine) and two P.
friedrichsthalianum (Berg-Niedenzu) accessions from Costa Rica as outgroups. Principal
component analysis (PCA) explained less than 30% of total variation and 14 characteristics
from trees (1), leaves (2) and fruits (11) were the most informative. PCA analysis separated
the germplasm into three major groups of accessions based on fruit size and weight, stem
14
diameter and leaf size. Significant differences in fruit yield were detected among accessions
and years, where P. guajava produced 36 kg/year/tree of fresh fruit while P. cattleianum and
P. friedrichsthalianum showed fruit yield lower than 7 kg/year/tree. The fruit yield broad
sense heritability was 0.25.
Molero et al. (2003) studied morphology of seven selections of guava against nematode
resistance. Collection of leaves, flowers and fruits in all the selections were made for the
study of leaf shape consisting leaf margin, color, width and length of the leaves, length of the
pedicel, the width/length relation, number of petals, number of veins, width and length of the
petals, flower parameters like flower diameter, shape of pollen grain; shape, diameter,
perimeter and fruit parameters like length and side of fruit, thickness of mesocarp, cavity of
the seed and color of the mesocarp. This morphological study showed differences between all
the selections for all the evaluated characteristics.
2.2 GENETIC ANALYSIS
The establishment and use of molecular markers for the detection and exploitation of DNA
polymorphism is one of the most significant developments in the field of molecular genetics.
Molecular DNA techniques allow researchers to identify genotypes at the taxonomic level,
assess the relative diversity within and among the species and locate diverse accessions for
breeding purposes. The differences that distinguish one plant from another are encoded in the
plant’s genetic material, the deoxyribonucleic acid (DNA). DNA is packaged in chromosome
pairs (strands of genetic material), one coming from each parent. The genes, which control a
plant’s characteristics, are located on specific segments of each chromosome. All of the
genes carried by a single gamete (i.e., by a single representative of each of all chromosome
pairs) is known as genome (King and Stansfield, 1990).
Advances in DNA sequencing, data analysis and PCR have resulted in powerful techniques
which can be used for the characterization and evaluation of germplasm and genetic
resources, and for the identification of markers for use in breeding programmes. The
development of randomly amplified polymorphic DNA (RAPD) markers, generated by the
polymerase chain reaction (PCR) using arbitrary primers, has provided a new tool for the
15
detection of DNA polymorphism (Williams et al., 1990). RAPD analysis has been used to
study genetic relationship in a number of guava species (Huff et al., 1993, Gunter et al.,
1996, Kolliker et al., 1999, Nair et al., 1999). Many molecular markers, such as Restriction
Fragment Length Polymorphism (RFLP), Amplified Fragment-Length Polymorphism
(AFLP), Sequence Characterized Amplified Regions (SCAR), Inter Simple Sequence Repeat
(ISSR), Microsatellites Simple Sequence Repeat (SSR) and Random Amplified
Polymorphism DNA (RAPD), Sequence Tag Sites (STSs), Cleaved Amplified Polymorphic
Sequences (CAPS), Expressed Sequence Tags (ESTs), Single Nucleotide Polymorphisms
(SNPs), and Diversity Arrays Technology (DArT) have been used in horticultural crop
research.
2.3 Markers system used for detection of diversity
Several molecular markers have been developed and as well as applied for analyses of
genetic diversity and similarity, but all marker systems have advantages and disadvantages
(Schlotterer, 2001). Choice of marker system can be determined by the aims and objectives
of the work as well as genetic makeup system of the species and also available financial
support.
2.3.1 Biochemical Markers
Biochemical markers are proteins in nature that are produced by gene expression. Isozymes,
that are different molecular forms of the same enzyme and can catalyze the same reaction,
are basically protein in nature. They may be the products of the numerous alleles of one
or many genes (Chawla, 2004). The enzymes are extracted, and run on denaturing
electrophoresis gels. Usually SDS are used for detection of denaturing component on the
gels, unravels the secondary and tertiary structures of the enzymes and are then separated on
the basis of net charge and mass. The sequences of nucleotide of the DNA are converted to
the sequences of amino acids of polypeptides by transcription and translation process
(Reiner and Hans, 2007).
16
Table 2.1 Classification of different markers systems
Polymorphic differences happens on the amino acid levels that allow singular peptide
polymorphism to be identified and utilized as a polymorphic biochemical marker. The
only fault with these markers is that most cultivars that are commercial breeds of plants,
are genetically similar and isozymes cannot produce a greater amount of polymorphism.
2.3.2 Protein-based markers
Protein based molecular markers give indirect information regarding plant genome structure.
Only one class of protein-based markers, isozymes, is widely used in the diversity analyses.
S. No
Technique name Description Scientist
1 Biochemical markers
Allozymes, Isozymes Tanksley and Morton,1983
Protein based markers 2 DNA Based markers (i) non PCR based
markers Restriction Fragment Length Polymorphisms (RFLPs)
(Botstein et al., 1980); Neale and Williams, (1991)
(ii) PCR based markers
Simple Sequence Repeats (SSRs), Inter Simple Sequence Repeats (ISSRs), Simple Sequence length polymorphism
Litt and Lutt, (1989); Hearne et al. (1992); Jarne and Lagoda, (1996)
Amplified Sequence length polymorphism (ASLP)
Maughan et al. (1995)
Cleaved Amplified Polymorphic Sequences (CAPS)
Akopyanz et al. (1991)
Heteroduplex Perez et al. (1999) Single Nucleotide Polymorphisms
(SNPs) Riedel et al. (1990)
Inter Simple Sequence Repeats (ISSRs)
Hayashi, (1992)
Random Amplified Polymorphic DNA (RAPD)
Williams et al. (1990)
Restriction Fragment Length Polymorphisms (RFLPs)
Witsenboer et al. (1997)
Amplified fragment length polymorphisms (AFLPs)
Vos et al. (1995)
17
2.3.3 Isozymes
The term ‘isozymes’ was firstly introduced by Markert and Moller (1959) and is referred as
“proteinious forms of an enzyme carrying same catalytic activity, and that can convert the
same substrate, but resulting in different molecular weights or in electric charges”. This
difference in size and charge can be caused by amino-acid substitutions peri or post-
translational modifications. Isozymes originate through amino acid alterations, which cause
changes in net charge, or the spatial structure (conformation) of the enzyme molecules and
also, therefore, their electrophoretic mobility. After specific staining the isozyme profile of
individual samples can be observed (Soltis and Soltis, 1989). Protein variants in isozymic
analysis are famous by gel electrophoresis and can be visualized by an enzymatic-specific
staining protocols, where co-factor, substrate and an oxidized salt are also included.
Isozymes may not be necessarily the products of same gene, and they may be activated either
at different life stages or even in different cell compartments. Types of isozymes encoded by
same locus but with different alleles are referred to as allozymes (Weising et al., 2005). The
main benefit of isozymic analysis, especially when allozyme enzymes are used, is that they
show co-dominant inheritance, which allows to differentiate between homozygous and
heterozygous genotypes, and is necessary for precise estimations of population genetics,
especially in cross-pollinated species.
The main disadvantage of isozyme analysis is that these detect variation only in specific
protein-coding loci and thus provides fewer markers as compared to DNA-based methods.
Furthermore, an isozymic system mostly show low variability and even in some cases no
variability because of low rate of mutational events.
2.3.4 DNA-based markers
With the discovery of restriction enzymes (Smith and Wilcox, 1970) and the
polymerase chain reaction (PCR) (Mullis and Faloona, 1987) have generated the
opportunity to imagine the configuration of an organism at the DNA level, and obtain a
supposed genetic fingerprinting (Kearsey and Pooni, 1996). The visualization of DNA bands
is routinely done by the separation, on a gel, of DNA-fragments and DNA markers which
result from a selected digestion of enzymes or fragments which may result from a
selected amplification applying PCR technique. DNA-fragments that result from different
18
gel bands between individuals or samples are so-called polymorphic markers that are the
visible differences on bands which gel results from differences at the DNA level. It is worthy
that not all types of markers are similar; the information content that is obtained may depends
on the methods that are used to obtain the data from particular marker and that population to
which that marker was scored. For example, it is not always possible to differentiate genome
fragments that exist in homozygous form, as compared to heterozygous fragments form. In a
population that is in heterogeneous condition like an F2, co-dominant markers similar to
RFLPs (Botstein et al., 1980) and co-dominantly marker like AFLPs (Vos et al., 1995) can
yield more information than that of dominant markers like RAPDs (Welsh and McClelland,
1990) and dominating scored markers like AFLPs. Advanced tools regarding the retrieval of
markers data and their subsequent analysis can be developed and that can allow a quick
and trustworthy analysis in most of the plant species. These developments with their
practical results have opened a new era in the field of genetics and selection (Moreau et al.,
1998).
Molecular markers are totally based on naturally arising polymorphisms in DNA
sequences (i.e. base pair deletions, additions, substitutions, or patterns). There are several
methods to detection and amplification of these polymorphisms so that these can be used for
breeding analysis purposes. Molecular markers are superior to morph physiological forms
of markers for marker assisted selection as these are comparatively simple to detect, occur
abundantly throughout the genomics of an organism even in highly bred cultivars, entirely
free of environmental conditions and can be identified virtually at any stage of plant
growth and development. Molecular markers can be applied for various applications
including genetic diagnostics, germplasm characterization, characterization of trans
formants, study of genome organization and phylogenetic relationship analysis.
Different kinds of DNA markers of different nature exist, such as RFLPs, AFLPs, RAPDs,
SNPs SSRs and. These may differ in a diversity of ways for instance their technical
requirements; the time amount, money and that of labour needed, that is the amount of
genetic markers that can be identified throughout the genomic of an organism; and
genetic variation amount founded in each marker with in a given population. The
information provided by these markers to the breeder are very dependent on the sort of
19
marker system used. Each marker has its advantages and disadvantages, and in the
future, other systems can also be developed (Korzun et al., 2003).
2.3.5 Amplified fragment length polymorphisms (AFLPs)
AFLPs markers are DNA fragments, normally range between 80 base pairs up to 500 base
pairs (bp) or more in length. These bases are first obtained by digesting DNA using
restriction enzymes (enzymes that cut DNA at particular sequences), then ligated with
oligonucleotide adapters and finally amplifying these sequences by PCR. The PCR primers
used are semi specific containing a core adaptor sequence, a restriction enzyme specific
sequence, and a tail of one to five other nucleotides (Faccioli et al., 1999). The higher the
amount of other nucleotides in the ‘tail’, the lower will be the number of bands obtained in
PCR and vice versa. Two rounds of amplification like pre-selective and selective are
normally carried out, the second round is important and consists of more specific primers, i.e.
more other nucleotides addition to the tail than the first step. The resulting AFLP banding
profiles are source of variation in restriction sites and in dominant regions. The AFLP
technique can generates products from many different sites in the genome, some results of
perhaps revealing 50 to 100 base pairs fragments in a single reaction. Generally the PCR
products are distinguished on polyacrylamide gels. Then these are visualized using different
radioactive, silver staining, fluorescent or other methods. Bands can be scored as present or
absent in individuals samples. When AFLPs comes to data analysis, these are generally
considered to have originated in nuclear DNA. However, they may or may rarely originate
from organelle DNA. PCR-based technique of generating molecular markers was defined by
(Vos, et al., 1995), giving rise to AFLP markers. With this technique, the DNA is first
treated with restriction enzymes and is then amplified with PCR which can allow selective
amplification of the restriction fragments. This gives rise large amount of useful markers that
can be located on the genome of an organism relatively quickly and reliably.
2.3.6 Restriction Fragment Length Polymorphisms (RFLPs)
Restriction Fragment Length Polymorphisms are markers that can be detected by treating
the DNA with restriction enzymes (enzymes that can cut DNA at a specific sequence)
(Botstein et al., 1980). For example, the EcoR1 restriction enzyme can cuts DNA
20
whenever the base sequence like GAATTC is found. Then the differences in the lengths
of DNA fragments will be seen if, for instance, the DNA of one organism contains that
sequence at a particular part of the genome, i.e. tip of chromosome 3, whereas in another
organism the sequence GAATTT, that is not cut by EcoR1(Table 2). RFLPs are the first
molecular markers that have been widely used. However, their use is time-consuming and
expensive, and reset of the simpler marker systems has later been developed.
Table 2.2 Comparison of different molecular marker systems
Character SNPs AFLPs SSRs RFLPs RAPDs
DNA quantity (ng/µg) 0.05 0.5-1.0 0.05 10 0.02
DNA quality High Moderate Moderate High High
PCR based Yes Yes Yes No Yes
Polymorphic loci 1 1.0-3.0 1.0-3.0 1.0-3.0 1.5-50
Ease of use Easy Easy Easy Not easy Easy
Reproducibility High Moderate High High Moderate
Developmental cost High Moderate High Low Low
2.3.7 Random Amplified Polymorphic DNA (RAPD)
RAPDs markers are ‘anonymous’ DNA amplified fragments using single short primers.
These are generally 10 bases long, of ‘arbitrary’ (also termed as ‘random’ or non-specific)
sequence (Matthes et al., 1998). Individual primer operates in both forward and reverse
directions, thus amplifying between inverted replications of the binding sequence, if the
repeats are close to each other. Usually a single primer is able to amplify simultaneously the
number of fragments, from around 5 to 20 sites in the genomic of an individual. Amplified
fragments are usually separated using agarose gel electrophoresis and polymorphism is
detected as the presence or absence of amplified bands following the methods of ethidium
bromide or other DNA staining methods to gels. Primarily polymorphisms arise because of
base variation at annealing sites with putative primer (primer can or cannot bind), even
though length differences are possible. When RAPDs comes to data analysis, these are
generally considered to have originated in nuclear DNA. However, these may or more rarely
originated from organellar DNA. Among PCR based molecular markers, RAPDs, DNA
21
markers were first described in 1990 (Williams et al., 1990). These can be identified using
the Polymerase Chain Reaction (PCR), which is a widespread molecular biology
method that can allow for the production of multiple copies (amplification) of particular
DNA sequences. The analysis procedure for RAPD markers is simple and quick, but the
results are very sensitive to laboratory conditions.
2.3.8 Inter Simple Sequence Repeats (ISSRs)
ISSRs markers are small DNA fragments located in between adjacent, oriented oppositely,
Simple Sequence Repeats. ISSRs are amplified by using ‘semi-specific’ primers and these
consist of SSRs with a few other nucleotides as presenters into non-repeat adjacent areas
(Reddy et al., 2002). The configuration of anchoring bases is changed in order to have
different products. The technique feats the abundance of SSRs in genomic of an organism
and generated about 10 to 60 fragments simultaneously. Products of ISSRs are detected by
gel electrophoresis and are generally scored for the presence or absence of bands. ISSRs are
generally considered to have their origin in nuclear DNA. However, they may or may not
more originate from organellar DNA.
2.3.9 Simple Sequence Repeats (SSRs)
SSRs also known as microsatellites markers are simple DNA sequences (e.g. AT and or/
GC), usually 2 or 3 bases long, repeated at variable numbers and times in tandem. They are
very easy to identify with PCR, and typical microsatellite markers have more variants
than those from other molecular marker systems. Initially identification of SSR markers is
time-consuming but when they are identified, emerged as very attractive tools for genetic
studies (Saghai-Maroof et al., 1994; Liu et al., 1994), because of their features like they are
totally PCR-based, primarily co-dominant, comparatively inexpensive, can reproduce across
mapping populations, and are multi-allelic in nature. SSR markers are recognized as neutral,
that they are not influenced by the expression of the linked gene. It is established fact about
SSR markers have that mutations in SSR repeat bases cause quantitative variation in the
transcriptional activity and biological function of human and mammalian genes (Kashi et al.,
1997). Data from SSR markers are being primarily used as single loci (if they are
unlinked), but they may also be employed for haplotyping, if they are physically linked.
22
Moreover, it must be emphasized that SSR markers also convey extra information, as
compared to other classes of markers. The variability in SSR marker loci is because of
the differences in the number of repeat units, e.g. di-, tri- or tetra nucleotide repeats.
In recent years, however, because of the accessibility of large Expressed Sequence Tag
(EST) datasets for the number of plant species and development of several
bioinformatics tools, it is now possible to identify and develop SSR based markers from
ESTs (Pillen et al., 2000; Thiel et al., 2003; Ramsay et al., 2004; Varshney et al., 2006). The
SSR markers that are derived from ESTs are known as EST-SSRs and the development of
these markers is easier and economical, in contrast to the earlier genomic SSRs (Varshney et
al., 2005).
2.3.10 Single Nucleotide Polymorphisms (SNPs)
SNPs are i.e. single base alterations in DNA sequence(Gupta et al., 2001; Marcel et al.,
2007), are become an increasingly vital class of molecular marker. The potential
number of SNP markers is very high, meaning that it should be possible to find them
in all parts of the genome, and micro-array procedures have been developed for
automatically scoring hundreds of SNP loci simultaneously at a low cost per sample.
2.3.11 Cleaved Amplified Polymorphic Sequences (CAPS)
CAPS markers are amplified DNA fragments, using specific primers that are afterwards
digested by specific restriction enzymes. The sequence polymorphism results from cutting of
products in different places, and these sources variants are considered as difference of lengths
when running these reactions products on agarose gels. The CAPS markers approaches are
sometimes known as Restriction Fragment Length Polymorphism (RFLP-) PCR, and this
technique bears similarities to the non-PCR-based older RFLP method and these can be
applied to organism specific nuclear sequences, or to organellar DNA using different
universal primers. As with SSRs, sequencing is mostly required in former cases in order to
have primer pairs. Similar to SSRs, CAPS can assess variation at particular one locus only in
a particular type of PCR.
23
Table 2.3 Comparison of molecular markers for their advantages and disadvantages.
2.4 Achievements through molecular markers approaches in guava
Molecular techniques are useful tools for characterizing the genetic diversity different
cultivars or species, for the purpose of identifying genes of commercially important and
development through genetic transformation techniques. Important achievements in guava
applying molecular approaches are presented in Table 3. Clonal identifications or
cauterization are traditionally based on several morphological parameters; however,
morphological characters are not reliable to distinguish between and among closely related
guava germplasm (Chandra et al., 2005). Most of the guava cultivars grown on commercial
scales are seedling origin from the specimen parents (Jaiswal et al., 1992). Some somatic
mutations and changes because of environmental conditions can create problem in accurate
identification and cauterization of germplasm. Presently, different molecular markers based
24
approaches like RAPD, RFLP, AFLP, SSRs, ISSR and VNTRS have been employed for the
inquiring of cultivar origins and relationships based on taxonomy for several plant species/
land races. Also recognition of genetic variation is very important for micropropagation,
collection and in vitro germplasm preservation to remove undesirable somaclonal variations.
Reports have been made in guava on assessment of genetic variation by using molecular
markers, i.e. Random Amplified Polymorphic DNA (RAPD) markers (Dahiya et al., 2002;
Prakash et al., 2002; Chen et al., 2007; Feria-Romero et al., 2009). Isolation of sufficient
quality of a genomic DNA for usage in PCR-based DNA marker technique has been facing
severe problems which may be the presence of inhibitors such as polysaccharides or
polyphenol, which inhibit the enzymatic DNA processing and inhibit the PCR reactions
(Prakash et al., 2002). A well-established protocol of modified CTAB (Porebski et al., 1997)
resulted in excel-lent DNA templates for the PCR amplification for guava (Prakash et al.,
2002). Prakash et al., (2002) analyzed molecular diversity of 41 different genotypes of guava
collected from different parts of India by using RAPD markers. As a review many authors
have purposed that the genetic base for Indian guava are rated as low to moderate diversity
and among germplasm triploid seedless cultivars of guava are not genetically similar and
possesses independent origins. Keeping in view the findings Dahiya et al. (2002) tried to
identify genetic relationship in 13 different north Indian cultivars of guava by using RAPD
markers. Chen et al. (2007) used RAPD markers to determine phylogenetic relatedness
among 18 cultivars of Taiwan.
Simple Sequence Repeats (SSRs), which are also known as microsatellites DNA markers,
have been widely used in plant genomic studies, and are considered more variable than
RFLPs and RAPDs (Krishna and Singh, 2007). Microsatellites markers were developed to
study genetic variation in guava by creating a genomic library with sequences enriched for
(GA) n and (GT) n dinucleotide tandem repeats and a group 23 nuclear SSR loci were chosen
to determine the diversity within three guava species (Risterucci et al., 2005). Hernandez-
Delgado et al. (2007) studied the AFLP analysis of genetic association among the 48 guava
cultivars that were grown in different locations of Mexico.
25
Table 2.4 Achievements made in guava through molecular makers based approaches
The guava accessions assembled in the Cuban germplasm collection at Alquizar (Havana
Province) was previously characterized by phenotypic descriptors. Rodriguez et al. (2004)
used applied SSR and AFLP DNA markers in the establishment of phylogenetic relationships
for Cuban germplasm. As a result three different guava molecular mapping populations were
produced which were resulted from controlled crosses with the dwarf cultivar ‘Enana Roja
cubana’ as the female parent. For integrated molecular linkage Map based on segregating
AFLP markers, of guava was constructed for the first mapping population MP1 under
investigation. The previously published linkage map was extended by arranging a total of
220 markers into 11 linkage groups possibly representing the 11 chromosomes (2n = 22) of
the guava genome with 11 to 30 markers per linkage group and a total genome length of
1379 cM.
The first report about genetic characterization of Mexican native guava germplasm by using
AFLP marker methodology and the results based on phenotypic and productive
characteristics suggest that germplasm was collected from open pollinated trees. To confirm
that, Hernandez-Delgado et al. (2007) used amplified fragment length polymorphism (AFLP)
technique to analyse a set of 48 guava (Psidium guajava L.) accessions cultivated in Mexico,
in order to characterize their genetic relationships. The study included two P. cattleianum
(Sabine) and two P. friedrichsthalianum (Berg-Niedenzu) accessions from Costa Rica as
Molecular markers Achievements References
RAPDs Molecular diversity of 41 guava genotypes
Prakash et al. (2002)
Genetic relationship in 13 guava cultivars
Dahiya et al.( 2002)
Molecular characterization of 18 guava cultivars of Taiwan
Chen et al. (2007)
SSRs Constriction of (GA)n and (GT)n enrich library
Risterucci et al. (2005)
QTLs Mexican guava Rodriguez et al. (2004)
AFLPs Genetic characterization of Mexican guava
Hernadez-Delgado et al. (2007)
26
outgroups. The AFLP analysis produced two clusters of Psidium accessions, the first
included P. cattleianum and P. friedrichsthalianum, and the second P. guajava accessions.
Rodriguez et al. (2004) studied a total of 49 accessions assembled in the Cuban guava
germplasm using phenotypic descriptors and AFLP analysis. For the molecular analysis, a
total of 226 polymorphic AFLP markers were used to investigate the genetic relatedness
within the guava collections and the results were compared to similar analysis based on
morphological and agronomic characters. The comparison of similarity matrices from both
data sets showed a low coefficient of correlation for qualitative to quantitative or to
molecular data.
Prakash et al. (2002) used RAPD markers to analyze molecular diversity of 41 genotypes of
guava mainly consisting of five Psidium species, 23 varieties, 12 selections and one hybrid.
93 amplified fragments were obtained by using eight primers. The results for genetic
dissimilarity matrix based on Squared Euclidean Distance indicated maximum genetic
distance of 54% between the variety Mirjapur seedling (P. guajava) and P. quadrangularis,
while the minimum distance was only 11% between SWY-1 and GR-1 Navalur selections.
Based on Ward's method of cluster analysis, all the individuals on the dendrogram were
grouped into two major clusters according to their geographical locations and species.
Chen et al. (2007) studied molecular markers as 18S rRNA, internal transcribed spacer (ITS)
region of ribosomal DNA, trnL intron and trnL-trnF Intergenic Spacer (IGS) of chloroplast
DNA (cpDNA), and RAPD marker for the molecular identification of 18 P. guajava samples
from collected from different indigenous tribes, consisting, from non-indigenous tribes, and
commercial cultivars from markets in Taiwan. They reported that molecular methods like
RFLP and Denatured Gradient Gel Electrophoresis (DGGE) were found be time consuming
and less efficient as compared to RAPD. For the genetic analysis, ten oligonucleotide primers
were used in RAPD to amplify the specific genes regions from 32 guava samples by using
four different primers, OPB 17, OPG 6, OPY 15 and OPY 18, which were able to direct the
amplification and yielded a total of 82 polymorphic RAPD patterns. As a result of
amplification thirty-two genotypes on the dendrogram were identified which were divided
into two major groups, the uncultivated and commercial cultivars. On the basis of cluster
27
analysis, the red-flesh Psidium samples which showed different bands were grouped
independently.
Mercado-Silva et al. (2002) characterized 12 guava (Psidium guajva L.) selections
phenotypically as well as genetically. Selections were analyzed using Random Amplified
Polymorphic DNA (RAPD's). The results from Cluster Analysis of RAPD data showed a
genetic simi- larity ranging from 88 to 96 % among selections. The Results indicated the
availability of selected guava germplasm showing similar bands with high productivity and
good quality indexes, will be adapted to the growing conditions in Mexico.
Perez et al. (1999) carried out genetic and morphological variability of 17 guava (Psidium
guajava L.) genotypes. The genotypes were studied by using RAPD (Randomly Amplified
Polymorphic DNA) sequences. A total of 226 bands were evaluated, out of which 84.07 %
were polymorphic. The Cluster Analysis of RAPD showed a maximum genetic similarity of
91.3 % among the genotypes MOR9 and MOR10 from Morelos, and a minimum similarity
of 41 % between the genotypes JROS22 and PAL8, respectively.
SR or microsatellite codominant technique, which has proven its advantages and suitability
in a broad range of applications in genetics, was developed in order to improve the
availability of best performing molecular tools for genetic studies and marker assisted
breeding in guava and its closely related species. Risterucci et al. (2005) constructed a GA
and GT microsatellite-enriched library and characterized 23 nuclear SSR loci in the guava
species. The results for all SSR loci were found polymorphic after screening for diversity in
different cultivars, and across-taxa amplification tests showed the potential transferability of
most SSR markers in Psidium species. The SSR resource could be a powerful tool for genetic
studies of guava, including cultivars identification and linkage mapping, as well as
potentially for interspecific genetic studies within the genus Psidium.
Valdes-Infante et al. (2007) determined the genetic diversity among different guava
accessions by using SSR loci. A total of 34 different alleles ranges were found from three to
seven accessions and the average number of putative alleles per locus were found 4.57. The
results indicated that except two genotypes, all the accessions were differentiated into
28
different groups as a result of the molecular analysis which were divided into six diversity
groups, showing an acceptable level of genetic variability in the collection assayed. The high
number of common alleles detected as a result of SSR locus suggests that most of the
analyzed plant material have a common genetic ancestry.
Lara et al. (2004) carried out the phenotypic and genetic diversity of four guava orchards
located in San Tadeo, Calvillo, Aguascalientes. The 48 trees were analyzed by the Random
Amplified Polymorphic DNA (RAPD) markers. The 15 RAPD oligonucleotides amplified
112 ADN fragments (7-8 fragments per oligonucleotide) showed high polymorphism. The
Cluster analysis of phenotypic and RAPD data did not demonstrated clear clustering of trees
in relation to the orchard of origin. The results for morphological and genetic diversity were
unfavorable to the variation in fruit shapes and mesocarp color, which affect uniformity and
quality of production.
Rueda et al. (2006) uses 27 accessions of guava for genetic variation analysis, RAPD
molecular markers (Ramdon Amplified Polymorphic DNA), using 6 polymorphic primers,
which resulted in a total of 43 polymorphic loci. A Similarity analysis and multiple
correspondence materix revealed that a large group (20 accessions) which separate the
accessions collected in the Americas, as well as the separation of guava accessions collected
in Africa and Surin, guava crown, Peruvian apple as a group and individual which is relative
high degree of genetic diversity within the germplasm.
Anju et al. (2008) studied PCR based Random Amplified Polymorphic DNA (RAPD) and
Directed Amplification of Minisatellite DNA (DAMD) markers for the genetic diversity and
relatedness among 22 guava accessions. DNA was isolated by CTAB method used for
amplification of 96 markers by using 7 RAPD primers and 56 workers generated by 40
DAMD primers. The results from genetic distance matrix based on Jaccard's coefficient
indicated maximum distance between Purple guava and Allahabad Safeda (43%), whereas
minimum distance was as low as 5.4% between two breeding lines HPSI-20 and HPSI-26.
Whereas half-sib progenies CISH-G-1 to CISH-G-6 showed slightly more distance ranging
from 10.8–24.0%. The RAPD clustering also revealed that most of the cultivars/accessions
29
are originated from Indo-Gangetic plains which were grouped together. The DAMD primer
was found to be suitable cluster for the cultivars from exotic origin or possessing exotic
parentage.
Sharma et al. (2007) examined the diversity among twenty-two Psidium guajava L. cultivars
and two species viz. P. catteilianum and P. friedrichthalianum using RAPD markers. Genetic
variation was analyzed using forty-one random decamer primers, thirty-nine showed
reproducibility. For the various genotypes, between 2-16 bands were obtained for each
primer. Out of total 376 clear and reproducible bands, 347 were polymorphic resulting to
92.29% polymorphism. The results for the amplified DNA fragment normally ranged from
0.12-2.2 kilo base pair and the primers screened indicated that RAPD fragment(s) are unique
for a particular genotype. PIC value for primers was found maximum for OPB-12 (0.916)
and least for OPD-11 (0.520) primers. The genetic similarity matrix constructed showed a
value ranged between 0.33-0.94 and the highest genetic similarity (0.94) was calculated
between Hybrid Red Supreme and Super Max Ruby. Similarly Hybrid Red Supreme and
Spear Acid were genetically most diverse (0.33). In the dendrogram, all the 22 genotypes
were grouped into two clusters. In the first cluster Chinese guava grouped together with P.
guajava L. cultivar Apple Colour and Spear Acid and the second cluster was filled by the
remaining 19 genotypes. This study thus explains the usefulness of the RAPD markers and
their effectiveness in assessing the genetic relationship among the various genotypes of
Psidium spp. examined.
Aranguren et al. (2010) evaluated the genotypic variability of guava landraces by using SSR
markers. Thirty one guava accessions were selected from different regions of Venezuela. All
samples were performed for 16 Simple Sequences Repeats (microsatellite loci), by primers
specific for guava crop. The results indicated that all evaluated loci showed high
polymorphism, giving the results up to 100% and finding several alleles per locus. These
results showed a great genetic diversity among the natural population of P. guajava, which
described to the link of accession with their geographical distribution. This study also
indicated that microsatellites are useful to characterize accessions of guava on genetic basis.
30
Ritter et al. (2010) mapped several identical SSR markers in different progenies, and
detected a potential association of linkage groups among different populations. An
integrated parental linkage maps in three guava mapping populations (‘Enana’ × “N”,
‘Enana’ × ‘Suprema Roja’ and ‘Enana’ × ‘Belic L-207’) using AFLP and SSR markers
were also constructed. They used between 102 and 119 AFLP primer combinations (PCs)
in each population by generating between 684 and 1163 segregating AFLP fragments.
The results distribution of parent-specific and common markers showed that ‘Enana’ was less
heterozygous than that of other parents and it also indicated that all parents shared a
considerable gene pool. In addition, SSR primer combinations (PCs) between 28 and
171evaluated linkage mapping among these populations. The parent specific linked
fragments were initially arranged into linkage groups. In all mapping population, 11different
linkage groups (LGs) corresponding to the 11 chromosomes for the haploid guava
genome were obtained for every parent. For available SSR markers, combined parental
linkage maps of each mapping population were created using as anchor points allelic SSR
fragments with common AFLP fragments. These maps contain between 408 and 850
markers and had lengths of 1885 to 2179 cM, respectively. The results for average
linkage group lengths vary between 160 and 198 cM in these maps and concluded on average
between 37 and 77 markers.
The use of microsatellites could play an important role in the detection of guava accessions.
To support this hypothesis Valdes-Infante et al. (2010) used the microsatellite (SSR) markers
for guava germplasm characterization and gene bank management in Cuba. They detected
total of 34 different SSR alleles containing three to seven per locus in the examined
genotypes and resulted with an average number of putative alleles per locus of 4.57. Among
these, twenty-four alleles were classified as common alleles, out of which 10 were
widespread and 14 sporadic. Out of remaining alleles, ten alleles were classified as rare,
from which 7 were sporadic and 3 localized. For all SSR loci, a large number of
homozygous genotypes were recognized, except for the SSR locus mPgCIR09. These results
indicate a medium to low levels of heterozygosity was detected, which ranged from 0.08 to
0.54 with 0.38 as the total average for this parameter. The plant material showing cultivar-
specific markers was 'Darío 19-2'; 'Belic L-98'; 'BG 76-23'; 'Belic L-205' and 'Microguayaba'
31
which could be important materials used for conservation purposes. All these results indicate
a positive sign for utilization of microsatellite markers for germplasm characterization.
Briceno et al. (2010) developed SSR markers for Psidium guajava for the characterization of
individuals belonging to the same genus and to the same family (Myrtaceae). The plant
samples used in this work were collected from two sharply different and geographically
isolated ecosystems. An analysis with more than 16 pairs of guava-derived SSR primers was
carried out and all primer combinations were evaluated in terms of efficiency of
amplification, number of loci, size of the amplification products, and that of degree of
polymorphism. A successful amplification of informative loci, validated the transfer of
most of the SSR primers to other Myrtaceae species.
Valdes-Infante et al. (2010) conducted agro-morphologic traits and compared molecular
markers in terms of their polymorphism level, discriminating power, and informativeness
for 23 different genotypes assembled in the Cuban guava germplasm collection. AFLP
and SSR markers are powerful techniques for guava basic and varietal identification, but
the high level of polymorphic loci attributed by the dominant AFLP marker determines
the discriminating capacity of the genetic marker. All of the individuals were identified
with a single AFLP primer combination, while only a few genotypes were differentiated
with a single SSR primer combination or by morphological variables. The higher values
of expected heterozygosity were detected by SSR which reflected the high level of
informativeness of about this marker, it is due to the multi allelic and co-dominant nature
of SSR marker which makes them suitable for diversity studies. As for as results for
morphologic diversity index is concerned, it provided a good estimate of diversity when
phenotypic traits of high heritability were used, and was comparable with the expected
heterozygosity scored with DNA markers. The value for morphologic diversity index was the
lowest for morphological markers, the assay efficiency index and marker index showed the
same pattern of variation than discrimination capacity, number of banding pattern, number
of unique banding pattern and number of effective pattern for both molecular markers which
are indexes that indicates for the discriminating capacity in guava.
32
Lepitre et al. (2010) created an integrated parental linkage map of diploid guava (Psidium
guajava L., 2n = 22) in support of accelerated breeding for guava by marker-assisted
selection, based on AFLP and SSR markers which has been derived for the mapping
population MP1 constructed from a cross between “Enana” x “N6” heterozygous guava
cultivars . A total of 1103 segregating AFLP markers were obtained from 119 AFLP
primer combinations were designed for determining linkage mapping. In addition, 171
SSR maker loci were also analyzed which generated 258 allelic fragments. A total of
1364 SSR and AFLP markers were available for determining linkage mapping for guava
cultivars. The integrated linkage map of MP1 contains 578 markers (452 AFLPs, 126
SSRs) distributed for 11 linkage groups matching to the 11 chromosomes of the haploid
guava genome. This resultant map had a length of 2179 cM and an average linkage group
length of 198 cM, and a total of 126 so-called RF0 markers and 146 so-called associated
markers were also determined.
Risterucci et al. (2010) reported a method for establishing genomic libraries enriched for
microsatellites development, and presented results on Psidium guava. The results indicated
that designing optimal SSR markers from bulk sequence data is quite laborious and time-
consuming process. SSR Analysis Tool (SAT) is a user-friendly web application which
minimizes the tedious manual operations and also reduces errors. This tool is facilitated
with the integration, analysis, and display of sequence data from SSR-enriched
libraries. SAT is also designed to perform base calling and quality assessment of
chromatograms, eliminate adaptors and low quality sequences, cloning vector, detect
chimera or partially digested sequences, search for SSR motifs, cluster, assemble the
redundant sequences, and design SSR primer pairs. The genetic variation has been
complemented by classical molecular methods (DNA techniques) and there seems a great
potential for the application of these molecular markers (DNA markers) to fruit crops.
Rodriguez-Medina et al. (2004) compared the data which were derived from individual
performance versus combined data sets for the purpose of varietal identification and
their diversity estimation in guava germplasm by using different DNA markers. The AFLP
based data permitted discrimination of all the accessions of guava that were evaluated with
different diversity groups. Seven diversity groups were detected with Microsatellite (SSR)
33
markers, but with these markers all the accessions were not differentiated. The combined
results of AFLP and SSR data showed similar results in case when AFLP markers
were used. Although coincidences were identified in individuals and combined
dendrograms, in addition of the information derived by these markers system permitted an
accurate estimation of diversity for guava germplasm.
2.5 Clonal Propagation
There are several reports, which have examined the possibilities of propagation of guava by
using softwood cutting from seedling-originated juvenile stock plants in which genetic
segregation is possible. Propagation by softwood cuttings from mature trees may be one of
the important options to avoid the genetic segregation and maintain the quality of the variety.
However, the information regarding the rooting ability of the cutting obtained from mature
stock plants of the species is very scarce. Luqman et al., (2004) used the basal portions of
semi hardwood guava cuttings, immersed for 12h in solutions containing different
concentrations of Indolbutyric acid (IBA). The treated cuttings were planted in bags of sand
medium. IBA had no significant effect on the number of days to bud sprouting and sprouting
percentage. Survival of cuttings (35.71%), number of leaves per cutting (13.93), number of
branches per cutting (3.80), branch length (7.2 cm), and number of roots per cutting (21.15),
root length (6.089 cm), root weight (4.57 g) and rooting percentage (39.28%) were greatest at
1000 ppm.
Ayaz et al. (2004) investigated the effect of different concentrations of paclobutrazol and
dipping period on the rooting of softwood cutting of guava. Fresh softwood cuttings of guava
having 3-4 leaves were dipped in 0, 10, 20, 30, 40, 50 and 60 ppm solution of paclobutrazol
for 1, 2, 3, 4 and 5 h and the plants were grown in plastic tubes containing sand and covered
with polythene sheet for maintaining humidity. In various paclobutrazol concentrations, 60
ppm resulted in maximum cutting success (73.3%), rooting (69.5%), shoot length (24.3 cm),
number of branches (4.3), number of roots (87.1) and root volume per plant (1.64 cm3). In
various dipping periods, five hours dipping resulted in maximum cutting success (34.9%),
34
rooting (30.1%), shoot length (10.6 cm) and number of roots (47.2) while four hours dipping
resulted in the maximum number of branches (2.6) and root volume per plant (1.05 cm3). In
interaction, the maximum cutting success (81.7%), shoot length (28.8 cm) and number of
branches (5.3) was observed in 60 ppm paclobutrazol concentration and 2 h dipping.
Paclobutrazol at 60 ppm concentration and 3 h dipping period gave the maximum rooting
(80%), number of roots (102.7) and root volume per plant (1.76 cm3).
Ullah et al. (2005) investigated the effect of plant growth regulators on the rooting of guava
cv. Allaabadi cuttings. Naphthalene acetic acid (NAA), IBA and paclobutrazol at 1000 ppm
were applied on hardwood, semi-hardwood and softwood type cuttings by immersing the
basal ends of cuttings in the solutions for 5 minutes. Cuttings were then planted in plastic
bags containing sand and covered with plastic sheets. Maximum sprouting (71.22%), number
of branches (3.44), root weight (1.46 g) and survival percentage (57.22%) were observed in
softwood cuttings treated with paclobutrazol. The maximum root number (59.66) and the
longest shoots (8.24 cm) were obtained from softwood cuttings treated with IBA. Semi-
hardwood and softwood cuttings showed early sprouting (17.68%) and maximum root length
(12.81 cm), respectively, under the NAA treatment.
Wally et al. (1991) described the scope of clonal propagation of guava by stem cutting
collected from mature stock plants. Cuttings were treated with 0, 0.2, 0.4 and 0.8% IBA
solution and rooted in the non-mist propagator. Rooted cuttings were allowed to grow in the
polythene bags filled with soil and cow-dung mixed in the ratio of 3: 1 (by volume) for three
months to assess the steckling capacity and initial growth performance. The highest rooting
percentage (60%) was observed in the cuttings treated with 0.4% IBA solution followed by
0.2% IBA and the lowest was in control treatment. The maximum number of primary roots
(32.7) was developed in the cuttings treated with 0.8% IBA solution followed by 0.4% IBA
and the lowest was in the cuttings without IBA treatment. The highest survival percentage
(70.9%) was observed in the cuttings rooted with 0.4% IBA and the lowest (58.3%) was in
the cuttings without any treatment. However, there was no significant variation in height of
cuttings due to IBA treatments in rooting.
35
Manan et al. (2008) tested the hypothesis that cutting treated with IBA and sucrose increases
the percentile of rooted cuttings as well as quality of the newly formed root system. They
conducted an experiment in an intermittent mist chamber. After the preparation of cuttings,
these were treated with IBA in immersion for 24 hours at concentrations of 0, 100, 200 and
300 mg L-1, 2% sucrose being added or not carrying 2 nodes and 1 pair of reduced leaves.
After the treatment these cuttings were planted in polyethylene sacks filled with sand as a
substrate. It was concluded that, among all the concentrations the IBA (300 mg L-1) was the
best for the percentage of rooted cuttings and number and average weight of the dry root
matter. It was also concluded that the presence of sucrose didn't present a significant effect
on the characteristics analyzed and the simple distinction of the leaves on the cuttings didn't
influence their rooting. It was also concluded that in those cuttings that were treated with 300
mg L-1 of IBA, for the leaves it was not necessary to persist for 60 days. Manan et al., 2008
conducted the studies on the hardwood cuttings of Guava using six to eight inches long
hardwood cuttings of two selections, cuttings were taken from 3-4 years old trees. The
cuttings were treated with IBA at 500 and 1000 ppm, and tap water was used as a control.
The results for number of sprouted cuttings, percentage of rooted cuttings, average number of
roots per cutting, average root length per cutting, , and for mortality rate were determined.
For commercial production of guava plants, quality is of the utmost importance to ensure
high productivity, uniformity, rapid formation, and early production and these qualities make
guava essential to vegetative propagation. With this aim Yamamoto et al. (2010) evaluated
the cuttings of guava that were immersed with different concentrations of indolebutyric acid
(IBA) with talc and alcohol as a vehicle. Herbaceous cuttings with 10-12cm length were
submitted to two different forms of treatment applications (alcoholic talc and alcoholic
solution) added with three doses (0, 1000 and 2000 mg L-1) of IBA. Then cuttings were
placed under the plastic boxes containing carbonized rice husk. After 95 days of treatment
applications, different variables were evaluated for IBA treatment, cutting survival rate (%)
rooted cuttings (%) root number per cutting root length per cutting, foliar retention (%) and
dry root matter per cutting (g). The best results were obtained for the percentage of rooting
(28.5%), number of roots per cutting (12.10) and root length (6.79cm) with the highest
concentration of IBA. These results indicated that the application of 2000 mg L-1 of IBA is
36
the most appropriate that provides the best results for rooting characteristics. Guava with the
talc carrier was more efficient than alcohol.
For understanding more efficient techniques in plant propagation of guava, Souza et al.
(2008) evaluated the effect of different concentrations of IBA in combination with substrates
on rooting of Psidium guajava L. experiments was conducted in greenhouse and was
designed completely randomized in the scheme of 2x5 factorial, with two substrates
(vegetable Plantimax, Plantimax forest) in combination of five levels of IBA, 250 mg / L
500 mg L-1, 1000 mg L-1, 2000 mg L-1, distilled water, respectively. The results indicated that
interaction was significant between substrate and all doses of IBA in almost all traits except
for number of rooted plants. Among all concentrations for different parameters the best
results were for concentrations with 1000 and 2000 mg L-1.
Colombo et al. (2008) evaluated the potential effect of different concentrations of IBA on the
rooting of guava selection 8501-1 by using 10-12 cm long herbaceous cuttings. They
prepared cuttings in two ways (with or without basal lesions) and submitted these cuttings to
four different concentrations of IBA (0 mg L-1, 1,000 mg L-1, 2,000 mg L-1 and 3,000 mg L-
1). After the treatment, they placed the cuttings in plastic boxes to root, containing carbonized
rice husk. After 85 days, the experiment was evaluated for the following variables, root
number per cutting, rooted cuttings (%), cuttings with callus but without roots (%), root
length per cutting, cutting survival rate (%), wet and dry root matter per cutting (g) and
foliar retention (%.). The results indicated that there was no significant contrast among the
different concentrations of IBA for the percentage of rooted cuttings, but there was a
significant difference for the root number per cutting and per cutting wet and dry root matter
under the doses of 2,000 and 3,000 mg L-1. Results for the basal lesions indicated that they do
not have improving effect on the cuttings rooting potential, and the concentration 3,000 mg
L-1 resulted in the largest number of roots per cutting.
Tavares et al. (1995) evaluated the effects of different seasons of cutting collection and effect
of different concentrations of IBA on the rooting of two different types of cuttings (apical
and mid cuttings) of guava (Psidium guajava L.). They used two clones on the basis of flesh
37
color, one of white flesh and another of red flesh fruits. They collected cuttings four times in
the year from the trees during February, April, June and August. The cuttings were prepared
by apical portion with two leaves and mid part branch cuttings carrying four leaves, both 15
cm length and these cuttings were treated with 0, 4000, 5000, 6000 or 7000 ppm of IBA by
making as powder mixture. These treatments were introduced to cuttings 1 cm of their basal
ends. The cuttings were planted in plastic bags filled with ash of rice husk as substrate and
were placed in greenhouse under mist conditions. After an interval of 60 days the cuttings
were uprooted for evaluation of the percentage of rooted cuttings, cuttings with callus,
cuttings with new leaves and root dry weight per cutting. The dates of collection of cuttings
had significant effects variation. The results indicated that the highest percentage of rooted
cuttings (51.52%) was noted on cuttings collected in February. Sprouting of cuttings was not
affected by time of collection of cuttings and IBA concentrations. Different concentrations of
IBA increased the dry weight of roots per cutting and percentage of rooting. Difference was
found between the two clones only with respect to the percentage of rooted cuttings and of
sprouting percentage that was higher for the white flesh clone than red flesh clone, cuttings
collected in October.
Figueiredo et al. (1995) studied the effect blanching IBA on the rooting of cuttings of Feijoa.
They carried out this work considering three different dates of blanching. The branches of
various plants were obtained, with uniform size and age prior to branch trimming in
greenhouse. Cuttings obtained from these branches at three different blanching times (0, 40
and 60 days) were treated with 0, 5000, 7000, 9000 and 11000 ppm of powder IBA. After
few weeks the cuttings were evaluated for number of rooted cuttings in order to calculate
percent rooting. The results indicated that the blanching was most effective in rooting and the
best time for rooting was variable to the date of branch blanching and the IBA concentration
for rooting showed negative effect on rooting.
Zietemann and Roberto, (2007) evaluated the effect of different substrates and collection
seasons on the performance of herbaceous cutting of Paluma and Seculo XXI guava verities.
The herbaceous cuttings were collected during spring and summer with 10 cm of length,
carrying two nodes with a pair of leaves in the superior node. The cuttings of both seasons
38
were treated with five different doses of IBA (0, 500, 1500 and 2000 mg L-1) and planted into
plastic boxes filled with hull rice coal and vermiculite as substrate. After 70 days interval, the
cuttings were evaluated for the rooting percentage, number of roots, roots length, fresh and
dry matter of roots, foliar retention, survival percentage and percentage of cuttings with
callus. The results indicated that the better rooting percentage was obtained in cuttings
collected in summer season and the concentrations 1500 and 2000 mg L-1 of IBA were found
the most appropriate for the best rooting characteristics of ‘Paluma’ and ‘Seculo XXI’
herbaceous cuttings.
Costa et al. (2003) carried out research to verify the effect of shading in the stock plants of
two guava cultivars by using indolebutyric acid as root promoter in the rooting of cuttings.
The factors that were studied to determine the influence of the application of IBA (0 and
2000 mg L-1) and shading the stock plants with 30 and 50 % as stock plants were grown with
plenty of sun. The cuttings were grown in an intermittent mist chamber under greenhouse.
They concluded that both cultivars Rica and Kumagai had different rooting capabilities. The
use of 30% shading in Kumagai provided the best results for rooting of cuttings. Similarly
the use of 30% shading with application of 2000 mg.L-1 IBA gave the best percentage of
rooting in the cuttings for Rica. It was also concluded that the use of IBA increased the
number of roots in cuttings.
Dantas et al. (1999) conducted research with objectives to determine the effect of ethephon.
Herbaceous branches of guava from commercial orchard of seven years old were used by
preparing cuttings from middle to tip portions, carrying two nodes and a pair of leaves which
were cut by the half. The cuttings were immersed with different levels of ethephon at the 0,
25, 50, 75 and 100 mg L-1 concentrations for 5 seconds and then were planted in
polyethylene bags filled with saw dust as substrate. After 60 days, the cuttings were
evaluated for, rooting percentage, number and weight dry matter of roots. The results
indicated that the tip cuttings presented good results for percentage of rooting, number and
weight of dry matter than the medium ones. It was also concluded that treatment of the
cuttings with Ethephon was not effective.
39
Santoro et al. (2010) evaluated the guava selection 8501-9 for rooting using herbaceous
cuttings with 10-12 cm of length in two different way of preparation (simple cut without
lesions and other cambium exposition), and carrying the three suppression intensity of the
leaves (without leaves, with half leaves and intact leaves). After treating the cuttings with
IBA, the cuttings were placed to root in plastic box (44 x 30 x 7 cm) filled with carbonized
rice hulls as growing media. After 78 days, the cuttings were evaluated for the parameters
like leaf retention, survival percentage of the cuttings, the number of rooted cuttings and of
roots, the length of roots, and fresh and dry mass of the roots. The result for the interaction
between cuttings with lesions and presence of leaves on cuttings was not significant, which
indicated that this factor acted independently in relation to the other factors studied. The
cuttings with leaves suppression resulted in death of cuttings. Results for the cambium
exposition have proportion vantage over rooted cuttings obtained from herbaceous portions.
Cuttings with carrying base lesion give addition of only 10% for foliar retention and
percentage of rooted cuttings. Cutting carrying a pair of leaves showed advantage over the
parameters of the fresh and dry weight of root in relation to the cutting carrying half leaves,
and the cuttings without leaves did not show any formation of roots although the presence of
leaves is fundamental to roots promotion.
In order to improve the rooting in guava, Prieto-Silva et al. (2004) evaluated the different
levels of IBA, 0 and 200 mg L-1 and 1 g L-1 of Benlate (50% Benomyl), with the substratum
earth worm humus plus organic river matter (RO) in the proportions 1:1; 2:1 and 3:1,
respectively. They submerged the cuttings in IBA+ Benlate for 14 h. after eight weeks; the
cuttings were examined for the alive (CAP), rooted (CRP) and health (CHP), rooting
percentage, root number by cutting (RN) and length for the longest root (LR). Results
indicated that the cuttings without IBA and planted in substratum 3HL: 1AR gave the
maximum CHP (16%), CAP (24%), LR (1.65) and CRP (20%). The maximum RN (2.2) was
determined in 2HL: 1AR with IBA.
Mukhtar et al. (1998) tested the different NAA and IBA treatments by dipping the base of
herbaceous cuttings of guava. The treatments (0, 2000, 4000, 6000 mg/1000 mg talc) were
made by adding IBA and ANA in combination with talc. The results obtained, with
40
maximum rooting (93%) in the application of the IBA 4000 mg. Form this experiment they
concluded that the response of NAA application was bad and they also come to know that the
rooting percentage is increased with increasing concentration up to 4000 mg and started to
decline with high concentrations.
Gonzalez and Schmidt, (1992) obtained 25% rooting in cuttings of Kumagai guava applied
with a concentration of 1000 mg L-1 IBA, while cuttings not treated with IBA resulted in
only 3.37% rooting. In this same study the use of NAA (50-10%) was less effective than IBA
application. Pereira et al. (1991) while applying NAA as a root inducer in different types of
leafy cuttings of guava cultivar J-3 obtained 70.22% of rooting with the concentration of
2000 mg L-1.
Marco et al. (1998) applied ethephon to the base of cuttings of guava, at doses of 0, 1,000,
2,000, 3,000 and 4000 mg L-1 IBA, maximum rooting percentage (28.02%) was obtained for
the AIB factor, with concentration of 3000 mg L-1. The concentration of 3000 mg L-1 was
resulted in the greatest number of roots per cutting (2.91).
Coutinho et al. (1991) conducted an experiment to examine the effect of IBA in semi-
hardwood cuttings of guava pine apple guava (Feijoa sellowiana Berg.), Guabiju
(Myrcianthes puncens Berg.), cherry-the-bush (Eugenia involucrata), Surinam cherry
(Eugenia uniflora L.) and yellow guava (Psidium cattlyanum Sat.), found that cuttings
Guabiju, cherry and Surinam cherry bush not rooted even when treated with IBA. Since the
cuttings of pineapple guava, yellow guava and showed low rooting both untreated (3.00 to
0.66% respectively) and with treatment with IBA (6.33% with 5000 mg L-1 to 2.66% with
1000 mg L-1).
Nachtigal et al. (1994) applied 200, 300 and 400 mg L-1 IBA, and the control with distilled
water, in semi-hardwood cuttings of strawberry guava. A better rooting percentage (69.6%)
and higher dry weight of roots (0.22 g / square) was obtained with the concentration of 200
mg L-1 of auxins.
41
Chapter 3
MATERIALS AND METHODS
A comprehensive understanding of the genetic diversity, degree of variation, genetic building
of the plant and heritability of genetic characters, among and within the genotypes would
help in identification and development of wide-ranging plant improvement programs.
Genetic variability is a gift from nature and its fruitful application in any crop species
requisites for systematic collection, description, evaluation and grouping of population based
on economic descriptors. The present research study was designed to investigate the
presence of diversity among guava accession using morphological and SSR molecular
marker. The present studies on the morphological and genetic variation and clonal
propagation of guava with were carried out in Institute of Horticultural sciences and Center
of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture,
Faisalabad particular focus on establishing genetic diversity for germplasm collection and to
develop asexual propagation system to avoid clonal degradation.
The details of the materials used and methods adopted for collection and analysis of data and
interpretations are described in this chapter. On field experiments were carried out survey,
mapping and tagging of germplasm from the district of Faisalabad and Sheikhupura during
winter 2011. These districts are the major areas of guava cultivation in Punjab province of
Pakistan. Two control conditions experiments genetic diversity and clonal propagation of
guava were carried out in Center of Agricultural Biochemistry and Biotechnology and mist
unit under green house in green house area of floriculture.
A detail of methodology is presented in this thesis under the following experiments.
1) Morphological variation analysis of guava
2) Genetic variation analysis of guava
3) Clonal propagation of guava
42
3.1 Experimental Materials
The guava accessions used for this study were surveyed, marked and collected from guava
growing areas of Punjab (Sheikhupura and Faisalabad) during 2011. The guava accessions
under study were seedling selections. In Faisalabad Postgraduate Agricultural Research
Station guava orchards were also surveyed for the genetic diversity analysis with their
passport data (Table 3.1.). The accessions were originally collected from three research
institutions of Faisalabad and main growing regions of Sheikhupura.
The list of collected germplasm is as under.
Table 3.1 List of accession collected from Faisalabad district of Punjab, Pakistan
Sr.# Accession Site Origin
1 Gola Square 9 UAF. Faisalabad Local
2 Surahi Square 9 UAF. Faisalabad Local
3 Rough gola Square 9 UAF. Faisalabad Local
4 Mota gola Square 9 UAF. Faisalabad Local
5 Bangladeshi gola Ayub Research Faisalabad Exotic
6 Lal gola Ayub Research Faisalabad Local
7 Surahi Ayub Research Faisalabad Local
8 Khatta Ayub Research Faisalabad Local
9 Surahi Ayub Research Faisalabad Local
10 Gola Ayub Research Faisalabad Local
11 Allah Abadi gola Ayub Research Faisalabad Local
12 Lal gola Ayub Research Faisalabad Local
13 Karalla Postgraduate Agriculture Research Station Faisalabad
Local
14 Lal gola Postgraduate Agriculture Research Station Faisalabad
Local
15 Gola Postgraduate Agriculture Research Station Faisalabad
Local
16 Sadabahar gola Postgraduate Agriculture Research Station Faisalabad
Local
17 Larkana gola Postgraduate Agriculture Research Station Faisalabad
Local
43
Table 3.2 List of accession collected from Sheikhupura district of the Punjab, Pakistan
Sr.# Accession Site Origin
18 Gola Malk pur Local
19 Mota gola (Traday wali) Sharqpur Local
20 Surahi Malk pur Local
21 Chota gola Malk pur Local
22 Gola Sharqpur Local
23 Surahi Malk pur Local
24 Gola Sharqpur Local
25 Surahi Sharqpur Local
26 Gola Sharqpur Local
27 Mota gola (Traday wali) Sharqpur Local
28 Lal gola Malk pur Local
29 Choti surahi Sharqpur Local
30 Larkana gola Malk pur Local
31 Desi gola Malk pur Local
32 Gola Sharqpur Local
33 Mota gola (Traday wali) Sharqpur Local
34 Moti surahi (Traday wali) Sharqpur Local
35 Gola (Traday wali) Sharqpur Local
36 Sadabahar gola (Traday wali) Sharqpur Local
37 Sadabahar gola (Traday wali) Sharqpur Local
44
3.2 Surveying of orchards for guava germplasm collection form
Faisalabad and Sheikhupura districts
A survey was conducted in districts of Faisalabad and Sheikhupura to evaluate the guava
germplasm for morphological and genetic diversity. The plantation of guava is extensively
distributed throughout the district of Faisalabad and Sheikhupura, the main hub of guava in
Punjab Province. On the basis of the agro-ecological similarity of the above mentioned
districts, altogether of 51 guava orchards with 37 accessions were surveyed and collected. 17
accessions of guava were collected from 13 orchards of Faisalabad district and 20 accessions
were collected form 38 orchards of Sheikhupura district (Table 3.3). During the survey,
questionnaire prepared on morphological parameters i.e. tree, leaves, flowers, fruits and
seeds following guava plant descriptor formulated by UPOV was filled up at the spot.
Guava accessions were surveyed with two basic groups Gola (Pome shape) and Surahi (Pear
shape) and named accordingly as their local names or their other pomological parameters like
fruit weight, fruit texture and fruit flesh colour (white, creamy, pink, yellow or red).
Tbale 3.3 Orchards surveyed and accession collection from district of Faisalabad and
Sheikhupura
District No. of Orchards No. of Accessions
Faisalabad 13 17
Sheikhupura 38 20
Total 51 37
3.3 Mapping and Tagging of plant material Mapping of accessions of guava was carried out by tagging plant and row number in a
particular direction in a random manner with the local name of the accessions in the districts
of Faisalabad and Sheikhupura indicated in Table 3.1 and 3.2 following the plant
morphological descriptor for discrimination of accessions as shown in figures of this chapter.
45
3.4 MORPHOLOGICAL ANALYSIS
Tree morphological characters like growth habit, leaf shape, flowering, fruit and seed
morphology and quality of selected germplasm was recorded at germplasm collection site
according to the plant descriptors (UPOV, 1987). Ten plants from each accession of guava
were selected for tree, leaf, fruit, flower and seed morphological analysis. Data was
registered from 25 leaves/flowers/fruits/seed per accession.
Following morphological characters are recorded.
3.4.1 Tree Parameters
1. Tree: Attitude of Branches
a. erect
b. spreading
c. drooping
2. Young shoot: color of stem
a. green
b. yellow green
c reddish
d. dark reddish
3.4.2 Leaf Parameters
Observations on the young leaves having 3-5 cm length were made during the period of
active growth.
1. Young leaf: anthocyanin coloration
a. absent
b. present
2. Young leaf: Intensity of anthocyanin coloration
a. weak
b. medium
c. strong
3. Young leaf: pubescence on lower side
a. absent or very sparse
b. sparse
c. medium
46
d. Dense
e. very dense
4. Fully developed shoot: thickness of stem
a. thin, b. medium, c. thick. Was measured as 3, 4, 5 mm
5. Fully developed leaf: Length of leaf blade
a. short b. medium c. large. Was recoded as 5, 10, 15 cm
6. Fully developed leaf: Width of leaf blade
4, 6, 8 cm was recorded for a. narrow, b. medium, c. broad
7. Fully developed leaf: Leaf blade length/ width ratio
a. low
b. medium
c. high
8. Fully developed leaf: Leaf shape
a. round
b. ovate
c. obvate
d. trullate
e. obtrullate
f. oblong
1 2 3 4 5 6 round ovate obovate trullate obtrullate oblong
9. Fully developed leaf: curvature in cross section
a. weak
b. medium
c. strong
47
10. Fully developed leaf: Leaf twisting
a. absent
b. present
absent present
11. Fully developed leaf: curvature of midrib
a. absent
b. present
absent present
12. Fully developed leaf: degree of curvature of midrib
a. weak
b. medium
c. strong
13. Fully developed leaf: Leaf variegation
a. absent
b. present
48
14. Fully developed leaf: Leaf green color
a. yellow green
b. grey green
c. green
d. dark green
15. Fully developed leaf: color of midrib on lower side
a. cream
b. yellow
c. reddish
16. Fully developed leaf: spacing of secondary veins
a. close
b. medium
c. wide
17. Fully developed leaf: relief of surface of leaf
a. smooth
b. medium
c. wrinkled
18. Fully developed leaf: pubescence on lower side
a. absent or very
b. sparse
c. sparse
d. medium
e. dense
f. very dense
19. Fully developed leaf: undulation of margin
a. absent
b. present
20. Fully developed leaf: degree of undulation of margin
a. weak
b. medium
c. strong
49
21. Fully developed leaf: shape of base
a. obtuse
b. rounded
c. cordate
obtuse rounded cordate
22. Fully developed leaf: shape of leaf tips
a. attenuate
b. apiculate
c. acute
d. obtuse
e. rounded
attenuate apiculate acute obtuse rounded
3.4.3 Flower Parameters
1. Inflorescence: Predominant Number of flowers
a. one
b. one to three
c. three
2. Flower: size
a. small, b. medium, c. large. Noted as 1, 2, 3 cm
3. Flower: Number of fully developed petals
a. few, b. medium, c. many. Ranged as <3, 3-4, 5
50
4. Flower: staminoide petals
a. absent
b. present
5. Flower: Number of staminoide petals
80-100, 100-180, 180-260 was counted for a. few, b. medium, c. many
3.4.4 Fruit Parameters
1. Fruit: length As 4, 6, 9 cm for a. short, b. medium, c. long
2. Fruit: width
Fruit width was ranged as 1.5-2, 3- 4, 5-7 cm for a. narrow, b. medium, c. broad
3. Fruit: Fruit length/width ratio
a. small, b. medium, c. large was calculated as 1, 3, 4.5 cm
4. Fruit: Fruit shape at stalk end
a. broadly rounded
b. rounded
c. truncate
d. pointed
e. necked
broadly rounded rounded truncate pointed necked
5. Fruit: Width of neck in relation to that of fruit
a. narrow
b. medium
c. broad
51
narrow medium broad
6. Fruit: Fruit color of skin
a. pale yellow-green
b. pale yellow
c. dark yellow
d. orange
e. orange green
f. dark green
g. red
7. Fruit: relief of surface
a. smooth
b. rough
c. bumpy
8. Fruit: Longitudinal ridges
a. absent
b. present
9. Fruit: Prominence of longitudinal ridges
a. weak
b. medium
c. strong
10. Fruit: Longitudinal grooves
a. absent
b. present
11. Fruit: Size of sepal
1, 2, 3 cm was noted for a. small, b. medium, c. large
12. Fruit: Diameter of calyx cavity in relation to that of fruit
a. small, b. medium, c. large. Was measured as 1, 2, 3 cm
52
small medium large
13. Fruit: Ridged collar around the calyx cavity
a. inconspicuous
b. conspicuous
inconspicuous conspicuous
14. Fruit: Length of stalk
a. short, b. medium, c. long. Was measured as 1, 2 and 3 cm
15. Fruit: Color of flesh
a. white
b. cream
c. pale pink
d. pink
e. dark pink
f. orange pink
g. orange
16. Fruit: Evenness of color of flesh
a. even
b. mottled
17. Fruit: Discoloration of flesh after cutting
a. absent
b. present
53
18. Fruit: Grittiness of outer flesh
a. absent
b. present
19. Fruit: Thickness of outer flesh in relation to core diameter
a. very thin, b. thin, c. medium, d. thick, e. very thick. Ranged as >5, 8-12, 15-20 mm
thin medium thick
20. Fruit: Puffiness
a. absent
b. present
21. Fruit: Degree of puffiness
a. weak
b. medium
c. strong
22. Fruit: Juiciness
a. dry
b. medium
c. juicy
23. Fruit: acidity
Reagents:
1. 0,1NNaOH: Dissolve 4g NaOH in 1000 ml distilled water.
2. Phenolphthalein Indicator Solution:
Dissolve 1g phenolphthalein in 70 ml100% alcohol and add 30 ml distilled water.
Method:
Mix 10 ml fruit puree with distilled water (amount of water is immaterial, use enough
water to make end point easily visible). Add 3drops phenolphthalein. Titrate with
54
0.1N NaOH till rose pink color persists (pH 8.1).
24. Fruit: sweetness
Sweetness of fruit was expressed as the amount of the total sugar. The total sugar was
expressed as the amount of the total soluble solids (TSS).
The TSS was measured by means of a hand refractometer and expressed in ˚Brix.
a. low, b. medium, c. high. Ranged as 4, 8, and 12 ˚Brix
25. Fruit: Muskiness
a. absent
b. present
26. Fruit: Number of seeds
a. very few, b. few, c. medium, e. many, f. very many
Counted as <30, 30-100, 100-200, 200-300, >300
3.4.5 Seed Parameters
1. Seed: size
1, 2, 3 mm for a. small, b. medium, c. large
2. Period from flowering to fruit maturity
130, 140, 150 days for a. short, b. medium, c. long
3.5 Statistical Analysis
The data was subjected to Principal Components (PC) (Franco and Hidalgo, 2003) analysis in
order to identify morphological variables. Data analysis was performed using statistical
software (Statistica 5.5 version).
3.6 GENETIC ANALYSIS
This part of study was carried out in the Plant Genomic and Fingerprinting lab, Center of
Agricultural Biology and Biotechnology (CABB), University of Agriculture, Faisalabad.
3.6.1 Plant material
Thirty seven accession of guava were collected form the different orchards of the districts of
Faisalabad and Sheikhupura that has already been mentioned in the Table 3.1 and 3.2 for
55
DNA extraction and genetic diversity analysis. 15 to 20 young leaves per accession of guava
were collected and immediately placed in liquid nitrogen and the stored in the freezer at-
70˚C.
3.6.2 DNA Extraction
Genomic DNA extractions of 37 accession of guava were done by following CTAB (Cetyle
Trimethyl Ammonium Bromide) with slight modification.
3.6.3 DNA Extraction Procedure:
1. Turned on water bath to 65ºC.
2. Weighed out approximately 50 mg of leaf tissue and placed in a 1.5 mL micro-
centrifuge tube
3. Poured liquid nitrogen around and in tube and crushed tissue with blue pestle
4. Added 700 μL of 2x CTAB and vortex tubes vigorously for ~10 sec
5. Placed tubes in water bath for 2.5 hours
6. Removed samples from water bath and allowed them to come to room temperature.
7. Added 700 μL of Chloroform Isoamyl Alcohol (CIA) (24:1) to each tube (working
under hood) and vortex/invert to mix
8. Centrifuged at 13,200 rpm for 10 min – TWICE
a. Only needed once if top layer is clear and colorless
9. Removed supernatant and placed in a new 1.5 mL micro-centrifuge tube
10. Discarded old tube. Added 700 μL of CIA to each new tube.
11. Inverted several times, and then placed on vortex briefly to ensure thorough mixing.
12. Centrifuged at 13,200 rpm for 10 minutes. (Repeat Twice) As in the previous step,
remove the top aqueous layer to a new tube and discarded the old tube.
13. Added 500 μL of cold isopropanol to each micro-centrifuge tube.
14. Inverted several times. DNA precipitated during this step. It will looked like strands
of hair in liquid.)
15. Placed in freezer (-20ºC) overnight.
16. Centrifuged at 6000 rpm for 10 minutes.
17. (Carefully poured out supernatant and inverted tubes on a paper towel to dry.
56
18. Washed pellet with 70% EtOH – TWICE. After washing, centrifuge for 1 or 2
minutes.
19. Poured out EtOH and allowed to air dry at room temperature.
20. Added 50 μL of TE to each tube.
21. Vortex samples for ~10 Sec.
22. Stored samples at -20°C
3.6.4 DNA Quantification
Quantification of DNA was visually carried out on gel electrophoresis and saving it by gel
doc. For quantification of genomic DNA 0.8 % agrose gel was used following the
electrophoresis technique.
The gel used for electrophoresis was prepared as under:
A volume of 100 ml 1 X TBE buffer was taken in a 250 ml flask and 0.8g of agrose powder
was added. To mix the material, the flask was gently swirled. The flask was wrapped with
sliver foil for heating. Heat the material in microwave (approximately for 1.30 min) till
agrose is dissolved. After heating the gel was left for approximately to cool to 50-60 ˚C and
then 5 µl of Ethidium Bromide with concentration of (10 µg/µl) was added for gel staining.
Melted gel together Ethidium Bromide was swirled to mix well and was pour immediately
into casting tray to set at room temperature. When the gel was load samples comb and side
pieces were removed. A sample mixture of 2 µl loading dye and 6 µl genomic DNA was
prepared and loaded in each well.
The gel was powered on for 20 minutes at 40 Volts and visualized on U. V. Trans-illuminator
and immediately saved on gel doc for comparison of florescence intensity of template DNA
bands with 1Kb DNA Ladder.
3.6.5 DNA amplification
Polymerase reaction (PCR) was taken for a variety of conditions in order to have
reproducible DNA amplification. PCR optimization with respect to template concentration,
primer concentration and number of thermal cycles and particularly optimization of MgCl2
concentration to have best amplification of guava template DNA.
57
3.6.6 Primer mix
The primer used in this study was custom synthesized from Eurofine® USA. and were
diluted to 100 µl deionized water for stock solution. The working dilution of 30 ng/µl of each
primer was made and aliquots were stored in freezer at -70˚C.
3.6.7 Genomic DNA concentration.
Six different concentrations of genomic DNA (20 ng, 25 ng 30n g, 40 ng, 45 ng and 50 ng/
µl) was used For best amplification.
3.6.8 MgCl2 concentration for guava
The most important component influencing the results of PCR reaction is MgCl2 because
Mg+2 act as cofactor for the activation of Taq DNA polymerase. Five different concentrations
(1.5 mM, 2.5 mM, 3.5 mM, 4.5mM and 5.5 mM) of MgCl2 were used to find out best
amplification.
3.6.9 dNTPs
The four individual dNTPs (dATP, dGTP, dCTP and dTTP) were obtained from Fermentas®
USA and mixed in equal quantities in separate aliquot and further diluted to make working
dilution of 25 mM and stored in freezer at -70˚C.
3.6.10 PCR temperature profile
A PeqLab 96 well primus thermal cycler was used in this study. The following amplification
profile was used for SSR (PCR) annealing of 37 accessions of guava.
An initial denaturation was carried out 94˚C for 4 minutes.
i. Denaturation 94˚C for 45 seconds
ii. Primer annealing 55˚C for 45 seconds
iii. Primer extension 72 ˚C for 60 seconds
iv. Final extension 72˚C for 5 minutes
v. Hold temperature 8˚C
Repeated cycles i, ii and iii 30 cycles
58
3.6.11 SSR Primers
The 23 base oligonucleotide primers custom synthesized from eurofins® USA were used for
amplification of genomic DNA. The oligonucleotide sequences of primers are given in table
3.4.
Table 3.4 Synopsis of characteristics of 23 nuclear SSR loci isolated from Psidium guajava (Risterucci et al., 2005)
3.6.12 Simple Sequence Repeat (SSR) Analysis
The extracted DNA was subjected to SSR analysis by using 23 different SSR primers
following the procedure of Rodriguez et al. (2007).
59
3.6.13 PCR Reaction:
Reagents 1 x PCR water 5.4 µl
10x buffer 2.0 µl
25mM MgCl2 (1.5 mM) 2.0 µl
Primer F 1.0 µl
Primer R 1.0 µl
dNTPs (0.2 mM) 6.4 µl
DNA (50 ng/µl) 2.0 µl
Taq (5 U/µl) 0.2 µl
Total 20 µl Except the template DNA, the master mix was equally distributed to PCR tubes (18
µl/tube) and later 2 µl of template DNA from the respective genotypes was added
making the final volume of 20 µl.
3.6.14 Gel Electrophoresis
After PCR reactions, a mixture from SSR amplification was processed for 3 % agarose gel
and stained with ethidium bromide solution.
3.6.15 Data Analysis
For establishing data matrix, an auto radiogram was visually scored for the presence (1) or
absence (0) of polymorphic bands. Assessments of genetic relationship among the genotypes
were done by cluster analysis, using POPgen software.
60
3.7 CLONAL PROPAGATION OF GUAVA
3.7.1 Preparation of Cuttings
Five year old trees of Gola accession were selected for taking softwood cuttings measuring
12 cm in length with 4 to 6 nodes and carrying at least 4 leaves from tips of current season
growth during August and September, 2009. A slanting cut of about 2 cm length was given
on basal portion of the cuttings to extend cambial contact with growth regulators.
3.7.2 Preparation of Growth Regulator Treatments
The IBA (0, 2000, 4000, 6000 and 8000 ppm) and NAA (0, 2000, 4000, 6000 and 8000 ppm)
were used to treat the cuttings for root induction. The treatments were prepared by weighing
0, 200, 400, 600 and 800 mg each of IBA and NAA and added 100g talcum powder (w/w) in
each treatment. The growth regulators and talcum powder were put in glass jar and mixed
thoroughly to make a homogenous mixture and similarly during the treatment of cuttings the
mixture was mixed again and again. Copper oxychloride solution (1%) was used as fungicide
to prevent any fungal contamination to cuttings.
3.7.3 Planting of Cuttings
The basal portion of cuttings (50 per treatment) was first dipped in copper oxychloride
solution, then inserted in 0, 2000, 4000, 6000 and 8000 ppm IBA or NAA as quick insert
method and gently knocked within the jar to remove extra mixture before planting. The
treated cuttings were 1/3 inserted into the sand medium under intermittent misting conditions
at 6 x 6 inch distance. The system was equipped with automatic humidifiers which turned
on/off as the humidity level went <75% or >85%, respectively. The temperature and
humidity ranged from 25 to 30oC and 80 to 85%, respectively and all these conditions were
maintained up to 6 weeks.
The rooted cuttings were transplanted in-to 6x6 inch polythene bags filled with coconut husk
and silt 3:1. The transplanted plantlets were then covered with polythene sheet for
acclimatization. After one week of transplanting the polythene sheet was removed and
plantlets were allowed to grow under normal environment.
61
3.8 Data Collection
Data pertaining to sprouting of cuttings was collected.
Number of rooted cuttings
Sprouting percentage
Average number of roots per cuttings
Average root length (cm)
Survival percentage
3.9 Statistical Analysis
There were five treatments including control and there were 50 cuttings for each treatment.
The whole experiment was repeated thrice and their averages were taken for data analysis. A
total of 250 cuttings for IBA and NAA each were planted separately. The experiment was
laid out according to Completely Randomized Design (CRD) for IBA and NAA and CRD 2
factors layout for their comparison. Data collected was analyzed using Statistica software and
significance among treatment means was compared using Duncan’s Multiple Range (DMR)
test (Steel et al., 1997).
62
Figure 3.1 Phenotypic characters of 37 accessions of guava collected from districts of Faisalabad and Sheikhupura, Punjab Pakistan.
Tree parameters
Leaf shapes that found in 37 accessions of guava collected from Faisalabad and Sheikhupura districts
Spreading Erect
round obvate oblong
63
Leaf twisting
trullate obtrullate
Present Absent
64
Different shapes of leaves recorded during surveying of different orchards of districts of
Faisalabad and Sheikhupura
Present Absent
Curvature of midrib
obtuse rounded
Leaf shape of leaf tips
obtuse rounded
65
Fruit shapes of 37 accessions of guava collected from Faisalabad and Sheikhupura districts
apicullate acute
Rounded truncate
pointed necked
66
Fruit flush color of 37 accessions of guava collected from during survey of orchards of Faisalabad and Sheikhupura districts.
Thickness of outer flush in relation to core diameter
Pink color Cream color
thin medium thick
67
Figure 3.2 Steps for preparation, treating of cuttings, insertion and providing mist conditions to softwood cuttings of guava
Prepared cuttings dipping in mixture
Planting of cuttings
Mist condition
68
Chapter 4
RESULTS AND DISCUSSIONS
This chapter describes the results and discussion of the experiments that were outlined in the
previous chapter. Different section of this chapter includes original research questions that
were pretended in the introduction of the thesis in the context of analysis of morphological
and genetic variation and clonal propagation of guava. The illustrations of different
components of the results and dissection are presented here under different heads.
4.1 Morphological variation analysis of guava accessions
To determine the existing morphological variation among and within different accessions of
guava collected from different districts, different statistical and principal component were
performed mean and standard deviation were calculated. The results of phenotypic
relationships are presented under the following headings.
o Tree parameters
o Leaf parameters
o Flower parameters
o Fruit parameters
o Seed parameters
4.1.1 Descriptive statistical analysis for phenotypic parameters
Mean, range and standard deviation of variation in morphological characters are widely used
to identify variations between and within populations (Endashaw and Bekele, 1996; Sharma
et al, 1995). Statistical summary (mean, minimum, maximum and standard deviation) over
the entire accessions of the guava germplasm are presented in Tables 4.1 to 4.5. In a
descriptive statistics, summary of statistics is used to review a number of set of observations
in order to determine and communicate the larger amount of explainable variables into a
simple as possible.
The statistical summary of phenotypic variables is described as under:
69
4.1.1.1 Statistical summary of tree parameters of guava accessions
The range (Max. and Mini.) of phenotypic characters of tree parameters showed variation
(Table 4.1). Range for young shoot color (2-1.15), fully developed shoot and thickness of
stem (7-4.67) showed considerable variation among the guava accessions. The variance for
tree variables ranged from 0.12 to 0.60. These parameters had also showed a note able mean
range in different accessions of guava (Table 1). However, attitude of branches did not show
any variation.
The statistical summary of tree parameters indicated that out of three parameters, young
shoot color and fully developed shoot: thickness of stem showed variation while attitude of
branches showed no statistical difference in guava accessions.
Table 4.1 Explainable statistical summary of tree parameters in guava accessions
Character Min Max Mean SD Var.
Young shoot color 1 2 1.15 0.35 0.12
Fully developed shoot thickness of stem 3 7 4.67 0.78 0.60
4.1.1.2 Statistical summary of leaf parameters in guava accessions
The range (Max and Mini) of phenotypic characters for leaf parameters in guava accessions
showed variation (Table 4.2). Range for young leaf anthocyanin coloration, fully developed
leaf twisting, fully developed leaf curvature of midrib was found (1-9); length of leaf blade
and width of leaf blade ranged (3-7) and fully developed leaf shape as1-6. The range for
young leaf intensity of anthocyanin coloration, young leaf pubescence, fully developed leaf
curvature in cross section, fully developed leaf degree of curvature of midrib, fully developed
leaf spacing of secondary veins and fully developed leaf relief of surface of leaf was
calculated as 3-5 with the highest variation while rest of the leaf parameters showed low
diversity. However, fully developed leaf degree of undulation of margin indicated no
diversity. The variance and standard deviation was recorded the highest in young leaf
anthocyanin coloration, length of leaf blade, leaf length/ width ratio, fully developed leaf
shape, fully developed leaf degree of
70
Table 4.2 Explainable statistical summary of leaf parameters in guava accessions
Character Min Max Mean SD Var.
Young leaf anthocyanin coloration 1 9 1.22 1.30 1.68
Young leaf Intensity of anthocyanin coloration 0 5 0.14 0.81 0.66
Young leaf pubescence 1 5 4.90 0.61 0.37
Length of leaf blade 3 7 5.10 1.17 1.37
Width of leaf blade 3 7 4.68 0.96 0.92
Leaf length/ width ratio 3 3 5.01 1.38 1.92
Fully developed Leaf shape 1 6 3.90 1.52 2.30
Fully developed leaf curvature in cross section 3 5 3.33 0.74 0.55
Fully developed Leaf twisting 1 9 1.43 1.81 3.28
Fully developed leaf curvature of midrib 1 9 1.43 1.81 3.28
Fully developed leaf degree of curvature of
midrib 0 5 0.26 1.09 1.20
Fully developed Leaf variegation 1 1 1.00 0.00 0.00
Fully developed Leaf green color 2 4 2.88 0.50 0.25
Fully developed leaf color of midrib on lower
side 1 2 1.19 0.39 0.15
Fully developed leaf spacing of secondary veins 3 5 4.97 0.23 0.05
Fully developed leaf relief of surface of leaf 3 5 3.34 0.75 0.57
Fully developed leaf pubescence on lower side 1 1 1.00 0.00 0.00
Fully developed leaf undulation of margin 1 1 1.00 0.00 0.00
Fully developed leaf degree of undulation of
margin 0 0 0.00 0.00 0.00
Fully developed leaf shape of base 1 2 1.77 0.42 0.18
Fully developed leaf shape of tips 1 5 3.79 1.06 1.11
71
curvature of midrib and fully developed leaf shape of tips with values (1.68-1.81). The
lowest variation and standard deviation was recoded (0.23-0.05) in young leaf intensity of
anthocyanin coloration, young leaf pubescence, fully developed leaf curvature in cross
section, fully developed leaf green color, fully developed leaf color of midrib on lower side,
fully developed leaf spacing of secondary veins, fully developed leaf relief of surface of leaf
and fully developed leaf shape of base.
These leaf parameters had also a note able mean range in different variables of guava ranging
from (1.00-5.10) (Table 4.2).
The statistical summary for leaves parameters explained that a much variation existed in leaf
parameters for explanation, identification and characterization of guava accessions collected
from both districts of Punjab.
4.1.1.3 Statistical summary for flower parameters in guava accessions
The explainable statistical summary for flower parameters exhibited no variation and showed
showing similar minimum and maximum range (5-5) for flower size and flower number of
fully developed petals, (2-2) inflorescence: predominant number of flowers showed (1-1)
staminoide petals and number of staminoide petals (Table 4.3). Result for standard deviation
was zero for all the flower parameters. Flower parameters also showed a note able mean
range of 0, 1, 2 and 5 for different variables of guava flower (Table 4.3).
The overall results indicated that the flower traits did not show any statistical variation. This
means all accessions of guava had similar number of predominant number of flowers, flower
size, number of fully developed petals, staminoide petals and number of staminoide petals.
Table 4.3 Explainable statistical summary for flower parameters in guava accessions
Character Min Max Mean S.D. Var.
Predominant number of flowers 2 2 2.00 0.00 0.00
Flower size 5 5 5.00 0.00 0.00
Flower number of fully developed petals 5 5 5.00 0.00 0.00
Staminoide petals 1 1 1.00 0.00 0.00
Number of Staminoide petals 0 0 0.00 0.00 0.00
72
4.1.1.4 Statistical summary for fruit parameters in guava accessions
Explainable statistical summary of fruit parameters indicated a high range of statistical
variation (Max. and Min.) for phenotypic characters (Table 4.4). The range for fruit
muskiness was 9-9 where as it was 3-7 for fruit length, fruit width, fruit length/width ratio,
fruit thickness of outer flesh in relation to core diameter and juiciness, fruit sweetness,
number of seeds and seed size. A lowest range of (0-5, 1-5, 3-5 and 5-5) was found in fruit
shape at stalk end, fruit width of neck in relation to that of fruit, prominence of longitudinal
ridges and fruit acidity respectively.
A range of standard deviation and variance was found in fruit parameters as (1.25 and 1.57)
for fruit shape at stalk end and fruit width of neck in relation to that of fruit (1.89 and 3.56).
Similarly a range of variation was found in mean of fruits parameters such as fruit muskiness
(9), fruit length, fruit width, fruit length/width ratio, fruit thickness of outer flesh in relation
to core diameter, juiciness (5.15, 5.05, 5.11, 5.05, 5.15, 5.12 and 5.00, respectively, and rest
of the parameters showed the lowest morphological variation.
Fruit parameters explained that a high diversity exited for exploitation, identification and
characterization of guava accessions.
4.1.1.5 Statistical summary of seed parameters in guava accessions
A note able minimum and maximum range was recoded in seed parameters for number of
seeds (1-7), seed size (3-7) and period from flowering to fruit maturity (5.5) (Table 4.5).
The highest standard deviation and variance was measured in number of seeds with values
1.45 and 2.11 while the lowest was found in seed size with value 0.95 and 0.90 and no
deviation was found in period from flowering to fruit maturity.
The means of seed traits showed difference with the range of 4.78 and 5.12 for number of
seeds, seed size and 5.00 for period from flowering to fruit maturity, respectively.
Among all the seed parameters, number of seeds showed high statistical variation, seed size
showed low variation and period from flowering to fruit maturity showed no variation for
explainable statistical analysis, which means that only number of seeds and seed size are
explainable characters for phenotypic diversity.
73
Table 4.4 Explainable statistical summary for fruit parameters in guava accessions
Character Min Max Mean SD Var.
Fruit length 3 7 5.15 0.87 0.76
Fruit width 3 7 5.05 0.76 0.58
Fruit length/width ratio 3 7 5.11 0.82 0.68
Fruit shape at stalk end 1 5 2.70 1.25 1.57
Fruit width of neck in relation to that
of fruit 0 5 0.90 1.89 3.56
Prominence of longitudinal ridges 0 5 0.12 0.76 0.57
Fruit longitudinal grooves 1 1 1.00 0.00 0.00
Diameter of calyx cavity in relation to
that of fruit 3 5 4.25 0.97 0.94
Fruit ridged collar around calyx cavity 1 2 1.56 0.50 0.25
Fruit evenness of color of flesh 1 1 1.00 0.00 0.00
Fruit discoloration of flesh after cutting 1 1 1.00 0.00 0.00
Fruit grittiness of outer flesh 1 1 1.00 0.00 0.00
Fruit thickness of outer flesh in
relation to core diameter 3 7 5.05 0.99 0.99
Fruit puffiness 1 1 1.00 0.00 0.00
Juiciness 5 7 5.15 0.53 0.28
Fruit acidity 3 5 3.65 2.02 4.07
Fruit sweetness 3 7 4.71 0.98 0.96
Muskiness 9 9 9.00 0.00 0.00
74
Table 4.5 Explainable statistical summary of seed and flowering to fruit maturity parameters in guava accessions
Character Min Max Mean SD Var.
Number of seeds 1 7 4.78 1.45 2.11
Seed size 3 7 5.12 0.95 0.90
Period from flowering to fruit maturity
5 5 5.00 0.00 0.00
4.1.2 Principal Component Analysis (PCA)
Principal component analysis, a statistical procedure, is performed to convert an orthogonal
transformation into a set of observations of possibly correlated variables into a new set of
values of linearly uncorrelated variables. PCA is a multivariate analysis that can reduce
dimensional spacing without losing any information. This bilinear modeling method gives us
an explainable overview of the main information in a multivariate and multidimensional data
set. The information of the original variables is projected into a smaller variable of
underlying latent variables that are called principal components. The 1st principal component
accounted much of variability in the data and the 2nd PC covered as much as possible of the
rest of the variation and it was also orthogonal to the 1st principal component as well.
Principal component analysis has been widely used in studying agro-morphological fruit
characterization in germplasm collections of many exotic crops such as sorghum (Sorghum
bicolor (L.) Moench) (Mujaju et al., 2008), Helianthus annuus L. (Nooryazdan et al., 2010),
groundnut (Vigna subterranea L.) (Onwubiko et al., 2011), castor bean (Ricinus communis
L.) (Goodarzi et al., 2012) and native indigenous fruit trees such as Uapaca kirkiana (Mwase
et al., 2006); tamarind (Tamarindus indica) (Fandohan et al., 2011) and Baobab (Adansonia
digitata) (Assogbadjo et al., 2011).
4.1.3 PCs:
PCs are composite variables, as they are linear functions of the original variables, are
estimated to contain, in a decreasing order, the main structured information it the data. PCs
are also called as latent variable and a PC is also same as a score vector. PCs are estimated in
PCA (Esbensen et al., 2000). Principal component analysis was computed following the
statistica software. The principal component analysis, used for data reduction, a data matrix
75
of 57 x 1425 was prepared for analysis by taking Eigen value greater than 1 as a measure of
significance for principal component analysis (PCA). Eight components were selected from
the mean of phenotypic traits of accessions. The principal component analysis is broken
down into two districts and their combine analysis is given as under.
4.1.4 Principal Component Analysis for guava accessions of district
Faisalabad
To study the multivariant analysis for accession of guava and their relationship to the
phenotypic characters, the data matrix of 57 x 969 was prepared for analysis and the Eigen
value greater than 1 was taken as significant for principal component analysis as given in the
Table 4.6. Out of 57, eight PCs exhibited more than one Eigen value but the 1st 8 PCs
showed 91.85% variability. A gross variance of 27.65, 44.54, 60.07, 70.20, 78.57, 83.68,
88.14 and 91.85% was taken out from 1 to the 8 components, respectively and 27.65% of
total variance was enlightened by the eight components.
The 1st component analysis accounted much of the variability and is discussed here for detail
explanation.
Table 4.6 Eigen values of correlation matrix and related statistics for guava accessions of district Faisalabad
4.1.4.1 Variability position of accessions on the basis of phenotypic traits
Morphological characterization is an essential step in the characterization and classification
of crop germplasm because a breeding program mainly relies on the magnitude of
morphological variability (Koffi et al., 2008). Table 4.7 indicates factor coordinates of
accessions based on their correlations for Faisalabad district for further explanation which is
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8
Eigenvalue 8.57 5.24
4.81
3.14
2.60
1.58
1.38
1.15
Proportion 27.65 16.89 15.53 10.12 8.37 5.10 4.46 3.72
Cumulative % 27.65 44.54 60.07 70.20 78.57 83.68 88.14 91.85
76
concluded in Table 4.8. The 1st factor analysis accounted much of the variability and was
more related to accession Mota gola containing the highest positive value (7.7801) and the
lowest value (0.3998) for Surahi of Faisalabad and Khata gola (0.7790), Rough gola
(0.7570), and Bangladeshi gola (6.7883) respectively. The second PC-Factor analysis was
related to accessions Khata gola (7.6426), Karalla (2.5245), Sadabahar gola (0.9030), Surahi
(0.9802), Lal gola (0.6028) and Surahi (0.9802) for their variation ranking. Rest of the
factors were related to accession Lal gola, gola, Karalla, Sadabahar gola, Khata, Surahi,
Rough gola and Gola and Surahi for their variability.
Table 4.7 Factor coordinates of 17 accessions of guava based on correlations for Faisalabad district
Accession Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
Gola -0.2177 -1.0534 0.5893 -0.1503 -1.0114 -0.3152
Surahi 0.3998 0.0246 -2.1729 -0.2724 -1.8643 2.1592
Rough gola 0.7570 -1.6406 -0.4767 0.4719 -0.7590 2.3740
Mota gola 7.7801 -0.9479 0.5925 0.5529 0.6853 -0.5269
Bangladeshi gola 6.7883 -0.8724 0.6255 0.4569 0.3191 -0.2712
Lal gola -1.8325 -1.7580 2.4160 -2.1069 0.7340 1.3090
Surahi -0.1418 -0.3387 -6.8660 -3.3363 0.7887 -1.1023
Khata 0.7790 7.6426 1.3138 -1.5777 1.7601 0.7794
Surahi -2.4855 0.9802 -0.5249 1.3740 -0.4002 0.2254
Gola -1.7453 -1.3981 1.2416 -0.2705 -0.0059 -0.4852
Allah Abadi gola -1.1823 -1.0583 0.9617 -0.3992 -1.5695 -1.0265
Lal gola -1.5956 -1.3506 2.1413 -2.0371 -0.2716 0.8600
Karalla -0.7535 2.5245 -0.8698 3.0285 -1.8111 -0.4893
Lal gola -1.0657 0.6284 2.5198 -1.6659 -0.8347 -1.9352
Gola -1.7344 -0.5799 0.2693 1.0241 0.8398 -2.1606
Sadabahar gola -1.2123 0.9030 -1.2242 2.3904 -1.3387 0.0583
Gola -2.5377 -1.7053 -0.5364 2.5175 4.7393 0.5473
77
Among the factors PC1 was poor for accessions Surahi, Gola, Lal gola Allah Abadi gola,
Karalla and Sadabahar gola; It is clear from Table 4.8. that the accession Mota gola showed
highest the variation on the basis of phenotypic characters studied and the lowest variation
was recorded in accession Surahi followed by Bangladeshi, Khata gola and Rough gola. On
the whole, we found that guava accessions exhibited a significant phenotypic variation.
These results are in agreement with that reported by Arshad et al. (2011), but were not
consistent with that reported by Balkaya et al. (2005). The difference in these data could be
attributed to the differences in accessions which could in turn be influenced by
environmental factors such as geographical area, elevation of temperature, and soil fertility.
Table 4.8 Variability position of accessions on the basis of phenotypic traits studied
Accession Factor 1 Values Rank
Mota gola 7.7801 1
Khata gola 0.7790 2
Rough gola 0.7570 3
Bangladeshi gola 6.7883 4
Surahi 0.3998 5
4.1.4.2 The accession vector view of the biplot to show similarities among accessions
Figure 4.1 is the accession-vector view of the biplot for the data presented in Table 4.7 which
indicated Eigen factors coordinating 17 accessions of guava, based on correlations of
phenotypic characters studies.
The biplot explained 44.54% of total variation of the accession Eigen factor (Table 4.6).
The lines that connected the test accessions to the biplot origin are called accession vectors.
The cosine of the angle between the vectors of two accessions approximates the correlation
between them. A8 and A2 were positively correlated (an acute angle), A7 and A1 were
negatively correlated (an obtuse angle), and A13 and A4 are moderately correlated
containing positive and negative values. There was no grouping in positive quadrate A8 and
78
A1
A2
A3
A4A5
A6
A7
A8
A9
A10A11
A12
A13
A14
A15
A16
A17
-4 -2 0 2 4 6 8 10
Factor 1: 27.65%
-4
-2
0
2
4
6
8
10
Fac
tor
2: 1
6.8
9%
A1
A2
A3
A4A5
A6
A7
A8
A9
A10A11
A12
A13
A14
A15
A16
A17
Figure 4.1 Diagram showing projection and the relationships among 17 accession of guava
based on the first two principal component factors. A1 Gola A10 Gola A2 Surahi A11 Allah Abadi gola A3 Rough gola A12 Lal gola A4 Mota gola A13 Karalla A5 Bangladeshi gola A14 Lal gola A6 Lal gola A15 Gola A7 Surahi A16 Sadabahar gola A8 Kata A17 Larkana gola A9 Surahi
79
Table 4.9 Factor coordinates of 57 variables of guava for Faisalabad district based on correlations Serial No. Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 61 Young shoot color of stem 0.3333 -0.5430 -0.0479 -0.6788 -0.2232 0.0978 2 Fully developed shoot, thickness of stem 0.0851 0.7967 0.1326 -0.2114 0.3017 0.2817 3 Fully developed leaf Length of leaf blade -0.3129 0.0049 -0.5453 0.0528 0.5290 -0.3966 4 Fully developed leaf Width of leaf blade -0.1514 0.3410 0.6172 0.5609 -0.2123 0.0620 5 Leaf blade length/ width ratio -0.1713 0.2079 -0.7699 -0.3295 0.0142 -0.3254 6 Fully developed leaf shape -0.6930 -0.4576 -0.4749 -0.0554 -0.0193 0.1815 7 Fully developed leaf curvature in cross section -0.1146 -0.4520 0.1766 0.3337 0.6478 -0.0036 8 Fully developed leaf green color 0.4478 0.4691 0.1211 -0.0043 -0.2992 -0.4251 9 Fully developed leaf color of midrib on lower side -0.1928 0.0438 0.3212 -0.1977 0.6809 -0.1590 10 Fully developed leaf spacing of secondary veins 0.0125 0.0381 0.8063 0.4853 -0.1261 0.2258 11 Fully developed leaf relief of surface of upper side -0.1298 -0.4354 -0.1140 0.1251 0.7917 0.1213 12 Fully developed leaf shape of base 0.3686 0.3093 -0.1031 -0.1505 -0.2753 -0.4656 13 Fully developed leaf shape of leaf tips 0.5075 -0.3406 -0.2778 0.2579 0.0788 0.1446 14 Fruit length 0.7567 0.5422 0.1237 -0.0171 0.2763 -0.0237 15 Fruit width 0.9376 -0.0900 0.0189 0.1929 0.1016 -0.1813 16 Fruit length/width ratio 0.7567 0.5422 0.1237 -0.0171 0.2763 -0.0237 17 Fruit shape at stalk end -0.3157 0.5744 -0.5726 0.0648 -0.2740 0.2531 18 Fruit Width of neck in relation to that of fruit 0.0563 0.5094 -0.5600 -0.4657 0.0676 0.2322 19 Fruit color of skin -0.0089 0.2998 -0.3347 0.7111 -0.1281 -0.1441 20 Fruit relief of surface 0.4829 -0.2243 0.2561 0.0262 -0.0869 0.3284 21 Fruit Size of sepal 0.6407 -0.3490 -0.4649 -0.1626 -0.0624 0.2630 22 Fruit Diameter of calyx cavity in relation to that of fruit 0.2657 -0.5547 0.4831 -0.3846 -0.2118 0.0242 23 Fruit Ridged collar around calyx cavity 0.1685 -0.4561 0.5870 -0.3664 -0.0581 -0.4293 24 Fruit Length of stalk 0.8887 0.1283 -0.2321 -0.1259 0.1399 0.2504 25 Fruit Color of flesh -0.1814 0.3225 0.5466 -0.5961 0.1231 0.1151 26 Thickness of outer flesh in relation to core diameter 0.9535 -0.1015 -0.1697 -0.0142 0.0020 0.1006 27 Fruit Juiciness 0.7983 -0.1255 0.0836 0.0930 0.1189 -0.1182 28 Fruit acidity 0.0546 0.9115 0.0992 -0.0010 0.1632 0.0905 29 Fruit sweetness 0.3607 -0.3910 -0.5753 0.5029 -0.1598 -0.0394 30 Number of seeds -0.9537 0.0894 0.0116 -0.0598 -0.1064 0.1477 31 Seed size 0.9386 -0.0784 -0.1576 0.0488 -0.0341 0.1346
80
A2 but most of the A8, A10 A11 were grouped together but they had negative effect on
relationship. A8 and A2 were most diverse phenotypes in all morphological characters
studied. Among all accessions, A8, A10 A11, A1, A7 and A17 had negative effect on
phenotypic characters studied, and Accessions A3, A4, A5, A9, A13, A14 and A16 have
moderate effect on phenotypic diversity or these accessions had moderate relationship.
4.1.4.3 Variability position of guava accessions for Faisalabad on the basis of
phenotypic traits studied A sound knowledge of the source of variation is essential for the application of sound
phenotypic diversity and management practices. Table 4.9 indicates the factor coordinates of
guava accessions, based on their correlations of Faisalabad district for further explaination
and are concluded in Table 4.10. The 1st factor analysis accounted much of the variability
and was more related to one tree parameter, five leaves parameters, thirteen fruit parameters
and one seed parameter. The tree PC factor was young shoot color (0.3333). Leaf parameters
were related to fully developed shoot thickness of stem (0.0851), fully developed leaf green
color (0.4478), fully developed leaf spacing of secondary veins (0.0125), fully developed leaf
shape of base (0.3686) and fully developed leaf shape of leaf tips (0.5075). Fruit parameters
are corresponded to fruit length (0.7567), fruit width (0.9376), fruit length/width ratio
(0.7567), fruit width of neck in relation to that of fruit (0.0563), fruit relief of surface
(0.4829), fruit size of sepal (0.6407), fruit diameter of calyx cavity in relation to that of fruit
(0.2657), fruit ridged collar around calyx cavity (0.1685), fruit length of stalk (0.8887),
thickness of outer flesh in relation to core diameter (0.9535), fruit juiciness (0.7983), fruit
acidity (0.0546) and fruit sweetness (0.3607) and seed parameter are related to seed size
(0.9386).
Among the PC-factor 1 the poor variation was related for various variables, attitude of
branches, young shoot color of stem, young leaf anthocyanin coloration, intensity of
anthocyanin coloration, pubescence on lower side and for fully developed length of leaf
blade, width of leaf blade, leaf blade length/ width ratio, leaf shape, curvature in cross
section, leaf twisting, curvature of midrib, degree of curvature of midrib, leaf variegation,
color of midrib on lower side, relief of surface of leaf, pubescence on lower side, undulation
of margin and degree of undulation of margin. The flower predominate parameters were
81
number of flowers, number of fully developed petals, staminoide petals and number of
staminoide petals. Fruit parameters included fruit shape at stalk end, fruit color of skin,
longitudinal ridges, prominence of longitudinal ridges, longitudinal grooves, color of flesh,
evenness of color of flesh, discoloration of flesh after cutting, grittiness of outer flesh,
puffiness, degree of puffiness, muskiness. The seed parameters like number of seeds and
period from flowering to fruit maturity also showed little variation.
Among tree characteristics a great diversity was observed for young shoot color of stem. This
interfered that this parameter showed variation due to a high cross pollinization (Lu-cheis,
1987).
The results are largely in agreement with those of previous studies. Yang et al. (2001)
determined nut width, average nut weight, nut shell thickness, nut kernel percentage, per
kernel weight, total fat content of kernel, total protein content and kernel yield per m tree-
crown projection area of walnut by principal component analysis.
In general, leaf parameters like fully developed leaf green color, fully developed leaf spacing
of secondary veins, fully developed leaf shape of base, fully developed leaf shape of leaf tips
showed variation. In leaf in-between variants were related with leaf and age of the plant, leaf
position in branches, leaf density within the plant and genetic constitution of plant. These
results are in agreement with the findings of Cardenas-Urdaneta (2004) and Jimenez-
Mendoza (2004). Various scientists studied the variation in leaves and linked with genetic
constitution, agro-climatic conditions of the region, management practices like pruning of the
plants, planting density of fruit plants, and other factors, like age and productivity of fruit
plants.
The observations recorded in the present investigation suggested that the different genotypes
varied markedly with respect to fruit length, weight and fruit length/width ratio. Fruit width
of neck in relation to that of fruit, fruit relief of surface, fruit size of sepal, fruit diameter of
calyx cavity in relation to that of fruit, fruit ridged collar around calyx cavity, fruit length of
stalk, thickness of outer flesh in relation to core diameter, fruit juiciness, fruit acidity and
fruit sweetness, obviously due to their differential genetic behavior.
The similar variations in the fruit characters were also observed by Dinesh and Reddy
(2001) Singh (1988), Morton (1984), Shukla and Vashishtha (2004) and Subramanyam and
Iyer (1993).
82
Table 4.10 Variability position of accessions of guava for Faisalabad district on the basis of phenotypic traits studied Variable PC 1 Values Numbers
Tree parameters
Young shoot color of stem 0.3333 1 Leaf parameters
Fully developed shoot, thickness of stem 0.0851 1 Fully developed leaf green color 0.4478 2
Fully developed leaf spacing of secondary veins 0.0125 3
Fully developed leaf shape of base 0.3686 4
Fully developed leaf shape of leaf tips 0.5075 5
Fruit parameters
Fruit length 0.7567 1
Fruit width 0.9376 2
Fruit length/width ratio 0.7567 3
Fruit Width of neck in relation to that of fruit 0.0563 4
Fruit relief of surface 0.4829 5
Fruit Size of sepal 0.6407 6
Fruit Diameter of calyx cavity in relation to that of fruit 0.2657 7
Fruit Ridged collar around calyx cavity 0.1685 8
Fruit Length of stalk 0.8887 9
Thickness of outer flesh in relation to core diameter 0.9535 10
Fruit Juiciness 0.7983 11
Fruit acidity 0.0546 12
Fruit sweetness 0.3607 13
Seed parameters
Seed size 0.9386 1
Total (Parameters) 20
83
Figure 4.2 Diagram showing projection and the relationships among 57 variable of guava
based on the first two principal component factors for Faisalabad district.
YSCS Young shoot color of stem FRS Fruit relief of surface
FDSTS Fully developed shoot, thickness of stem FSS Fruit Size of sepal
FDLGC Fully developed leaf green color FDCCRF Fruit Diameter of calyx cavity in relation to that of fruit
FDLSSV Fully developed leaf spacing of secondary veins FRCCC Fruit Ridged collar around calyx cavity
FDLSB Fully developed leaf shape of base FLS Fruit Length of stalk
FDLST Fully developed leaf shape of leaf tips TOFRCD Thickness of outer flesh in relation to core diameter
FL Fruit length FJ Fruit Juiciness
FW Fruit width FA Fruit acidity
FRLW Fruit length/width ratio FS Fruit sweetness
FWNRF Fruit Width of neck in relation to that of fruit SS Seed size
Location=1Projection of the variables on the factor-plane ( 1 x 2)
YSCS
FDSTS
FDLLB
FDLWB
LLWR
FDLS FDLCCS
FDLGC
FDLCMLS FDLSSV
FDLRSUS
FDLSB
FDLST
FL
FW
FRLW FSSE
FWNRF
FCS
FRS
FSS
FDCCRF
FRCACC
FLS
FCF
FTOFRCD FJ
FA
FS_1
FNS
SS
-1.0 -0.5 0.0 0.5 1.0
Factor 1 : 27.65%
-1.0
-0.5
0.0
0.5
1.0
Fac
tor
2 : 1
6.89
%
YSCS
FDSTS
FDLLB
FDLWB
LLWR
FDLS FDLCCS
FDLGC
FDLCMLS FDLSSV
FDLRSUS
FDLSB
FDLST
FL
FW
FRLW FSSE
FWNRF
FCS
FRS
FSS
FDCCRF
FRCACC
FLS
FCF
FTOFRCD FJ
FA
FS_1
FNS
SS
84
4.1.4.4 The phenotypic-vector view of the biplot to show phenotypic similarities among different traits
Figure 4.2 is the phenotypic vector view of the biplot for the data in Table 4.9 while indicates
Eigen factors coordinating 57 phenotypic traits of guava based on correlations of phenotypic
characters studies. The biplot explained 44.54% of the total variation of the variables for
Eigen factors 1and 2 (Table 4.6).
The lines that connect the phenotypic traits to the biplot origin are called variable vectors.
The cosine of the angle between the vectors of two factors approximates the correlation
between them. The variables FA, FDSTS, FRLW, FWNRF, FDLSB, FLS and FDLSSV
positively correlated (an acute angle) and there were no groups in this quadrate. Variables
FDLS, FDLRSUS and FDLCCS were negatively correlated (an obtuse angle) and formed a
group of FDLRSUS and FDLCCS. The variable FNS, FSS, FDLWB, FCS, FDLLB, FRS,
FDLST, FSS, FS, FRCACC, FDCCR and YSGS were moderately correlated containing
positive and negative values and have moderate effects on phenotypic diversity or these
accession had moderate relationship.
Sen (1983) pointed out that correlation coefficients between FW and KR as well as between
FW and KR were very high in walnut. In addition, Sen (1985) stated that there is high
significant correlation between KW and FL. Akça and Sen (1992) underlined that there were
statistically significant correlations between KW and FL, FW, KR. Firouz and Bayazid
(2003) indicated that the correlation between average small diameter and average seed
weight, kernel weight and percentage, shell weight was positive and significant.
4.1.4.5 Group constellation and linkage distance based on phenotypic characters of guava accessions of district Faisalabad
The description and assessment of phenotypic variation is important in the selection of
genotypes providing high yields and qualitative traits acceptable to consumers. The
assessment of phenotypic variability among guava accessions is useful for conservation of
germplasm, broadening the genetic resources of cultivars and protection of cultivars
(Yuzbasıo et al., 2006). In the present study, the most phenotypic representative variables for
describing diversity of the 37 accessions of guava were defined with linkage analysis to
85
established groups among guava accessions. The morphological characters of 17 guava
accessions with 57 phenotypic characters of Faisalabad district were used to explain
phenotypic similarity matrices. The morphological diversity presented (Table 4.9) was
further used to produce a dendrogram as Figure 4.3 by using Statistica software, which
explained the morphological linkage distance (4.13) and grouping between all guava
accessions. A maximum similarity value of 19.6 (Table 4.11) was observed between two
main groups and eight sub groups. The sub group I in main group I had 3 sub-groups with 5
accessions as Mota gola, Bangladeshi gola, Khata, Surahi, as indicated in Table 4.12 Sub
group II and III was comprised of the accessions like Khata and Surahi, respectively. Major
group II produced 4 sub-groups that was constituted of 12 accessions. Sub group I of main
group II contained 3 accessions that were Lal gola, Lal gola and Lal gola of different
orchards of same district while Sub group II was comprised of single accession Karalla and
Sub group III and V had 4 accessions each. Karalla and Khata were most diverse in both
major groups, and formed a separate group. Gola, Mota gola, Lal gola and Bangladeshi gola
were closely associated in main group I and II. Again Lal gola and surahi of different
orchards were closely associated in both groups. Similarly gola and Larkana gola were
associated with each other but were less diverse. Hilsy et al. (2005) divided the 53 accessions
in 3 main groups and sub sub-sub groups with closely associated accessions. Samilar results
are also figure out by Fernandez-Santos et al. (2010). Figure 4.3 clearly indicates a high to
moderate phenotypic diversity among the guava accessions of Faisalabad district. This may
be because of that these accessions are natural seedling selections. Similar results were also
previously reported by Prakash et al. (2002). The Euclidean distance analysis derived from
morphological characterization that divided the guava accessions into two main groups
at a similarity distance of 0.92-9.84. The maximum value (9.84) was with accession
Khata and grouped with two other accessions Mota gola and Bangladeshi gola. The lowest
value (0.92) was of Gola and Mota gola of the same sub group. All accessions were affected
by environment, genotype, growing season, growing practice (Lindsay and Bosland, 1996;
Martinez et al., 2005). The variation level observed in the collected accessions showed there
a very high potential for developing guava varieties for table and processing purposes.
86
Table 4.11 Phenotypic distances between all possible pairs of guava accessions of district Faisalabad calculated as linkage distances A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17
A1 0.00 6.20 2.54 6.47 6.23 3.24 6.96 8.82 3.74 2.54 2.45 3.25 5.60 4.12 2.90 3.04 3.58 A2 6.20 0.00 5.97 8.88 8.76 6.93 4.24 7.18 6.14 6.60 6.55 6.96 7.46 7.38 6.73 5.83 6.97 A3 2.54 5.97 0.00 6.34 6.17 3.74 6.93 9.04 3.99 3.17 3.22 3.96 5.84 4.78 3.68 3.74 3.94 A4 6.47 8.88 6.34 0.00 1.35 8.04 9.55 9.84 8.52 7.69 7.57 7.80 8.33 7.79 7.49 7.46 7.96 A5 6.23 8.76 6.17 1.35 0.00 7.86 9.47 9.83 8.42 7.51 7.39 7.62 8.25 7.63 7.35 7.29 7.86 A6 3.24 6.93 3.74 8.04 7.86 0.00 7.82 8.80 4.63 3.49 3.67 1.16 6.86 3.38 4.22 4.82 4.57 A7 6.96 4.24 6.93 9.55 9.47 7.82 0.00 8.32 6.77 7.50 7.45 7.85 8.21 8.33 6.83 6.76 6.92 A8 8.82 7.18 9.04 9.84 9.83 8.80 8.32 0.00 8.46 8.53 8.54 8.68 8.36 7.84 8.91 8.71 9.37 A9 3.74 6.14 3.99 8.52 8.42 4.63 6.77 8.46 0.00 3.02 3.11 4.85 5.51 4.61 3.92 2.96 4.28
A10 2.54 6.60 3.17 7.69 7.51 3.49 7.50 8.53 3.02 0.00 0.92 3.68 6.01 3.35 3.39 3.77 3.88 A11 2.45 6.55 3.22 7.57 7.39 3.67 7.45 8.54 3.11 0.92 0.00 3.66 5.97 3.27 3.27 3.62 4.10 A12 3.25 6.96 3.96 7.80 7.62 1.16 7.85 8.68 4.85 3.68 3.66 0.00 6.73 3.20 4.06 4.75 4.65 A13 5.60 7.46 5.84 8.33 8.25 6.86 8.21 8.36 5.51 6.01 5.97 6.73 0.00 6.99 5.11 4.44 5.79 A14 4.12 7.38 4.78 7.79 7.63 3.38 8.33 7.84 4.61 3.35 3.27 3.20 6.99 0.00 4.40 5.30 5.47 A15 2.90 6.73 3.68 7.49 7.35 4.22 6.83 8.91 3.92 3.39 3.27 4.06 5.11 4.40 0.00 3.84 2.74 A16 3.04 5.83 3.74 7.46 7.29 4.82 6.76 8.71 2.96 3.77 3.62 4.75 4.44 5.30 3.84 0.00 4.04 A17 3.58 6.97 3.94 7.96 7.86 4.57 6.92 9.37 4.28 3.88 4.10 4.65 5.79 5.47 2.74 4.04 0.00
A1 Gola A10 Gola A2 Surahi A11 Allah Abadi gola A3 Rough gola A12 Lal gola A4 Mota gola A13 Karalla A5 Bangladeshi gola A14 Lal gola A6 Lal gola A15 Gola A7 Surahi A16 Sadabahar gola A8 Kata A17 Larkana gola A9 Surahi
87
Figure 4.3 Dendrogram showing phenotypic diversity between 17 accessions based on
morphological characterization for Faisalabad district.
Group II
Group I
Gola
Rough gola
Gola
Mota gola
Surahi
Sadabahar gola
Gola
Larkana gola
Karalla
Lal gola
Lal gola
Lal gola
Surahi
Surahi
Khatta
Mota gola
Bangladeshi gola
88
Table 4. 12 Group constellations of 17 accessions of guava belonging to Faisalabad district based on 57 phenotypic characters
Sub groups Number of accessions Accession names
Main group I
Sub group I 2 Mota gola, Bangladeshi gola
Sub group II 1 Khata
Sub group III 2 Surahi, Surahi
Main Group II
Sub group I 3 Lal gola, Lal gola, Lal gola
Sub group II 1 Karalla
Sub group III 4 Larkana gola, Gola, Sadabahar gola, Surahi
Sub group V 4 Mota gola, Gola, Rough gola, Gola
The present study also showed that the guava accessions distributed to a wide range of
geographic conditions of Faisalabad district showed a significant variation in terms of most
of the morphological characters.
Further detailed information can be obtained by using DNA markers and different molecular
techniques. Geleta et al. (2005) described that both phenotypic diversity and AFLP markers
generally separate genotypes according to phenotypic traits. The plant material investigated
in this study indicated both districts are very rich in guava germplasm and the breeders have
the advantage to utilize this diversity in future breeding programs.
4.2 Principal Component Analysis of guava accessions of district
Sheikhupura The principal component analysis is summarized and presented for guava accessions in
Table 4.13. By taking Eigen value greater than 1 as a measure of significance for principal
component analysis (PCA), nine components were selected from the mean of phenotypic
traits of accessions. Out of 20, nine PCs exhibited more than one Eigen value but the 1st 8
PCs showed about 93.10% variability.
89
A gross variance of about 27.26, 44.32, 56.93, 67.92, 76.39, 81.56, 85.92, 89.88 and 93.10%
was taken out from the 1 to 9 components, respectively and 27.65 % of the total variance was
depicted by the eight components.
The 1st component analysis accounted much of the variability and is discussed here for
detailed explanation.
4.2.1 Variability position of guava accessions of district Sheikhupura on the basis of morphological traits studied
Table 4.14 indicates factor that coordinates of guava accessions district of Sheikhupura on
the basis of their correlation and are summarized in Table 4.15 The 1st component analysis
account much of the variability. Regarding more phenotypic diverse accessions, the
accessions with positive values like Mota gola showed the highest variation (7.4557) and
Sadabahar gola with value 0.2098 showed the lowest variation. Mota gola, Mota gola (from
different orchards), Larkana gola, Desi gola, Gola and Gola (from different orchards) also
showed variation. The accessions with negative values like Gola (-0.5409), Surahi (-2.0286),
Chota gola (-3.5758), Gola (-0.6931), Surahi (-1.2117), Gola (-0.7586), Surahi (-1.6649),
Gola (-0.8408), Lal gola (-1.5287) and Choti surahi (-4.3346) showed little variation.
It is clear from Table 4.15 that the accession Mota gola showed the highest variation on the
basis of phenotypic characters studied and the lowest variation was recorded in accession
Sadabahar gola, Mota gola, Mota gola, Larkana gola, Desi gola, Gola and Gola.
Table 4.13 Eigen values of correlation matrix and related statistics for guava accessions of
district Sheikhupura
Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
Factor 6
Factor 7
Factor 8
Factor 9
Eigenvalue 8.45 5.29 3.91 3.41 2.63 1.60 1.35 1.23 1.00
Proportion 27.26 17.06 12.61 8.47 10.99 5.17 4.36 3.96 3.21
Cumulative % 27.26 44.32 56.93 67.92 76.39 81.56 85.92 89.88 93.10
90
Table 4.14 Factor coordinates of guava accessions of district of Sheikhupura based on correlations
Accession Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
Gola
-0.5409
1.1249
4.1255
1.7757
-2.0084
-0.4824
Mota gola 7.3524 -0.5027 -4.1959 0.3537 -0.0224 0.3018
Surahi -2.0286 -1.8122 -2.9385 -1.5822 -4.7296 -2.4025
Chota gola -3.5758 1.3156 -0.4219 -0.3482 1.7705 -0.8530
Gola -0.6931 2.1328 -0.0964 -0.2767 1.5275 -1.3273
Surahi -1.2117 -2.5057 1.5071 -3.7428 1.5962 0.2459
Gola -0.7586 1.0711 -0.3262 0.5918 0.5647 -1.4050
Surahi -1.6649 -3.3750 0.9436 -3.5605 0.3165 -0.3160
Gola -0.8408 0.3480 -1.2217 1.4140 1.4463 -0.7391
Mota gola 2.3809 -0.9724 3.0840 0.4227 0.1339 -0.2429
Lal gola -1.5287 6.2081 -0.4921 -2.5475 -2.3148 2.8717
Choti surahi -4.3346 -3.4488 -3.1097 2.2819 0.6166 2.4041
Larkana gola 0.1151 0.7748 -0.7406 1.3396 0.6822 -0.4194
Desi gola 0.1853 1.4310 0.0352 0.4124 0.4762 -0.4039
Gola 0.2292 1.7375 -0.0440 0.2678 0.9183 -0.2818
Mota gola 7.4557 -0.9944 1.0813 -1.0522 -0.3517 0.4490
Moti surahi -0.1028 -2.0278 0.6357 -0.9547 0.4916 1.9090
Gola 0.2098 1.0011 0.0236 0.8422 0.5311 -0.1031
Sadabahar gola -0.8578 -2.5070 2.1272 3.5207 -2.1758 0.8979
Sadabahar gola 0.2098 1.0011 0.0236 0.8422 0.5311 -0.1031
91
Table 4.15 Variability position of guava accessions of district Sheikhupura on the basis of morphological traits studied
Accession PC 1 Values Rank
Mota gola 7.4557 1
Mota gola 7.3524 2
Mota gola 2.3809 3
Larkana gola 0.1151 4
Desi gola 0.1853 5
Gola 0.2292 6
Gola 0.2098 7
Sadabahar gola 0.2098 8
4.2.2 The guava accession-vector view of the biplot of district Sheikhupura Figure 4.4 presents the guava accession-vector view of the biplot for the data of Table 4.14
which indicates Eigen factors coordinates of 20 accessions of guava of district Sheikhupura,
based on correlations of phenotypic characters studied.
This biplot explained 44.32% of total variation of the guava accession Eigen factor Table
4.14. The highest projection was for guava accessions A28 and the lowest A26, A19 and
A33. A32, A31, A30 and A33 were correlated positively (an acute angle), A29, A25, A23,
A36, A34 and A20 were negatively correlated (an obtuse angle), and rest of the guava
accessions are moderately correlated containing positive and negative values.
There was grouping in positive quadrate for accession A32, A31and A30. Accessions A23
and A36 grouped together but had negative effect on association and AccessionsA19, A33,
A24, and A19 had moderate phenotypic diversity or these accessions had moderate
relationship. A28, A19 and A33 were the most diverse guava accessions regarding all
morphological characters studied among all accessions.
4.2.3 Variability position of accessions on the basis of phenotypic traits studied
Since the study of phenotypic variability and genetic diversity for diverse morpho economic
and secondary metabolite traits in the available germplasm is a prelude to potential guava
92
crop improvement. Considerable variation was observed among the selected accessions in
terms of plant morphology and growth behavior.
Table 4.16 indicates the factor coordinates of guava accessions district of Sheikhupura, based
on their correlations and are summarized in Table 4.17. The 1st factor analysis accounted
much of the variability and was related to five leaf parameters, eleven fruit parameters and
one seed parameter. The PC factor for leaf parameters showed variation for leaf shape
(0.0712), curvature in cross section (0.2100), spacing of secondary veins (0.3743), shape of
base (0.4289) and shape of leaf tips (0.3203). Fruit parameters were varied to fruit length
(0.8756), fruit width (0.8770), fruit length/width ratio (0.8770), fruit relief of surface
(0.8710), fruit size of sepal (0.2714), fruit diameter of calyx cavity in relation to that of fruit
(0.3713), fruit ridged collar around calyx cavity (0.3919), fruit length of stalk (0.6743),
thickness of outer flesh in relation to core diameter (0.8974), fruit Juiciness (0.9068) and fruit
sweetness (0.3816). The only variant seed parameter was seed size (0.9179). Among the PC-
factor1 the poor variation was related to variables like young shoot color (-0.0797), fully
developed shoot thickness of stem (-0.0756), fully developed length of leaf blade (-0.1342),
fully developed leaf width of leaf blade (-0.0744), leaf blade length/ width ratio (-0.0964),
fully developed leaf green color (-0.3403), fully developed leaf color of midrib on lower side
(-0.1720), fully developed leaf relief of surface of upper side (-0.0454), fruit shape at stalk
end (-0.5314), fruit width of neck in relation to that of fruit (-0.2778), color of skin (-0.0402),
color of fruit flesh (-0.1238), number of seeds (-0.9349) and fruit acidity (-0.1821).
(Szkudlarz, (2003); Ngulube et al. (1997); Ali Ghars et al. (2006) and Goulart et al. (2006)
found variability in both fruit and seed characteristics, with seed traits demonstrating larger
interpopulational and internal variability. Interpopulational variability of seed and fruit traits
has also been reported in different species. In accord with our results, Bednorz (2007) also
noted higher variability of size and shape traits in seeds than in fruits which was probably
influenced by the environment of the maternal plants, which together with internal variables
affects fruit and seed morphology (Krannitz, 1997).
Leaf parameters of fully developed leaf showed variation for characters like fully developed
leaf shape, leaf curvature in cross section, leaf spacing of secondary veins, leaf shape of base
and shape of leaf tips.
93
Figure 4.4 Diagram showing projection and the relationships among 20 guava accession
based on the first two principal component factors A18 Gola A28 Lal gola
1A9 Mota gola A29 Choti surahi
A20 Surahi A30 Larkana gola
A21 Chota gola A31 Desi gola
A22 Gola A32 Gola
A23 Surahi A33 Mota gola
A24 Gola A34 Moti surahi
A25 Surahi A35 Gola
A26 Gola A36 Sadabahar gola
A27 Mota gola A37 Sadabahar gola
A18
A19
A20
A21
A22
A23
A24
A25
A26
A27
A28
A29
A30
A31A32
A33
A34
A35
A36
A37
-6 -4 -2 0 2 4 6 8 10
Factor 1: 27.26%
-4
-2
0
2
4
6
8
Fac
tor
2: 1
7.06
%
A18
A19
A20
A21
A22
A23
A24
A25
A26
A27
A28
A29
A30
A31A32
A33
A34
A35
A36
A37
94
Table 4.16 Factor coordinates of the variables of the guava accessions based on correlations for district Sheikhupura Serial No. Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 1 Young shoot color -0.0797 0.4142 -0.2929 -0.4570 0.2453 -0.2722 2 Fully developed shoot, thickness of stem -0.0756 -0.5062 0.0672 -0.5730 0.1988 -0.0035 3 Fully developed leaf Length of leaf blade -0.1342 -0.3869 -0.8267 -0.1987 -0.0566 0.0105 4 Fully developed leaf Width of leaf blade -0.0744 0.1006 -0.4475 -0.3618 -0.2034 -0.4981 5 Leaf blade length/ width ratio -0.0964 -0.2270 -0.9345 0.0509 -0.0526 -0.0079 6 Fully developed leaf shape 0.0712 0.2729 -0.6386 0.2787 0.3646 0.2556 7 Fully developed leaf curvature in cross section 0.2100 0.3343 0.0834 -0.1418 0.5607 0.1667 8 Fully developed leaf green color -0.3403 -0.2236 0.6526 -0.2628 0.0326 -0.0312 9 Fully developed leaf color of midrib on lower side -0.1720 0.4766 0.1327 -0.4132 -0.2136 -0.1984 10 Fully developed leaf spacing of secondary veins 0.3743 0.3912 0.5231 -0.0648 0.4341 -0.0002 11 Fully developed leaf relief of surface of upper side -0.0454 -0.0029 -0.4428 -0.6502 0.4133 -0.1537 12 Fully developed leaf shape of base 0.4289 0.2211 0.3891 -0.2852 -0.4514 -0.1743 13 Fully developed leaf shape of leaf tips 0.3203 -0.6337 0.2203 0.1304 -0.0980 -0.4240 14 Fruit length 0.8756 -0.0180 0.1848 -0.1195 -0.1810 -0.0770 15 Fruit width 0.8770 0.0258 0.0353 -0.1565 -0.2085 -0.0642 16 Fruit length/width ratio 0.8770 0.0258 0.0353 -0.1565 -0.2085 -0.0642 17 Fruit shape at stalk end -0.5314 -0.6994 0.0190 -0.2836 -0.1064 0.2789 18 Fruit Width of neck in relation to that of fruit -0.2778 -0.6114 0.0712 -0.5035 0.2394 0.3741 19 Fruit color of skin -0.0402 -0.6700 -0.1662 0.2418 0.2571 -0.4597 20 Fruit relief of surface 0.8710 -0.1113 -0.2693 -0.0647 -0.0395 0.1014 21 Fruit Size of sepal 0.2714 -0.4548 0.3973 -0.6281 0.1458 0.0892 22 Fruit Diameter of calyx cavity in relation to that of fruit 0.3713 0.3951 -0.0739 -0.4588 0.6159 -0.0501 23 Fruit Ridged collar around calyx cavity 0.3919 0.7449 -0.0080 0.1493 0.3114 -0.2646 24 Fruit Length of stalk 0.6743 -0.4381 -0.0730 -0.5229 -0.0951 0.0052 25 Fruit Color of flesh -0.1238 0.6355 -0.0586 -0.3248 -0.3362 0.5340 26 Thickness of outer flesh in relation to core diameter 0.8974 -0.0619 -0.1914 0.1405 0.0462 0.2055 27 Fruit Juiciness 0.9068 -0.1334 -0.1741 -0.0555 -0.0357 0.0911 28 Fruit acidity -0.1821 0.3266 -0.3291 -0.4046 -0.7211 0.0244 29 Fruit sweetness 0.3816 -0.7792 0.0737 0.3685 0.1054 0.0306 30 Number of seeds -0.9349 0.0636 0.1981 -0.0726 0.0191 -0.1078 31 Seed size 0.9179 -0.0468 -0.0079 0.2044 -0.0359 0.1724
95
From the observations, it was apparent that there is a considerable degree of divergence
between phenotypic variables of guava. There was three parameters that played the greatest
role in differentiation were leaf, fruit and seed. It was reported that leaf and fruit
characteristics can be used for differentiation of guava accessions grown under comparable
conditions. Molero et al. (2003) studied leaf characters in guava and reported that Cubana
and Blanca showed predominant lanceolate leaves whereas Montalban was with cylindrical
shape. Cas had proportions of elliptic and lanceolate shapes and that Criolla Roja resulted in
the high diversity of shapes. Similarly shape of leaf blades of guava were noticed as ovoid-
lanceolate, oblongs and oblong elliptic (Anez and Bautista, 1995). Also, for leaf shapes
Cardenas-Urdaneta and Jimenez-Mendoza (2004) described shapes as trapezoid in "Criolla
Roja" regions with the acute tips, apex enlarging and narrowing toward base that creates
trapezium/oblong shape. The trapezoid shape was absent in plant descriptor of guava made
by Sanchez-Urdaneta (1997). Diversity in mature leaf shapes and shape of base and tip
between and inside plants plant could be related to genetic factors, leaves correspondence to
one leaves organs and higher phenotypic plasticity of the leaf, which can modify leaf size and
morphology (Silva et al., 1999). The results for fully developed leaf spacing of secondary
veins (number of veins pair) showed medium variation in leaf blades of all accessions P.
guajava and P. friedrichsthalianum and P. guajava had an average of 18.7 vein pairs but
Montalban showed 20.5 pairs whereas Cas showed 9.3 vein pairs.
The observations recorded on fruit parameters like fruit length, fruit width, fruit length/width
ratio, fruit relief of surface, fruit size of sepal, fruit diameter of calyx cavity in relation to that
of fruit, fruit ridged collar around calyx cavity, fruit length of stalk, thickness of outer flesh
in relation to core diameter and fruit juiciness varied markedly in different accessions
obviously due to their differential genetic behavior.
Overall results indicated high diversity for all guava accessions, both vegetative and
reproductive characters in the district Sheikhupura. These diverse guava accessions could be
used in crop improvement program and future breeding. Clusters with superior
morphological characters have been identified. This can be exploited for their further genetic
potential.
96
Table 4.17 Variability position of guava accessions of district Sheikhupura on the basis of trait studied
Variables PC 1 Values Numbers
Leaf parameters
Fully developed leaf shape 0.0712 1
Fully developed leaf curvature in cross section 0.2100 2
Fully developed leaf spacing of secondary veins 0.3743 3
Fully developed leaf shape of base 0.4289 4
Fully developed leaf shape of leaf tips 0.3203 5
Fruit parameters
Fruit length 0.8756 1
Fruit width 0.8770 2
Fruit length/width ratio 0.8770 3
Fruit relief of surface 0.8710 4
Fruit Size of sepal 0.2714 5
Fruit Diameter of calyx cavity in relation to that of fruit 0.3713 6
Fruit Ridged collar around calyx cavity 0.3919 7
Fruit Length of stalk 0.6743 8
Thickness of outer flesh in relation to core diameter 0.8974 9
Fruit Juiciness 0.9068 10
Fruit sweetness 0.3816 11
Seed parameters
Seed size 0.9179 1
Total (Parameters) 17
97
Figure 4.5 Diagram showing projection and the relationships among 57 variables of guava accessions of district Sheikhupura based on the first two principal component factors.
YSCS Young shoot color of stem FRS Fruit relief of surface
FDSTS Fully developed shoot, thickness of stem FSS Fruit Size of sepal
FDLGC Fully developed leaf green color FDCCRF Fruit Diameter of calyx cavity in relation to that of fruit
FDLSSV Fully developed leaf spacing of secondary veins FRCCC Fruit Ridged collar around calyx cavity
FDLSB Fully developed leaf shape of base FLS Fruit Length of stalk
FDLST Fully developed leaf shape of leaf tips TOFRCD Thickness of outer flesh in relation to core diameter
FL Fruit length FJ Fruit Juiciness
FW Fruit width FA Fruit acidity
FRLW Fruit length/width ratio FS Fruit sweetness
FWNRF Fruit Width of neck in relation to that of fruit SS Seed size
Location=2Projection of the variables on the factor-plane ( 1 x 2)
YSCS
FDSTS
FDLLB
FDLWB
LLWR
FDLS FDLCCS
FDLGC
FDLCMLS
FDLSSV
FDLRSUS
FDLSB
FDLST
FL FW FRLW
FSSE
FWNRF FCS
FRS
FSS
FDCCRF
FRCACC
FLS
FCF
FTOFRCD
FJ
FA
FS_1
FNS
SS
-1.0 -0.5 0.0 0.5 1.0
Factor 1 : 27.26%
-1.0
-0.5
0.0
0.5
1.0
Fac
tor
2 : 1
7.06
%
YSCS
FDSTS
FDLLB
FDLWB
LLWR
FDLS FDLCCS
FDLGC
FDLCMLS
FDLSSV
FDLRSUS
FDLSB
FDLST
FL FW FRLW
FSSE
FWNRF FCS
FRS
FSS
FDCCRF
FRCACC
FLS
FCF
FTOFRCD
FJ
FA
FS_1
FNS
SS
98
4.2.4 The phenotypic-vector view of the biplot to show phenotypic similarities among different phenotypic traits of guava accessions of district Sheikhupura
Figure 4.5 is the phenotypic vector view of the biplot for the data in Table 4.16 which
indicates Eigen factors coordinating 57 phenotypic traits of guava accessions of district
Sheikhupura based on correlations of phenotypic characters. The biplot explained 44.32%
total variation of the variables for Eigen factors 1 and 2 as in (Table 4.16).
The variables with positive values like FRCACC, FDLCCS, FDCCRF, FDLSSV, FDLSB,
FL and FW correlated positively (an acute angle) with a single group (FDCCR and
FDLSSV). The variables FSSE, FWNRF, FCS, FDSTS, FDLLB, LLWR and FDLGC were
correlated negatively (an obtuse angle), and the variables FLS, FSS, FDLST, FS, FDLR,
FDLWB, FA, YSCS, FDLCMLS and FCF had moderate effect on phenotypic diversity of
these guava accessions.
4.2.5 Group constellation and linkage distance of guava accession of district
Sheikhupura based on phenotypic characters
The phenotypic characters of 20 different accessions of guava belonging to district
Sheikhupura were used to explain morphological similarity. The morphological factors from
Table 4.16 were used to produce a graphical presentation in the form of dendrogram (Figure
4.6). A maximum similarity value of 22.6 was noted between the two main groups and 7
sub-groups. The major group I with linkage distance value of 16.3 (Table 4.18.) was
consisted of 3 sub- groups and six sub-sub groups containing 16 accessions that were Gola,
Desi gola, Larkana gola, Gola, Mota gola, Larkana gola, Lal gola, Chota gola, Surahi,
Sadabahar gola, Gola, Lal gola, Chota gola, Surahi and Sadabahar gola (Table 4.19). Sub
group I had accessions as Gola, Desi gola, Larkana gola, Gola, Gola, Mota gola, which was
divided into 2 sub-sub groups. Sub group II was consisted of two accessions Mota gola and
Mota gola with no sub-sub group. Sub group III was comprised of the accessions like Lal
gola, Chota gola, Surahi, Sadabahar gola, Mota gola, Gola and was divided into 4 sub-sub
groups. Sub-Sub group I was consisted of 2 accessions as Lal gola, Chota gola, Sub-Sub
group II had accession (Surahi), Sub-Sub group III had 1 Sadabahar gola and Sub-Sub group
V had 2 accessions (Mota gola and Gola). Main group II contained 3 sub-groups. Sub group
99
I has 1 accession, Choti surahi, Sub group II had 1 accession that was Surahi, Sub group III
had 2 accessions (Surahi and Surahi).
Desi gola and Gola were most divergent and closely associated in both groups whereas
Surahi of different orchards was different from Desi gola and Gola accessions. Another
group, gola and Mota gola and surahi of different orchards and Chota gola, gola, Lal gola and
Mota gola were closely associated but less diverse with other accessions.
Gola and Desi gola were the most diverse groups in both main groups as indicated by the low
value of the linkage distances 0.29 and 0.36, respectively. The two accessions ‘Sadabahar
gola and gola were found to be quite diverse. The linkage distance values ranged from 0.29-
11.3 and clearly express a high phenotypic diversity among the guava phenotypes. This may
be due to natural seedling selections, soil and climatic factors.
The data review indicates that guava accessions represent a wide range of diversity for
morphological traits. The very extensive variation was found in Cameroon and Nigeria
(Waruhiu et al., 2004; Anegbeh et al., 2005) and other indigenous fruit trees such as Irvingia
gabonenis (Aubry-Lecompte ex O’Rorke) Baill. ex Lanen (Atangana et al., 2001, 2002),
Sclerocarya birrea (A. Rich), Ziziphus mauritiana Lam. (Koné et al., 2009), Allanblackia
floribunda Oliver (Atangana, 2010) and Adansonia digitata L. (Kouyaté et al., 2011).
The investigation is also very useful in choosing the precious accessions for further breeding
programs. Results of accession classification revealed that accessions within each cluster
belonged to different group which suggested that there was a clear relationship between
accessions and phenotypic diversity. Therefore, more emphasis has to be directed to
accessions as a source of diversity in this germplasm. Such results have been reported in
different crops by several studies on white clover (Jahufer et al., 1997) and durum wheat
(Annicchiarico et al., 2000). These results could be attributed to free exchange of materials
that may have overlapped the previous diversity distribution pattern of the domesticated
pecies (Jaradat and Shahid, 2006). However, phenotypic evaluation is influenced by
environment and might not distinguish between closely related accessions. Therefore, further
molecular investigations are needed to verify germplasm.
100
Table 4.18 Phenotypic distances between all possible pairs of guava accessions of district Sheikhupura calculated as linkage distances
A18 A19 A20 A21 A22 A23 A24 A25 A26 A27 A28 A29 A30 A31 A32 A33 A34 A35 A36 A37
A18 0.00 9.9 6.77 6.3 4.93 6.9 5.42 7.5 5.58 3.33 6.82 9.2 5.48 4.87 4.92 6.8 7.82 4.97 3.82 4.97
A19 9.86 0.0 8.46 10.2 7.40 10.3 7.13 10.3 6.90 8.52 8.77 9.9 6.32 6.71 6.66 5.7 9.29 6.66 8.68 6.66
A20 6.77 8.5 0.00 6.1 5.59 6.7 5.17 6.2 4.98 7.25 6.98 7.6 5.42 5.79 5.85 8.2 7.44 5.84 4.73 5.84
A21 6.31 10.2 6.11 0.0 5.25 7.5 5.21 7.8 5.16 7.30 5.72 7.5 5.46 5.48 5.56 9.6 8.22 5.55 5.34 5.55
A22 4.93 7.4 5.59 5.3 0.00 6.7 0.92 7.1 1.47 5.01 4.63 6.9 1.93 1.57 1.56 7.3 6.23 1.57 4.19 1.57
A23 6.94 10.3 6.66 7.5 6.70 0.0 6.79 1.8 6.83 6.93 8.16 6.7 7.01 6.87 6.90 8.9 3.16 6.94 5.91 6.94
A24 5.42 7.1 5.17 5.2 0.92 6.8 0.00 7.1 0.90 5.51 4.86 6.7 1.76 1.80 1.84 7.4 6.33 1.78 4.32 1.78
A25 7.55 10.3 6.20 7.8 7.14 1.8 7.06 0.0 7.04 7.67 8.70 6.7 7.43 7.39 7.41 9.2 4.08 7.43 6.37 7.43
A26 5.58 6.9 4.98 5.2 1.47 6.8 0.90 7.0 0.00 5.66 4.95 6.7 1.69 1.89 1.93 7.3 6.41 1.87 4.34 1.87
A27 3.33 8.5 7.25 7.3 5.01 6.9 5.51 7.7 5.66 0.00 7.18 9.6 5.36 4.79 4.81 5.2 7.41 4.91 4.78 4.91
A28 6.82 8.8 6.98 5.7 4.63 8.2 4.86 8.7 4.95 7.18 0.00 8.5 4.92 4.57 4.58 8.7 7.73 4.62 6.30 4.62
A29 9.20 9.9 7.64 7.5 6.90 6.7 6.73 6.7 6.69 9.59 8.52 0.0 6.61 7.03 7.03 11.3 5.82 6.95 7.20 6.95
A30 5.48 6.3 5.42 5.5 1.93 7.0 1.76 7.4 1.69 5.36 4.92 6.6 0.00 1.46 1.51 6.7 6.37 1.46 4.31 1.46
A31 4.87 6.7 5.79 5.5 1.57 6.9 1.80 7.4 1.89 4.79 4.57 7.0 1.46 0.00 0.32 6.5 6.20 0.29 4.19 0.29
A32 4.92 6.7 5.85 5.6 1.56 6.9 1.84 7.4 1.93 4.81 4.58 7.0 1.51 0.32 0.00 6.5 6.17 0.36 4.25 0.36
A33 6.82 5.7 8.21 9.6 7.30 8.9 7.43 9.2 7.28 5.19 8.74 11.3 6.74 6.50 6.48 0.0 8.99 6.58 7.23 6.58
A34 7.82 9.3 7.44 8.2 6.23 3.2 6.33 4.1 6.41 7.41 7.73 5.8 6.37 6.20 6.17 9.0 0.00 6.20 6.59 6.20
A35 4.97 6.7 5.84 5.6 1.57 6.9 1.78 7.4 1.87 4.91 4.62 6.9 1.46 0.29 0.36 6.6 6.20 0.00 4.23 0.00
A36 3.82 8.7 4.73 5.3 4.19 5.9 4.32 6.4 4.34 4.78 6.30 7.2 4.31 4.19 4.25 7.2 6.59 4.23 0.00 4.23
A37 4.97 6.7 5.84 5.6 1.57 6.9 1.78 7.4 1.87 4.91 4.62 6.9 1.46 0.29 0.36 6.6 6.20 0.00 4.23 0.00
A18 Gola A28 Lal gola 1A9 Mota gola A29 Choti surahi A20 Surahi A30 Larkana gola A21 Chota gola A31 Desi gola A22 Gola A32 Gola A23 Surahi A33 Mota gola A24 Gola A34 Moti surahi A25 Surahi A35 Gola A26 Gola A36 Sadabahar gola A27 Mota gola A37 Sadabahar gola
101
Figure 4.6 Dendrogram showing phenotypic diversity between 20 guava accessions of districts of Sheikhupura based on morphological characterization.
Gola
Mota gola
Sadabahar gola
Surahi
Chota gola Lal gola
Mota gola Mota gola
Mota gola
Gola
Gola
Larkana gola
Desi gola
Gola
Gola
Sadabahar gola
Surahi
Surahi Moti surahi
Choti surahi
Group I
Group II
102
Table 4. 19 Group constellation of 20 guava accessions of Sheikhupura district based on 57 phenotypic characters studied
Sub groups Number of
accessions Accession names
Main group I
Sub group I 6 Gola, Desi gola, Larkana gola, Gola, Gola, Mota gola Sub-Sub group I 2 Desi gola, Gola Sub-Sub group II 4 Larkana gola, Gola, Gola, Mota gola Sub group II 2 Mota gola, Mota gola Sub group III 6 Lal gola, Chota gola, Surahi, Sadabahar gola, Mota gola, Gola Sub-Sub group I 2 Lal gola, Chota gola Sub-Sub group II 1 Surahi Sub-Sub group III 1 Sadabahar gola Sub-Sub group V 2 Mota gola, Gola
Main Group II
Sub group I 1 Choti surahi Sub group II 1 Surahi Sub group III 2 Surahi and Surahi Unique accessions 2 Sadabahar gola, Gola
4.3 Principal component analysis for guava accession of district Faisalabad and Sheikhupura
The principal component analysis is summarized and presented for guava accession in Table
4.20 by taking Eigen value greater than 1 as a measure of significance for principal
component factor analysis; eight components were selected from the phenotypic traits of
accessions. Out of 57 factors, eight PCs factors exhibited more than one Eigen value but the
1st 8 PCs showed about 82.14 % variability.
A gross variance of about 25.14, 39.40, 51.57, 60.02, 67.43, 73.91, 78.21 and 82.14 % was
taken out from the 1 to 8 components, respectively, and 25.14 % of total variance was
observed eight components.
The 1st component analysis accounted much of the variability and is discussed here for
detailed explanation.
103
Table 4.20 Eigen values calculated by statistica software for 57 phenotypic variables of guava accession of district Faisalabad and Sheikhupura
Table 4.21 PC. Factors coordinating for 37 of guava accessions of district Faisalabad and Sheikhupura
Accession Factor 1 Factor 2 Factor 3 Factor 4
Gola (FSD) -0.0170 1.1673 -0.4815 -0.5726
Surahi (FSD) 0.1992 -2.1651 -0.5694 -1.3932
Rough gola (FSD) 0.9869 -0.1844 -1.4256 -0.1889
Mota gola (FSD) 7.5069 -0.0573 0.0146 0.1258
Bangladeshi gola (FSD) 6.5894 0.1507 0.0826 -0.3608
Lal gola (FSD) -1.4990 3.6659 0.4573 1.8579
Surahi (FSD) -0.4253 -4.0431 -2.2062 1.8444
Khata (FSD) -0.3019 -2.4057 8.6285 2.5174
Surahi (FSD) -2.4092 -0.8295 0.2459 0.6356
Gola (FSD) -1.6269 2.2908 -0.6952 0.1315
Allah Abadi gola (FSD) -1.0276 1.8123 -0.3132 -1.0832
Lal gola (FSD) -1.1779 3.1923 0.7549 1.2795
Karalla (FSD) -0.8949 -3.0191 1.5397 -0.6594
Lal gola (FSD) -1.4134 2.4888 2.7401 -0.4288
Gola (FSD) -1.3909 0.7260 -0.4824 0.8407
Sadabahar gola (FSD) -1.1147 -1.7777 -0.1684 -1.2160
Larkana gola (FSD) -2.0024 0.8406 -2.8186 3.8441
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8
Eigenvalue 7.79 4.42 3.77 2.62 2.29 2.01 1.33 1.22
Proportion 25.14 14.26 12.16 8.46 7.40 6.49 4.30 3.92
Cumulative % 25.14 39.40 51.57 60.02 67.43 73.91 78.21 82.14
104
Accession Factor 1 Factor 2 Factor 3 Factor 4
4.3.1 Variability position of guava accessions of districts Faisalabad and Sheikhupura
on the basis of their traits
Know ledge of the extent of genetic diversity and the identification, differentiation
and characterization of genotypes and populations, provided information for the
detection of duplicates in collections and also better characterization and utilization
in breeding (Hornokova et al., 2 003) Table 4.21 indicates the coordinates of accessions,
based on their correlations for Faisalabad and Sheikhupura districts for further explanation
and are summarized in Table 4.22. The 1st factor accounted much of the variability and in
guava accessions. The accessions with positive values like Mota gola of Sheikhupura (SKP)
(7.8509) showed the highest variation and Gola (SKP) with value 0.0093 showed lowest
variation. Guava accessions Mota gola (FSD) (7.5069), Bangladeshi gola (FSD) (6.5894),
Mota gola (SKP) (6.3715) and Mota gola (SKP) (2.8991) also showed much variation. The
Mota gola (SKP) 6.3715 -0.3975 -1.9018 4.1381
Surahi (SKP) -1.5464 -3.4182 0.8529 2.1234
Chota gola (SKP) -3.7284 1.0652 -0.6639 -1.5621
Gola (SKP) -0.9955 1.8710 -0.7828 -0.1449
Surahi (SKP) -0.4630 -2.2822 1.0117 -1.2579
Gola (SKP) -1.1117 1.1360 -0.5846 -0.1110
Surahi (SKP) -0.8482 -3.1100 1.2170 -0.6140
Gola (SKP) -1.1063 0.7544 -1.2741 -0.0861
Mota gola (SKP) 2.8991 0.2238 -0.0239 -3.0674
Lal gola (SKP) -1.8018 3.3593 2.8263 1.5199
Choti surahi (SKP) -4.5644 -3.9096 -3.6155 1.0228
Larkana gola (SKP) -0.1770 0.7200 -1.0143 -0.1016
Desi gola (SKP) -0.0106 1.4831 -0.4076 -0.4179
Gola (SKP) 0.0093 1.5568 -0.7154 -0.3543
Mota gola (SKP) 7.8509 -0.0589 0.0658 -0.1494
Moti surahi (SKP) 0.1992 -2.1651 -0.5694 -1.3932
Gola (SKP) -0.0170 1.1673 -0.4815 -0.5726
Sadabahar gola (SKP) -0.9563 -2.1598 0.3718 -2.2152
Sadabahar gola (SKP) -0.0170 1.1673 -0.4815 -0.5726
105
guava accessions with negative values were Gola (FSD), Lal gola (FSD), Surahi (FSD),
Khata (FSD), Surahi (FSD), Gola (FSD), Allah Abadi gola (FSD),Lal gola (FSD), Karalla
(FSD), Lal gola (FSD), Gola (FSD), Sadabahar gola (FSD), Larkana gola (FSD), Gola
(SKP), Lal gola (SKP), Choti surahi (SKP), Larkana gola (SKP), Desi gola (SKP), Sadabahar
gola (SKP), Sadabahar gola (SKP), Surahi (SKP), Chota gola (SKP), Gola (SKP), Surahi
(SKP), Gola (SKP), Surahi (SKP) and Gola (SKP) which showed no variation or had no
contribution in variation.
It is clear from Table 4.22 that the accession Mota gola (SKP) showed the highest variation
on the basis of phenotypic characters studied and the lowest variation was recorded in
accession Gola (SKP) and Mota gola (FSD), Bangladeshi gola (FSD), Mota gola (SKP),
Mota gola (SKP), Rough gola (FSD), Surahi (FSD), Moti surahi (SKP), Gola (SKP) and
Gola (SKP).
No documented information is available on varietal characteristics as well as parentage of
Pakistani guava seedling selections/accessions. Moreover, its propagation through seeds
(commercial method of propagation) further eliminates the distinct characteristics of varieties
established in orchards. The guava trees cannot come true from seeds due to mechanisms of
pollination, both self-pollination and cross pollination occur in guava. The amount of cross-
pollination ranges from 25.7 to 41.3%. However, in our study the Gola accessions fell in
Group I which indicates that it is probably due to frequent cross pollination between gola
accessions as it is dominated accession in almost every orchard. Therefore, genetic analysis
grouped them in one cluster. However, one Surahi accession grouped in gola group (group I)
which might be overlapped togather possible crossing between Gola and Surahi.
The present work has also identified the relationships among major guava accessions. In the
current study, results supported this contention considerable variability was found among
collected populations. Even within a certain region, variation of plant and fruit type was
observed to understand the potential of these genetic resources for future breeding efforts,
it is important to evaluate the most useful morphological characters. Knowledge of
variation found in cultivated species and its pattern of distribution is important for the
development of breeding programs (Gil and Ron, 1992; Balkaya and Ergün, 2007). In
conclusion, it can be pointed out that the current study revealed considerable variation in
106
accessions of guava and this phenotypic variation has considerable implications for future
collection, storage and breeding.
Table 4.22 Variability position of guava accessions of district Faisalabad and Sheikhupura on the basis of their traits.
Accession PC 1 Score Rank
Mota gola (SKP) 7.8509 1
Mota gola (FSD) 7.5069 2
Bangladeshi gola (FSD) 6.5894 3
Mota gola (SKP) 6.3715 4
Mota gola (SKP) 2.8991 5
Rough gola (FSD) 0.9869 6
Surahi (FSD) 0.1992 7
Moti surahi (SKP) 0.1992 8
Gola (SKP) 0.0323 9
Gola (SKP) 0.0093 10
FSD= Faisalabad and SKP= Sheikhupura
4.3.2 The accession vector view of the biplot to show similarities among 37 guava
accessions of districts Faisalabad and Sheikhupura Figure 4.7 shows the accession-vector view of the biplot for the data in Table 4.21 which
indicated Eigen factors coordinates of 37 guava accessions of district of Faisalabad and
Sheikhupura, based on correlations of phenotypic characters.
The biplot showed 39.14% of the total variation in the guava accessions for Eigen factor
(Table 4.22) the highest projection was for guava accession A29 (SKP) and the lowest A3
(FSD), A7 (FSD), A33 (FSD) and A6 (FSS).
A5 (FSD) and A27 (SKP) were correlated positively (an acute angle) while A29, A7, A20,
(A13 and A25), (A8, A23, A36 and A16) were grouped together and correlated negatively
107
(an obtuse angle), while rest of the accessions moderately correlated having positive and
negative values.
Figure 4.7 Diagram showing projection and the relationships among 37 guava accessions of
guava based on the first two principal component factors A1 Gola A10 Gola A18 Gola A28 Lal gola
A2 Surahi A11 Allah Abadi gola 1A9 Mota gola A29 Choti surahi
A3 Rough gola A12 Lal gola A20 Surahi A30 Larkana gola
A4 Mota gola A13 Karalla A21 Chota gola A31 Desi gola
A5 Bangladeshi gola A14 Lal gola A22 Gola A32 Gola
A6 Lal gola A15 Gola A23 Surahi A33 Mota gola
A7 Surahi A16 Sadabahar gola A24 Gola A34 Moti surahi
A1
A2
A3A4
A5
A6
A7
A8
A9
A10
A11
A12
A13
A14
A15
A16
A17
A18
A19
A20
A21
A22
A23
A24
A25
A26
A27
A28
A29
A30
A31A32
A33
A34
A35
A36
A37
-6 -4 -2 0 2 4 6 8 10
Factor 1: 25.14%
-5
-4
-3
-2
-1
0
1
2
3
4
5
Fac
tor
2: 1
4.26
%
A1
A2
A3A4
A5
A6
A7
A8
A9
A10
A11
A12
A13
A14
A15
A16
A17
A18
A19
A20
A21
A22
A23
A24
A25
A26
A27
A28
A29
A30
A31A32
A33
A34
A35
A36
A37
108
A8 Kata A17 Larkana gola A25 Surahi A35 Gola
A9 Surahi A26 Gola A36 Sadabahar gola
A27 Mota gola A37 Sadabahar gola
There was grouping in negative quadrate (A13 and A25) and (A8, A23, A36 and A16), and
accessions A4 and A33; A6, A28 and A12; A14, A1; A11 and A22; A33 and A31; A17,
A24, A16, A26 and A30 were grouped as moderate quadrate and had moderate effect on
phenotypic diversity or these accessions had moderate relationship. Guava accession A29,
A7 and A33 were the most diverse on the basis of all morphological characters among all
accessions.
4.3.3 Variability position of guava accessions in districts Faisalabad and Sheikhupura
on the basis of phenotypic their different traits Natural phenotypic variation study within a species is the first step in any tree improvement
program. It permits grouping of phenotypic variation into between-tree, among-site and
sometimes within-tree sources. Table 4.23 indicates the factor coordinates of guava
accessions, based on their correlations for Faisalabad and Sheikhupura districts and is
summarized in Table 4.24. The 1st factor analysis accounted much of the variability and was
related to one tree parameter [ Young shoot color (0.1529)], four leaf parameters, [fully
developed leaf shape of leaf tips (0.4626), fully developed leaf shape of base (0.3825), fully
developed leaf green color (0.109), fully developed leaf curvature in cross section (0.0105)]
eleven fruit parameters [thickness of outer flesh in relation to core diameter (0.9032), fruit
length (0.8088), fruit width (0.8793), fruit
juiciness (0.8557), fruit length of stalk (0.7922), fruit length/width ratio (0.7902), fruit relief
of surface (0.6135), fruit size of sepal (0.5153), fruit sweetness (0.3901), fruit diameter of
calyx cavity in relation to that of fruit (0.3247), fruit ridged collar around calyx cavity
0.2671)] and one seed parameter [seed size (0.9185)] (Table 4.24).
In PC-factor 1 the poor variation was related to guava variables like fully developed shoot,
thickness of stem (-0.0010), fully developed leaf length of leaf blade (-0.2285), fully
developed leaf width of leaf blade (-0.1134), leaf blade length/ width ratio (-0.1831), fully
developed leaf shape ( -0.2720), fully developed leaf color of midrib on lower side (-0.1625),
fully developed leaf relief of surface of upper side (-0.0560), number of seeds (-0.9285),
109
Table 4. 23 Factor coordinates of the variables based on correlations of guava accession in district Faisalabad and Sheikhupura Serial No. Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 1 Young shoot color 0.1529 0.2716 -0.2661 -0.0561 0.6995 0.3022 2 Fully developed shoot, thickness of stem -0.0010 -0.3510 0.6270 0.1810 0.1556 -0.2178 3 Fully developed leaf Length of leaf blade -0.2285 -0.4481 -0.2687 0.6678 0.0035 0.0706 4 Fully developed leaf Width of leaf blade -0.1134 0.0452 0.2842 0.2249 -0.3559 0.1745 5 Leaf blade length/ width ratio -0.1831 -0.4463 -0.2639 0.5830 0.0736 0.5216 6 Fully developed leaf shape -0.2720 0.1464 -0.5281 0.4293 0.0161 0.1675 7 Fully developed leaf curvature in cross section 0.0105 0.3674 -0.3408 0.2455 -0.1742 -0.6784 8 Fully developed leaf green color 0.1090 -0.2146 0.4709 -0.5061 0.0797 0.0418 9 Fully developed leaf color of midrib on lower side -0.1625 0.3590 0.3039 0.4099 0.0802 -0.2673 10 Fully developed leaf spacing of secondary veins 0.2350 0.5428 0.2569 -0.3094 -0.3020 -0.3178 11 Fully developed leaf relief of surface of upper side -0.0560 0.1334 -0.4007 0.4734 0.0668 -0.6542 12 Fully developed leaf shape of base 0.3825 -0.0657 0.3898 -0.2341 -0.0196 0.2131 13 Fully developed leaf shape of leaf tips 0.4626 -0.3310 -0.3367 -0.1705 -0.0657 -0.1406 14 Fruit length 0.8088 -0.0413 0.3836 0.1402 -0.1334 -0.1407 15 Fruit width 0.8793 0.0366 0.0901 0.1659 -0.1678 -0.0608 16 Fruit length/width ratio 0.7902 -0.0501 0.4057 0.2639 -0.1527 -0.0875 17 Fruit shape at stalk end -0.4259 -0.7936 0.1407 -0.1090 0.1655 -0.1125 18 Fruit Width of neck in relation to that of fruit -0.1218 -0.6498 0.1917 0.0172 0.5741 -0.2398 19 Fruit color of skin -0.0234 -0.5408 -0.1411 -0.1258 -0.5989 0.0304 20 Fruit relief of surface 0.6135 0.1042 -0.0141 0.2484 -0.0443 0.1036 21 Fruit Size of sepal 0.5153 -0.3293 -0.1432 -0.2289 0.5349 -0.3220 22 Fruit Diameter of calyx cavity in relation to that of fruit 0.3247 0.5807 -0.1100 -0.0775 0.4366 0.0262 23 Fruit Ridged collar around calyx cavity 0.2671 0.8046 -0.0896 -0.0553 0.0921 0.2970 24 Fruit Length of stalk 0.7922 -0.3880 0.1100 0.1581 0.2997 -0.1508 25 Fruit Color of flesh -0.1778 0.3926 0.6359 0.3339 0.2797 0.0407 26 Thickness of outer flesh in relation to core diameter 0.9032 -0.0872 -0.1476 0.0859 0.0420 0.1339 27 Fruit Juiciness 0.8557 -0.0191 -0.0895 0.1822 -0.0362 0.0918 28 Fruit acidity -0.0899 -0.2464 0.7629 0.3534 -0.1427 0.1151 29 Fruit sweetness 0.3901 -0.4808 -0.6289 -0.2978 -0.1994 -0.0578 30 Number of seeds -0.9285 0.0300 0.0939 -0.1371 0.0456 -0.1395 31 Seed size 0.9185 -0.0721 -0.1146 0.0095 -0.0101 0.1268
110
Table 4.24 Variability position of guava accessions in district of Faisalabad and Sheikhupura on the basis of their different traits.
Variable PC 1 Score Numbers
Tree parameters Young shoot color 0.1529 1
Leaves parameters
Fully developed leaf shape of leaf tips 0.4626 1 Fully developed leaf shape of base 0.3825 2 Fully developed leaf green color 0.109 3 Fully developed leaf curvature in cross section 0.0105 4
Fruit parameters
Thickness of outer flesh in relation to core diameter 0.9032 1 Fruit length 0.8088 2 Fruit width 0.8793 3 Fruit Juiciness 0.8557 4 Fruit Length of stalk 0.7922 5 Fruit length/width ratio 0.7902 6 Fruit relief of surface 0.6135 7 Fruit Size of sepal 0.5153 8 Fruit sweetness 0.3901 9 Fruit Diameter of calyx cavity in relation to that of fruit 0.3247 10 Fruit Ridged collar around calyx cavity 0.2671 11
Seed parameters
Seed size 0.9185 1
Total (Parameters) 17
fruit color of flesh (-0.1778), fruit acidity (-0.0899), fruit shape at stalk end (-0.4259), fruit
width of neck in relation to that of fruit (-0.1218) and fruit color of skin (-0.0234). As the
variability of the tree, leaf, flower, fruit and seed characters assayed appears to be low, based
on the diversity estimates obtained. These results indicated that green color of young shoot
predominated in both districts (Faisalabad and Sheikhupura). Generally, young shoot color of
stem is related with age of the plants and to that it could be partially related to soil (structure,
texture, moisture, fertility and others), the environment, pruning of the tree and genetic
makeup of the plants (Cardenas-Urdaneta and Jimenez-Mendoza, 2004). The leaf blade
characters showed variation in fully developed leaves.
111
Apex shape and leaf blades base was of obtuse and rounded shape. Cardenas-Urdaneta and
Jimenez-Mendoza (2004) observed 80% acute shape of leaf blade apex and base as major
character of leaf. Similarly, Anez and Bautista (1995) agreed with both shapes as 70% of
acute and 30% obtuse. Difference in between two studies linked to leaves variability may be
due to genetic constitution and likewise to agro-practices and climatic conditions of the
observed area.
In the present study, significant differences were found among sites for any phenotypic
characteristics that were analyzed. This phenotypic variation contrasts with the results of
Atangana et al. (2001) in Irvingia gabonensis and could likely be explained by the relative
homogeneity of ecological conditions of the sites. Information on the reproductive biology of
the accessions could also provide insights into phenotypic variation in the measured
characters. The broad variability found between and within accession in this study is
typically found in populations of an outbreeding species, and indicates that extensive
phenotypic diversity exists in fruits and seeds of guava accessions. The findings of this
research are also in agreement with the results obtained by Cardenas-Urdaneta and Jimenez-
Mendoza (2004) in Mara region for "Criolla Roja" and the results of Trujillo and South of
Lake. They concluded that apparently, the shape of base of leaf was related to the insertion
angle of the leaf, since leaf blades that have minor angles indicate an enlarged of leaf blades
base and having a tendency to be round in base shape.
The recorded observations in this investigation suggests that different genotypes varied
significantly with respect to thickness of outer flesh in relation to core diameter, fruit length,
fruit width, fruit juiciness, fruit length of stalk, fruit length/width ratio, fruit relief of surface,
fruit size of sepal, fruit sweetness, fruit diameter of calyx cavity in relation to that of fruit and
fruit ridged collar around calyx cavity.
In conclusion, it is clear from this study that extensive phenotypic variation exists in leaf and
fruit traits of guava phenotypic variables. As no genetic information is available, expected
progress towards improvement of the fruit species for selection process, a detailed
investigation is need for genetic variation.
112
Figure 4.8 Diagram showing projection and the relationships among 57 variables of guava
accessions of district Faisalabad and Sheikhupura based on the first two principal component factors.
YSCS Young shoot color of stem FRS Fruit relief of surface FDSTS Fully developed shoot, thickness of stem FSS Fruit Size of sepal
FDLGC Fully developed leaf green color FDCCRF Fruit Diameter of calyx cavity in relation to that of fruit
FDLSSV Fully developed leaf spacing of secondary veins FRCCC Fruit Ridged collar around calyx cavity FDLSB Fully developed leaf shape of base FLS Fruit Length of stalk
FDLST Fully developed leaf shape of leaf tips TOFRCD Thickness of outer flesh in relation to core diameter
FL Fruit length FJ Fruit Juiciness FW Fruit width FA Fruit acidity FRLW Fruit length/width ratio FS Fruit sweetness FWNRF Fruit Width of neck in relation to that of fruit SS Seed size
YSCS
FDSTS
FDLLB
FDLWB
LLWR
FDLS
FDLCCS
FDLGC
FDLCMLS
FDLSSV
FDLRSUS
FDLSB
FDLST
FL
FW
FRLW
FSSE
FWNRF
FCS
FRS
FSS
FDCCRF
FRCACC
FLS
FCF
FTOFRCD
FJ
FA
FS_1
FNS
SS
-1.0 -0.5 0.0 0.5 1.0
Factor 1 : 25.14%
-1.0
-0.5
0.0
0.5
1.0
Fac
tor
2 : 1
4.26
%
YSCS
FDSTS
FDLLB
FDLWB
LLWR
FDLS
FDLCCS
FDLGC
FDLCMLS
FDLSSV
FDLRSUS
FDLSB
FDLST
FL
FW
FRLW
FSSE
FWNRF
FCS
FRS
FSS
FDCCRF
FRCACC
FLS
FCF
FTOFRCD
FJ
FA
FS_1
FNS
SS
113
4.3.4 The phenotypic vector view of the biplot to show phenotypic similarities among
different phenotypic traits of guava accessions of districts Faisalabad and
Sheikhupura
Figure 4.8 is the phenotypic vector view of the biplot for the data in Table 4.23 indicating
Eigen factors coordination of 57 phenotypic traits of guava based on correlation of
phenotypic characters. This biplot explained 39.4% of the total variation of the variables for
Eigen factors 1 and 2 as shown in Table 4.20.
The variables having positive values (FRCACC, FDCCRF, FDLSSV, FDLCCS, YSCS, FRS
and FW) were correlated positively with a single group (FDCCRF and FDLSSV), the
variables with negative values (FSSE, FWNRF, FDSTS and FA) were correlated negatively
and formed a separate group (FDLL and FDLWR). FLS, FS, FDL, FDLGC, FCF. FDLC,
MLS, FDLS, FDLRSVS and FDLC were moderately correlated and had moderate effect on
phenotypic diversity and containg group (FCF, FDLC and MLS).
4.3.5 Group constellation and linkage distance of guava accessions of districts
Faisalabad and Sheikhupura based on phenotypic characters
Knowledge of correlations among accessions is useful in designing an effective
breeding program for any crop. Morphological analysis showed a significant diversity
within the accessions of guava. The phenotypic diversity data of 37 different accessions of
guava belonging to Sheikhupura and Faisalabad districts was used to explain morphological
linkage distances and similarity as indicated in dendrogram (Figure 4.9). The elucidation of
the morphological linkage grouping and distance of all accessions of guava is presented in
Table 4.26. A maximum similarity value of 34.1 was noted between two main groups and 12
sub-groups and 8 sub-sub groups. The major group I had linkage distance value of 29.2
(Table 4.25.) and was comprised of 9 sub-groups and 8 sub-sub groups containing 25
accessions as Mota gola (SKP), Bangladeshi gola (FSD), Rough gola (FSD) and Mota gola
(FSD), Mota gola (SKP) and Gola (SKP), Chota gola (SKP), Sadabahar gola (SKP) and
Sadabahar gola (FSD), Karalla (SKP) and Surahi (SKP), Lal gola (SKP) and Lal gola (FSD),
Lal gola (FSD) and Lal gola (FSD), Allahabadi gola (FSD) and gola (FSD), Surahi (FSD),
Larkana gola
114
Table 4.25 Phenotypic distances between all possible pairs of guava accessions of district Faisalabad and Sheikhupura calculated as linkage distances
A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 A27 A28 A29 A30 A31 A32 A33 A34 A35 A36 A37 A1 0.00 6.20 2.54 6.5 6.2 3.24 6.96 8.8 3.74 2.54 2.45 3.25 5.60 4.12 2.90 3.04 3.58 4.97 6.7 5.84 5.6 1.57 6.9 1.78 7.4 1.87 4.91 4.62 6.9 1.46 0.29 0.36 6.6 6.20 0.00 4.23 0.00 A2 6.20 0.00 5.97 8.9 8.8 6.93 4.24 7.2 6.14 6.60 6.55 6.96 7.46 7.38 6.73 5.83 6.97 7.82 9.3 7.44 8.2 6.23 3.2 6.33 4.1 6.41 7.41 7.73 5.8 6.37 6.20 6.17 9.0 0.00 6.20 6.59 6.20 A3 2.54 5.97 0.00 6.3 6.2 3.74 6.93 9.0 3.99 3.17 3.22 3.96 5.84 4.78 3.68 3.74 3.94 5.46 6.8 5.98 5.9 2.62 6.8 2.89 7.3 2.91 4.76 5.25 7.2 2.91 2.54 2.47 6.5 5.97 2.54 4.80 2.54 A4 6.47 8.88 6.34 0.0 1.4 8.04 9.55 9.8 8.52 7.69 7.57 7.80 8.33 7.79 7.49 7.46 7.96 6.73 5.7 7.94 9.4 7.18 8.7 7.30 9.1 7.14 5.14 8.63 11.1 6.61 6.40 6.37 0.5 8.88 6.47 7.06 6.47 A5 6.23 8.76 6.17 1.4 0.0 7.86 9.47 9.8 8.42 7.51 7.39 7.62 8.25 7.63 7.35 7.29 7.86 6.51 5.8 7.93 9.2 7.01 8.6 7.14 9.0 6.97 4.98 8.51 11.0 6.39 6.15 6.13 1.4 8.76 6.23 6.87 6.23 A6 3.24 6.93 3.74 8.0 7.9 0.00 7.82 8.8 4.63 3.49 3.67 1.16 6.86 3.38 4.22 4.82 4.57 5.70 8.4 6.67 5.9 3.26 7.6 3.50 8.0 3.58 6.22 3.78 7.5 3.79 3.24 3.24 8.1 6.93 3.24 5.67 3.24 A7 6.96 4.24 6.93 9.5 9.5 7.82 0.00 8.3 6.77 7.50 7.45 7.85 8.21 8.33 6.83 6.76 6.92 9.31 8.2 7.91 9.0 7.10 5.8 6.96 5.8 6.99 8.99 8.52 4.3 6.69 7.03 7.00 9.7 4.24 6.96 7.81 6.96 A8 8.82 7.18 9.04 9.8 9.8 8.80 8.32 0.0 8.46 8.53 8.54 8.68 8.36 7.84 8.91 8.71 9.37 9.09 10.8 7.97 9.7 8.80 6.6 8.89 6.9 8.85 9.13 7.45 9.9 8.81 8.76 8.78 10.0 7.18 8.82 8.33 8.82 A9 3.74 6.14 3.99 8.5 8.4 4.63 6.77 8.5 0.00 3.02 3.11 4.85 5.51 4.61 3.92 2.96 4.28 6.75 7.9 5.53 5.6 3.63 6.9 3.55 7.3 3.47 7.01 4.68 6.2 3.77 3.84 3.82 8.7 6.14 3.74 4.42 3.74
A10 2.54 6.60 3.17 7.7 7.5 3.49 7.50 8.5 3.02 0.00 0.92 3.68 6.01 3.35 3.39 3.77 3.88 5.43 7.9 6.35 4.8 2.20 7.1 2.61 7.7 2.73 5.60 3.48 7.2 2.91 2.51 2.53 7.8 6.60 2.54 4.57 2.54 A11 2.45 6.55 3.22 7.6 7.4 3.67 7.45 8.5 3.11 0.92 0.00 3.66 5.97 3.27 3.27 3.62 4.10 5.39 7.7 6.31 4.7 2.15 7.0 2.52 7.7 2.80 5.54 3.53 7.3 2.85 2.44 2.46 7.7 6.55 2.45 4.45 2.45 A12 3.25 6.96 3.96 7.8 7.6 1.16 7.85 8.7 4.85 3.68 3.66 0.00 6.73 3.20 4.06 4.75 4.65 5.45 8.3 6.34 5.7 3.24 7.4 3.43 7.8 3.63 6.05 3.80 7.6 3.78 3.23 3.25 7.9 6.96 3.25 5.41 3.25 A13 5.60 7.46 5.84 8.3 8.3 6.86 8.21 8.4 5.51 6.01 5.97 6.73 0.00 6.99 5.11 4.44 5.79 6.60 8.7 4.59 6.6 5.12 7.1 4.95 7.1 4.93 6.72 7.26 7.5 5.11 5.57 5.58 8.5 7.46 5.60 4.44 5.60 A14 4.12 7.38 4.78 7.8 7.6 3.38 8.33 7.8 4.61 3.35 3.27 3.20 6.99 0.00 4.40 5.30 5.47 5.54 8.7 6.21 4.8 4.09 7.2 4.21 7.7 4.26 6.25 2.72 8.3 4.41 4.04 4.10 7.9 7.38 4.12 5.17 4.12 A15 2.90 6.73 3.68 7.5 7.3 4.22 6.83 8.9 3.92 3.39 3.27 4.06 5.11 4.40 0.00 3.84 2.74 5.87 7.1 4.81 5.1 2.44 6.9 1.97 6.8 2.00 6.07 5.17 6.6 2.98 2.93 2.96 7.6 6.73 2.90 4.53 2.90 A16 3.04 5.83 3.74 7.5 7.3 4.82 6.76 8.7 2.96 3.77 3.62 4.75 4.44 5.30 3.84 0.00 4.04 5.30 7.6 5.29 6.1 3.06 6.4 3.13 7.0 3.31 5.56 5.82 6.2 3.18 3.12 3.08 7.6 5.83 3.04 2.81 3.04 A17 3.58 6.97 3.94 8.0 7.9 4.57 6.92 9.4 4.28 3.88 4.10 4.65 5.79 5.47 2.74 4.04 0.00 6.48 7.3 5.87 6.2 3.18 7.5 3.14 7.5 3.14 6.55 5.73 6.6 3.75 3.66 3.55 8.1 6.97 3.58 5.23 3.58 A18 4.97 7.82 5.46 6.7 6.5 5.70 9.31 9.1 6.75 5.43 5.39 5.45 6.60 5.54 5.87 5.30 6.48 0.00 9.9 6.77 6.3 4.93 6.9 5.42 7.5 5.58 3.33 6.82 9.2 5.48 4.87 4.92 6.8 7.82 4.97 3.82 4.97 A19 6.66 9.29 6.84 5.7 5.8 8.36 8.25 10.8 7.91 7.86 7.71 8.28 8.75 8.67 7.08 7.65 7.33 9.86 0.0 8.46 10.2 7.40 10.3 7.13 10.3 6.90 8.52 8.77 9.9 6.32 6.71 6.66 5.7 9.29 6.66 8.68 6.66 A20 5.84 7.44 5.98 7.9 7.9 6.67 7.91 8.0 5.53 6.35 6.31 6.34 4.59 6.21 4.81 5.29 5.87 6.77 8.5 0.00 6.1 5.59 6.7 5.17 6.2 4.98 7.25 6.98 7.6 5.42 5.79 5.85 8.2 7.44 5.84 4.73 5.84 A21 5.55 8.22 5.89 9.4 9.2 5.92 9.03 9.7 5.59 4.78 4.74 5.71 6.61 4.77 5.11 6.06 6.18 6.31 10.2 6.11 0.0 5.25 7.5 5.21 7.8 5.16 7.30 5.72 7.5 5.46 5.48 5.56 9.6 8.22 5.55 5.34 5.55 A22 1.57 6.23 2.62 7.2 7.0 3.26 7.10 8.8 3.63 2.20 2.15 3.24 5.12 4.09 2.44 3.06 3.18 4.93 7.4 5.59 5.3 0.00 6.7 0.92 7.1 1.47 5.01 4.63 6.9 1.93 1.57 1.56 7.3 6.23 1.57 4.19 1.57 A23 6.94 3.16 6.75 8.7 8.6 7.58 5.75 6.6 6.93 7.06 7.03 7.42 7.06 7.24 6.87 6.44 7.45 6.94 10.3 6.66 7.5 6.70 0.0 6.79 1.8 6.83 6.93 8.16 6.7 7.01 6.87 6.90 8.9 3.16 6.94 5.91 6.94 A24 1.78 6.33 2.89 7.3 7.1 3.50 6.96 8.9 3.55 2.61 2.52 3.43 4.95 4.21 1.97 3.13 3.14 5.42 7.1 5.17 5.2 0.92 6.8 0.00 7.1 0.90 5.51 4.86 6.7 1.76 1.80 1.84 7.4 6.33 1.78 4.32 1.78 A25 7.43 4.08 7.27 9.1 9.0 8.00 5.75 6.9 7.33 7.70 7.65 7.78 7.12 7.66 6.76 6.95 7.54 7.55 10.3 6.20 7.8 7.14 1.8 7.06 0.0 7.04 7.67 8.70 6.7 7.43 7.39 7.41 9.2 4.08 7.43 6.37 7.43 A26 1.87 6.41 2.91 7.1 7.0 3.58 6.99 8.9 3.47 2.73 2.80 3.63 4.93 4.26 2.00 3.31 3.14 5.58 6.9 4.98 5.2 1.47 6.8 0.90 7.0 0.00 5.66 4.95 6.7 1.69 1.89 1.93 7.3 6.41 1.87 4.34 1.87 A27 4.91 7.41 4.76 5.1 5.0 6.22 8.99 9.1 7.01 5.60 5.54 6.05 6.72 6.25 6.07 5.56 6.55 3.33 8.5 7.25 7.3 5.01 6.9 5.51 7.7 5.66 0.00 7.18 9.6 5.36 4.79 4.81 5.2 7.41 4.91 4.78 4.91 A28 4.62 7.73 5.25 8.6 8.5 3.78 8.52 7.4 4.68 3.48 3.53 3.80 7.26 2.72 5.17 5.82 5.73 6.82 8.8 6.98 5.7 4.63 8.2 4.86 8.7 4.95 7.18 0.00 8.5 4.92 4.57 4.58 8.7 7.73 4.62 6.30 4.62 A29 6.95 5.82 7.20 11.1 11.0 7.51 4.30 9.9 6.15 7.24 7.29 7.63 7.54 8.28 6.59 6.18 6.64 9.20 9.9 7.64 7.5 6.90 6.7 6.73 6.7 6.69 9.59 8.52 0.0 6.61 7.03 7.03 11.3 5.82 6.95 7.20 6.95 A30 1.46 6.37 2.91 6.6 6.4 3.79 6.69 8.8 3.77 2.91 2.85 3.78 5.11 4.41 2.98 3.18 3.75 5.48 6.3 5.42 5.5 1.93 7.0 1.76 7.4 1.69 5.36 4.92 6.6 0.00 1.46 1.51 6.7 6.37 1.46 4.31 1.46 A31 0.29 6.20 2.54 6.4 6.1 3.24 7.03 8.8 3.84 2.51 2.44 3.23 5.57 4.04 2.93 3.12 3.66 4.87 6.7 5.79 5.5 1.57 6.9 1.80 7.4 1.89 4.79 4.57 7.0 1.46 0.00 0.32 6.5 6.20 0.29 4.19 0.29 A32 0.36 6.17 2.47 6.4 6.1 3.24 7.00 8.8 3.82 2.53 2.46 3.25 5.58 4.10 2.96 3.08 3.55 4.92 6.7 5.85 5.6 1.56 6.9 1.84 7.4 1.93 4.81 4.58 7.0 1.51 0.32 0.00 6.5 6.17 0.36 4.25 0.36 A33 6.58 8.99 6.49 0.5 1.4 8.13 9.66 10.0 8.68 7.80 7.67 7.90 8.54 7.89 7.62 7.64 8.11 6.82 5.7 8.21 9.6 7.30 8.9 7.43 9.2 7.28 5.19 8.74 11.3 6.74 6.50 6.48 0.0 8.99 6.58 7.23 6.58 A34 6.20 0.00 5.97 8.9 8.8 6.93 4.24 7.2 6.14 6.60 6.55 6.96 7.46 7.38 6.73 5.83 6.97 7.82 9.3 7.44 8.2 6.23 3.2 6.33 4.1 6.41 7.41 7.73 5.8 6.37 6.20 6.17 9.0 0.00 6.20 6.59 6.20 A35 0.00 6.20 2.54 6.5 6.2 3.24 6.96 8.8 3.74 2.54 2.45 3.25 5.60 4.12 2.90 3.04 3.58 4.97 6.7 5.84 5.6 1.57 6.9 1.78 7.4 1.87 4.91 4.62 6.9 1.46 0.29 0.36 6.6 6.20 0.00 4.23 0.00 A36 4.23 6.59 4.80 7.1 6.9 5.67 7.81 8.3 4.42 4.57 4.45 5.41 4.44 5.17 4.53 2.81 5.23 3.82 8.7 4.73 5.3 4.19 5.9 4.32 6.4 4.34 4.78 6.30 7.2 4.31 4.19 4.25 7.2 6.59 4.23 0.00 4.23 A37 0.00 6.20 2.54 6.5 6.2 3.24 6.96 8.8 3.74 2.54 2.45 3.25 5.60 4.12 2.90 3.04 3.58 4.97 6.7 5.84 5.6 1.57 6.9 1.78 7.4 1.87 4.91 4.62 6.9 1.46 0.29 0.36 6.6 6.20 0.00 4.23 0.00
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Figure 4.9 Dendrogram showing phenotypic diversity among 37 guava accessions of district Faisalabad and Sheikhupura based on morphological characters
Group I
Group II
Gola (FSD) Gola (SKP)
Sadabahar gola (SKP) Desi gola (SKP)
Gola (SKP) Rough gola (FSD)
Gola (SKP) Gola (SKP) Gola (SKP)
Larkana gola (SKP) Gola (FSD)
Larkana gola (FSD) Surahi (FSD)
Gola (FSD) Allah Abadi gola (FSD)
Lal gola (FSD) Lal gola (FSD) Lal gola (FSD) Lal gola (SKP) Karalla (FSD) Surahi (SKP)
Sadabahar gola (FSD) Sadabahar gola (SKP)
Chota gola (SKP) Gola (SKP)
Mota gola (SKP) Mota gola (FSD)
Rough gola (FSD) Bangladeshi gola (FSD)
Mota gola (SKP) Surahi (FSD)
Moti surahi (SKP) Surahi (SKP) Surahi (SKP) Surahi (FSD)
Choti surahi (SKP) Khata (FSD)
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Table 4. 26 Group constellation of 37 guava accessions of district Faisalabad and Sheikhupura belonging to Faisalabad district based on 57 phenotypic characters
Sub groups Number of accessions
Accession names
Main group I
Sub group I 1 Mota gola (SKP)
Sub-Sub group I 1 Bangladeshi gola (FSD)
Sub-Sub group II 2 Rough gola (FSD) and Mota gola (FSD)
Sub group IIa 2 Mota gola (SKP) and Gola (SKP)
Sub group IIb 1 Chota gola (SKP)
Sub-Sub group IIc1 2 Sadabahar gola (SKP) and Sadabahar gola (FSD)
Sub-Sub group IIc2 2 Karalla (SKP) and Surahi (SKP)
Sub group IIIa 2 Lal gola (SKP) and Lal gola (FSD)
Sub-Sub group IIIb 2 Lal gola (FSD) and Lal gola (FSD)
Sub group Va1 2 Allahabadi gola (FSD) and gola (FSD)
Sub-Sub group Va1 1 Surahi (FSD)
Sub group Va2 2 Larkana gola (FSD) and Gola (FSD)
Sub group Va3i 1 Rough gola (FSD)
Sub group Va3ii 1 Larkana gola (SKP)
Sub-Sub group Va3iia 2 Gola (SKP) and Gola (SKP)
Sub-Sub group Va3iib 1 Gola (SKP)
Sub group V2 2 Gola (SKP) and Desi gola (SKP)
Main Group II
Sub group I 1 Khata (SKP)
Sub group IIa 2 Choti surahi (SKP) and Surahi (FSD)
Sub group IIbi 2 Surahi (SKP) and Surahi (SKP)
Unique accessions 5 Sadabahar gola (SKP), Gola (SKP), Gola (FSD), Choti surahi (SKP) and Surahi (FSD)
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(FSD) and Gola (FSD), Rough gola (FSD), Larkana gola (SKP), Gola (SKP) and Gola
(SKP), Gola (SKP), Gola (SKP) and Desi gola (SKP) (Table 4.26). Sub group I had 1
accession that was Mota gola (SKP), Sub-Sub group I with 1 accession Bangladeshi gola
(FSD), Sub-Sub group II with 2 accessions Rough gola (FSD) and Mota gola (FSD), Sub
group IIa with 2 accessions Mota gola (SKP) and Gola (SKP), Sub group IIb with1ccession
Chota gola (SKP), Sub-Sub group IIc1 with 2 accessions Sadabahar gola (SKP) and
Sadabahar gola (FSD), Sub-Sub group IIc2 with 2 accessions Karalla (SKP) and Surahi
(SKP), Sub group IIIa with 2 accessions Lal gola (SKP) and Lal gola (FSD), Sub-Sub group
IIIb with 2 accessions Lal gola (FSD) and Lal gola (FSD), Sub group Va1 with 2 accessions
Allahabadi gola (FSD) and gola (FSD), Sub-Sub group Va1 with1 accession Surahi (FSD),
Sub group Va2 with 2 accessions Larkana gola (FSD) and Gola (FSD), Sub group Va3i with
1 accession Rough gola (FSD), Sub group Va3ii with 1 accession Larkana gola (SKP), Sub-
Sub group Va3iia with 2 accessions Gola (SKP) and Gola (SKP), Sub-Sub group Va3iib
with1 accession Gola (SKP), Sub group V2 with 2 accession Gola (SKP) and Desi gola
(SKP). Main group II had 3 sub-groups that consist of 5 accessions as Sub group I with 1
accession Khata (SKP), Sub group IIa with 2 accessions Choti surahi (SKP) and Surahi
(FSD) and Sub group IIbi with 2 accessions Surahi (SKP) and Surahi (SKP). The accessions
Sadabahar gola (SKP), Gola (SKP), Gola (FSD), Choti surahi (SKP) and Surahi (FSD) and
were collected from two different districts. The linkage distances among sub groups varied
from 0.29 in cluster I to a maximum distance of 11.3 in the Sub group IIbi. This
indicates the presence of high phenotypic diverse accessions with in different clusters. Sub
group I with accession Mota gola (SKP), Sub-Sub group I with accession Bangladeshi gola
(FSD), Sub group IIb with accession Chota gola (SKP), Sub-Sub group Va.1 with accession
Surahi (FSD), Sub group Va3i with accession Rough gola (FSD), Sub group Va3ii with
accession Larkana gola (SKP) and Sub-Sub group Va3iib with accession Gola (SKP) in main
group I and Sub group I with accession Khata (SKP) with separate cluster were diverse
accessions collected from Faisalabad and Sheikhupura districts. A group of accessions
including Mota gola, Rough gola, Lal gola, Gola, Allahabadi gola (FSD) and Gola and
Surahi (SKP) were diverse but closely associated in main groups and sub groups. Gola, Mota
gola, Lal gola (SKP) and Gola and Larkana gola (FSD) was the second diverse group that
was closely associated. Similarly Choti Surahi and Karla (FSD) and Surahi were less diverse
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but associated each other. Similar results were obtained by Junior et al. (2008) who evaluated
63 accessions of guava and found accessions Compos and Coma the most divergent with
other closely associated accessions and phenotypic characters studied. The reasoning for
solitary clusters formation is due to complete inhibition and isolation of gene flow or
either may be due to severe natural/human selection procedure of accessions for divergence
adaptiveness. It is advisable to select accessions from solitary and unique accessions with
high phenotypic diversity as parents in further breeding and selection programs for
getting desirable divergent.
The main group and sub-sub group diversity are expected to provide the general information
of varietal performance and relationship according to their environment, but are
nececcessarily dependent on geographical origin and or even pedigree relations, and
accessions that display high phenotypic similarity might not to be genetically similar because
the environment can manipulate phenotypic expressions (Poehlman, 1987).
Morphological variation within and among populations can either be due to genotypic
differentiation or to phenotypic plasticity. Genotypic differentiation among plant populations
is common (Heslop-Harrison, 1964; Langlet, 1971) and has been shown to occur on spatial
scales as small as a couple of meters or even decimeters (Jain and Bradshaw, 1966;
Antonovics, 1971; Shaver, Chapin, and Billings, 1979; McGraw and Antonovics, 1983).
Although genotypic differentiation is widely reported, large morphological differences
among populations in different environments may also be due to phenotypic plasticity
(Heathcote, Davies, and Etherington, 1987; Williams and Black, 1993). Plasticity should be
selected in heterogenous environments, rather than specialized phenotypes, given that there is
no added cost of plasticity (Sultan, 992). For example, leaf size and leaf area of many alpine
plants change with altitude (Meinzer, Goldstein, and Rundel, 1985; Korner et al., 1989), and
some arctic plants may produce more or larger leaves during warmer summers than during
colder ones (Havstrom et al., 1995; Stenstrom and Jonsdottir, 1997).
Morphological characterization had been widely used to describe varieties, although new
molecular techniques have been deployed increasingly for the identification of different
crops. Morphological characters were used to describe three potential citrus rootstocks
(Jaskani et al., 2006). The advantages of morphological characters are savings in cost and
time and the provision of horticultural characters. Because of the plasticity and instability of
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phenotypic characters, cultivar identification and diversity were often in contrast to actual
genetic diversity. Previously Yonemoto et al. (2007) and Struwig et al. (2009) used both
morphological and molecular analysis. Furthermore, important horticultural characters are
reported to be controlled by multiple genes (Campos et al., 2005; Liu and Deng, 2007) and
are of low heritability. Thus, morphological characterization could be an essential component
since most of the horticultural characters cannot be evaluated through molecular markers.
The analysis using morphological characters revealed considerable amount of diversity
among 37 guava accessions that can be used in selecting diverse parents in breeding
program. This is also crucial in utilizing the genetic potential of these genotypes for
improvement of traits needed for adaptation to various conditions. However, there is
the need for complementing similar work with other techniques such as DNA genetic
markers to further accurately classify guava accessions existing in both districts.
4.4 GENTIC VARIATION ANALYSIS OF GUAVA ACCESSIONS Quantification of genetic variation is an important component of crop improvement. It can
help evaluate genetic variability of selected parental cultivars, for hybridization as well
as for making new genetic recombination of selected inbred lines or parents. It also
facilitates the identification of materials which should be preserved for maximum genetic
variation in germplasm.
4.4.1 DNA extraction and quality estimation The DNA of 37 guava genotypes was successfully extracted following CTAB protocol. DNA
samples were electrophoresed on 0.8 % agrose gel and saved for quality of DNA as shown in
Figure 4.11. Extracted DNA as observed in well revealed bands of different florescences
Intensities are indicating different quantities of DNA in each sample. The quantity extracted
with this method was high. Stock concentrations obtained with method ranged from 857
ng/µl to 2455 ng/µl and the clear and bright bands indicated a good quality of DNA. It is
clear that CTAB is convenient method to use and a good quality of DNA can be extracted.
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4.4.2 Optimization of polymerase chain reactions (PCR)
The amplification of microsatellites can be influence by variable factors, such as annealing
temperature of primers, template quality and quantity, MgCl2 and Taq polymerase
concentration (Penner et al. 1993). The reaction was optimized for template DNA
concentration, MgCl2 concentration and Taq DNA polymerase, before conducting the final
analysis.
4.4.3 Template DNA
The routine DNA extraction methods includes ethanol precipitation of DNA, all the traces of
70 % ethanol were removed before amplification. The DNA concentrations (20 ng, 25 ng 30n
1 Gola 13 Karalla 25 Surahi 2 Surahi 14 Lal gola 26 Gola 3 Rough gola 15 Gola 27 Mota gola 4 Mota gola 16 Sadabahar gola 28 Lal gola 5 Bangladeshi gola 17 Larkana gola 29 Choti surahi 6 Lal gola 18 Gola 30 Larkana gola 7 Surahi 19 Mota gola 31 Desi gola 8 Kata 20 Surahi 32 Gola 9 Surahi 21 Chota gola 33 Mota gola 10 Gola 22 Gola 34 Moti surahi 11 Allah Abadi gola 23 Surahi 35 Gola 12 Lal gola 24 Gola 36 Sadabahar gola
37 Sadabahar gola
Figure 4.11 Quality of DNA for 37 accessions of guava extracted through CTAB method
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
20 21 22 23 24 25 26 27 2 8 29 30 31 32 33 34 35 36 37
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g, 40 ng, 45 ng and 50 ng/ µl) of genomic DNA were used for optimization and it was found
50 ng/µl genomic DNA was found to have good quality banding pattern. Other
concentrations of genomic DNA of guava samples did not show any results. The extracted
and purified genomic DNA (50 ng/µl) of guava genotypes was used as template DNA for
each reaction, which is clear from the results in this study. The final working dilutions of the
concentration (50 ng/µl) from all the stock DNA of all guava genotypes for SSR (PCR)
annealing.
4.4.4 MgCl2 concentration
As the DNA amplification is greatly affected by the concentration of the MgCl2
concentration which results in lower or higher intensity. Basically Mg+2 act as a cofactor for
Taq polymerase. Free Mg+2 in high concentrations affects the dNTPS and Taq concentration,
therefore it was optimized to a concentration of 0.2 µl (Taq 5 U/µl) per reaction for
amplification reaction.
To find out most suitable concentration of MgCl2, five different concentrations (1.0 mM, 2.0
mM, 3.0 mM, 4.0 mM and 5.0 mM) were used. Out of these concentrations 2.0 mM
concentration was found to the bright bands for each primer. The variation in the
concentration of MgCl2 in PCR reactions resulted in qualitative changes in DNA banding
pattern. This optimum concentration of MgCl2 for every primer was dependent of GC/AC
contents and the number of bands amplified. However, by increasing the concentration also
resulted in lower banding pattern.
4.4.5 Taq DNA polymerase
Taq DNA polymerase concentration is another factor to optimize for the best amplification.
Taq DNA polymerase concentration ranging from 0.5 to 2.5 units per 20 µl reaction and
other reaction mixture was constant were tested for best amplification. 0.5 unit/ 20 µl
reaction was found optimum in the present studies. If inhibitors are present in the reaction
mixture then template DNA used in high concentration with high amount of Taq DNA
polymerase are helpful to have better yield of amplification (Rehman and Zafar, 2001).
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4.4.6 Annealing temperature
The purity and yield of the reaction products depend on several parameters, one of which is
the annealing temperature. At both sub- and super-optimal annealing temperature values,
non-specific products may be formed, and the yield of products is reduced. Optimizing the
annealing temperature is especially critical when long products are synthesized or when total
genomic DNA is the substrate for PCR. Conventional PCRs were run at 40°, 45°, 50°, 55°,
60° and 65°C for 30 cycles. After evaluation, 55 ˚C was optimized for amplification of SSR
primers for this study.
4.4.7 dNTPs
Different concentration (3.4, 4.5, 5.5 and 6.4 µl/ 20µl of reaction mixture) were tested for
clear and bright bands. The best concentration was founded 6.4 for each reaction mixture as
clear from results of this studied.
4.4.8 Agarose gel electrophoresis The amplified products along with 2 ml of loading dye (bromophenol blue) from each tube
were separated on 3% per cent high resolution agarose gel at 80 volts containing 1x TAE
buffer (pH 8.0) in buffer tank and gel containing ethidium bromide (0.5 µg ml-1). The gels
were photographed and saved.
4.4.9 Scoring the amplified fragments
The 23 base oligonucleotide primers obtained from eurofins USA were used for
amplification of genomic DNA The amplified product was scored as (1) for the presence and
(0) for the absence of a band generating binary matrix (1 and 0 matrix) and per cent
polymorphism was calculated by using the following formula:
Number of polymorphic bands Percent polymorphism = _________________________________________ X 100 Total number of bands
123
4.4.10 Primer screening
The DNA samples of 37 accessions were amplified with 23 different SSR primers out of
these, 18 exhibited polymorphic bands (Table 4.27). A total of 23 nuclear SSR loci were
characterized for revealing polymorphisms by screening different accessions (Risterucci et
al., 2005). All SSR primer pairs successfully amplified in P. guajava, with an average
number of 4.2 alleles per locus. None of the primer was informative enough to distinguish
all the accessions, however, primers mPgCIR05, mPgCIR10, mPgCIR16, mPgCIR18,
mPgCIR19, mPgCIR21, mPgCIR22 and mPgCIR26 sufficiently discriminate the accessions.
Out of these 23 primers pairs 18 were showing polymorphic bands. Maximum polymorphism
was recorded in in primers mPgCIR16 and mPgCIR25 with value 83.33% and 83.78%
respectively. Valdes-Infante et al. (2007) also reported the similar results for guava
fingerprinting studies.
4.4.11 Group constellation 37 accessions of belonging to Faisalabad and Sheikhupura districts
Genetic relationship among 37 accessions of guava based on SSR is indicated in the
dendrogram (4.12) which clearly define that how genetically accessions are diverse. It can be
clearly evident from the cluster among guava accessions that were divided into two main
groups along with fourteen sub clusters. Cluster pattern revealed that, main cluster II was the
largest cluster consisting of 20 genotypes, followed by main cluster II containing 11
accessions, while the six genotypes appeared unique that didn’t show any cluster as indicated
from Table 4.16. Main cluster I, sub cluster I with 1 accession (Gola (SKP)), sub cluster II
with 3 accessions that were Chota gola (SKP), (Sadabahar gola (SKP) and Mota gola (SKP)),
sub cluster III containing 4 accessions (Surahi (SKP) and Surahi (SKP)), (Mota
Gola (SKP) and Surahi (SKP)) and Sub cluster V that contains 3 accessions was with Rough
gola (FSD) and (Surahi (FSD) and Gola (FSD).
II main cluster includes accessions in sub cluster are as sub group I with 1 accession Gola
(SKP), Sub cluster II with 2 accessions (Surahi (FSD) and Khata (FSD)), Sub cluster III with
6 accessions (Gola (FSD) and Lal gola (FSD)), Sadabahar gola (FSD), (Larkana gola (FSD)
and Lal gola (FSD)), Sadabahar gola (FSD), Sub cluster V with 3 accessions (Mota gola
(SKP) and Desi gola (SKP)), Surahi (SKP), Sub cluster VI with 1 accession Gola (SKP), Sub
124
Table 4. 27 SSR primers showing amplification, polymorphism, and size among 37 accessions of Psidium guajava
Sr No.
Primer Sequence No. of Alleles
Allele size range
1 mPgCIR02 F:AGTGAACGACTGAAGACC R:ATTACACATTCAGCCACTT
3 232-260
2 mPgCIR03 F:TTGTGGCTTGATTTCC R:TCGTTTAGAGGACATTTCT
2 202-220
3 mPgCIR05 F:GCCTTTGAACCACATC R:TCAATACGAGAGGCAATA
3 230-295
4 mPgCIR07 F:ATGGAGGTAGGTTGATG R:CGTAGTAATCGAAGAAATG
4 160-170
5 mPgCIR08 F:ACTTTCGGTCTCAACAAG R:AGGCTTCCTACAAAAGTG
3 215-235
6 mPgCIR09 F:GCGTGTCGTATTGTTTC R:ATTTTCTTCTGCCTTGTC
3 160-185
7 mPgCIR10 F:GTTGGCTCTTATTTTGGT R:GCCCCATATCTAGGAAG
4 265-290
8 mPgCIR11 F:TGAAAGACAACAAACGAG R:TTACACCCACCTAAATAAGA
4 298-320
9 mPgCIR14 F:TAAACACAACAAGGGTCA R:CAGTTTTCATATCGTCCTC
2 190-200
10 mPgCIR15 F:TCTAATCCCCTGAGTTTC R:CCGATCATCTCTTTCTTT
5 240-270
11 mPgCIR16 F:AATACCAGCAACACCAA R:CATCCGTCTCTAAACCTC
7 270-300
12 mPgCIR17 F:CCTTTCGTCATATTCACTT R:CATTGGATGGTTGACAT
3 150-240
13 mPgCIR18 F:TAAGCTGCATGTGTGC R:ATGGCTTTGGATGAAA
2 180-200
14 mPgCIR19 F:AAAATCCTGAAGACGAAC R:TATCAGAGGCTTGCATTA
4 265-280
15 mPgCIR20 F:TATACCACACGCTGAAAC R:TTCCCCATAAACATCTCT
3 275-300
16 mPgCIR21 F:TGCCCTTCTAAGTATAACAG R:AGCTACAAACCTTCCTAAA
4 250-285
17 mPgCIR22 F:CATAAGGACATTTGAGGAA R:AATAAGAAAGCGAGCAGA
3 200-278
18 mPgCIR25 F:GACAATCCAATCTCACTTT R:TGTGTCAAGCATACCTTC
5 170-200
125
cluster VII with 1 Lal gola (SKP), Sub cluster VIII with 1 accession Surahi (FSD), Sub
cluster X with 2 accessions (Mota gola (SKP) and Karalla (FSD)), Sub cluster XI with
1accession Choti Surahi (SKP) and Sub cluster XII with 2 accessions (Gola (FSD) and
Sadabahar gola (SKP)). All these sub clusters were interlinked with each other and with main
clusters as indicated Figure 4.9. There were 6 unique accessions (Bangladeshi gola (FSD),
Lal gola (FSD), Larkana gola (FSD), Gola (SKP), Moti surahi (SKP) and Sadabahar gola
(SKP)) that didn’t cluster with any group.
Figure 4.12 The pattern of polymorphic bands for 37 accession of guava by primer mpgCIR-
18
M: 1 Kb DNA Ladder 1 Gola 13 Karalla 25 Surahi 2 Surahi 14 Lal gola 26 Gola 3 Rough gola 15 Gola 27 Mota gola 4 Mota gola 16 Sadabahar gola 28 Lal gola 5 Bangladeshi gola 17 Larkana gola 29 Choti surahi 6 Lal gola 18 Gola 30 Larkana gola 7 Surahi 19 Mota gola 31 Desi gola 8 Kata 20 Surahi 32 Gola 9 Surahi 21 Chota gola 33 Mota gola 10 Gola 22 Gola 34 Moti surahi 11 Allah Abadi gola 23 Surahi 35 Gola 12 Lal gola 24 Gola 36 Sadabahar gola
37 Sadabahar gola
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 6 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 M
126
Table 4.28 Similarity matrix data of 37 Psidium genotypes obtained using SSR markers. op ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
1 **** 0.968 0.936 0.848 0.730 0.730 0.775 0.800 0.776 0.753 0.779 0.754 0.709 0.800 0.797 0.779 0.757 0.900 0.915 0.931 0.863 0.840 0.932 0.838 0.817 0.796 0.837 0.754 0.753 0.730 0.775 0.730 0.689 0.730 0.730 0.882 0.730
2 0.032 **** 0.938 0.821 0.707 0.707 0.750 0.774 0.751 0.729 0.754 0.730 0.729 0.774 0.772 0.754 0.733 0.904 0.919 0.901 0.869 0.849 0.902 0.848 0.791 0.771 0.810 0.771 0.729 0.707 0.750 0.707 0.708 0.707 0.707 0.854 0.707
3 0.066 0.065 **** 0.886 0.707 0.707 0.750 0.811 0.751 0.729 0.754 0.730 0.686 0.811 0.810 0.754 0.772 0.904 0.886 0.867 0.902 0.813 0.869 0.811 0.751 0.771 0.810 0.730 0.729 0.707 0.750 0.707 0.667 0.707 0.707 0.886 0.707
4 0.165 0.198 0.121 **** 0.743 0.743 0.788 0.852 0.831 0.766 0.792 0.767 0.721 0.774 0.729 0.792 0.810 0.848 0.828 0.801 0.912 0.743 0.842 0.852 0.789 0.767 0.851 0.724 0.721 0.743 0.788 0.743 0.744 0.743 0.743 0.931 0.743
5 0.314 0.347 0.347 0.297 **** 1.000 0.943 0.834 0.894 0.970 0.853 0.918 0.970 0.834 0.873 0.853 0.873 0.730 0.743 0.785 0.756 0.800 0.756 0.834 0.894 0.918 0.873 0.918 0.970 1.000 0.943 1.000 0.943 1.000 1.000 0.743 1.000
6 0.314 0.347 0.347 0.297 0.000 **** 0.943 0.834 0.894 0.970 0.853 0.918 0.970 0.834 0.873 0.853 0.873 0.730 0.743 0.785 0.756 0.800 0.756 0.834 0.894 0.918 0.873 0.918 0.970 1.000 0.943 1.000 0.943 1.000 1.000 0.743 1.000
7 0.255 0.288 0.288 0.239 0.059 0.059 **** 0.836 0.843 0.915 0.854 0.865 0.915 0.836 0.823 0.804 0.823 0.775 0.788 0.832 0.757 0.754 0.802 0.836 0.843 0.865 0.874 0.865 0.915 0.943 0.889 0.943 0.889 0.943 0.943 0.788 0.943
8 0.224 0.256 0.210 0.160 0.182 0.182 0.180 **** 0.886 0.860 0.800 0.861 0.809 0.783 0.774 0.800 0.819 0.800 0.813 0.777 0.828 0.834 0.828 0.783 0.793 0.765 0.774 0.813 0.860 0.834 0.836 0.834 0.836 0.834 0.834 0.774 0.834
9 0.254 0.286 0.286 0.186 0.112 0.112 0.171 0.121 **** 0.922 0.810 0.821 0.868 0.746 0.781 0.810 0.781 0.735 0.747 0.746 0.803 0.805 0.761 0.839 0.850 0.821 0.830 0.821 0.868 0.894 0.896 0.894 0.843 0.894 0.894 0.789 0.894
10 0.284 0.316 0.316 0.267 0.030 0.030 0.089 0.151 0.081 **** 0.827 0.890 0.941 0.809 0.847 0.827 0.847 0.753 0.766 0.809 0.779 0.825 0.779 0.809 0.868 0.890 0.847 0.890 0.941 0.970 0.915 0.970 0.915 0.970 0.970 0.766 0.970
11 0.250 0.283 0.283 0.233 0.159 0.159 0.158 0.223 0.210 0.190 **** 0.929 0.827 0.889 0.791 0.864 0.884 0.779 0.831 0.836 0.725 0.810 0.806 0.800 0.810 0.880 0.837 0.832 0.879 0.853 0.854 0.853 0.854 0.853 0.853 0.831 0.853
12 0.282 0.315 0.315 0.266 0.086 0.086 0.145 0.150 0.198 0.116 0.073 **** 0.890 0.909 0.851 0.880 0.951 0.754 0.809 0.810 0.780 0.826 0.780 0.813 0.872 0.895 0.851 0.895 0.946 0.918 0.919 0.918 0.919 0.918 0.918 0.767 0.918
13 0.345 0.316 0.377 0.328 0.030 0.030 0.089 0.212 0.142 0.061 0.190 0.116 **** 0.809 0.847 0.827 0.847 0.709 0.721 0.761 0.733 0.825 0.733 0.860 0.868 0.890 0.847 0.946 0.941 0.970 0.915 0.970 0.972 0.970 0.970 0.721 0.970
14 0.224 0.256 0.210 0.256 0.182 0.182 0.180 0.245 0.293 0.212 0.118 0.096 0.212 **** 0.910 0.845 0.910 0.800 0.813 0.818 0.788 0.834 0.788 0.783 0.793 0.813 0.819 0.813 0.860 0.834 0.836 0.834 0.836 0.834 0.834 0.813 0.834
15 0.227 0.259 0.211 0.316 0.136 0.136 0.195 0.257 0.248 0.166 0.235 0.161 0.166 0.094 **** 0.884 0.905 0.797 0.729 0.813 0.784 0.829 0.784 0.728 0.830 0.801 0.810 0.851 0.900 0.873 0.823 0.873 0.823 0.873 0.873 0.770 0.873
16 0.250 0.283 0.283 0.233 0.159 0.159 0.218 0.223 0.210 0.190 0.147 0.127 0.190 0.169 0.123 **** 0.884 0.779 0.752 0.753 0.806 0.810 0.806 0.756 0.810 0.783 0.837 0.832 0.879 0.853 0.804 0.853 0.854 0.853 0.853 0.792 0.853
17 0.278 0.311 0.259 0.210 0.136 0.136 0.195 0.200 0.248 0.166 0.123 0.050 0.166 0.094 0.100 0.123 **** 0.797 0.770 0.813 0.825 0.786 0.784 0.774 0.878 0.851 0.857 0.851 0.900 0.873 0.874 0.873 0.874 0.873 0.873 0.810 0.873
18 0.105 0.101 0.101 0.165 0.314 0.314 0.255 0.224 0.308 0.284 0.250 0.282 0.345 0.224 0.227 0.250 0.227 **** 0.949 0.895 0.863 0.803 0.932 0.761 0.735 0.754 0.757 0.754 0.753 0.730 0.689 0.730 0.732 0.730 0.730 0.915 0.730
19 0.088 0.084 0.121 0.189 0.297 0.297 0.239 0.207 0.291 0.267 0.185 0.211 0.328 0.207 0.316 0.285 0.262 0.052 **** 0.910 0.842 0.817 0.912 0.813 0.747 0.809 0.770 0.767 0.766 0.743 0.744 0.743 0.744 0.743 0.743 0.897 0.743
20 0.072 0.104 0.143 0.222 0.243 0.243 0.184 0.252 0.294 0.212 0.179 0.211 0.273 0.201 0.207 0.284 0.207 0.111 0.094 **** 0.778 0.824 0.927 0.777 0.833 0.855 0.813 0.810 0.809 0.785 0.786 0.785 0.740 0.785 0.785 0.874 0.785
21 0.148 0.141 0.103 0.092 0.280 0.280 0.278 0.189 0.220 0.250 0.321 0.248 0.310 0.238 0.244 0.216 0.193 0.148 0.172 0.251 **** 0.794 0.821 0.828 0.803 0.780 0.825 0.737 0.733 0.756 0.802 0.756 0.757 0.756 0.756 0.877 0.756
22 0.175 0.164 0.207 0.297 0.223 0.223 0.282 0.182 0.217 0.193 0.211 0.191 0.193 0.182 0.187 0.211 0.241 0.219 0.202 0.194 0.231 **** 0.869 0.792 0.760 0.826 0.742 0.872 0.825 0.800 0.801 0.800 0.849 0.800 0.800 0.780 0.800
23 0.071 0.103 0.141 0.172 0.280 0.280 0.221 0.189 0.274 0.250 0.216 0.248 0.310 0.238 0.244 0.216 0.244 0.071 0.092 0.076 0.197 0.140 **** 0.788 0.803 0.780 0.825 0.780 0.779 0.756 0.757 0.756 0.757 0.756 0.756 0.877 0.756
24 0.177 0.165 0.210 0.160 0.182 0.182 0.180 0.245 0.175 0.212 0.223 0.207 0.151 0.245 0.317 0.280 0.257 0.273 0.207 0.252 0.189 0.233 0.238 **** 0.839 0.861 0.910 0.861 0.809 0.834 0.885 0.834 0.836 0.834 0.834 0.813 0.834
25 0.203 0.235 0.286 0.237 0.112 0.112 0.171 0.232 0.163 0.142 0.210 0.137 0.142 0.232 0.187 0.210 0.130 0.308 0.291 0.183 0.220 0.274 0.220 0.175 **** 0.872 0.927 0.821 0.868 0.894 0.949 0.894 0.843 0.894 0.894 0.747 0.894
26 0.228 0.261 0.261 0.266 0.086 0.086 0.145 0.267 0.198 0.116 0.127 0.111 0.116 0.207 0.222 0.245 0.161 0.282 0.211 0.157 0.248 0.191 0.248 0.150 0.137 **** 0.851 0.895 0.890 0.918 0.919 0.918 0.865 0.918 0.918 0.809 0.918
27 0.178 0.211 0.211 0.161 0.136 0.136 0.134 0.257 0.187 0.166 0.177 0.161 0.166 0.200 0.211 0.177 0.154 0.278 0.262 0.207 0.193 0.299 0.193 0.094 0.076 0.161 **** 0.801 0.847 0.873 0.926 0.873 0.823 0.873 0.873 0.810 0.873
28 0.282 0.261 0.315 0.323 0.086 0.086 0.145 0.207 0.198 0.116 0.185 0.111 0.056 0.207 0.161 0.185 0.161 0.282 0.266 0.211 0.305 0.137 0.248 0.150 0.198 0.111 0.222 **** 0.946 0.918 0.865 0.918 0.919 0.918 0.918 0.724 0.918
29 0.284 0.316 0.316 0.328 0.030 0.030 0.089 0.151 0.142 0.061 0.129 0.056 0.061 0.151 0.106 0.129 0.106 0.284 0.267 0.212 0.310 0.193 0.250 0.212 0.142 0.116 0.166 0.056 **** 0.970 0.915 0.970 0.915 0.970 0.970 0.721 0.970
30 0.314 0.347 0.347 0.297 0.000 0.000 0.059 0.182 0.112 0.030 0.159 0.086 0.030 0.182 0.136 0.159 0.136 0.314 0.297 0.243 0.280 0.223 0.280 0.182 0.112 0.086 0.136 0.086 0.030 **** 0.943 1.000 0.943 1.000 1.000 0.743 1.000
31 0.255 0.288 0.288 0.239 0.059 0.059 0.118 0.180 0.110 0.089 0.158 0.084 0.089 0.180 0.195 0.218 0.134 0.373 0.296 0.241 0.221 0.221 0.278 0.123 0.053 0.084 0.077 0.145 0.089 0.059 **** 0.943 0.889 0.943 0.943 0.744 0.943
32 0.314 0.347 0.347 0.297 0.000 0.000 0.059 0.182 0.112 0.030 0.159 0.086 0.030 0.182 0.136 0.159 0.136 0.314 0.297 0.243 0.280 0.223 0.280 0.182 0.112 0.086 0.136 0.086 0.030 0.000 0.059 **** 0.943 1.000 1.000 0.743 1.000
33 0.373 0.345 0.406 0.296 0.059 0.059 0.118 0.180 0.171 0.089 0.158 0.084 0.029 0.180 0.195 0.158 0.134 0.313 0.296 0.302 0.278 0.164 0.278 0.180 0.171 0.145 0.195 0.084 0.089 0.059 0.118 0.059 **** 0.943 0.943 0.744 0.943
34 0.314 0.347 0.347 0.297 0.000 0.000 0.059 0.182 0.112 0.030 0.159 0.086 0.030 0.182 0.136 0.159 0.136 0.314 0.297 0.243 0.280 0.223 0.280 0.182 0.112 0.086 0.136 0.086 0.030 0.000 0.059 0.000 0.059 **** 1.000 0.743 1.000
35 0.314 0.347 0.347 0.297 0.000 0.000 0.059 0.182 0.112 0.030 0.159 0.086 0.030 0.182 0.136 0.159 0.136 0.314 0.297 0.243 0.280 0.223 0.280 0.182 0.112 0.086 0.136 0.086 0.030 0.000 0.059 0.000 0.059 0.000 **** 0.743 1.000
36 0.126 0.158 0.121 0.072 0.297 0.297 0.239 0.256 0.237 0.267 0.185 0.266 0.328 0.207 0.262 0.233 0.210 0.088 0.109 0.135 0.131 0.249 0.131 0.207 0.291 0.211 0.210 0.323 0.328 0.297 0.296 0.297 0.296 0.297 0.297 **** 0.743
37 0.314 0.347 0.347 0.297 0.000 0.000 0.059 0.182 0.112 0.030 0.159 0.086 0.030 0.182 0.136 0.159 0.136 0.314 0.297 0.243 0.280 0.223 0.280 0.182 0.112 0.086 0.136 0.086 0.030 0.000 0.059 0.000 0.059 0.000 0.000 0.297 ****
127
Figure 4.13 Dendrogram showing genetic relationship between 37 Psidium accessions based on SSR marker analysis.
Cluster II Cluster I
Gola (F
SD
)
Surahi (F
SD
)
Rough gola (F
SD
)
Gola (S
KP
)
Mota gola (S
KP
)
Surahi (S
KP
)
Surahi (S
KP
)
Mota gola (S
KP
)
Sadabahar gola (SK
P)
Chota gola (S
KP
)
Gola (S
KP
) B
angladeshi gola (FS
D)
Lal gola (F
SD
) L
arkana gola (FSD)
Gola (S
KP
) M
oti Surahi (S
KP
) G
ola (SK
P)
Sadabahar gola (SK
P)
Gola (F
SD
)
Choti S
urahi (SKP
)
Karalla (F
SD
)
Mota gola (S
KP
)
Surahi (F
SD
)
Lal gola (S
KP
)
Gola (S
KP
) S
urahi (SK
P)
Desi gola (S
KP
) M
ota gola (SK
P)
Allah A
badi gola (FS
D)
Lal gola (F
SD
)
Larkana gola (FSD
)
Sadabahar gola (F
SD
)
Lal gola (F
SD
)
Gola (F
SD
)
Khata (FSD
)
Surahi (F
SD
)
Gola (S
KP
)
128
Table 4. 29 Group constellation of 20 accessions of guava belonging to Faisalabad district based on 57 phenotypic characters studied
(FSD)= Faisalabad, (SKP) = Sheikhupura These accessions were considered to be the most diverse genotypes. Gola, Surahi, Karala,
Mota gola (FSD) and were most divergent in both group but closely associated with each
other. Gola, Mota gola, Surahi, Desi gola (SKP) and Lal gola, Larkana gola and Gola (FSD)
were associated divergent groups in first and second group. Surahi, Mota gola, Sadabahar
gola (SKP) were associated in main group I. Similarly Lal gola, Gola, Khata and Surahi were
almost similar and were associated with each other but less diverse. Mercado-Silva et al.
(2002) indicated same results from clustering of RAPD that divided the guava accessions in
two main divergent groups with many closely associated divergent sub and sub-sub groups.
Sub groups Number of accessions
Accession names
Main Cluster I
Sub Cluster I 1 Gola (SKP)
Sub Cluster II 3 Chota gola (SKP), (Sadabahar gola (SKP) and Mota gola (SKP))
Sub Cluster III 4 (Surahi (SKP) and Surahi (SKP)), (Mota gola (SKP) and Surahi (SKP))
Sub Cluster V 3 Rough gola (FSD) and (Surahi (FSD) and Gola (FSD))
Main Cluster II
Sub Cluster I 1 Gola (SKP) Sub Clusterv II 2 (Surahi (FSD) and Khata (FSD)) Sub Cluster III 6 (Gola (FSD) and Lal gola (FSD)), Sadabahar gola
(FSD), (Larkana gola (FSD) and Lal gola (FSD)), Sadabahar gola (FSD)
Sub Cluster V 3 (Mota gola (SKP) and Desi gola (SKP)), Surahi (SKP)
Sub Cluster VI 1 Gola (SKP) Sub Cluster VII 1 Lal gola (SKP) Sub Cluster VIII 1 Surahi (FSD) Sub Cluster X 2 (Mota gola (SKP) and Karalla (FSD)) Sub Cluster XI 1 Choti Surahi (SKP) Sub Cluster XII 2 (Gola (FSD) and Sadabahar gola (SKP)) Unique Accessions 6 (Bangladeshi gola (FSD), Lal gola (FSD),
Larkana gola (FSD), Gola (SKP), Moti surahi (SKP) and Sadabahar gola (SKP))
129
4.4.12 Genetic diversity in 37 different accessions of guava
Quantification of genetic variability existing among and in-between groups of genotypes, is
much important, particularly helpful in proper selection of parents for understanding
higher heterosis and obtaining promising recombinants and divergent. Several methods
have been proposed by various workers to determine the genetic diversity and
divergence species in crop plants (Murthy and Arunachalam, 1966). Cluster analysis by
different statistic software is a unique way to discriminate, morphological similarities,
phylogenetic relationships and eco-geographical diversity. Based on Popgen software
values 37 accessions were grouped into 14 clusters, 20 accessions were present in main
cluster II, while main cluster I, had 11 accessions and 6 accessions were unique (quite
divers) in the dendrogram with a maximum similarity value of (1.00).
The solitary clusters formation may be due to total prevention and isolation of gene
flow or may be due to intensive natural/human selection procedure for a complexes of
diverse adaptiveness. The distances among sub cluster varied from (0.029) in cluster I
to a maximum distance of (1.00) in the cluster XII. This indicates the presence of diverse
accessions within different clusters. The sub cluster similarity value ranged widely, with
minimum value of (0.029) (Table 4.28.) between sub cluster I and sub cluster XII and
maximum value of (1.00) indicating high similarity among the genotypes. The sub
clusters I in main cluster, sub cluster VI, sub cluster VII, sub cluster VIII and sub cluster XI
with solitary accessions were the most divergent accessions with a maximum sub
cluster distances with accessions Gola (SKP), Gola (SKP), Gola (SKP), Lal gola (SKP),
Surahi (FSD) and (SKP). The accessions Bangladeshi gola (FSD), Lal gola (FSD), Larkana
gola (FSD), Gola (SKP), Moti surahi (SKP) and Sadabahar gola (SKP) are unique one, which
mean they are genetically quite diverse. It is most desirable to select accessions from
above mansion unique accessions showing high (0) cluster distances and also with high
genetic diversity as parents in further recombination breeding and selection program for
getting desirable segregates.
The assortment of these genotypes in groups, 50% similarity was considered as cut off point.
Surahi (SKP) and Surahi (SKP) of different orchards, Mota gola (SKP) and Sadabahar gola
(SKP), Gola (SKP) and Mota gola (SKP) and Gola (FSD) and Surahi (FSD) were similar
within the main group I. these Gola and Surahi identical in the same group indicated that they
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are genetically same with different local names. Khata (FSD) and Surahi (FSD), Surahi and
Desi gola (SKP) and karalla and Mota gola (FSD) were similar in genetics but different in
local names.
The genetic similarities of SSR matrix indicate substantial genetic diversity (68 %) to (100%)
among all accessions studied. Similarity matrix (Table 4.28) depicted the high linkage
distance among Surahi (SKP) and Mota gola (SKP) are most diverse in both groups with
68% similarity, Rough Gola (FSD), Surahi (FSD) and Gola (SKP) ranked second with
similarity (70%) and linked togather with 0.347 distances belonging to two different districts.
Desi gola (SKP) and Gola (SKP) are showing 100 % similarity.
This main cluster and sub cluster diversity in two different districts is due to high diverse
genetic makeup of accession and are independent of environmental conditions (Singh and
Bains, 1968). Similar results were observed by Walia and Garg (1996) and Dotlacil et al.
(2000) in which they indicated a non-parallelization between geographic effects and genetic
diversity. This also indicated that genetic diversity is not only connected with geographic
origin or genetic makeup of accessions but also some other characters. In conformation with
this results earlier Sharma et al. (1997) had also reported that the genotypes of
heterogeneous origin/place of release often grouped together in same clusters. It was also
suggesting that these are to some degree of the ancestral relationship among the genotypes.
The genetic diversity studied in this research by SSR markers revealed that variation
measured among guava accessions were more collected from Sheikhupura than Faisalabad
district.
The low genetic diversity in the species is a matter of concern which may be obstacle in
further improvement. It is well documented that plant improvement is based on information
about the genetic linkages among accessions and species. Moreover, plant breeder also select
breeding material to breed for elite lines on genetic bases among breeding material. The
accessions of guava showing the tendency of 100% similarity have been extensively used as
parental material for breeding purpose or there is repetition of the same material which has
led to very low genetic diversity. It might be possible that they are the same clones but
planted at different locations.
Overall diversity studied in guava accessions on the basis of phenotypic and genetic markers
(SSR) showed a diverse behavior with obvious distances. For example Mota gola (SKP) is
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more diverse phenotypically but genetically is not diverse accession. Similarly there are
some accession that phenotypically diverse in Sheikhupura are not genetically and vice versa
and are genetically quite diverse so called unique one in both techniques.
Because the morphological markers are influenced by environmental conditions and are
considered as reliable source for morphological identification or genetic closeness among
guava accessions but the molecular markers are independent of these factors and are
dependent of genetic constitution of individual genotype. Therefore they are more reliable
and should be performed for further genetic studies of guava and it will be more appropriate
to advice that the conclusion of this study may be accomplished with ISSR, AFLP or SARC
studies.
The findings presented here have some implication for guava future selection and breeding
programs. The genetic differences and linkage distance presented in this study of various
guava accessions would be helpful for guava breeding programs regarding varietal
improvement and selection of genetically diverse parents for germplasm collection and
conservation as well as for vegetative propagation. There is a great need for exploitation of
these accessions for improved new varieties, particularly for marketing and export purpose.
4.4.13 Estimating phenotypic data with genetic data for 37 accessions of guava
The clustering of morphological and molecular data of 37 accessions indicated diverse
results, when compared the major groups I and II. The major group I in phenotypic diversity
had 27 accessions and genetic group I had 11 accessions and among these all accessions 8
accessions like Mota gola (SKP), Gola (SKP), Chota (SKP), Sadabahar (SKP), Surahi (SKP),
Rough gola (FSD), Surahi (FSD), Gola (FSD) were common in phenotypic and genetic
groups (Table 4.26). The phenotypic cluster main group II had 11 accessions while genetic
group II had 27 accessions and the accessions Surahi (FSD), Choti Surahi (SKP), Surahi
(SKP) and Khatta (SKP) were common in both groups (Table 4.29). Similarly among unique
accessions, accessions Sadabahar gola (SKP) and Gola (SKP) were common. These results
indicate that most of the accessions studied phenotypic and genotypic basis did not match
with each other in group I or group II. Phenotypic characters provide a simple way of
quantifying genetic variation under normal growing environments but morphological traits
are limited in number, modified by the environment and may be controlled by epistatic and
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pleiotropic gene effects (Van Beuningen and Busch, 1997). Similarly, our results indicate
that phenotypic characters were in conformity with the genetic analysis i.e. 21.0 % in group I
and 12.5 % in group II. Hence the genetic analysis is more reliable to assess genetic diversity
of germplasm.
4.5 CLONAL PROPAGATION OF GUAVA
Clonal propagation is asexual propagation of many new plants from an individual (all has the
same genotype) and is a rapid multiplication of superior plants that maintain the uniformity.
The propagation of various cultivars of guava by conventional methods such as budding,
grafting and layering has been practiced. These techniques are laborious and time consuming
as far as the production of a large number of homogeneous plants is concerned. Moreover,
the rate of propagation of guava by these methods is rather low. Another aspect, sexual
propagation by seed, plants cannot maintain the genetic purity of the variety due to the
segregation and recombination of characters. Since guavas cannot be depended upon to come
true from seed, vegetative propagation through cuttings treated with growth regulators may
be one of the important options to avoid the genetic segregation and maintain the quality of
the variety. Plants originated through asexual means are true to type. Although there are
few reports available on clonal multiplication of guava, further study is needed to prescribe
an optimal technique for rapid clonal propagation of a large number of divergent genotypes
which would serve as a basic tool in establishing a gene bank for future breeding programs.
The research studies embodied in this section were carried out to ascertain the effect of
different concentration of IBA and NAA 0, 2000, 4000, 6000 and 8000 ppm on rooting of
soft wood cuttings under mist unit.
Softwood cuttings were collected from current season growth of five year old trees of gola
accessions, measuring 12 cm in length carrying 4 to 6 nodes and at least 4 leaves on tips.
The cuttings were prepared as shown in Figure 4.13.
The data collected from the various parameters was statistically analyzed for comparison of
treatment means. Detailed accounts of the results obtained are being presented under the
different parameters:-
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4.5.1 Experiment (A): Effect of IBA on rooting of soft wood cuttings of guava
4.5.1.1. Number of rooted cuttings
Data regarding number of rooted cuttings was collected and means of each treatment were
subjected to statistical analysis and the results are presented as analysis of variance in Table
4.30. Highly significant results were obtained for all the concentrations of IBA. The results
for the number of rooted cuttings assessed at 0, 2000, 4000, 6000 and 8000 ppm showed
varying results (Table 4.31). Cuttings treated with IBA 4000 ppm showed maximum number
of rooted cuttings (43.67) than 6000 ppm (24.00), 2000 ppm (23.67) and 8000 ppm (16.33).
It was also observed for number of rooted cutting, that the cuttings treated with IBA, had
statistically higher rooting than the untreated cuttings (control). This indicates that the IBA
mixture affected the integrity of cuttings tissues at their base which showed rooting response.
According to Mura et al. (1995), Hartmann et al. (1982) and Mancuso et al. (1997)
successful application of growth substances improve or promote rooting in cuttings. At 35
days after planting, they observed differences in statistics for the number of roots and length
with concentrations above 2000 mg L-1 IBA and NAA. Tavares et al. (1995) and Kersten and
Ibanez (1993) reported that concentrations which suits for rooting in guava cuttings range
between 4000 and 8000 mg L-1 and 4000 and 5000 mg L-1 IBA, respectively. However, it is
known that the possible reasoning for rooting of guava, in addition to growth regulators, may
be factors such as carbohydrates, presence of leaves and shoots at cuttings, cultivar or
species, mother plant age, the time of collecting cuttings, light and relative humidity in
rooting beds affect the root formation in cuttings (Wang and Andersen, 1989; Hafeez et al.,
1991; Mitra and Bose, 1996; Hartmman et al., 1997). IAA was subsequently tested for its
activity in promoting roots on stem segments, and investigators demonstrated the practical
use of this material in stimulating root formation on cuttings (Thimann and Koepfli, 1935).
Later on it was proved (Zimmerman and Fordham, 1985) that two synthetic auxins, indole-3-
butyric acid (IBA) and α-naphthalene acetic acid (NAA), were even more effective than the
naturally occurring or synthetic IAA for rooting. Today, IBA and NAA are still the most
widely used auxins for rooting stem cuttings and for rooting tissue-culture-produced
microcuttings. It has been repeatedly confirmed that auxin is required for initiation of
134
adventitious roots on stems, and indeed, it has been shown that division of the first root initial
cells are dependent upon either applied or endogenous auxins (Strömquist and Hansen, 1980;
Gaspar and Hofinger, 1988). IBA has been found to occur naturally. The formation of root
primordium cells depends on the endogenous auxins in the cutting and on a synergic
compound such as a diphenol. These substances lead to the synthesis of ribonucleic acid
(RNA), which act upon root primordium initiation (Hartmann et al., 1982).
4.5.1.2. Percent of rooted cuttings
Data collected regarding percent of rooted cuttings and means of each treatment was
subjected to statistical analysis and the results obtained are presented as analysis of variance
in Table 4.30. Highly significant results were obtained for all the concentrations of IBA
regarding percent of rooted cuttings.
The result presented in Table 4.31 indicated significant maximum rooted cuttings (87.33%)
treated with 4000 ppm IBA and minimum (16.00%) success was obtained with higher
concentration of IBA (8000 ppm). No rooting was observed in control.
These results can be compared with results of Hafeeze et al. (1988) who noted 94.44%
rooting with 12 ppm paclobutrazol treatment but in contrast Mukhtar et al. (1998) got 64%
rooting with paclobutrazol concentration of 1000 ppm. The results from experiments
indicated that higher concentration and more treatment time are helpful to suppress
gibberellins that generally inhibit the adventitious root formation. With such supperessional
action, there may be more cell division, resulting in callus formation and more percentage of
rooting. The growth regulators also cause a greater mobilization of sugars and nitrogenous
substances, which are helpful in initiation of root primordia (Detweiler, 1942).
Table 4.30 Analysis of variance for percent of rooted cuttings in guava
S.O.V D.F S.S M.S F. Value Prob.
IBA 4 2973.94 746.100 95.06*** 0.00
Error 10 78.21 0.933
Total 14 3052.16
*** = Highly Significant (p<0.05)
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Table 4.31 Means number of rooted cuttings and percent rooted cuttings in guava
IBA concentrations (ppm) Number of rooted
cuttings
Percent rooted cuttings
0 0.00±0.00D 0.00±0.00C
2000 23.67±0.33B 48.67±2.40B
4000 43.67±0.33A 87.33±1.76A
6000 24.00±1.00B 49.33±5.70B
8000 16.00±0.58C 43.33±4.37B
Means sharing similar letter are statistically non-significant (P>0.05) and the ± values given in the table are indicating the standard deviation.
Table 4. 32 Analysis of variance for sprouting percentage
S.O.V D.F S.S M.S F. Value Prob.
IBA 4 11548.3 2887.07 79.61*** 0.0000
Error 10 362.7 36.27
Total 14 11910.9
*** = Highly Significant (p<0.05)
4.5.1.3. Average number of roots per cutting
The data collected for average number of roots per cutting was statistically analyzed and
highly significant differences were obtained for all the concentration for root formation in
softwood cuttings (Table 4.33).
A differential effect was found on the average number of roots per cutting in different
concentrations of IBA (Table 4.34). A significant maximum root number (38.00) was found
in 4000 ppm and minimum (12.00) was with 8000 ppm and there was no rooting in control
treatment.
The increase in root number can be correlated to rooting in guava variety 'Paluma which
showed maximum (80.73) number of roots per cutting treated with 500 mg L-1 IBA (Pereira
et al., 1991). These results show that the number of roots per cutting was affected linearly by
IBA concentrations, the varietal performance increased by increase power in the
136
concentration of IBA by resulting in increased the numbers of roots per cutting, but the
results could be opposite due to imbalance between auxins, carbohydrates and other factors
that are involved in the process of root formation. Similarly different reports have shown
that physiologically age and maturity of bud wood may also effect on rooting, younger
propagation material is more suitable for successful adventitious rooting. Additionally,
this effect of age is very strong, e.g. it can be seen each year and is often independent
of climate conditions (Morgan et al., 1980, Plietzsch and Heiliger, 1997). It has
generally been reported that physiologically maturation increased with chronological
age (cyclophysis).
Table 4.33 Analysis of variance for average number of roots per cutting
S.O.V D.F S.S M.S F. Value Prob.
IBA 4 2425.024 606.256 9385.2190*** 0.0000
Error 8 0.517 0.065
Total 14 2425.548
*** = Highly Significant (p<0.05)
Table 4.34 Average number of roots per cutting of guava
IBA concentrations (ppm) IBA
0 0.00±0.00E
2000 21.33±0.19C
4000 38.00±0.19A
6000 24.67±0.04B
8000 12.00±0.12D
Means sharing similar letter are statistically non-significant (P>0.05) and the ± values given in the table are indicating the standard deviation.
137
4.5.1.4 Average root length (cm)
The data collected for average root length was statistically analyzed and highly significant
differences were obtained for average root length as described in Table 4. 35.
Cuttings treated with IBA concentration 4000 ppm yielded maximum root length (8.00 cm)
followed by 2000 ppm (6.67 cm), 6000 ppm (5.00 cm) and 8000 ppm (3.00 cm) (Table 4.36).
For the characteristic like average length of roots, Pereira et al. (1991) and Bacarin et al.
(1994) while working with guava (Psidium guajava L.) observed the superiority of the
variety 'Paluma' which showed maximum root length. The largest root was statistically
superior to that of varieties 'Ogawa' and Pedro Sato, and likewise the number of roots and
root length had greater throughout the growing period of cuttings. After 70 days of planting,
cuttings from the variety 'Paluma' produced the largest root of 8.51 cm and similar results
were also found by Dutta and Mitra (1991), 30 days after the planting of cuttings of Harijha
guava (14.62 cm).
Differences in polar auxin transport ability also contribute to rooting success and root length.
Marks et al. (2002) demonstrated that polar auxin transport was more intense in
Syringa, where a lesser rooting length was observed compared to Forsythia. The
transport of exogenously applied IBA was more intense in the cuttings of plants that
tend to root better (Ludwig-Müller, 2009).
Table 4.35 Analysis of variance for average root length (cm) in guava cuttings
S.O.V D.F S.S M.S F. Value Prob
IBA 4 119.067 29.767 1927.0616*** 0.0000
Error 8 0.124 0.015
Total 14 119.235
*** = Highly Significant (p<0.05)
138
Table 4.36 Means for average root length (cm) in guava
IBA concentrations (ppm) Root length
0 0.00±0.00E
2000 6.67±0.04B
4000 8.00±0.12A
6000 5.00±0.00C
8000 3.00±0.12D
Means sharing similar letter are statistically non-significant (P>0.05) and the ± values given in the table are indicating the standard deviation.
4.5.1.5. Survival percentage of plantlets
The data regarding survival percentage was collected after planting cuttings in 6 x 6 inches
polythene bags. The media used was soil and silt at (50:50 ratios). After 20 days the data was
collected for survival of rooted cuttings and statistically analyzed (Table 4.37).
The results indicated that there was a significant interaction between concentrations IBA and
survival percentage. The highest survival percentage (91.33%) was obtained from cuttings
treated with 4000 ppm and the lowest in 8000 ppm with 75.33% (Table 4.39).
These results cope with observations made on the survival percentage of cuttings for variety
'Paluma', that showed the highest rate of survival in the three evaluation experiments, which
may possibly be of greater potential to produce greater roots and root length (Franzon et al.,
2004). According to Lionakis (1981) the presence of leaves on cuttings more number roots
and root length guarantees to the survival of the cuttings. This may be either by synthesis of
carbohydrates by photosynthesis or providing auxins and some other substances, that are
more important in the process of root induction and stimulating the cambial activities, and
process of differentiation in the cells. Furthermore, Franzon et al. (2004) noted that rooting
of cuttings and survival percentage was good at a concentration of 1.00 mg L-1 and 3.00 mg
L-1of IBA (30% and 4%, respectively).
139
Table 4.37 Analysis of variance for survival percentage of guava plantlets
S.O.V D.F S.S M.S F. Value Prob.
IBA 4 5735.494 4240.40 1062117.46*** 0.00
Error 8 0.011 13.60
Total 14 5735.521
*** = Highly Significant (p<0.05)
Table 4.38 Survival percentage of guava plantlets
IBA concentrations (ppm) Survival percentage of guava plantlets
0 0.00±0.00D
2000 85.33±1.45AB
4000 91.33±0.88A
6000 80.00±3.61BC
8000 75.33±2.60C
Means sharing similar letter are statistically non-significant (P>0.05) and the ± values given in the table are indicating the standard deviation.
140
Figure 4.14. Rooting behavior of softwood cuttings of guava treated with different concentrations of IBA
IBA (0 ppm) IBA (2000 ppm) IBA (4000 ppm)
IBA (6000 ppm) IBA (8000 ppm) IBA (4000 ppm)
141
4.5.2 Experiment (B): Effect of NAA on rooting of soft wood cuttings of guava
4.5.2.1. Number of rooted cuttings
Analysis of variance shows highly significant differences for NNA concentrations (Table
4.40). A maximum rooted cuttings (15.00) were noted in softwood cuttings treated with
concentration 2000 ppm whereas minimum rooted cuttings (13.00) was observed in 8000
ppm concentration of NAA g and no rooting was observed in control (Table 4.40) which can
be seen in figure 4.16.
Hartmann (1982) and Mitra and Bose (1996) supposed that growth regulators are responsible
factors for induction of roots and secondly stored food in the form of carbohydrates in the
cuttings can produce sufficient amounts of roots.
Tomar et al. (1999), Bhagat et al. (1998) and Chandrappa and Gowda, (1998) also noted that
2000 ppm of growth regulators (IAA, IBA or NAA) promotes rootings. Significant results
were also noted by Khattak et al. (1983) at 2000 ppm NAA.
Table 4.39 Analysis of variance for number of rooted cuttings in guava
S.O.V D.F S.S M.S F. Value Prob.
Treatment 4 414.27 103.567 221.9286 0.0000
Error 8 3.73 0.467
Total 12
*** = Highly Significant (p<0.05)
Table 4.40 Number of rooted cuttings and percent rooted cuttings in guava
NAA concentrations (ppm) Number of rooted cuttings Percent rooted cuttings
0 0.00±0.00E 0.00±0.00D
2000 15.00±0.00A 30.00±0.00A
4000 13.00±0.58B 26.00±0.58B
6000 9.00±0.58C 18.00±0.58C
8000 6.67±0.33D 13.33±0.33D
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4.5.2.2. Percent rooted cuttings
The results for percent rooted cuttings were significantly different at p ≤ 0.05 as affected by
different concentrations of NAA (Table 4.40). Maximum significant sprouting (30.00%) was
observed in softwood cutting that were treated with 2000 ppm NAA. In 8000 ppm of NAA
induced minimum roots on cuttings (13.00 %) (Table 4.41). Control did not induce any roots
in softwood cuttings.
The possible reason may be high concentration and increased treatment time which may be
helpful to suppress the effect of gibberellin that generally can inhibit the normal adventitious
root formation. With such supperessional phenomenon, there can be more cell division that
results in formation of callus and high percentage of rooting.
Although plant growth regulator NAA has significant potential for rooting of cuttings, but it
may not be used singly in high concentration as it can cause toxicity to the tissue. According
to Sulladmath and Kololgi (1969) NAA had synergistic effects on the rooting whenever
mixed with lBA formulation.
Bhagat et al. (1998) also noted that 2000 ppm of plant growth regulators (IAA, IBA or NAA)
was optimum for more rooting. Tomar et al. (1999) also recorded similar results in softwood
cuttings of guava.
Table 4.41 Analysis of variance for percent rooted cuttings
S.O.V D.F S.S M.S F. Value Prob.
NAA 4 1657.067 414.267 1129.8182*** 0.000
Error 8 2.93 0.367
Total 12
*** = Highly Significant (p<0.05)
143
4.5.2.3. Average number of roots per cuttings
The data collected for average number of roots per cutting was statistically analyzed and
highly significant results were obtained that are presented in Table 4. 42.
The average number of roots significantly differed with different concentrations of NAA,
maximum roots (19.66) were obtained in softwood cuttings treated with NAA concentration
of 2000 ppm and minimum numbers of roots (6.00) were in 8000 ppm as given in the Table
4.43. No roots were induced in control.
Teaotia and Pandey (1961) noted that both NAA (50 ppm) and IAA (100 ppm) were more
effective on semi hardwood cuttings. Blommaert (1958) obtained 75% to 90% rooting with
softwood cuttings under mist system along with IBA treatments.
Table 4.42 Analysis of variance for average number of roots per cuttings in guava
S.O.V D.F S.S M.S F. Value Prob.
Growth regulator 4 646.544 161.636 883.9492 0.0000
Error 8 1.463 0.183
Total 12
*** = Highly Significant (p<0.05)
Table 4.43 Means for average number of roots per cutting in guava
NAA concentrations (ppm) Average number of roots per cutting
0 0.00±0.00E
2000 19.66±0.20A
4000 13.00±0.29B
6000 10.00±0.29C
8000 6.33±0.19D
Means sharing similar letter are statistically non-significant (P>0.05) and the ± values given in the table are indicating the standard deviation.
144
4.5.2.4. Average root length (cm)
The data collected for average root length was assessed by analysis of variance and results
indicated a highly significant difference for different concentrations as represented in Table
4.44. The longest roots were recorded in softwood cuttings treated with 2000 ppm and
minimum root length was noted in 8000 ppm (Table 4.45). These results are in coordination
with the results of Lanphear et al. (2007) who reported that auxins have increasing effect on
the root length. These results were also in conformity with the results of Lanphear and Meahl
(1963) who reported that growth regulators are helpful in stimulating the rooting in cuttings
when endogenous level and climatic factors are also favorable.
Table 4.44 Analysis of variance for average root length in guava
S.O.V D.F S.S M.S F. Value Prob.
Growth regulator 4 57.980 14.495 96633.5851 0.0000
Error 8 0.001 0.000
Total 12
*** = Highly Significant (p<0.05)
Table 4.45 Average root length in guava cuttings
NAA Concentrations (ppm) Average root length (cm)
0 0.00±0.00E
2000 6.00±0.00A
4000 4.33±0.01B
6000 3.33±0.02C
8000 3.00±0.01D
Means sharing similar letter are statistically non-significant (P>0.05) and the ± values given in the table are indicating the standard deviation.
145
4.5.2.5. Survival percentage of guava plantlet
Table 4.46 shows highly significant results when collected data for survival percentage was
statistically analyzed. The results clearly indicate the highest survival of 15.00 % was
observed in softwood cuttings treated with 2000 ppm NAA followed by 4000 ppm (93.00%),
2000 ppm (85.67%) and 8000 ppm (73.67%) (Table 4.47). The data regarding the percentage
of survival indicate the highest value in IBA as compared to NAA treated cuttings. The
increased survival percentage in IBA than NAA may be because of high number of roots and
root length observed in soft-wood cuttings (Hafeez et al., 1988).
Table 4.46 Analysis of variance for survival percentage of guava plantlets
S.O.V D.F S.S M.S F. Value Prob.
Growth regulator 4 17260.000 4315.000 385.2679 0.0000
Error 8 89.600 11.200
Total 12
*** = Highly Significant (p<0.05)
Table 4.47 Means for survival percentage of guava plantlets
NAA Concentrations (ppm) Survival percentage
0 0.00±0.00D
2000 85.67±1.45A
4000 93.00±1.00AB
6000 81.00±3.79BC
8000 73.67±0.67C
Means sharing similar letter are statistically non-significant (P>0.05) and the ± values given in the table are indicating the standard deviation.
146
Figure 4.15 Rooting behavior of softwood cuttings of guava treated with different concentrations of NAA
NAA (0 ppm) NAA (2000 ppm) NAA (4000 ppm)
NAA (6000 ppm) NAA (8000 ppm)
147
4.5.3 Experiment (C): Comparison of IBA and NAA for rooting of softwood cuttings of guava
4.5.3.1 Number of rooted cuttings
Comparison of two different growth regulators indicated that number of rooted cuttings was
significantly affected by different growth regulators, their concentrations and interaction of
growth regulators and their concentration (Table 4.48). High number of rooted cuttings was
found in growth regulator IBA (21.47) as compared to NAA (8.73). Overall the comparison
of concentrations showed that the 4000 ppm was the best for rooted cuttings (28.33). The
interaction of growth regulators and concentration represented the IBA with 4000 ppm
(43.67) the effective combination for more number of roots. The better rooted cuttings
softwood cuttings may be on the account of an accumulation of endogenous growth
promoting substances in the tissues. IBA may be having synergistic effect with endogenous
hormones and hence resulted in better number of roots.
Rahman et al. (2004) obtained maximum rooting (71.22%) in softwood cuttings of guava
treated with paclobutrazol at 100 ppm solution as compared to NAA at concentration of 1000
ppm.
Table 4.48 Analysis of variance for number of rooted cuttings (IBA and NAA) of guava
Source of variation Degrees of freedom
Sum of squares Mean squares F-value
Group
Conc.
Group x Conc.
Error
1
4
4
20
1216.03
2623.20
775.47
14.00
1216.03
655.80
193.87
0.70
1737.19**
936.86**
276.95**
Total 29 4628.70
NS = Non-significant (P>0.05); * = Significant (P<0.05); ** = Highly significant (P<0.01)
148
Table 4.49 Comparison of IBA and NAA for number of rooted cuttings in guava
Conc. (ppm)
Growth regulators Mean IBA NAA
0 0.00 ± 0.00f 0.00 ± 0.00f 0.00 ± 0.00E
2000 23.67 ± 0.33b 15.00 ± 0.00cd 19.33 ± 1.94B
4000 43.67 ± 0.33a 13.00 ± 0.58d 28.33 ± 6.86A
6000 24.00 ± 1.00b 9.00 ± 0.58e 16.50 ± 3.39C
8000 16.00 ± 0.58c 6.67 ± 0.33e 11.33 ± 2.11D
Mean 21.47 ± 3.78A 8.73 ± 1.41B
Means sharing similar letter in a row or in a column are statistically non-significant (P>0.05). Small letters represent comparison among interaction means and capital letters are used for overall mean. The ± values given in the table are indicating the standard deviation.
4.5.3.2. Percent rooted cuttings
When comparison of growth regulators, concentration and their interaction was performed,
highly significant results were obtained for percent rooted cuttings of guava (Table 4.50).
The maximum percent rooted cuttings were noted in 4000 ppm (56.67%) in growth regulator
IBA as compared to NAA. Overall comparison of percent rooted cuttings proved IBA with
4000 ppm (87.33%) was the best combination for percent rooted cuttings.
The possible reason may be the antagonistic effect of IBA with gibberellin and with other
factors that has promoting effect on root formation. These findings also cope with results of
Wahab et al. (2001) who investigated the influence of IBA, IAA and NAA with
concentrations 1000, 2000, 3000, 4000, 5000 and 6000 ppm. They found significant increase
in percent rooted cuttings of guava with IBA (1000 and 2000 ppm) and NAA (2000 ppm)
(78.84, 75.96 and 76.59%, respectively).
149
Table 4.50 Analysis of variance for percent rooted softwood cuttings (IBA and NAA) in guava
Source of variation Degrees of freedom
Sum of squares Mean squares F-value
Group
Conc.
Group x Conc.
Error
1
4
4
20
5992.5
10209.9
2995.5
367.3
5992.53
2552.47
748.87
18.37
326.27**
138.97**
40.77**
Total 29 19565.2
NS = Non-significant (P>0.05); * = Significant (P<0.05); ** = Highly significant (P<0.01)
Table 4.51 Comparison of IBA and NAA for percent rooted cuttings of guava
Conc. (ppm)
Growth regulators Mean IBA NAA
0 0.00 ± 0.00e 0.00 ± 0.00e 0.00 ± 00.00D
2000 48.67 ± 2.40b 30.00 ± 0.00c 39.33 ± 04.31B
4000 87.33 ± 1.76a 26.00 ± 0.58c 56.67 ± 13.74A
6000 49.33 ± 5.70b 18.00 ± 0.58cd 33.67 ± 07.46BC
8000 43.33 ± 4.37b 13.33 ± 0.33d 28.33 ± 06.99C
Mean 45.73 ± 7.53A 17.47 ± 2.81B
Means sharing similar letter in a row or in a column are statistically non-significant (P>0.05). Small letters represent comparison among interaction means and capital letters are used for overall mean. The ± values given in the table are indicating the standard deviation.
4.5.3.3. Average number of roots per cutting
Significantly positive results were obtained when comparison of growth regulators,
concentration and interaction was statistically analyzed for average number of roots per
cutting (Table 4.52). The results for interaction means for growth regulators and
concentrations showed that IBA (4000 ppm) had high (38.00) average roots per cutting.
Overall mean maximum number of roots per cutting was noted in softwood cuttings treated
150
with IBA (19.20) as compared to NAA (9.80). Among the concentrations, 4000 ppm induced
the maximum number (25.50) of roots (Table 4.53).
These results are comparable with the results of Noor et al. (2004) who found that IBA
increased the number of roots and root length significantly. They further investigated that
cuttings treated with 500 to 1000 ppm of IBA concentration increased the rooting percentage,
number of roots per cutting as well as length of the roots per cutting. The formation of high
number of roots per cutting may be the fact that the cambial activity is involved in root
induction (Hafeez et al., 1991).
Table 4.52 Analysis of variance for average number of roots/cutting (IBA and NAA)
Source of variation Degrees of freedom
Sum of squares Mean squares F-value
Group
Conc.
Group x Conc.
Error
1
4
4
20
662.98
2421.96
649.61
1.99
662.982
605.490
162.402
0.099
6672.75**
6094.11**
1634.54**
Total 29 3736.54
NS = Non-significant (P>0.05); * = Significant (P<0.05); ** = Highly significant (P<0.01)
Table 4.53 Comparison of IBA and NAA for average number of roots/cutting
Conc. (ppm)
Growth regulators Mean IBA NAA
0 0.00 ± 0.00e 0.00 ± 0.00e 0.00 ± 0.00E
2000 21.33 ± 0.19c 19.66 ± 0.20d 20.49 ± 0.39B
4000 38.00 ± 0.19a 13.00 ± 0.29e 25.50 ± 5.59A
6000 24.67 ± 0.04b 10.00 ± 0.29g 17.33 ± 3.28C
8000 12.00 ± 0.12f 6.33 ± 0.19h 9.17 ± 1.27D
Mean 19.20 ± 3.40A 9.80 ± 1.76B
Means sharing similar letter in a row or in a column are statistically non-significant (P>0.05). Small letters represent comparison among interaction means and capital letters are used for overall mean. The ± values given in the table are indicating the standard deviation.
151
4.5.3.4. Average root length (cm)
Analysis of variance for average root length showed highly significant results for both group
of growth regulators, concentration and interaction (Table 4.54). The results indicated that
average longest roots were recorded in softwood cuttings that were treated with IBA (4000
ppm) (8.00 cm) as compared to cuttings treated with NAA. The comparison of growth
regulators showed the superiority of IBA over NAA for long roots. The means of
concentrations were also compared and 2000 ppm induced the longest roots in (6.33 cm) in
treated cuttings of guava.
These results are in accordance with Vale et al. (2008) who concluded that the IBA and NAA
concentration of 3000 mg L-1 resulted in the highest rooting percentage, root length and the
root number. The results are also supported by Lanphear and Meahl (1963) for root length
and number of roots per cutting.
4.5.3.5. Survival percentage of guava plantlet
The ANOVA (Table 4.56) for percentage survival for guava plantlet showed non-significant
results for IBA and NAA and highly significant results for interaction (Table 4.56). The
results for maximum overall survival percentage (92.17%) were noted with 0.4g
concentration.
Similar results were described by Rahman et al. (2004) who found the highest survival of
57.22% in softwood cuttings that were treated with paclobutrazol followed by 54.97% with
IBA. NAA showed low (15.83%) survival of guava plantlets. The highest value for survival
also observed with paclobutrazol and then IBA, while minimum in NAA treated cuttings
(Hafeez et al., 1988). This increase in survival percentage in paclobutrazol and IBA is
because of high number of roots.
152
Table 4.54 Analysis of variance for average root length (IBA and NAA) for softwood cuttings of guava
Source of variation Degrees of
freedom Sum of squares Mean squares F-value
Group
Conc.
Group x Conc.
Error
1
4
4
20
10.824
162.817
14.229
0.171
10.8240
40.7043
3.5573
0.0085
1268.44**
4770.04**
416.88**
Total 29 188.041
NS = Non-significant (P>0.05); * = Significant (P<0.05); ** = Highly significant (P<0.01)
Table 4.55 Comparison of IBA and NAA for average root length of guava softwood cuttings
Conc. (ppm)
Growth regulators Mean IBA NAA
0 0.00 ± 0.00h 0.00 ± 0.00h 0.00 ± 0.00E
2000 6.67 ± 0.04b 6.00 ± 0.00c 6.33 ± 0.15A
4000 8.00 ± 0.12a 4.33 ± 0.01e 6.17 ± 0.82B
6000 5.00 ± 0.00d 3.33 ± 0.02f 4.17 ± 0.37C
8000 3.00 ± 0.12g 3.00 ± 0.01g 3.00 ± 0.05D
Mean 4.53 ± 0.75A 3.33 ± 0.53B
Means sharing similar letter in a row or in a column are statistically non-significant (P>0.05). Small letters represent comparison among interaction means and capital letters are used for overall mean. The ± values given in the table are indicating the standard deviation.
153
Table 4.56 Analysis of variance for survival percentage for (IBA and NAA) of guava plantlet
Source of variation Degrees of
freedom Sum of squares Mean squares F-value
Group
Conc.
Group x Conc.
Error
1
4
4
20
0.5
34212.1
9.5
243.3
0.53
8553.03
2.37
12.17
0.04NS
702.99**
0.19NS
Total 29 34465.5
NS = Non-significant (P>0.05); * = Significant (P<0.05); ** = Highly significant (P<0.01)
Table 4.57 Comparison of growth regulators and concentrations for survival percentage of
guava plantlet
Conc. (ppm)
Growth regulators Mean IBA NAA
0 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00D
2000 85.33 ± 1.45 85.67 ± 1.45 85.50 ± 0.92B
4000 91.33 ± 0.88 93.00 ± 1.00 92.17 ± 0.70A
6000 80.00 ± 3.61 81.00 ± 3.79 80.50 ± 2.35BC
8000 75.33 ± 2.60 73.67 ± 0.67 74.50 ± 1.26C
Mean 66.40 ± 9.02A 66.67 ± 9.09A
Means sharing similar letter in a row or in a column are statistically non-significant (P>0.05). Small letters represent comparison among interaction means and capital letters are used for overall mean. The ± values given in the table are indicating the standard deviation.
154
Figure 4.16. Comparison of root induction behavior of softwood cuttings of guava treated
with growth regulator and non-growth regulator
Control (non-growth regulator) Growth regulator (IBA)
155
Chapter 6
SUMMARY
The most salient results of the study are summarized in this chapter. The objective of this
study was to assess the morphological and diversity and determine the magnitude of
releatadnace in guava accessions collected major guava producing areas of Punjab province.
The present research revealed a wide morphological and genetic diversity in guava
accessions.
Multivariate Principal Component Analysis was performed in order to estimate the
phenotypic diversity the morphological diversity was determined by analyzing the data of
accessions collected from districts Faisalabad and Sheikhupura. All the PCs revealed highly
significant variability for all the traits tested such as tree, leaves, flower, fruit and seed
parameters. Fruit traits such as thickness of outer flesh in relation to core diameter, fruit
length, fruit width, fruit Juiciness, fruit length of stalk, fruit length/width ratio, fruit relief of
surface, fruit size of sepal, fruit sweetness, fruit diameter of calyx cavity in relation to that of
fruit, fruit ridged collar around calyx cavity were identified as the most diverse and important
marker traits of effectively distinguish different guava accessions. Other useful marker traits
were of leaf, i.e. fully developed leaf shape of leaf tips, fully developed leaf shape of base,
fully developed leaf green color, fully developed leaf curvature in cross section, tree and seed
traits were young shoot color and seed size. Simple classification of accession into specific
groups based on helpful key markers traits such as tree young shoot color, fruit shape, color ,
size and other leaf and other seed parameters will enable the effective management,
conservation and sustainable use of guava accessions in future.
The morphological based dendrogram grouped guava accessions in many diverse groups,
Mota gola of Sheikhupura was found most diverse phenotypic accession along with other
phenotypic divers groups and sub groups. The accessions so called unique accessions were
also isolated for phenotypic divergent accessions that are Sadabahar gola (SKP), Gola (SKP),
Gola (FSD), Choti surahi (SKP) and Surahi (FSD) are belonging two different districts. The
heterogeneity of pedoclimatic and conditions and selection pressure of farmer’s traits across
many accessions has created a significant of differentiation at seedling selection that resulted
156
in an important accumulation phenotypic diversity in guava accessions. Since genetic
diversity of accessions, landraces, varieties and species is most economically important
component of global biodiversity.
The establishment, detection and exploitation of molecular markers is one of the most
significant transformation in the field of molecular genetics. Genetic diversity can further be
explained by cluster analyses to determine the genetic relationships among accessions that
were computed for all the accessions. The SSR based dendrogram divided the accessions into
two main clusters and sub clusters due to diverse genetic makeup of accessions. The results
further indicated that Bangladeshi gola (FSD), Lal gola (FSD), Larkana gola (FSD), Gola
(SKP), Moti Surahi (SKP) were genetically diverse and unique one. It is most advisable to
select accessions from these genetically diverse accessions as parents in further
recombination breeding and selection programs.
For clonal propagation of guava, both IBA and NAA appeared to have broad ranges of root
enhancing activity; however, with in the effective range of IBA evaluated, 4000 ppm
produced the higher rooting percentage. This study also revealed the potential of clonal
propagation technique in guava for nursery plants production as it is quick, easy and
economical method for vegetative propagation.
Both morphological and DNA markers concluded a precise information about the diversity
in all the guava accessions and further the clonal propagation also created a great opportunity
for nurseryman for vegetative propagation of guava. However, a more comprehensive and in
depth inventory of guava accessions, their related species and wild guava occurring
throughout the country supported with gene banks of live plant specimens and also an
annexed fruit crop catalogue needs to be carried out keeping in view the further
breeding/improvement programs in guava.
157
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