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MOLECULAR AND PHYLOGENETIC DIVERSITY STUDIES OF SOME ACHA (Digitaria spp.)LANDRACES OF THE JOS PLATEAU AND ITS ENVIRONMENTS

Davou Dung Nyam B.Sc. M.Sc. (Jos) UJ/2012/PGNS/0323

A thesis in the Department of PLANT SCIENCE AND TECHNOLOGY, Faculty of Natural Sciences,Submitted to the School of Postgraduate Studies, University of Jos, in partial fulfilment of therequirements for the award of the degree of DOCTOR OF PHILOSOPHY in CYTOGENETICS AND PLANT BREEDING of the UNIVERSITY OF JOS

JUNE 2017

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DECLARATION

I hereby declare that this work is the product of my research efforts, undertaken under the supervision of Professor Emmanuel Hala Kwon-Ndung, and has not been presented elsewhere for the award of a degree or certificate. All sources have been duly distinguished and appropriately acknowledged.

------Davou Dung Nyam (B.Sc, M.Sc) UJ/2012/PGNS/0323

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CERTIFICATION

This is to certify that this thesis has been examined and approved forthe award of the degree of DOCTOR OF PHILOSOPHYinCYTOGENETICS AND PLANT BREEDING of the University of Jos

______PROF. EMMANUEL HALA KWON-NDUNG Date Supervisor

______DR. PONCHANG A. WUYEP Date Head of Department

______PROF. (MRS) GEORGINA S. MWANSAT Date Dean, Faculty of Natural Sciences

______PROF. NUHU A. GWORGWOR Date Internal Examiner

______PROF. GODFREY AKPAN IWO Date External Examiner

DEDICATION iv

This Thesis is dedicated to my wife Margaret Davou Nyam (CHOM DD) and all the children (Paul-Mado, Nerat-Dagwi, Yongrat, Kim-Zadok and John-Suga & Hwelleng, Kim,Juliet and Dorcas) God has blessed us with.

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ACKNOWLEDGEMENTS

I am indeed, very grateful to Prof.Emmanuel Hala Kwon-Ndung for not only accepting to take over the supervision of this work after the retirement of my first supervisor, Prof. Onyekwere P. Ifenkwe, but also believing in me and pushing me to the successful completion of this work. Your interest and continued encouragement, advice and positive criticisms at various stages of the study which has made the preparation of this thesis possible, is highly appreciated. Prof. Onyekwere P. Ifenkwe, you had desired to graduate me before your retirement from active service but this could not be. Nevertheless, I remain truly grateful for your kind understanding of the predicaments that militated against me and all the encouragements I received from you. I particularly want to acknowledge the support and encouragement of Dr.

Ponchang A. Wuyep, HOD of the Department of Plant Science and Technology. I am indeed very grateful to the Dean of Natural Sciences, Prof. Georgina S. Mwansat, for her interest, concern and push. I sincerely register my profound gratitude to Prof.

Dana‟an A. Dakul, the immediate past Dean for his concern and continued encouragement which has in no small measure propelled this work to its logical conclusion..I particularly want to acknowledge the support, encouragement and excellent criticisms and contributions of the workby Prof. Bashir A. Ajala and Dr.

Ahmed D. Ali.My big brother, Da Dr.Michael D. Sila-Gyang, words indeed, cannot express my appreciation for your selfless contributions in diverse ways to the success of this work.I am also very grateful to Dr. Marta Vicente Crespo of the Institute of

Biomedical Research, Kampala International University, Ishaka, Uganda for her valuable training sessions during my visit to her laboratory.I had also, received a lot of encouragement from Dr. Bitrus Yakubu, Dr. Luka Pam, Ms. Anvou D. Jambol (my teacher in the lab), Mrs. Dinchi Davouand all the other staff of the Department of

Biotechnology, National Veterinary Research Institute (NVRI), Vom. Thank you very much. vi

I sincerely appreciate the efforts of my late parents, Da Chief Anthony D.

Nyam and Ngo Vou D. Nyam for their parental role in my life. I could not have had better parents. Rest in perfect peace with the Lord.My mother, Ngo Vou D. Nyam; thank you for being a loving and caring mother to your children. Even though you did not go to school, you truly appreciated the value of education and did everything possible to ensure we were properly educated. My sincere appreciation to all my siblings, for the love, respect and encouragement. May the good Lord continue to keep us together. I love you all!

My fine wife, Margaret D. Nyam, my „CHOMDD‟, I cannot thank you enough for your concern, support and encouragement are beyond imagination. Our children, I thank you all for being my very loving and wonderful children. The good Lord bless and keep you. I love you all! My special appreciation goes to the academic and non- academic staff of the Department of Plant Science and Technology University of Jos.

I am very grateful to the University of Jos authority for its financial support and sponsorship of this study. I salute and appreciate the entire staff of the School of

Postgraduate Studies for your kind advice and suggestions towards the successful compilation of this study. I extend my profound gratitude to Mr. Gimba Mohammed who helped with the statistical analyses and to Mrs. Bamijoko O. Oroye (Mama

Tosin) who aided in formatting the various chapters of the thesis. Thank you.

My praises and thanksgiving to my Lord and Saviour, JESUS CHRIST for His

Mercies concerning me that has endured forever. Thank you for the people you have sent my way in the course of this work.

TABLE OF CONTENTS

CONTENT PAGE

TITLE PAGE ------i

DECLARATION------ii

CERTIFICATION ------iii vii

DEDICATION ------iv

ACKNOWLEDGEMENTS ------v

TABLE OF CONTENTS ------vii

LIST OF TABLES ------x

LIST OF FIGURES ------xii

LIST OF PLATES ------xiii

LIST OF ABBREVIATIONS AND SMBOLS- - - - - xiv

LIST OF APPENDICES ------xvi

ABSTRACT ------xvii

CHAPTER ONE INTRODUCTION

1.1 BACKGROUND OF THE STUDY - - - - - 1

1.2 STATEMENT OF THE PROBLEM - - - - - 4

1.3 JUSTIFICATION ------5

1.4 AIM ------5

1.5 SPECIFIC OBJECTIVES ------5

1.6 HYPOTHESES ------6

1.7 SCOPE OF THE STUDY ------6

CHAPTER TWO LITERATURE REVIEW

2.1 BOTANY OF THE CROP ------8

2.2 ORIGIN AND DOMESTICATION - - - - - 13

2.3 CULTIVATION AND PRODUCTION - - - - 13

2.4 USES ------15

2.5 CROP ECOLOGY, AGRICULTURAL PRACTICES AND SEED SYSTEM ------15

2.6 COMPOSITION ------20

2.7 CHALLENGES OF ACHA PRODUCTION - - - - 23 viii

2.8 PHYLOGENETIC STUDIES ------24 2.8.1 Molecular Phylogenetics ------25 2.8.2 Techniques and Applications ------25 2.8.3 Characteristics and Assumptions of Molecular Systematics - - 28 2.8.4 Isozymes ------31 2.8.5 Amplified Fragment Length Polymorphism (AFLP) - - 32 2.8.6 Random Amplified Polymorphic DNA (RAPD) - - - 35 2.8.7 Single Sequence Repeat (SSR) - - - - - 35

CHAPTER THREE MATERIALS AND METHODS 3.1 FIELD WORK ------37 3.1.1 Location and Description of Experimental Site - - - 37 3.1.2 Source of Planting Materials ------37 3.1.3 Experimental Treatments and Layout/Design- - - - 37 3.1.5 Cultural Practices ------40 3.1.6 Field Observations/Data Collection ------40 3.1.7 Data Analysis ------41

3.2 LABORATORY WORK ------42 3.2.1 Germination and Seedling Development - - - - 42 3.2.2 DNA Extraction ------42 3.2.3 Agarose Gel Electrophoresis ------43 3.2.4 Amplification ------44

CHAPTER FOUR RESULTS

4.1 MORPHOLOGICAL CHARACTERISTICS - - - 47 4.1.1 Plant Height ------47 4.1.2 Stem Girth ------49 4.1.3 Leaf Length ------49 4.1.4 Leaf Width ------52 4.1.5 Number of Days to 75% Maturity------54 4.1.6 1000 Seed Weight ------54

4.2 CORRELATION ------57

4.3 PRINCIPAL COMPONENT ANALYSIS (PCA) - - - 61

4.4 GENETIC RELATEDNESS OF ACCESSIONS - - - 66

4.5 MOLECULAR ANALYSIS ------82

CHAPTER FIVE DISCUSSION

5.1 MORPHOLOGICAL CHARACTERISTICS- - - - 102 5.1.1 Plant height ------102 5.1.2 Stem girth ------104 5.1.3 Leaf length ------105 5.1.4 Leaf width ------105 ix

5.1.5 Number of days to 75% Maturity - - - - - 105 5.1.6 1000 seed weight ------106

5.2 CORRELATION ANALYSES - - - - - 106

5.3 PRINCIPAL COMPONENT ANALYSIS (PCA) - - - 106

5.4 MOLECUAR ANALYSIS ------108

CHAPTER SIX SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS

6.1 SUMMARY OF FINDINGS ------111

6.2 CONCLUSION ------113

6.3 RECOMMENDATIONS ------114

6.4 LIMITATIONS OF THE STUDY - - - - - 115

6.5 SUGGESTIONS FOR FURTHER STUDY - - - - 115

6.6 CONTRIBUTION TO KNOWLEDGE - - - - 115

REFERENCES ------117

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LIST OF TABLES

TABLE PAGE

1 List of Acha Accessions used in the Study - - - - 37

2 List of Selected Specific Microsatellite Primers Used - - - - 45

3 Mean Height (cm)and combined Mean of 30 Accessions of Acha (Digitaria spp.) Grown in 2012, 2013 and 2014 Rainy Seasons at Binkan Near Jos------48

4 Mean Stem Girth (cm)and Combined Mean of 30 Accessions of Acha (Digitaria spp.)Grown in 2012, 2013 and 2014 Rainy Seasons at Binkan Near Jos ------50

5 Mean Leaf Length (cm) and Combined Mean of 30 Accessions of Acha (Digitaria spp.) Grown in 2012, 2013 and 2014 Rainy Seasons at Binkan Near Jos ------51

6. Mean Leaf Width (cm) and Combined Mean of 30 Accessions of Acha (Digitaria spp.) Grown in 2012, 2013 and 2014 Rainy Seasons at Binkan Near Jos ------53

7. Mean Number of Days to 75% Maturity and Combined Mean of 30 Accessions of Acha (Digitaria spp.) Grown in 2012, 2013 and 2014 Rainy Seasons at Binkan Near Jos - - - - - 55

8 Mean 1000 Seed Weight (g)and Combined Mean of 30 Accessions of Acha (Digitaria spp.) Grownin 2012, 2013 and 2014 Rainy Seasons at Binkan Near Jos ------56

9 Correlation Coefficient between Pairs of Morphological Traits2012: PH(cm), SG(cm), LL(cm), LW(cm), DM, 1000S(g). - - - 58

10 Correlation Coefficient between Pairs of Morphological Traits 2013: PH (cm),SG(cm), LL(cm), LW(cm), DM, 1000S(g). - 59

11 Correlation Coefficient between Pairs of Morphological Traits 2014: PH(cm),SG(cm), LL(cm), LW(cm), DM, 1000S(g). - 60

12 Eigen analysis of the Correlation Matrix of Morphological traits for 2012 Season ------62

13 Eigen analysis of the Correlation Matrix of Morphological traits for 2013 Season ------63

14 Eigen analysis of the Correlation Matrix of Morphological traits for 2014 Season ------65

15Cluster Analysis of Observations of Morphological Characters for 2012 ------76

16Cluster Analysis of Observations of Morphological Characters xi

for 2013 ------78

17 Cluster Analysis of Observations of Morphological Characters for 2014 ------80

18 List of Selected Accessions Used for the molecular analyses - - 83

19 Sequences of Primers Used for RAPD and Microsatellite PCR of Acha (Digitaria sp) genomic DNA and the Polymorphism Obtained - - 100

20 Sequences of Primers Used for RAPD and Microsatellite PCR of Acha(Digitaria sp) genomic DNA and the Percentage Polymorphism 101

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LIST OF FIGURES FIGURE PAGE

1 Acha seeds of D. exilis Stapf and D. iburua Stapf - - - 12

2 Map of West Africa Showing Varietal Density of Acha (Digitaria exilis)- 17

3 Map of West Africa Showing Areas of Cultivation and Importance of Acha: ------18

4 Map of Nigeria Exhibiting its 36 States and the Federal Capital Territory, Showing the Area (States of Collection) of the Accessions- - - 22

5 Dendrogram of Morphological Characters Showing the Linkage Among Thirty Accessions of Acha for 2012 Cropping Season. - - 67

6 Cluster Analysis of Morphological Variables for 2012 - - - 68

7 Dendrogram of Morphological Characters Showing the Linkage Among Thirty Accessions of Acha for 2013 Cropping Season. - - 70

8 Cluster Analysis of Morphological Variables for 2013 - - - 71

9 Dendrogram of Morphological Characters Showing the Linkage Among Thirty Accessions of acha for 2014 Cropping Season - - - 73

10 Cluster Analysis of Morphological Variables for 2014 - - - 74

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LIST OF PLATES

1 A Rack of collections of Acha accessions in the Department of Plant Science and Technology, University of Jos - - - - 39

2 Showing the seeds of the three species of Digitaria - - - 81

3 Gel electrophoregram of primer De 01 on the 11 accessions - 83

4 Gel electrophoregram of primer De 04 on the 11 accessions - - 86

5 Gel electrophoregram of primer De 06 on the 11 accessions - 87

6 Gel electrophoregram of primer De 08 on the 11 accessions - - 89

7 Gel electrophoregram of primer De 10 on the 11 accessions - - 90

8 Gel electrophoregram of primers De 14 on the 11 accessions - - 91

9 Gel electrophoregram of primer De 17 on the 11accessions - - 93

10 Gel electrophoregram of primer De 19 on the 11 accessions - 94

11 Gel electrophoregram of primer De 22 on the 11 accessions - - 96

12 Gel electrophoregram of primer De 26 on the 11accessions - - 97

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LIST OF ABBREVIATIONS AND SYMBOLS

A - adenine

AFLP - Amplified Fragment Length Polymorphism

ANOVA - analysis of variance bp - base pair

C - cytosine

°C - degree Celsius cm - centimeter

CTAB - cetytrimethylammonium bromide

DNA - deoxyribonucleic acid dNTP - deoxyribonucleotide triphosphate

EDTA - ethylenediamine tetra-acetic acid

FAO - Food and Agriculture Organization of the United Nations

EDGE - Evolutionarily Distinct and Globally Endangered

G - guanine g - gram ha - hectare

IUCN - The International Union for Conservation of Nature

Kg - kilogram

LiCl - Lithium Chloride m - meter

Mbp - Mega (or million) base pairs mg - milligram min - minute ml - milliliter mm - millimeter xv mM - millimole ng - Nanogram

PCR - polymerase chain reaction

PCA - principal component analysis pH - Hydrogen proton

PVP - Polyvinylpyrrolidone

RAPD - Random Amplified Polymorphic DNA

RFLP - Restriction Fragment Length Polymorphism

RNAse - ribonuclease

SD - standard deviation

SE - standard error sec - second

SNP - Single Nucleotide Polymorphism

SSR - Simple Sequence Repeat

T - thymine t - ton

U - Uracyl

V - volt

WAP - Weeks after planting

WWF - World Wide Fund for Nature

μl - microliter

LIST OF APPENDICES

APPENDIX PAGE

A INSTRUMENTS USED FOR THE STUDIES - - - 132 xvi

A1 Field Plan for 2012 Cropping Season - - - - - 132 A1 Field Plan for 2013 Cropping Season - - - - - 133 A1 Field Plan for 2014 Cropping Season - - - - - 134 A2 Analysis of Variance for Plant Height in 2012 - - - 135 A2 Analysis of Variance for Plant Height in 2013 - - - 135 A2 Analysis of Variance for Plant Height in 2014 - - - 135 A2 Analysis of Variance for Stem Girth in 2012 - - - 136 A2 Analysis of Variance for Stem Girth in 2013 - - - 136 A2 Analysis of Variance for Stem Girth in 2014 - - - 136 A2 Analysis of Variance for Leaf Length in 2012 - - - 137 A2 Analysis of Variance for Leaf Length in 2013 - - - - 137 A2 Analysis of Variance for Leaf Length in 2014 - - - 137 A2 Analysis of Variance for Leaf Width in 2012 - - - 138 A2 Analysis of Variance for Leaf Width in 2013 - - - 138 A2 Analysis of Variance for Leaf Width in 2014 - - - 138 A2 Analysis of Variance for Days to 75% Maturity in 2012 - - 139 A2 Analysis of Variance for Days to 75% Maturity in 2013 - - 139 A2 Analysis of Variance for Days to 75% Maturity in 2014 - - 139 A2 Analysis of Variance for 1000 Seed Weight in 2012 - - 140 A2 Analysis of Variance for 1000 Seed Weight in 2013 - - 140 A2 Analysis of Variance for 1000 Seed Weight in 2014 - - 140 A3 Correlation Coefficient Distance, Single Linkage Amalgamation Steps for Cluster Analysis of Variables for 2012. - - - 141

A3 Correlation Coefficient Distance, Single Linkage Amalgamation Steps for Cluster Analysis of Variables for 2013. - - - 141

A3 Correlation Coefficient Distance, Single Linkage Amalgamation Steps for Cluster Analysis of Variables for 2014. - - - 141

xvii

ABSTRACT

The evaluation of the molecular and phylogenetic diversity ofAcha (Digitaria spp.) landraces of the Jos plateau and its environs, was carried out between 2012, 2013 and

2014.The phenotypic and genotypic characters of the different accessions showed that variability due to effect was more in genotype had more variability in genetic diversity for the morphological traits.Thirty accessions were collected from Tafawa

Balewa Local Government Area(LGA) of Bauchi State; six LGAs of Plateau State and Jab LGA of Kaduna State and were screened during the cropping seasons of

2012, 2013and 2014 to evaluate their yield potentials and degree of relatedness. The randomized complete block design (RCBD) with three replications was employed for the field experiment. DNA extraction was carried out and Agarose Gel

Electrophoresis was conducted on the restricted amplified DNA extracts using microsatellite primers developed for Digitaria exilis. The results showed that a high level of variability existed between the accessions with respect to plant height, stem girth, leaf length, leaf width, days to maturity and a 1000 seed weight. The principal component one (PC1) contributed 87.1, 78.3 and 91.5% in 2012, 2013 and 2014 respectively, of the total variation.Some of the accessionswhich were identified asDigitariaiburua, took the longest number of days to maturity and had the heaviest

1000 seed weight with an average of 0.72g. Those that were identified as

Digitariaexilis, despite its early maturity date of an average of 132 days had an average 1000 seed weight of 0.62g. One of the accessions identified as D. barbinodis had the least number of days to maturity at 130 days and a mean 1000 seed weight of

0.51g. Correlation analyses revealed a highly significantand positive correlation between yield and the yield components.Dendrogramanalyses in all the years show that two distinct clusters separated the accessions into two morphotypes:

Digitariaiburuagroup and D. exilis group.D. barbinodis, a different species was found to be highly related to the D. exilisgroup. The traits in the 2012 planting season were xviii separated between the 96.79 to 93.59%, this was observed to be between the 100 and

94.62%, and the 99 and 96% for 2013 and 2014 cropping seasons respectively, indicating that there were no traits that were 100% similar. Selection for taller accessions, longer leaf length, wider stem girth and broader leaf width could to some extent, lead to higher yield in Acha production.RFLPs appear to suggest a clear separation of the 3 species (D. iburua, D. exilis and D. barbinodis) demonstrating the extent of their genetic differences at the molecular/DNA level.Evaluation of the phenotypic and genotypic characters for the different accessions in this study shows that the genotype had more variability in genetic diversity for plant height, leaf length, days to maturity and 1000 seed weight in the three year field trials.

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CHAPTER ONE INTRODUCTION

1.2 BACKGROUND OF THE STUDY

One of the basic needs of man has remained food, particularly in Africa while famine has continuously been the enemy of peace and stability. Africans have been sustained by most of its traditional food crops such as rice (Oriza sativa L.), pearl millet (Pennisetum glaucumL.) R. Brown, finger millet (Eleusine coracanaL.), sorghum (Sorghum bicolor L. Moench) and acha, also known fonio, cun (Digitaria exilis Kippist Staph. L. Digitaria iburua Staph and DigitariabarbinodisHenrad).

These food crops could be used as effective tools in fighting hunger in the continent

(National Research Council, NRC, 1996). Sometimes considered as a small seed with a big promise, acha provides food early in the season when other crops are yet to mature for harvest (Ibrahim, 2001). Acha grains have been reported to be the tastiest and most nutritious of all the grains (NRC, 1996). Temple and Bassa (1991) reported that acha contains 7% crude protein, which is high in leucine (19.8%),methionine and cystine of about (7%) and valine (5%). It has also been reported to form the staple food in some of the producing areas where it is processed into various kinds of menus

(Kwon-Ndung et al., 2001). In Nigeria, the Central bank has reported that about

70,000 metric tons of the crop is produced annually (CBN, 1998) and the economic returns of acha when computed showed that it is profitable to grow the crop compared to other crops like rice, sorghum and cowpea (Dauda and Luka, 2003).

Small seeded plants have been known to be among the earliest crops to be domesticated by man (Baltensperger, 1996). These are believed to form diverse groups of crop plant species from different grass genera grown all over the world.

These small seeded cereals may be minor in term of global food production, but they are crops of local importance in semi-arid regions, especially in marginal and drought-prone areas of Africa and Asia (Hilu, 1994) where they constitute along with 2 sorghum, a principal source of energy, protein and vitamins (Wendorf et al., 1992).

Obilana and Manyasa (2002) have reported that the bulk of the production is achieved in sub-saharan Africa and over 130 million people depend daily on these small seeded cereals otherwise known as millets. Acha which is also known as Cun, Fundi, kabuga,

Pom and Fonio in many areas where it is cultivated across the west and central

African regioncutting across Senegal to Chad, represent a unique component of millet diversity traditionally grown in the savannah zone of West and central Africa.

They have shown high tolerance to drought, flooding and disease and due to their hardy nature, these traditional millets are regarded as priority crops in West Africa where they are essential to the diets of millions of the inhabitants and deserve high value in their cultural traditions (Adoukonou-Sagbadja, 2010).

It is a staple food of most of the indigenes of Plateau State, particularly in the

Northern zone of the state. It is also, cultivated in some other states within the middle belt region of Nigeria in appreciable quantities. The place of acha in diet and dietary supplements has been on the increase in recent times. It is a nutritious food and has been reported to be one of the best tasting of all grains (NRC, 1996) with about 7% crude protein and is high in leucine (9.8%), methionine (5.6%) and valine (5.8%)

(Temple and Bassa, 1991). Kwon-Ndung and Misari (1999)and Jideani (1999) have reported the enormous traditional and technological uses of acha. The most interesting aspect of acha as a food item is its exclusive use as a food item for patients suffering from Diabetes mellitus and the related metabolic disorders. It is possible that its relatively low carbohydrate, high protein and appreciable fat content and unique amino acid composition accounts for its suitability as a food item for patients suffering from this disorder. Compared with isolated corn protein fractions of high methionine contents, acha is 3% higher than glutelin (3.7%) but 30% lower than the alcohol-soluble reduced glutelin (6.3%) (Paulis,1982). The total sulphur amino acid content of 7.3% is exceptional to the acha, and makes acha a very good complement 3 to legumes. This underscores its importance as food item and therefore, the need for the productive capacity of this crop to be improved.

During the last 20 – 35 years, there has been a resurgence of molecular and related cellular studies in plants, including the molecular mapping of plant genomes.

It has been reported that the early successes in plant biotechnology has led to the realisation that further molecular improvement of plants will require a thorough understanding of the molecular basis of plant development and the identification and characterization of genes that regulate agronomically important multigenic traits.

The erosion of genetic diversity in most native plant species has been observed to be a product of large scale farming, urbanization and preferential land uses that destroys natural vegetation and the likes. This is one of the major problems of the low production and loss of genetic diversity in most cereal crops and particularly the acha

(Digitaria spp.) in Nigeria. The production of acha has been on the downward trend in preference for other cash crops like potatoes, maize, wheat, rice etc because of the ease of cultivation and yield at the end of the cropping season. Banjo (1988) has confirmed the dominance of “high yielding” cultivars of other crops in the loss of local landraces and species in the developing world. These highly productive new cultivars are rapidly replacing landraces of major economic importance. These landraces have however, evolved in stressful environments and using limited inputs.

This is particularly true of the acha (Digitaria spp.) where it thrives in poor soils, needing very little nutrients. It becomes certain therefore, that such landraces contain genetic systems that equip them to withstand adverse climatic, soil or other constraints. It has become imperative to address the problem of the loss of local genetic resources due to replacements with the improved species. This problem of genetic erosion is particularly serious in Africa. Banjo (1988) enumerated the factors that have contributed and, are still contributing to the loss of genetic diversity in crop plants to range from the indiscriminate destruction of natural habitats to which wild 4 species are adapted, to lack of clear perception of the importance of wild relatives of crop species by policy-makers and the inadequacy of resources.

Crops that have been introduced from other countries are replacing the indigenous species in some cases, with the consequent neglect of the adopted native plants. Ideally, such under-utilised and under-exploited native species should not be allowed to become extinct and unavailable for use by future generations. The genetic resources saved from extinction today, may provide the solution for tomorrow‟s problem of unexpected pests, diseases and or other calamities.In 1995, the National

Cereal Research Institute Badeggi, established a national acha genetic improvement programme through an approved mandate by the National Council on Agriculture.

The institute was given the responsibility of the genetic improvement of acha. The institute has reported a germplasm assemblage, (Kwon-Ndung et al., 1998) which formed the nucleus of a national grmplasm of acha in Nigeria.

1.2 STATEMENT OF THE PROBLEM

Acha is a native species that has been cultivated over a very long time. The local accessions maintained by farmers over the years have drastically reduced in view of changes in cropping systems, urbanization and land use management systems.

These and other factors have led to lower crop yields and production status among local farmers.

Germplasm collection, characterisation and conservation are very important in considering the future of native species in Africa and particularly Nigeria. This is because the more diversity is conserved and made available for future use, the better the chances of meeting tomorrow‟s needs. Before any conservation decision can be made, a basic understanding of the taxonomy, genetic diversity, geographical distribution, ecology and ethno-botany of a plant group is very important.

5

1.6 JUSTIFICATION

The fact that the crop is in danger of extinction or genetic erosion cannot be overemphasized. The need for germplasm conservation is a top priority for this crop.

This study is therefore, imperative so that the morphological and molecular studies can place the appropriate taxonomic status of some accessions collected from Jos andits environments.

Differences exist in name, colour, size and even shape in the acha in the different areas of cultivation. These differences can only be ascertained by molecular studies. Moncadaet al. (2001) have rightly observed that nucleic acid isolation is a critical, but often overlooked step that determines the success of molecular techniques in plant research and breeding. It is therefore, necessary to carryout genetic finger printing, using the most advanced technologies to ascertain the level of such differences, environmental and or genetic. This is crucial in the subsequent strategies to improve the status of the genetic potential of the crop.

1.7 AIM

The aim of this work is to study the morphological and genetic diversity of some acha landraces on the Jos Plateau and its environments. Information from this research will provide a basis of classication in line with the breeding objectives of any future work on this crop.

1.8 SPECIFIC OBJECTIVES

This work therefore, sets out to achieve the following specific objectives: i. Collection and assemblage of germplasm from some areas of production in

Bauchi, Plateau and Kaduna states. ii. Field and laboratory evaluation of the collections using both the morphological

and molecular techniques. iii. Characterization and assessment of the degree of relatedness of the accessions

collected through laboratory and evaluation. 6

1.6 HYPOTHESES i. The acha accessions collected from Bauchi, plateau and Kaduna states do not

differ significantly in their species and varieties. ii. There is no significant difference in the morphological traits of the accessions. iii. There is no significant difference in the band homology of DNA of the

accessions. iv. There is no significant difference in the degree of relatedness of the accessions.

1.7 SCOPE OF THE STUDY

This study was restricted to three acha producing states of Bauchi, Plateau and

Kaduna. Trips were made to Tafawa Balewa and Bogoro Local Government for the collections of the accessions in Bauchi state. In Plateau state, expeditions were undertaken to Bassa, Jos North and South, Riyom, Barkin Ladi, Mangu, Pankshin and

Kanke Local Government areas for the collection of the accessions while, Jaba Local

Government Area was visited to collect the accessions from Kaduna state.

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CHAPTER TWO LITERATURE REVIEW

Acha (Digitaria exilis Kippist.Stapf, Digitaria iburua Stapf. and Digitaria barbinodis Henrard.) is probably the oldest African cereal. For thousands of years,

West Africans have been cultivating it across the dry savannas and it was once their major food. This crop still remains very important in areas scattered from Cape Verde to Lake Chad. It has been reported to either be the staple or major part of the diet in

Mali, Burkina Faso, Guinea and Nigeria (NRC, 1996). It has also been reported that each year, West African farmers devote approximately 300,000 hectares to cultivating the acha and that the crop supplies food to 3 – 4 million people. Its production is restricted to certain ethnic groups for which it has high socio-cultural and economic value (NRC, 1996).

Acha is an important crop that has many names, some of which include the following: Fonio, Fundi, Kabuga Fundo, Chun and Pom. The Europeans called it

„Hungry Rice‟ and is considered by some authors as misleading (Kwon-Ndung and

Misari, 1999; Ibrahim, 2001; Annonymous, 1995). Acha has been suggested to be the oldest indigenous African cereal with a cultivation record dating back to 7000 years

(Cruz, 2004). Recently, discoveries show that the acha is cultivated and used as food and forage in the Dominican Republic island of South America (Pablo et al., 2003).

Cruz (2004) has reported that the global land area being put to its production is estimated to be 380,000ha, with an annual production of 250,000 tons. The average production per hectre remaining very low: ranging from600 – 700 kg/ha. NRC (1996) and Aslafy(2003) have reported that this crop fits into the low-input farming systems of the resource-poor African farmers because of its unique ability to tolerate poor and marginal soils and withstand drought. Dachi and Omueti (2000) reported that acha responds positively to fertilizer application even though the crop has been neglected in the past (Kwon-Ndung and Misari, 1999). This crop has been reported to 8 beconsidered important for improvement as a cultivated species (Ibrahim, 2001,

Morales-Payan et al., 2002).

Acha, has an estimated annual production to be about 100,000 tonnes (Anon.

1995). It can be used as porridge or added to other cereals as meal. It can also be used as fodder and its stems can be used as roofing material. Acha will tolerate marginal land and, on poor soil will grow better than any other crop. In fact farmers say that if nothing else will survive on a particular piece of land, then that is the place to sow acha (Anon. 1995).

In Mali, its price is triple that of millet and it is sold at double the price of rice or millet flour (Anon. 1995). In Nigeria, Plateau State accounts for more than 90% of the production and forms the staple food item amongst the indigenes that cultivate it.

Its price competes with that of rice and triples the price of maize, millet and other cereals. The consumption of acha (Digitariaspp.) is expanding rapidly among the

Nigerian population as an alternative to rice and consumers acknowledge that it has exceptional nutritional qualities. In contrast to other cereals where the germ, which is rich in fats, never disappears completely when hulled, the hulled grains of acha contain particularly no lipids (Anon., 1995). This is another reason why interest in this crop is growing among agriculturists. Many national programmes are incorporating the crop in their research strategic plan. For example, in June 1994, the programme for the promotion of indigenous cereals in Bamako, Mali dedicated an international workshop to acha. Similarly, the National Cereals Research Institute (NCRI) acha programme in Nigeria commenced in 1994.

2.1 BOTANY OF THE CROP

The genus Digitaria Haller is represented by about 300 species of annual and perennial grasses (Bogdan, 1977). A large number of these species such as Digitaria horizontalis, D. leptorachis and D. sanguinalis are recognised as weeds. Others such as D. decumbens Stent, D. eriantha Steud and D. macroblephoraStapf, have been 9 cultivated as forage crops and yet others such as D. exilis Stapf, and D. iburua Stapf, have been cultivated as human cereal crop (Holm et al., 1977; Oates et al., 1959;

Foster and Mundy, 1961; Romney, 1961; Steele, 1976). While species of Digitaria are well known as weeds or forage grass, they are only cultivated for food in some restricted areas such as on the Jos Plateau area of Nigeria.

The genus Digitaria belongs to the family Poaceae (Graminae). It is an annual grass, about 45 – 120cm tall with tillers and branches bearing linear, glabrous leaves approximately 15cm long. The terminal inflorescence is made up of panicle bearing spikes which form a digitate panicle. Each raceme approximates 15cm in length and bears spikelets in a single row or double rows along one side of the axis. The upper florescent of each pair in a spikelet is a hermaphrodite but the lower one is stable with a small palea enclosed by a lemna which fulfils the protective function of the very small lower glume. The upper glume is as long as the spikelet (about 2mm) with 3-5 nerves (Fig. 1). They have clearly digitate or subdigitate panicles and are turfted, with some members stoloniferous in the same species (Bogdan, 1977). Digitariahas perfectly glabrous and somewhat more turgid spikelets (Stapf, 1915). Digitaria iburua, which is more cultivated in some areas of the Jos Plateau and the Atakora

Mountains of Togo and the Republic of Benin, differs from D. exilis in the packed arrangement of spikelets, the angular scabrid pedicels and the short, very delicate upper glume (Stapf, 1915).

The crop is a cereal crop belonging to the grass family Poaceae, sub-family

Panicoideae, tribe Paniceae and the genus DigitariaHaller. This genus is reported to comprise of between 230 – 325 annual and perennial grass species with a wide range of geographic distribution in the tropics and subtropics (Henrard, 1950; Clayton and

Renvoize, 1986). Adoukonou-Sagbadja (2010) has observed that the acha/fonio millets appear to be the most economically important crop in this genus althoughD. sanguinalis L. has also been grown as millet in Eastern Europe from the middle Age 10 to the beginning of the 20th century. It is on record however that D. crucita Camus, domesticated at the late nineteenth century and D. compacter Veldkemp (Rishan) are still grown in India (Nesbitt, 2005). Quite a number of other wild species are reported to be valuable forage grasses throughout the tropics. Many others have been harvested in the past for food in times of famine or food scarcity in Africa (Haq and Ogbe,

1995; Adoukonou-Sagbadja et al., 2006). This is particularly true of the Beroms and other tribes of the Jos Plateau and environs who cultivate the acha.

Acha species are C4 plants, which are a group of plants that posses 4-carbon molecules present after the first product of carbon fixation. They are mostly adapted to marginal lands in the hot, drought-prone arid and semi arid regions of Africa, Asia and the Americas. Often called tropical or warm season plants, they reduce carbon dioxide (CO2) captured during photosynthesis to useable components by first converting carbon dioxide (CO2) to oxaloacetate (Oxalo acetic acid), a 4-carbon acid.These C4 plants have developed adaptaions which minimize the losses to photorespiration and therefore, more efficient in photosynthesis than the C3 plants.

They occur in grasses, sugar cane, maize, sorghum, Amaranthus and Atriplex. The acha are free-tillering and annual herbaceous plants with erect, slender and glabrous culms. D. exilis is usually up to 80 cm tall while D. iburua, usually, can reach 1.5 m.

Leaves are glabrous with a proximal sheathing base and distal strap shaped blade.

Their inflorescence is a finger-shaped panicle having 2-5 digitate (D. exilis) or 4-10 sub-digitate (D. iburua) racemes of 5-12 cm length. In D. iburua, the lowest raceme is somewhat distant from the remaining. The spikelet contains two bisexual florets with the lower unfertile whilst the upper is fertile having three stamens with yellowish anthers, two lodicules and a pink or purplish stigma. The reproductive system in these species remains less understood. For some authors, fonio species are likely self- fertilized crops (Watson and Dallwitz, 1992; Sarker et al., 1993); however

11 an outbreeding system has also been advocated (Fogg, 1976; Hilu et al. 1997). Grains are extraordinarily tiny (0.5-1mm diameter, 0.75-2mm length) with 1,000 weighting

0.5-0.6g. The caryopsis is tightly enclosed within two brown husks (lemma and palea). In D. iburua, the husks are intensively dark-brown; hence it is commonly named black fonio in contrast to D. exilis known as fonio or white fonio. Within each species, diverse varieties with a growth cycle varying from 60 to 130 days are recognized by farmers.

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Figure 1 Acha seeds of D. exilis Stapf and D. iburua Stapf Adapted from Porteres (1976): African cereals: In: Origins of African Plant domestication

13

2.2 ORIGIN AND DOMESTICATION

Digitaria exilis Stapf and D. iburua Stapf are two underutilised crops grown throughout the savanna zone of West AfricaPurseglove, (1985). Digitaria barbinodis is another cultivar The cultivation of D. exilis scatters from Cape Verde in the West to the Lake Chad in the East, from the edge of the Sahara in the north to the beginning of the rain forest in the South (Fig. 2). Porteres (1955) reported that there are about fifteen types of D. exilis in the Futa-Djallon highland and its surroundings. It is also found along the upper basins of the Senegal and Niger rivers, and on the edges of the basins. However, as you go eastwards as far as Chad, the number decreases. He suggested that the centre of discernible varietal diversification is situated in the upper basins of the Niger from the river‟s source to the central delta (Fig.2).

Ethnic migrations toward the south, as a result of the pluvio-climatic deterioration of the southern Sahara, may suggest that the centre of origin was probably located further to the North (Gani, 1988). But the diverse species of

Digitaria are not, on the whole, adapted to the excessively dry conditions. Porteres

(1955) reported that the cultural limit of D. exilis is situated on the annual isohyets of

1500mm, while sorghum and pennisetum can be limited to the isoyet of 200 – 250mm and that precocious variety of D. exilis are cultivated in dry conditions and late ones in wet conditions.

Digitaria iburua is known to be cultivated in Plateau, Kaduna and Nasarawa

States of Nigeria and the Atakora Mountains of Togo and the Republic of Benin. It is reported that this species has been domesticated during the Neolithic Saharan age

(Porteres, 1946).

2.3 CULTIVATION AND PRODUCTION

It has been rightly observed that acha (fonio/acha) are small-scale farmers‟ crops and their production is still essentially at the subsistence level. The total production of acha in West-Africa is not known as precise production statistics are 14 lacking for many producing countries. According to Bezpaly (1984), approximately

300,000 ha are devoted yearly to the cultivation of the crop in the region. In

2008/2009 agricultural season, the available statistics indicated a total of 448,247 ha harvested with 480,227 tons of grains produced (FAOSTAT, 2009). Most widely grown, white fonio/acha furnishes the quasi-totality of the recorded production while black acha accounts only for the negligible part (Ndoye and Nwasike, 1993).

A survey of FAO production statistics shows that the last two decades indicates Guinea and Nigeria are the two leading acha producers in the region, followed by Mali, Côte d‟Ivoire and Burkina Faso (Fig. 3). Elsewhere, the production is minimal because of the tediousness of acha cultivation and processing, strong competition from maize and other cash crops like cotton, absence of modern varieties, etc. (Sanou, 1993, Adoukonou-Sagbadja et al.,2006). Adoukonou-Sagbadja et al.

(2006) have also reported that productivity in the crop varies greatly across growing areas, years and is highly influenced by climate hazards. According to them, the regional average yield oscillates between 0.6-0.9 t/ha with the best productivity reaching 1.5 t/ha. In the Sahelian zone, yields are extremely low and fall often under

0.2 t/ha. Acha is essentially produced for human consumption although reported use for fodder purposes has been reported in the Dominican Republic (Pablo et al., 2003).

It is an important household food security crop as the grains can be conserved many years without insect damage.

Acha is well appreciated for its tasty and easily digestible grains and serves either as staple or co-staple food for several millions of tribal people. For instance in many tribal areas of Guinea, Mali, Togo and Nigeria, acha can be consumed two to three times a day and is preferred to other cereals (Haq and Ogbe, 1995). It is also the most prestigious and hence the first food choice reserved for guests or special occasions, for example, ceremonies. Diverse biochemical investigations indicated that the nutritive value of acha grain is favorably comparable with that of other cereals 15

(Haq and Ogbe, 1995). Acha has excellent protein composition (9-12%) that is advantageously rich in methionine and cystine, two vital amino-acids almost deficient in the major cereals like sorghum, rice, wheat or barley (NRC,1996).

2.3 USES

Traditionally, acha is routinely consumed as stiff or thin porridge, couscous, and canbe mixed with other flours to make breads. It is also popped or used to brew local alcoholic or non-alcoholic drinks. Nowadays, acha foods are gaining importance in many urban centers particularly in Guinea, Mali and Nigeria while precooked products are timidly entering European market under the bio label.

Utilization of acha grain as animal feed is not significant. However, the chaff and straw are important valued by-products widely used as livestock feed while the latter is often used by farmers in confecting mattresses, kitchen and barn roof. Acha has also a number of folk medicinal values, for example, it is a useful diet for those suffering from diabetes or for delivering women (Jideani, 1999; Adoukonou-Sagbadja et al., 2006). Aside these usages, acha is associated to the cultural and religious traditions of farmers. For instance, in the cosmology of Dogons (Mali), it is believed that the universe was born from a grain of D. exilis (Griaule and Dieterlen, 1950).

2.5 CROP ECOLOGY, AGRICULTURAL PRACTICES AND SEED SYSTEM

In West-Africa, fonio millets are grown in traditional rain-fed farming system under awide range of agro-climatic conditions (Adoukonou-Sagbaja, 2010). Digitaria exilis is cultivated from sea level up to 1500 m altitude and mainly in areas receiving annually 700 to 1,000 mm rainfall; however the crop easily enters pluvial areas of critical rainfall deficiency with its current cultural limit at the annual isohyet of 150 mm whereas in general sorghum and pearl millet are limited by isohyets of 200-250 mm (Portères, 1976). Southwards, the cultivation becomes rare when the annual rainfall reaches 2,000 mm (Diallo, 2003). D. iburua is grown in similar but mostly in 16 upland conditions (Portères 1976). Both crops are adapted to various soils including poor, sandy, degraded or marginal soils but heavy and saline ones are less suitable. In

Fouta Djallon for instance, D.exilis copes well with acidic clays with high aluminium content, a combination often toxic to most food crops (Haq and Ogbe, 1995). The optimal growing temperature range is 25-30°C with approximately 12 hour daylight.

In general, in contrast to black fonio, white fonio seems to be sensitive to day length

(Adoukonou-Sagbadja, 2010).

Acha cultivation is fairly simple and remains exclusively manual. The crop is mainly grown in pure culture with rare associations with sorghum, pearl millet, guinea millet (Brachiaria deflexa Robyns), okra (Hibiscus esculentus L.), Roselle (Hibiscus sabdariffa L.) etc. Considered as a very low demanding crop, fonio occupies generally the last place in rotation systems after beans/groundnut and pearl millet/sorghum. Farm size is small and often below 1 ha.

The sowing period varies among producing zones and depends on the onset of the rainy season. Soil preparation is minimal limiting to slight hoeing. Seeds are mainly broadcast-sown, at a seeding rate of ca. 10 to 30 kg of seed/ha. The weeding is performed manually two tothree times from planting to heading. Pesticides and fertilizers are not applied by farmers and adequate information on the nutrient requirements of acha not well documented. At maturity, acha is harvested by uprooting or cutting the straw.

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Figure 2Map of West Africa showing Varietal Density of Acha (Digitaria exilis) Adapted from Porteres (1976): African cereals: In: Origins of African Plant Domestication

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Figure 3 Map of West Africa showing Areas of Cultivation and Importance of Acha Adapted from Porteres (1976): African cereals: In: Origins of African Plant Domestication

19

Weeding is the most labour consuming activity, involving the farmer, his family and friends as reported by Kwon-Ndung et al. (1999)although Jideani,(1990) was of the opinion that harvesting is more tedious. Threshing is performed by beating or trampling the fonio sheaves. Adoukonou-Sagbadja et al. (2006) reported that the grains are well storable (5-10 years) and that their viability seems to decrease considerably after two years. According to them, farmers generally grow only one landrace but some rare households can grow two to three, depending on labor availability. In the entire cultivation zone, landraces are inherited from generation to generation.

Acha seeds destined to be sown the next season are directly taken from the new harvested stock. In case of shortfall, farmers can obtain planting seeds from relatives or friends in exchange with other crops but buying from local market is not or less practiced because of possible seed mixture of different landraces.

The cultivated Digitaria species are usually sown during the first rains, on well prepared fine tilt soil. The crop does not appear to benefit from being planted deeper than 5cm (Irvin, 1974). It is usually grown in pure stand, but may sometimes be mixed with sorghum or millet (Purseglove, 1972). Seeds could be broadcast or sown in rows (Dalziel, 1937). The seeds usually germinate within 3 – 5 days (Irvin,

1974) but sometimes it takes longer, particularly with D. iburua. When the plant shoots up, it is carefully weeded and at times it may need no weeding if the seeding rate is high (Purseglove, 1985). A number of cultivars are recognised, with varying periods of maturity of 90 – 130 days (Purseglove, 1985). The stems which are slender, are then bent to the earth by the mere weight of the grains, along the direction of wind. Harvesting is done with hooked sickles. During harvesting, the plant is tied up in small sheaves and placed in a dry place. The grains on the straw then become swollen to their covering, for ease of removal from the straw. The grains are trodden with feet or beaten by sticks and then dried in the sun for ease of the removal of the 20 skin (seed covering) which is carried out by pounding in wooden mortars. It is afterwards winnowed with cane flail.

Porteres (1955) reported that heavy soils are rather ill-suited to the majority of cultivated varieties. In general however, the crop is adapted to a wide range of soil types: sandy, limey, gravely or pebbly soils, slopes plateaus and valleys or river banks. Dalziel (1937) reported that its cultivation in Nigeria is largely done by the tribes who inhabit the Jos – Plateau where the soil is for the most part, poor and sandy.

The yield of the crop is very low, in the region of 600 – 800Kg ha-1, although yields over 1000Kg ha-1 have been recorded (Purseglove, 1985). Field trial reports from Nigeria indicate yield ranges of 700 – 1200kg/ha (Dachi and Omueti, 2000)

2.6 COMPOSITION

The acha crop is a nutritious food with approximately the following contents:

Protein 8.7% (typical of cereals), Fats 1.1%, Carbohydrate 81%, Fibre 1.1% and Ash

2.1% (Purseglove, 1972, 1985). The acha has been reported to have a very high methionine content (Anon. 1995) and this was confirmed by Benito et al (1986). It was also reported that the acha has a very high methionine + cysteine content of 7.3%

(Anon. 1995). Like other cereals, it is complementary to legumes with low methionine content. The methionine reported for the acha is higher than for a number of other cereals.

The acha has been reported to have been cultivated for millennia in thedry savannahs of West Africa, but much remains to be learned about its nutritional properties. In the work of Glew et al. (2013) in which achawas collected in four villages in Northern Nigeria and analyzed for fatty acids, minerals, amino acidsand antioxidant content, fatty acids accounted for 1.91% of the dry weight, with 47.4% linoleic acid and30.5% oleic acid. The content of the essential minerals, copper, magnesium, molybdenum, zinc andcalcium averaged 4.88, 1060, 0.23, 23.0 and 172 21

μg/g, respectively. The protein content was 6.53% andthe essential amino acid pattern, except for lysine, compared favorably to a World Health Organization(WHO) reference protein. According to them, the total polyphenolic content of methanolic extracts of acha matched that ofcommon cereals (for example, maize, rice, wheat) and the extracts contained substantial amounts of free-radical scavenging substances.

Thus, acha is a source of many nutrients critical to human health.

On poor soils where few crops can thrive, acha is widely grown as amongst the tribes of the Jos – Plateau in Nigeria, and the Semi-Bantu tribes, in Guinea and

Guinea Bissau (Fig. 3). The small grain has an attractive flavour, and is suitable for porridge and could be ground into flour and mixed with meal from other cereals. In

Guinea, Father Lambert (1788) cited in Porteres (1955) claimed that it is white in colour, easily ground into flour and can be made into a thick gruel for eating, after butter has been added. Dalziel (1973) stated that it makes a substitute for semolina for the Europeans. In the Jos – Plateau of Nigeria, it is a major staple food and is consumed as a beverage (Gbirik cun, Kunun acha). The grain could be boiled and eaten with fish, meat and spinach, or used in soup. Alcohol (local beer) is also made from the grain (Irvin, 1974). The straw is used for stuffing mattresses or for bedding for livestock. It is also burnt and the ash extracted with water to make soup (as among the Beroms and the other ethnic tribes of the Jos - Plateau).

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Figure 4 Map of Nigeria Exhibiting its 36 States and the Federal Capital Territory, Showing the Area (States of Collection) of the Accessions.

KEY: A. Bauchi (Tafawa Balewa) B. Plateau C. Kaduna (South)

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2.7 CHALLENGES OF ACHA PRODUCTION

Despite their importance in traditional agriculture, research efforts to improve the acha production are still at a low level. In consequence, the crops remain primitive facing diverse agronomical and technological problems. First, acha cultivation relies only on traditional landraces which are, despite their adaptability to marginal farming system, less productive. In addition, traditional farming practices (e.g. systematic use of poor and eroded soils, poor husbandry, etc.) and frequent droughts occurrence, etc., may considerably affect the performance of the crops. Lodging is a serious drawback in acha cultivation because of the fragile shoot of the plant; it renders the harvest tedious and contributes notably to the yield loss. Besides, seed shattering at maturity, though limited in the crops, can become important if the harvest is delayed (up to 25% according to Vodouhè et al. (2003).

While both acha species have shown low susceptibility to pests and diseases, the fungi Phyllachorasphearosperma and Helminthosporium spp. have been seen to affect the crops. Fonio is also found to be susceptible to rust caused by Puccinia cahuensis(Adoukonou-Sagbadja, 2010). The parasitic Striga, particularly S. rowlandi known to abundantly occur in West-Africa, causes serious damage to the crop (Sanou,

1993; Haq and Ogbe, 1995). Apart from losses due to insect pests, significant seed losses are also reported to occur occasionally from the effect of lodging and shattering in the event of late harvesting.

The current low ranking of acha/fonio in regional cereal production makes them less competitive than other major cereals like pearl millet, sorghum or maize and hampers their improvement through breeding, as the interest of breeders has been low.

Adoukonou-Sagbadjaet al. (2007a), has rightly observed that progress in the genetic improvement of acha has also been hindered by the biological characteristics of the crop and the fact that nothing is yet known on the inheritance of traits of agronomic relevance in the acha. The biological limitations among others include the miniature 24 size of floral organs, the dearth of information on reproductive biology but also a poor knowledge of the level and organization of the genetic diversity present in the crops.

Accordingly therefore, great efforts are needed to characterize and exploit acha genetic resources for the improvement of this valuable but neglected crop not only in

Nigeria, but in West-Africa. Another very serious issue is Weeding. This appears to be the single most important constraint in acha husbandry. Farmers spend a lot of time in manual weeding of the crop (Kwon-Ndung and Misari, 1999).

2.8 PHYLOGENETICSTUDIES

Phylogeny has been defined as the history of descent of a group of taxa such as species from their common ancestors including the order of branching and sometimes the time of divergence (Anand et al., 2014). In a review article by Anand et al. (2014), an overview of the uses of molecular markers employed by researchers for the purpose of phylogenetic studies was given. According to them, in molecular phylogeny, the relationships among organisms or genes are studied by comparing homologues of DNA or protein sequences. Dissimilarities among the sequences indicated genetic divergences as a result of molecular evolution during the course of time. They further explained that while classical phylogenetic approach relies on morphological characteristics of an organism, the molecular approaches depend on nucleotide sequences of RNA and DNA and sequences of aminoacids of a protein which are determined using modern techniques. By comparing homologous molecules from different organisms, it is possible to establish their degree of similarity thereby establishing or revealing a hierarchy of relationships, a phylogenetic tree. They further stressed that both classical morphology based methods and molecular analysis based methods are of importance as the basic bio-molecular framework of all organisms are similar and morphology of an organism is actually the manifestations of its genome, proteome and transcriptome profiles. A combination of the morphological based methods and molecular analysis based methods thus strengthens 25 the exercise of the determination of phylogenetic relationships of organisms to a great extent. According to them, efforts of classical biologists who have been basing their phylogeny analyses on morphological studies of both external and internal features of an organism should be encouraged and, that in combination with studies using molecular genetic markers and morphology, relatively full proof systems can be devised by phylogenetic studies.

2.8.1 Molecular Phylogenetics

Molecular phylogenetics also known as molecular systematics is the use of the structure of molecules to gain information on an organism‟s evolutionary relationships. The result of a molecular phylogenetic analysis is expressed in a phylogenetic tree.

2.8.2 Techniques and Applications

Every living organism contains DNA, RNA and the proteins. Closely related organisms generally have a high degree of agreement in the molecular structure of these substances, while the molecules of organisms distantly related usually show a pattern of dissimilarity. Conserved sequences, such as mitochondrial DNA, are expected to accumulate mutations over time, and assuming a constant rate of mutation, provide a molecular clock for dating divergence. Molecular phylogeny uses such data to build a “relationship tree” that shows the probable evolution of various organisms. Not until recent decades, however, has it been possible to isolate and identify these molecular structures.

The most common approach is the comparison of homologous sequences for genes using sequence alignment techniques to identify similarity. Another application of molecular phylogeny is in DNA barcoding, where the species of an individual organism is identified using small sections of mitochondrial DNA (Wikipedia, http://en.wikipedia.org/wiki/Molecular_phylogeny). Another application of the techniques that make this possible can be seen in the very limited field of human 26 genetics, such as the ever more popular use of genetic testing to determine a child‟s paternity, as well as the emergence of a new branch of criminal forensics focused on evidence known as genetic fingerprinting (Felsenstein, 2004).

Early attempts at molecular systematics were also termed as chemotaxonomy and made use of proteins, enzymes, carbohydrates and other molecules which were separated and characterized using techniques such as chromatography. These have been largely replaced in recent times by DNA or RNA segments extracted using different techniques. These are generally considered superior for evolutionary studies since the actions of evolution are ultimately reflected in the genetic sequences (Edna and Victor, 2008).

At present, it is still a long and expensive process to sequence the entire DNA of an organism (its genome), and this has been done for only a few species (Edna and

Victor, 2008). However, it is quite feasible to determine the sequence of a defined area of a particular chromosome. Typical molecular systematic analyses require the sequencing of around 1000 base pairs. At any location within such a sequence, the bases found in a given position may vary between organisms. The particular sequence found in a given organism is referred to as its haplotype. In principle, since there are four base types, with 1000 base pairs, we could have 41000 distinct haplotypes.

However, for organisms within a particular species or in a group of related species, it has been found empirically that only a minority of sites shows any variation at all and most of the variations that are found are correlated, so that the number of distinct haplotypes that are found is relatively small(Ahlquist and Sibley, 1999).

In a molecular systematic analysis, the haplotypes are determined for a defined area of genetic material; ideally a substantial sample of individuals of the target species or other taxon is used. However, many current studies are based on single individuals. Haplotypes of individuals of closely related, but supposedly different taxa, are also determined. Finally, haplotypes from a smaller number of individuals 27 from a definitely different taxon are determined: these are referred to as an out group.

The base sequences of the haplotypes are then compared. In the simplest case, the difference between two haplotypes is assessed by counting the number of locations where they have different bases: this is referred to as the number of substitutions

(other kinds of differences between haplotypes can also occur, for example the insertion of a section of nucleic acid in one haplotype that is not in another). Usually the difference between organisms is re-expressed as a percentage divergence, by dividing the number of substitutions by the number of base pairs analysed: the hope is that this measure will be independent of the location and length of the section of DNA that is sequenced (Hillis and Moritz, 1996).

An older and superseded approach was to determine the divergences between the genotypes of individuals by DNA-DNA hybridization. The advantage claimed for using hybridization rather than gene sequencing was that it was based on the entire genotype, rather than on particular sections of DNA. Modern sequence comparison techniques overcome this objection by the use of multiple sequences (Soltis et al.,

1992).

Once the divergences between all pairs of samples have been determined, the resulting triangular matrix of differences is submitted to some form of statistical cluster analysis, and the resulting dendrogram is examined in order to see whether the samples cluster in the way that would be expected from current ideas about the taxonomy of the group, or not. Any group of haplotypes that are all more similar to one another than any of them is to any other haplotype may be said to constitute a clade. Statistical techniques such as bootstrapping and jacknifing help in providing reliability estimates for the positions of haplotypes within the evolutionary trees

(Wikipedia, http://en.wikipedia.org/wiki/Molecular_phylogeny).

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2.8.3 Characteristics and Assumptions of Molecular Systematics

According to Wikipedia, (http://en.wikipedia.org/wiki/Molecular_phylogeny) the following constitute the characteristics and assumptions of molecular systematics:

1. Molecular systematic is an essentially cladistic approach: it assumes that

classification must correspond to phylogenetic descent, and that all valid taxa

must be monophyletic.

2. Molecular systematic often uses the molecular clock assumption that quantitative

similarity of genotype is a sufficient measure of the recency of genetic divergence.

Particularly in relation to speciation, this assumption could be wrong if either a. Some relatively small genotypic modification acted to prevent interbreeding

between two groups of organisms, or b. In different subgroups of the organisms being considered, genetic modification

proceeded at different rates.

3. In animals, it is often convenient to use mitochondrial DNA for molecular

systematic analysis. However, because in mammals mitochondria are inherited

only from the mother, this is not fully satisfactory, because inheritance in the

paternal line might not be detected.

These characteristics and assumptions are not wholly uncontroversial among biological systematists. As a cladistic method, molecular systematics is open to the same criticisms as cladistics in general. It can also be argued that it is a mistake to replace a classification based on visible and ecologically relevant characteristics by one based on genetic detail that may not even be expressed in the phenotype (Hillis and Moritz, 1996). However, the molecular approach to systematics, and its underlying assumptions, are gaining increasing acceptance. As gene sequencing becomes easier and cheaper, molecular systematics is being applied to more and more groups, and in some cases is leading to radical revisions of accepted taxonomies. 29

The theoretical framework for molecular systematics was laid in the 1960s in the works of Emile Zuckerandl, Linus Pauling and Walter M. Fitch. (Edna and Victor,

2008). Applications of molecular systematics were pioneered by Charles G. Sibley

(birds), Herbert C. Dessauer (herpetology), and Morris Goodman (primates), followed by Allan C. Wilson, Robert K. Selander, and John C. Avise (who studied various groups). Work with protein electrophoresis began around 1956. Although the results were not quantitative and did not initially improve on morphological classification, they provided tantalizing hints that long-held notions of the classifications of birds, for example, needed substantial revision. In the period of 1974 – 1986, DNA-DNA hybridization was the dominant technique (Ahlquist and Sibley, 1999).

Phylogenetic diversity is a measure of biodiversity which incorporates

Phylogenetic difference between species. It is defined and calculated as "the sum of the lengths of all those branches that are members of the corresponding minimum spanning path" in which 'branch' is a segment of a cladogram and the minimum spanning path is the minimum distance between the two nodes. It can also be seen as a measure of a species taxonomic distinctiveness and can be estimated by looking at the phylogenetic relationships among taxa. Keriann et al. (2007) believes that species- specific metrics on phylogenetic diversity can be used to determine conservation priorities at various biogeograhical scales.This definition is distinct from earlier measures which attempted to incorporate phylogenetic diversity into conservation planning, such as the measure of 'toxic diversity' introduced by Vane-Wright et al.

(1991).

The concept of phylogenetic diversity has been rapidly adopted in conservation planning, with programs such as theZoological Society of London'sEvolutionarily

Distinct and Globally Endangered (EDGE) of Existence programmefocused on evolutionary distinct species. Similarly, the World Wide Fund for Nature‟s

(WWF's)Global 2000 also includes unusual evolutionary phenomena in their criteria 30 for selecting target ecoregions.Some studies have indicated that alpha diversity is a good proxy for phylogenetic diversity PD, so suggesting the term has little use, but a study in the Cape Floristic Region showed that using phylogenetic diversity led to selection of different conservation priorities than using species richness. It also demonstrated that PD led to greater preservation of 'feature diversity' than species richness alone. Keriannet al, (2007) have also, observed that there are a few ecological and behavioural data on populations of some of the African primates that represent the highest levels of phylogenetic diversity. They were of the opinion that studies of primate taxa with high phylogenetic diversity rankings should focus on identifying sites suitable for intensive studies of population densities, feeding ecology and reproductive behaviour and then suggested that phylogenetic diversity metrics could serve as an important, complementary data set in the International Union for

Conservation of Nature(IUCN) ranking system (particularly for primates).

Plant genetic resources have played very important role in generating new high yielding crop varieties with resistance to biotic and abiotic stresses (Murray et al., 2008). It is worthy of note that morphological, biological and molecular procedures have been exploited for evaluating these resources. Whereas, phenotypic variation is not always truly indicative of genetic variation partly due to significant gene-environment interaction, until recently however, either qualitative or quantitative morphological characters were exploited for most of the crop characterization and evaluation.

Molecular markers are powerful genetic tools in modern agriculture. These have been used for investigating and characterizing genetic variability in any organism including plants. They have been used for genome and comparative mapping, phylogeny and population genetics, parental selection and species identification, association studies and quantitative trait loci (QTL) analysis. The use of molecular markers started with the discovery of biochemical markers (storage 31 proteins, isozymes) in the 1960‟s (Lewontin and Hubby, 1966). Along with the increase in knowledge on the genetic properties of DNA, numerous novel molecular techniques that detect directly polymorphisms at DNA level have evolved. The most commonly used DNA marker techniques in plant genetics are:Restriction Fragment

Length Polymorphisms (RFLPs), Amplified Fragment Length Polymorphisms

(AFLPs), Random Amplified DNA Polymorphisms (RAPDs), Inter Simple Sequence

Repeats (ISSRs), Simple Sequence Repeats or Microsatellites (SSRs) and Single

Nucleotide Polymorphisms (SNPs). These methods are used solely or as complementary tools to the traditionalagro-morphological markers, known to be often subjected to environmental influences. In general, molecular methods differ in their principle, application, type and amount of polymorphism detected (reviewed by

Semagn et al., 2006).Furthermore, each genetic marker system has its own benefits and drawbacks.Therefore, choosing the most appropriate marker system will depend on manyfactors such as the precise purpose, the desired levels of polymorphism, theavailability of technical facilities, as well the efficiency in terms of costs and timerequirements (Vendramin and Hansen, 2005).

2.8.4 Isozymes

Isoenzymes or isozymes are the earliest molecular markers used to detect genetic variation in organisms. They are homologous enzymes differing in their amino acid sequences but share the same catalytic function (Markert and Möller,

1959). Isozymes are expressed either by different alleles at the same locus (yet referred as allozymes) or by distinct loci. These differences may arise either from changes at the DNA level, which causes amino-acid substitutions and changes in charge of the protein or from post-translational modifications (e.g. glycozylation) which lead to changes in molecular weight. The ability to observe allelic variation at isozyme loci has revolutionized research in the fields of biochemical genetics, population genetics as well as in systematic and evolution studies (Hamrick, 32

1989;Crawford, 1989; Gao and Hong, 2000). Isozymes have the advantages that their analysis requires non sophisticated equipment; they are usually co-dominant making them appropriate forheterozygocity estimates in genetic diversity studies. However, the main drawbacksto their use are the limited number of available enzyme systems

(only 10 to 30 available for a given organism, reviewed by Avise, 2004), the use of specific detection methods for each enzyme, and only genomic regions coding for expressed proteins can be analyzed resulting in low polymorphism (Lewontin, 1991).

Nowadays, isozymes are largely superseded by modern DNA-based approaches that are more informative and offer broader genome representation and higher prospects for selective neutrality. As the cheapest and quickest marker systems to develop, isozymes remain nonetheless an excellent choice for studies that only need toidentify low levels of genetic variation, for instance in quantifying mating systems (Zeidler,

2000).

2.8.5 Amplified Fragment Length Polymorphism (AFLP)

Amplified Fragment Length Polymorphism (AFLP) is among the most commonly used DNA-based molecular marker techniques and has been applied to a variety of questions in plant biology, including genetic diversity and population genetics (Carr et al., 2003; Seehalak et al., 2006), molecular taxonomy and evolution

(Bänfer et al., 2004; Milla et al., 2005), species/cultivar identification (Portis et al.,

2004), genetic mapping and linkage analysis (Nissan-Azzouz et al., 2005), etc.

Originally developed by Vos et al. (1995), the essence of AFLP procedure lies in the combined use of two basic tools in molecular biology: the restriction endonuclease

(Danna and Nathans, 1971), which reduces the target genomic DNA into a pool of fragments and the Polymerase Chain Reaction (PCR) (Mullis et al., 1986), which allows amplification of a subset of these restriction fragments using primers with arbitrary selective extensions. 33

In higher plants, fragment amplification is usually conducted in two steps: a pre-amplification and an amplification using primers with one and three selective nucleotides at their 3‟-end, respectively. This allows a sequential reduction in complexity of the restricted patterns generated (i.e. to 1/16 and 1/4096 respectively).

The presence or absence of the selective nucleotides in the genomic fragments being amplified and the restriction fragment size variation provide the basis for revealing polymorphism in AFLPs. This polymorphism can be due to differences in restriction sites, mutations around the restriction sites or inherent to insertions or deletions within the amplified restriction fragment (Bonin et al., 2005). The size and number of the resulting AFLP products make them ideally suitable for size-fragmentation and visualization as bands by polyacrylamide gel electrophoresis. In general, 20 to 150 polymorphic bands (markers) can be expected for any single assay, depending on the size and structure of the target species genome (Bonin et al., 2005). Their size range is typically between 50 to 500 bp. Successful AFLP analysis requires high quality DNA free of any contaminants that could otherwise alter the banding profiles. Besides, the choice of the restriction enzyme can be important.

The two most commonly used enzymes in AFLP studies are MseI / EcoRI

(four-base / six-base cutter); TaqI / PstI are the main respective alternatives found in the literature. In general, to increase the informativeness of the AFLP technique, different combinations of primer pairs leading to more polymorphic markers are usually used. The AFLP fingerprinting technique offers several advantages compared to other molecular markers. It has the capacity to detect a higher number of polymorphic loci in a single assay than RFLPs or RAPDs (Powell et al., 1996), has a higher discrimination efficiency in comparison to RAPDs (Uptmoor et al., 2003,

Wagner et al., 2005) and ISSRs (Archak et al., 2003), and produces highly reproducible results (Jones et al., 1998). However, like RAPDs and ISSRs, AFLPs show generally dominant inheritance which is the main detrimental aspect of the 34 technique. The use of AFLP markers to study genetic diversity and population genetics in crops is promising because many polymorphic loci can be obtained fairly easily, in a relatively short time and without any prior knowledge of the genome of the species under study (Vos et al., 1995). Therefore, they are found to be particularly attractive for the genetic diversity and differentiation studies, particularly in minor and neglected crops such as Eragrostis tef (Ayele et al., 1999), finger millet (Le

Thierry d‟Ennequin et al., 2000), proso millet (Karam et al., 2004), or African rice

(Barry et al., 2006). The AFLP technique was also reported to work well for genetic relationships and phylogenetic studies in closely related species (Sharma et al., 1996,

Le Thierry d‟Ennequin et al., 2000, Bänfer et al., 2004) and found as efficient as microsatellites in parentage analysis and mating system determination (Gerber et al.,

2000, Thomson and Ritland, 2006). In general, the estimation of allele frequencies and subsequently the population genetic parameters (e.g. number of alleles per locus, average heterozygocity or gene diversity, FST, GST, etc.) for dominant markers such as AFLPs present some statistical limitations because of the inability in distinguishing between homo- and heterozygote dominant genotypes (Lynch and Milligan, 1994).

These difficulties canbe resolved by using indirect methods such as the Bayesian approach (Lynch and Milligan, 1994, Zhivotovsky, 1999) and/or alternative estimators like Shannon diversity index and Amova-based ΦST that rely on band frequencies (Shannon and Weaver, 1949, Excoffier et al., 1992). On the other hand, for stable and biologically relevant results, Kimberling et al. (1996) suggested sampling a high number of loci as possible. In relation to this, Kremer et al. (2005) using AFLP markers, showed that the monolocus estimation of genetic diversity has the potential to vary strongly with variations in the fixation index, but that the multilocus estimate is rather robust to deviations in Hardy-Weinberg equilibrium, because of the mechanistic effect of compensation between negative and positive 35 biases of genetic diversity estimates for different AFLP loci exhibiting contrasting frequencies of the null homozygote.

2.8.6 Random Amplified Polymorphic DNA (RAPD)

Marker–assisted selection can enhance the speed and effectiveness of plant breeding. Welsh & McClelland (1990) and Williams et al.(1990) have observed that

Molecular markers successfully developed during the last two decades have largely overcome the problems that are associated with phenotype-based markers and one of such techniques is the use of random amplified polymorphic DNA (RAPD).Many studies have been devoted to assessing patterns of sorghum genetic variation based on morphology (Appa-Rao et al., 1996; Djè et al., 1998) or pedigree (Jordan et al.,

1998). More recently, DNA-based techniques have been used successfully in DNA fingerprinting of plant genomes (Hongtrakul et al., 1997; Cervera et al., 1998) and in genetic diversity studies (Paul et al., 1997; Sonnate et al., 1997; Barrett and Kidwell,

1998; Chowdari et al., 1998b; Zhu et al., 1998; De-Bustos et al., 1999). Among them, random amplified polymorphic DNA (RAPD) analysis is quick (Colombo et al.,

1998; Fahima et al., 1999) and well adapted for nonradioactive DNA fingerprinting of genotypes (Cao et al., 1999). However, problems with the reproducibility in amplification of RAPD markers and with data scoring have been reported (Jones et al., 1998). Although major bands from RAPD reactions are highly reproducible, minor bands can be difficult to repeat due to the random priming nature of this PCR reaction and potential confounding effects associated with co-migration with other markers (Tessier et al., 1999).

2.8.7 Single Sequence Repeat (SSR)

Single sequence repeat (SSR) markers are attractive for DNA fingerprinting studies for several reasons. They are co-dominant and highly informative. They generally display high levels of polymorphism (Beckmann and Soller, 1990; Brown et al., 1996; Senior et al., 1998) and are amenable to automated genotyping strategies. 36

They also can be amplified by PCR and efficiently detect DNA polymorphism (Pejic et al., 1998). Finally, radioisotopes are not required in the detection of SSR markers, because sequence polymorphism usually can be detected by separation in agarose gels

(Burr, 1994). Although SSRs are well established for human and mammalian genetics, these markers have only recently become available in plant species. They have been identified in many plant genomes including those of maize (Senior and

Heun, 1993; Shatuck-Eidens et al., 1990; Taramino and Tingey, 1996); soybean

(Akkaya et al., 1992; Morgante and Olivieri, 1993); Brassica spp. (Poulsen et al.,

1993); rice (Wu and Tanksley 1993); barley (Saghai-Maroof et al., 1994); pearl millet

(Chowdari et al., 1998a); Arabidopsis (Depeige et al., 1995); tomato (Broun and

Tanksley, 1996); conifers (Tsumura et al., 1997); and sorghum (Brown et al., 1996;

Taramino et al., 1997; Dean et al., 1999). The results of studies using SSR markers in these species suggest that they may provide an outstanding new tool for genetic analysis of plant species. Harlan and DeWet (1972) classified cultivated sorghum based on agronomic and morphological characteristics. The utilities of isozymes

(Morden et al., 1989; Aldrich et al., 1992), RFLP (Aldrich and Doebley, 1992), and

RAPD (de Oliveira et al., 1996; Menkir et al., 1997; Ayana et al., 2000) markers have been used to study genetic diversity in sorghum germplasm. Several efforts have been made to utilize SSR markers in plants to study genetic diversity, characterize germplasm, and evaluate population dynamics (Zhang et al., 1997; Liu and Wu, 1998;

Senior et al., 1998; Struss and Plieske, 1998). Agrama and Tuinstra (2003), have shown that a comparison of RAPD and SSR marker techniques in sorghum was timely, even though the utility of different molecular markers for corn (Smith and

Helentjaris, 1996), soybeans (Powell et al., 1996) and barley (Russell et al., 1997) germplasm already has been reported.

37

CHAPTER THREE MATERIALS AND METHODS

This research work was carried out in two phases; field and the Laboratory.

3.1 FIELD WORK

3.1.1 Locaton and Description of Experimental Site

A field trial was carried out in 2012, 2013 and 2014 rainy seasons between

May and November each year to investigate the morphological variations of some acha accessions collected from different parts of Plateau, Bauchi and Kaduna states.

The field trial was conducted at Binkan, near Deeper Life Camp Ground, off Zaria road in Bassa Local Government Area of Plateau state, Nigeria. The site is located at latitude 9o20‟ N; 8o90‟ Eand 1208metres altitude above sea level. The temperature fluctuates between 13o - 34.5oC. The soil type is sandy loam with a past cropping history of maize, sorghum, acha, millet, potatoes, soybeans cropped in no particular order.

3.1.2 Source of Planting Materials

The thirty (30) accessions used in the experiment were obtained from the germplasm collection in areas of acha production in Plateau state and its environs (Kaduna and

Bauchi) (Table 1).

3.1.3 Experimental Treatments and Layout/Design

The experimental treatments consists of the 30 accessions of acha, where the experimental layout had 30 plots in which each plot measured 3m by 2m (6m2), laid out in a randomised complete block design (RCBD) and replicated three times with

1m inter replicate space. A total experimental area of 11m x60m was used (Appendix

A1).

38

Table 1 List of Acha Accessions Used in the Study SN Accession Accession Name Description of Scientific Name code seed coat colour 1 P1 Chwu Kperie 08 Dark brown Digitaria iburua 2 P2 Were Zawan Dirty brown Digitaria exilis 3 P3 Aburu Bwoi 07 Reddish brown Digitaria iburua 4 P4 Nding Zawan Dark brown Digitaria barbinodis 5 P5 Gyetel Burum 06 Light brown Digitaria exilis 6 P6 Chwe Rihwe Ansie 08 Brown Digitaria exilis 7 P7 Chisu Mangu 08 Slight brown Digitaria exilis 8 P8 Lub Bungha 07 Light brown Digitaria exilis 9 P11 Aburu Bakkanik 06 Mixed brown Digitaria iburua 10 P12 Udin Gyetel 06 Slight brown Digitaria exilis 11 P13 Takwal Fier 06 Brown Digitaria exilis 12 P15 Chwe Rikwi Kpa Guriea 08 Brown Digitaria exilis 13 P16 Mayama Du 03 Brown Digitaria exilis 14 P17 Udin Bakkanik 06 Lite brown Digitaria exilis 15 P19 Mbulus Nga 07 Light brown Digitaria exilis 16 P21 Peng Madu 03 Dark brown Digitaria iburua 17 P22 Sala Gyel 01 Dark brown Digitaria exilis 18 P23 Bwut Madu 03 Dark brown Digitaria iburua 19 P24 Chwu Zashi 08 Dark brown Digitaria iburua 20 P25 Jan Buru Chiga 06 Dark reddish brown Digitaria iburua 21 P26 Chikarai Jwakras 08 Slight brown Digitaria exilis 22 P27 Gindiri Vwang 06 Slight dirty brown Digitaria exilis 23 K1 Ziyan 08 Brown Digitaria exilis 24 K2 Wuwam 08 Brown Digitaria exilis 25 K3 Zealt 06 Brown Digitaria exilis 26 K4 Halat Jaba 08 Dark brown Digitaria iburua 27 B1 Wyandat 08 Light brown Digitaria exilis 28 B3 Wyant 06 Brown Digitaria exilis 29 B4 Hlad 06 Reddish brown Digitaria exilis 30 B5 Chid Kusung 06 Brown Digitaria exilis

Key: P – Collections from Plateau State

K – Collections from Kaduna State

B – Collections from Bauchi State

Subsequently, the accession code numbers in the above table would be used to refer to

the different accessions.

39

Plate 1 A Rack of collections of the Acha accessions in the Department of Plant Science and Technology, University of Jos 40

3.1.5 Cultural Practices

Land preparation was done manually by using hand-hoe on May 7th in the

2012 rainy season, May 14thand May 10th in the 2013 and 2014 rainy seasons, respectively.

Sowing was done on the same day the land was prepared. This was to avoid the rains falling before the seeds were uniformly broadcast and as this would prevent proper covering by the soil, making the seeds vulnerable to marauding birds.

Sowing per plot was by broadcast of 30g seed weight. Weeding was carried out by rouging out the weeds at 6 weeks after sowing (WAS) as this could not be done earlier because of the difficulty in distinguishing the acha plants from the other grasses/weeds that may be growing in the experimentalplots. Weeding was subsequently sustained by manual removal of the weeds at random periods from the trial till harvest. No fertilizer was applied.

3.1.6 Field Observations/Data Collection

Field observations and data on the following parameters were collected at various growth stages of the plants.

3.1.6.1 Plant height (cm)

The above ground plant heights of ten randomly selected plant stands per plot were measured with a metre rule from the point of first node to the tip of the terminal leaf. This was done from11WAS during which more than 75% of the panicle had already emerged and at harvest.The average plant height was calculated and used in the analyses.

3.1.6.2 Stem girth per plant (cm)

Ten plant stands were sampled as in 3.1.6.1 above and the stem girth was measured with Vernier caliper. This was done at maturity and the average stem girth was calculated for all the plots and recorded.

41

3.1.6.3 Number of leaves per plant

The number of leaves (opened) was counted per plant for ten plants randomly sampled within each plot at maturity. The average number of leaves for all the plots was calculated.

3.1.6.4 Leaf length per plant (cm)

The leaf length was measured by randomly selecting ten plants within each plot with the aid of a metre rule. This was done at the point of panicle emergence. At the end of the experiment, the average leaf length per plot of the different accessions was calculated.

3.1.6.5 Leaf width per plant (cm)

The leaf width was measured by randomly selecting ten plants within the differentplots. The mean leaf width was calculated.

3.1.6.6 Number of days to75% maturity

The number of days it took for each of the plots to mature is when full grain filling was evidenced and both stalk and panicle had completely turned brown, with evidence of lodging of the plants along the direction of the wind.

3.1.6.7 1000 Seed weight (g) One thousand seeds from the harvested plants were counted from the different plots and weighed on a digital top-loading balance (LP 502A, B. Bran by Scientific and Instrument Company, England) and the weight used in the analyses.

3.1.7 Data Analysis

Data collected were subjected to the analysis of variance (ANOVA) according to the „F‟ test as described by Snedecor and Cochran (1969). Significance of mean difference among the accessions (treatments) weres eparated using the Duncan

Multiple Range Test. 42

Simple correlation coefficient was determined to find the relationship between a 1000 seed weight and plant height, stem girth, leaf length, leaf width and number of days to 75% maturity.

Principal Component Analysis (PCA) was also employed to further elucidate the contributions of variability among the different variables of the accessions, in which dendrograms were constructed to illustrate the level of identity and degree of relatedness among the various groups of the accessions.

3.2 LABORATORY WORK

3.2.1 Germination and Seedling Development

One gram (1g) of seeds from the different accessions was germinated in petri- dishes overlaid beneath with Whatman filter paper. These were kept moist by the addition of water during the period of germination and seedling development. These seedlings were harvested whole after 10 days and used for the DNA extraction.

3.2.2 DNA Extraction

DNA extraction was carried out using the protocol described by Yin et al. (2011) at the Biotechnology laboratory of the National Veterinary ResearchInstitute, Vom,

Plateau state.

The following solutions and reagents were used in this process:

a. Extraction buffer: 2% CTAB; 100mM Tris-HCL, pH 8.0; 1.5M NaCl; 2%

polyvinylpyrrolidone (PVP)-40; 20mM ethylene diamine tetraacetic acid

(EDTA), pH 8.0; 1% β-mercaptoethanol. 50µg/ml proteinase K was added

immediately before use. Others included:

b. 8M Lithium Chloride (LiCl),

c. Chloroform,

d. 75% Ethanol,

e. 100% Ethanol,

f. RNase-free water. 43

Freshly harvested seedlings of the acha were crushed in a mortar with a pestle until a very soft paste was made. This was to enable the disruption of cells from the plant tissue. 100mg of the sample paste was transferred into 10ml centrifuge tubes. To each sample 5ml of extraction buffer was added, and mixed thoroughly by vortexing and incubated at 650C for 20 minutes with occasional swirling. Samples were cooled to room temperature and then, 3ml of Chloroform was added to each tube. This was then vortexed and incubated on ice for 5 minutes. The mixture was then centrifuged at

8000 rpm for 20 min at 40C to pellet the cellular debris, protein, and polysaccharides.

The supernatant was transferred to a new tube. 1.5ml of 8M Lithium Chloride was added to each tube and gently mixed and incubated at -800C for 2 hours. These were again centrifuged at 8000 rpm at 40C for 20 minutes. The solution was decanted and the pellet was washed with 75% ethanol and centrifuged at 10000 rpm at 40C for 10 min. The pellet was air dried for 10 min before dissolving in 100µl RNase free water.

3.2.3 Agarose Gel Electrophoresis

A 2% agarose gel was prepared. 1μl of 6X gel loading dye was added to 2μl of each DNA sample before loading the wells of the gel. The dye was added to allow for the monitoring of the extent to which the samples would have migrated during electrophoresis, so that it could be halted at the appropriate stage. One well was loaded with uncut, good quality λDNA as molecular weight standards. The submarine electrophoretic gel was run at 100V for 30 minutes till the dye had migrated one-third of the distance in the gel. The extracts were checked for DNA on a 1% agarose gel before storing at -200C until needed.

The DNA was then visualized using a BIO-RAD Gel Doc™ XR+ molecular imager.

3.2.4 Amplification 44

Ten microsatellite primers developed by Barnaud et al. (2012) were synthesized and used for amplification on the DNA extracts. These markers had been developed for Digitaria exilis. The primers used are given in Table 2.

45

Table 2 List of Selected Specific Microsatellite Primers Used Locus Forward primers (5 ′ – 3 ′) Reverse primers (5 ′ – 3 ′) Size (bp)

De-01 CTAACTCCTTCTCCCTCACC TGGCACTGACACAGTAAC 257

De-04 CATTTTCCCGAAGACAGAGG GACCTTGTGGCACCCATC 258

De-06 AGGAATGGCCTCAATACAT AGAAAGCAGTTGGATTGGT

207

De-08 TTGGTGGATATTGGAATTATG TTTACCCAACGCATAGGTAG

206

De-10 TCTTTTGTTTCTGGGATG ACTTGAGACCTGCAAAGA

203

De-14 CGAGACCTGATTTGTTTAGC CAAGTCTTTGATTTCCGTCT

199

De-17 GTAACGAACATCGGGTGA CTGATGGCAAGGATGTGT

201

De-19 CATCTTCGAGGTTCTTGGT AGCAGTGATTCGGTAGGAC

164

De-22 ATCGAGAGTTCAGTGAGTCC GATCATCAAACCATTTACCC 193

De-26 AATACATTTTCCCCTTCGTC GGATCTCGTTCATGTGCTAT 181

46

The reagent mix and cycling program was also adopted from Barnaud et al.

(2012) with some slight modifications. These are also presented below:

3.2.4.1 Reagent Master Mix

1 X (µl)

Nuclease free water 12.5

10X PCR Buffer 2.0 dNTP mix (10mM) 0.2

MgCl2 (25mM) 0.8

Forward Primer (4 µmol) 1.0

Reverse Primer (4 µmol) 1.0

Taq polymerase (0.5U) 0.5 18.0 DNA 2.0

20.0

3.2.4.2 Cycling Program

Initial denaturation 940C for 5 min

Denaturation 940C for 30 sec

Annealing 580C for 90 sec 35 cycles

Extension 720C for 90 sec

Final extension 720C for 10 min

The amplification was carried out on a GeneAmp® PCR system 9700

(Applied Biosystems). The PCR products were electrophoresed on a 2% agarose gel stained with ethidium bromide using a SIGMA PS 2000-2 machine and the gel viewed in a BIO-RAD Gel Doc™ XR+ molecular imager. The gel was run at 100 V for 30 minutes.

47

CHAPTER FOUR RESULTS

The results are presented in two sections: section A deals with the morphological characteristics of the accessions of Digitaria Spp. and section B deals with the molecular aspect of the work.

The most widely cultivated acha accession is the Digitaria exilis, followed by

D. iburua and D. barbinodis being the least cultivated in the study area (Bauchi,

Plateau and Kaduna states). Regarding its geographical distribution, the results show that the genetic diversity in D. barbinodis, D. exilis and D. iburua is concentrated on the Jos Plateau. This is confirmed by the number of accessions prevalent within the

Jos Plateau than those obtained in Bauchi and Kaduna states.

4.1 MORPHOLOGICAL CHARACTERISTICS

4.1.1 Plant Height

The results for plant height are presented in Table 3. The results show that significant differences existed among the accessions with respect to plant height.

During the 2012 study, accession P21 produced the tallest plant height while accession P17 produced the shortest. In the 2013 study, accession P1 produced the tallest plant height while accession P17 produced the shortest. In the 2014 study, P1 produced the tallest plant height while P5 and P13 produced the shortest plant height.

Across the years, P21 had reasonably taller plant heights. Plant height did not show any particular trend in performance as it appeared tallest in P21 in 2012, P1 in 2013 and P21 in 2014. Plant height varied between 68.4cm in P17 and 109.7cm in P21 in the 2012 trial, between 68.6cm in P4 and P15 and, 110.0cm in P1 in the 2013 trial and, between 68.6cm in P5 and P13 and 110.0cm in P21 in 2014 trial (Table 3,

Appendix A2).

The three year combined mean for plant height showed that accession P21 had the tallest plant height with the average value of 109.7cm. This was closely followed 48

Table 3 Mean Height (cm) and Combined Mean of 30 Accessions of Acha (Digitaria spp.) Grown in 2012, 2013 and 2014 Rainy Seasons at Binkan near Jos Mean Plant Height (cm) Three Year Accession Accession Name 2012 2013 2014 Combined code Mean P1 Chwu Kperie 08 106.2 b 110.0 a 106.0 b 107.4 b P2 Were Zawan 69.50 op 69.00 kl 70.00 ghi 69.50 ijkl P3 Aburu Bwoi 07 98.20 g 99.70 ef 100.0 c 99.30 e P4 Nding Zawan 69.10 qr 68.60 l 70.00 ghi 69.23 kl P5 Gyetel Burum 06 69.90 lmn 70.33 ijk 68.60 i 69.61 ijkl P6 Chwe Rihwe Ansie 08 70.10 lm 71.00 i 70.40 ghi 70.50 hijkl P7 Chisu Mangu 08 77.20 h 76.60 g 78.00 d 77.27 f P8 Lub Bungha 07 73.40 i 73.80 h 74.60 e 73.93 g P11 Aburu Bakkanik 06 100.1 e 102.2 d 106.7 b 103.0 d P12 Udin Gyetel 06 70.20 l 71.20 i 69.70 ghi 70.37 ijkl P13 Takwal Fier 06 69.23 pqr 69.00 kl 68.60 i 68.94 kl Chwe Rikwi Kpa 69.37 jkl P15 69.30 pq 68.60 l 70.20 ghi Guriea 08 P16 Mayama Du 03 71.50 j 71.20 i 70.80 ghi 71.17 hi P17 Udin Bakkanik 06 68.40 s 68.50 l 69.33 hi 68.74 l P19 Mbulus Nga 07 69.30 pq 70.20 ijk 69.60 ghi 69.70 ijkl P21 Peng Madu 03 109.7 a 109.2 a 110.2 a 109.7 a P22 Sala Gyel 01 73.60 i 74.00 h 73.60 ef 73.73 g P23 Bwut Madu 03 99.50 f 98.90 f 100.2 c 99.53 e P24 Chwu Zashi 08 105.7 c 106.2 b 105.8 b 105.9 c P25 Jan Buru Chiga 06 104.9 d 104.6 c 105.2 b 104.9 c P26 Chikarai Jwakras 08 71.20 j 71.40 i 73.80 e 72.13 h P27 Gindiri Vwang 06 69.70 no 69.40 jkl 70.20 ghi 69.77 ijkl K1 Ziyan 08 71.20 j 70.30 ijk 71.80 fg 71.10 hij K2 Wuwam 08 71.40 j 70.60 ij 71.80 fg 71.27 hi K3 Zealt 06 70.60 k 71.20 i 69.90 ghi 70.57 hijk K4 Halat Jaba 08 99.50 f 100.8 e 100.4 c 100.2 e B1 Wyandat 08 68.90 r 69.20 jkl 69.60 ghi 69.23 kl B3 Wyant 06 69.70 no 69.60 jkl 70.40 ghi 69.90 ijkl B4 Hlad 06 70.20 l 70.20 ijk 71.20 gh 70.53 hijkl B5 Chid Kusung 06 69.80 mno 70.00 ijk 70.20 ghi 70.00 ijkl

SE (±) 0.1155 0.425 0.6525 1.2 CV (%) 0.25 0.9 1.41 1.498 Means followed by the same letter(s) in a column are not significantly different at 5% level of probability (Duncan‟s new multiple range test)

49 by P1, P24 and P25 with corresponding values of 107.4cm, 105.9 and 104.9cm respectively. The shortest across the three years was observed to be P17, with a mean value of 68.74 (Table 3).

4.1.2 Stem Girth

The results for the stem girth are presented in Table 4. The results show that significantdifferences exists among the accessions. In 2012 study, P23 and P25 produced the widest stem girth while P16 had the narrowest stem girth. In 2013, the widest stem girth was observed in P24, with the least measure in P13, B1 and B3. In the 2014 study, P25 had the widest stem girth while P4, P15, P17, P19, P27, K1 and

B3 had the narrowest. Across the years, P23 and P25 appeared to maintain a better performance with respect to the stem girth (Table 4, Appendix A2).

The combined mean for the three years showed that the widest stem girth was observerved in P25 with a mean value of 2.467cm. This was followed by P21, P23,

P1, P3 and P11 with the corresponding mean values of 2.456, 2.433, 2.399, 2.310 and

2.301cm, respectively (Table 4).

4.1.3 Leaf Length

The results for leaf length are presented in Table 5. The results show that significant differences existed among the accessions. In the three years of study, accession P25 produced the longest leaf length with no such trend being observed for the shortest. However, in 2012 and 2013 trials, the shortest leaf length was observed in P4, while K1 had the shortest in 2014 (Table 5, Appendix A2).

The combined mean for the three years also revealed that the leaf length had its average peak at 25.10cm with P25. This was followed by P21 and K4 with

23.53cm each while the least value of 13.20cm was observed in P4. The remaining accessions fell within these extremes (Table 5).

50

Table 4 Mean Stem Girth (cm) and Combined Mean of 30 Accessions of Acha (Digitariaspp) Grown in 2012, 2013 and 2014 at Binkan near Jos Mean Stem Girth (cm)

Accession Three Year Accession Name 2012 2013 2014 code Combined Mean P1 Chwu Kperie 08 2.400 ab 2.410 a 2.387 a 2.399 a cdefg P2 Were Zawan 0.9000 def 0.9067 cd 0.9333 cd 0.9133 P3 Aburu Bwoi 07 2.300 ab 2.310 a 2.320 a 2.310 a P4 Nding Zawan 0.8000 ef 0.8100 d 0.8000 d 0.8033 fg P5 Gyetel Burum 06 0.9000 def 0.9067 cd 0.9000 cd 0.9022 defg P6 Chwe Rihwe Ansie 08 1.100 cd 1.200 b 1.000 bcd 1.100 cdef P7 Chisu Mangu 08 1.200 c 1.200 b 1.100 bc 1.167 cd P8 Lub Bungha 07 1.100 cd 1.100 bc 1.200 b 1.133 cde P11 Aburu Bakkanik 06 2.300 ab 2.303 a 2.300 a 2.301 a cdefg P12 Udin Gyetel 06 0.9000 def 0.9000 cd 1.000 bcd 0.9333 P13 Takwal Fier 06 0.8000 ef 0.8000 d 0.9000 cd 0.8333 fg efg P15 Chwe Rikwi Kpa Guriea 08 0.9000 def 0.9000 cd 0.8000 d 0.8667 P16 Mayama Du 03 0.6333 f 1.067 bc 0.9333 cd 0.8778 defg P17 Udin Bakkanik 06 0.8000 ef 0.9000 cd 0.8000 d 0.8333 fg efg P19 Mbulus Nga 07 0.9000 def 0.9000 cd 0.8000 d 0.8667 P21 Peng Madu 03 2.467 a 2.500 a 2.400 a 2.456 a P22 Sala Gyel 01 1.200 c 1.100 bc 1.000 bcd 1.100 cdef P23 Bwut Madu 03 2.500 a 2.400 a 2.400 a 2.433 a P24 Chwu Zashi 08 1.200 c 2.500 a 2.400 a 2.033 b P25 Jan Buru Chiga 06 2.500 a 2.400 a 2.500 a 2.467 a c P26 Chikarai Jwakras 08 1.300 c 1.200 b 1.100 bc 1.200 fg P27 Gindiri Vwang 06 0.8000 ef 0.9000 cd 0.8000 d 0.8333 K1 Ziyan 08 0.9000 def 0.9000 cd 0.8000 d 0.8667 efg K2 Wuwam 08 1.067 cde 1.000 bcd 1.000 bcd 1.022 cdefg cdef K3 Zealt 06 1.200 c 1.100 bc 1.000 bcd 1.100 K4 Halat Jaba 08 2.200 b 2.300 a 2.400 a 2.300 a efg B1 Wyandat 08 0.9000 def 0.8000 d 0.9000 cd 0.8667 B3 Wyant 06 0.8000 ef 0.8000 d 0.8000 d 0.8000 g cdefg B4 Hlad 06 0.9000 def 1.000 bcd 1.000 bcd 0.9667 efg B5 Chid Kusung 06 0.8000 ef 0.9000 cd 0.8667 d 0.8556

SE (±) 0.0817 0.0614 0.0662 11.3 CV (%) 10.98 8.2 8.7 0.244

Means followed by the same letter(s) in a column are not significantly different at 5% level of probability (Duncan‟s new multiple range test).

51

Table 5 Mean Leaf Length (cm) and Combined Mean of 30 Accessions of Acha (Digitaria spp.) Grown in 2012, 2013 and 2014 Rainy Seasons at Binkan near Jos

Mean Leaf Length (cm)

Three Year Accession Accession Name 2012 2013 2014 Combined code Mean P1 Chwu Kperie 08 16.00 j 20.40 e 20.80f 19.07 f P2 Were Zawan 17.02 g 16.90 f 16.60 h 16.84 g P3 Aburu Bwoi 07 21.60 d 22.10 c 23.10 c 22.27 cd P4 Nding Zawan 12.10 r 13.60 l 13.90 lm 13.20 m P5 Gyetel Burum 06 14.60 mn 14.80 i 14.90 j 14.77 ijk P6 Chwe Rihwe Ansie 08 14.60 mn 14.70 ij 15.00 j 14.77 ijk P7 Chisu Mangu 08 15.40 k 15.60 h 15.80 i 15.60 hi P8 Lub Bungha 07 16.60 h 16.90 f 16.80 gh 16.77 g P11 Aburu Bakkanik 06 22.70 c 23.20 b 23.10 c 23.00 bc P12 Udin Gyetel 06 15.07 l 15.50 h 15.60 i 15.39 ij P13 Takwal Fier 06 13.70 op 13.80 kl 13.90 lm 13.80 klm ijk P15 Chwe Rikwi Kpa Guriea 08 14.60 mn 14.60 ij 14.80 jk 14.67 P16 Mayama Du 03 16.30 i 16.40 g 16.60 h 16.43 gh P17 Udin Bakkanik 06 14.60 mn 14.80 i 14.60 k 14.67 ijk P19 Mbulus Nga 07 13.60 pq 14.00 k 13.90 lm 13.83 klm P21 Peng Madu 03 23.60 b 23.40 b 23.60 b 23.53 b P22 Sala Gyel 01 13.90 o 14.00 k 14.10 l 14.00 klm P23 Bwut Madu 03 20.60 f 21.60 d 21.10 e 21.10 e P24 Chwu Zashi 08 21.30 e 21.60 d 21.40 d 21.43 de P25 Jan Buru Chiga 06 25.40 a 24.80 a 25.10 a 25.10 a P26 Chikarai Jwakras 08 13.90 o 14.60 ij 14.60 k 14.37 kl P27 Gindiri Vwang 06 16.30 i 16.67 f 16.90 g 16.62 g K1 Ziyan 08 13.40 q 13.60 l 13.80 m 13.60 lm K2 Wuwam 08 14.60 mn 14.80 i 14.80 jk 14.73 ijk K3 Zealt 06 13.70 op 14.00 k 14.10 l 13.93 klm K4 Halat Jaba 08 23.60 b 23.40 b 23.60 b 23.53 b B1 Wyandat 08 14.80 m 14.60 ij 14.80 jk 14.73 ijk B3 Wyant 06 14.50 n 14.60 ij 14.80 jk 14.63 ijk B4 Hlad 06 13.60 pq 13.90 k 14.00 lm 13.83 klm B5 Chid Kusung 06 14.40 n 14.50 j 14.80 jk 14.57 jkl

SE (±) 0.0898 0.0879 0.078 3.1 CV (%) 0.94 0.9 0.79 0.854

Means followed by the same letter(s) in a column are not significantly different at 5% level of probability (Duncan‟s new multiple range test) 4.1.4 Leaf Width 52

Results for the leaf width are presented in Table 6. The results show that significant differences existed among the accessions. The widest mean leaf width of

1.3cm was observed in accession P23 in the 2012 trial, while the narrowest was produced by accession P22 with a mean value of 0.6000cm. In the 2013 trial, the widest leaf width was produced by accessions P23 and P24 with a mean value of

1.200cm each, while the least was observed in accessions P12, P13, P15, P19, P27,

B3 and B5 with mean values of 0.7000cm. In the 2014 trial however, accession K4 had the widest leaf width with a value of 1,200cm while the narrowest was observed in accessions P15, P19, P22, B1 and B4 with a mean value of 0.7000cm (Table 6,

Appendix A2).

Mean leaf width over the three year period was widest in P23 with a value of

1.222cm. This was followed by 1.167, 1.133 and 1.078cm in K4, P24 and P1. P11 and

P21 had 1.033cm each (Table 6).

Table 6 Mean Leaf Width (cm) and Combined Mean of 30 Accessions of Acha (Digitaria spp.) Grown in 2012, 2013 and 2014 Rainy Seasonsat Binkan near Jos 53

Mean Leaf Width (cm)

Three Year Accessi Accession Name 2012 2013 2014 Combined on code Mean P1 Chwu Kperie 08 1.033 bc 1.100 ab 1.100 abc 1.078 bcd P2 Were Zawan 0.9000 cd 0.8000 def 0.9000 de 0.8667 e P3 Aburu Bwoi 07 1.000 bc 1.000 bc 1.000 bcd 1.000 d P4 Nding Zawan 0.8000 de 0.9000 cd 0.9333 cde 0.8778 e P5 Gyetel Burum 06 0.9000 cd 0.8000 def 0.9333 cde 0.8778 e P6 Chwe Rihwe Ansie 08 0.7000 ef 0.7200 ef 0.8000 ef 0.7400 ghi P7 Chisu Mangu 08 0.9000 cd 0.9400 cd 0.9000 de 0.9133 e P8 Lub Bungha 07 0.9000 cd 0.9300 cd 0.9000 de 0.9100 e P11 Aburu Bakkanik 06 1.000 bc 1.000 bc 1.100 abc 1.033 d P12 Udin Gyetel 06 0.6500 ef 0.7000 f 0.8000 ef 0.7167 hi P13 Takwal Fier 06 0.7000 ef 0.7000 f 0.8000 ef 0.7333 hi P15 Chwe Rikwi Kpa Guriea 08 0.7000 ef 0.7000 f 0.7000 f 0.7000 i P16 Mayama Du 03 0.7000 ef 0.8000 def 0.8000 ef 0.7667 fghi P17 Udin Bakkanik 06 0.7000 ef 0.8000 def 0.8000 ef 0.7667 fghi P19 Mbulus Nga 07 0.6000 f 0.7000 f 0.7000 f 0.6667 i P21 Peng Madu 03 1.000 bc 1.100 ab 1.000 bcd 1.033 d P22 Sala Gyel 01 0.6000 f 0.7000 f 0.7000 f 0.6667 i P23 Bwut Madu 03 1.300 a 1.200 a 1.167 ab 1.222 a P24 Chwu Zashi 08 1.100 b 1.200 a 1.100 abc 1.133 abc P25 Jan Buru Chiga 06 1.100 b 1.100 ab 1.000 bcd 1.067 cd P26 Chikarai Jwakras 08 0.8000 de 0.8500 cdef 0.8000 ef 0.8167 efgh P27 Gindiri Vwang 06 0.7000 ef 0.7000 f 0.8000 ef 0.7333 hi K1 Ziyan 08 0.8000 de 0.8000 def 0.9000 de 0.8333 efg K2 Wuwam 08 0.9000 cd 0.8000 def 0.9000 de 0.8667 e K3 Zealt 06 0.9000 cd 0.8667 cde 0.8000 ef 0.8556 ef K4 Halat Jaba 08 1.100 b 1.200 a 1.200 a 1.167 ab B1 Wyandat 08 0.7000 ef 0.8000 def 0.7000 f 0.7333 hi B3 Wyant 06 0.7000 ef 0.7000 f 0.8000 ef 0.7333 hi B4 Hlad 06 0.8000 de 0.8000 def 0.7000 f 0.7667 fghi B5 Chid Kusung 06 0.7000 ef 0.7000 f 0.8000 ef 0.7333 hi

SE (±) 0.0565 0.0469 0.0556 6.0 CV (%) 11.57 9.3 10.89 0.0845

Means followed by the same letter(s) in a column are not significantly different at 5% level of probability (Duncan‟s new multiple range test)

4.1.5 Number of Days to 75% Maturity

The results for number of days to 75% maturity are presented in Table 7. The

results show that significant differences existed among the accessions.In all the years 54 of study, accession P23 had the highest number of days to 75% maturity of 157, 160 and 159 for 2012, 2013 and 2014 respectively. The least mean number of days to 75% maturity was observed in accession P4 with 130, 130 and 131.7 days for 2012, 2013 and 2014 respectively. The highest number of days to 75% maturity was observed in

2013 while the least was recorded in 2012 and 2013 (Table 7, Appendix A2).

The three year combined mean number of days to 75% maturity was highest in

P3 over the three year period at a value of 158.7days. This was followed by 157.7 and

157 days in K4 and P24. Others in this category include 155.4 days in P24, 154 days in P1 and P25, 153.7 in P3 and 152.7 in P11. The least number of days to 75% maturity was recorded in K1 with a value of 131.7 days. The remaining accessions in this category fall within these extremes (Table 7).

4.1.6 1000 Seed Weight

The results for 1000 seed weight are presented in Table 8. The results show that significant differences existed among the accessions. Accession P23 had the heaviest seed weight, with means of 0.7543, 0.7564 and 0.7567 for 2012, 2013 and

2014 respectively. The least values were observed in accessions P15 in 2012, P19 in

2013 and P4 in 2014 with mean values of 0.5112, 0.5124 and 0.5124 in 2012, 2013 and 2014 respectively (Table 8, Appendix A2).

The three year combined mean 1000 seed weight was highest in P23 with a value of 0.7558g. This was followed by 0.07239 and 0.7237 and 0.7225g in K4, P24 and P11 respectively. Others in the same category included 0.7189g in P1, 0.7161g in

P21, 0.7154 in P25 and 0.7146 in P3. The least mean weight was observed in P19 with a value of 0.5121g. The remaining accessions in this category were intermediates between the extremes (Table 8).

Table 7 Mean Number of Days to 75% Maturity and Combined Mean of 30 Accessions of Acha (Digitaria spp.) Grown in 2012, 2013 and 2014 Rainy Seasons at Binkan near Jos

55

Mean Number of Days to 75% Maturity Three Accession years Accession Name 2012 2013 2014 code Combined mean P1 Chwu Kperie 08 153.0 cd 154.0 cd 155.0 c 154.0 d P2 Were Zawan 132.0 jkl 133.0 ghij 133.0 hi 132.7 kl P3 Aburu Bwoi 07 152.0 d 154.0 cd 155.0 c 153.7 d P4 Nding Zawan 130.0 l 130.0 j 131.7 i 130.6 m P5 Gyetel Burum 06 132.0 jkl 133.0 ghij 133.0 hi 132.7 kl P6 Chwe Rihwe Ansie 08 134.0 hij 134.0 ghi 134.7 gh 134.2 ij P7 Chisu Mangu 08 135.0 hi 135.0 gh 135.0 gh 135.0 hi P8 Lub Bungha 07 138.0 g 140.0 f 138.0 f 138.7 g P11 Aburu Bakkanik 06 152.0 d 152.0 d 154.0 c 152.7 d P12 Udin Gyetel 06 136.0 h 136.0 g 136.0 fg 136.0 h P13 Takwal Fier 06 134.0 hij 133.0 ghij 136.0 fg 134.3 ij P15 Chwe Rikwi Kpa Guriea 08 132.0 jkl 133.0 ghij 133.0 hi 132.7 kl P16 Mayama Du 03 145.0 e 143.0 e 144.0 d 144.0 e P17 Udin Bakkanik 06 133.0 ijk 133.0 ghij 132.0 i 132.7 kl P19 Mbulus Nga 07 135.0 hi 135.0 gh 135.0 gh 135.0 hi P21 Peng Madu 03 156.0 ab 157.0 b 158.0 ab 157.0 b P22 Sala Gyel 01 132.0 jkl 134.0 ghi 133.0 hi 133.0 jkl P23 Bwut Madu 03 157.0 a 160.0 a 159.0 a 158.7 a P24 Chwu Zashi 08 154.3 bc 156.0 bc 156.0 bc 155.4 c P25 Jan Buru Chiga 06 152.0 d 154.0 cd 156.0 bc 154.0 d P26 Chikarai Jwakras 08 141.0 f 140.0 f 141.0 e 140.7 f P27 Gindiri Vwang 06 131.0 kl 132.0 hij 133.0 hi 132.0 kl K1 Ziyan 08 132.0 jkl 131.0 ij 132.0 i 131.7 lm K2 Wuwam 08 132.0 jkl 133.0 ghij 133.0 hi 132.7 kl K3 Zealt 06 132.0 jkl 134.0 ghi 134.0 ghi 133.3 jk K4 Halat Jaba 08 157.0 a 158.0 ab 158.0 ab 157.7 ab B1 Wyandat 08 132.0 jkl 133.0 ghij 133.0 hi 132.7 kl B3 Wyant 06 132.0 jkl 132.0 hij 133.0 hi 132.3 kl B4 Hlad 06 132.0 jkl 134.0 ghi 132.0 i 132.7 kl B5 Chid Kusung 06 132.0 jkl 133.0 ghij 133.0 hi 132.7 kl

SE (±) 0.6689 0.9406 0.7164 0.6 CV (%) 0.83 1.16 0.88 1.293 Means followed by the same letter(s) in a column are not significantly different at 5% level of probability (Duncan‟s new multiple range test)

56

Table 8 Mean 1000 Seed Weight (g) and Combined Mean of 30 Accessions of Acha (Digitaria spp.) Grown in 2012, 2013 and 2014 Rainy Seasons

Mean 1000 Seed Weight (g) Three Year Accession Accession Name 2012 2013 2014 combined code Mean P1 Chwu Kperie 08 0.7122 b 0.7321 b 0.7123 c 0.7189 bcd P2 Were Zawan 0.5231 jk 0.5243 m 0.5241 l 0.5238 m P3 Aburu Bwoi 07 0.7125 b 0.7201 cd 0.7112 c 0.7146 d P4 Nding Zawan 0.5117 k 0.5132 n 0.5123 m 0.5124 o P5 Gyetel Burum 06 0.5213 jk 0.5234 m 0.5203 l 0.5217 mn P6 Chwe Rihwe Ansie 08 0.5321 ij 0.5354 l 0.5342 k 0.5339 l P7 Chisu Mangu 08 0.5143 k 0.5232 m 0.5124 m 0.5166 no P8 Lub Bungha 07 0.5134 k 0.5223 m 0.5136 m 0.5164 no P11 Aburu Bakkanik 06 0.7213 b 0.7246 c 0.7216 b 0.7225 bc P12 Udin Gyetel 06 0.5612 f 0.5634 i 0.5612 i 0.5619 j P13 Takwal Fier 06 0.5432 hi 0.5432 k 0.5431 j 0.5432 k no P15 Chwe Rikwi Kpa Guriea 08 0.5112 k 0.5134 n 0.5212 l 0.5153 P16 Mayama Du 03 0.5532 fgh 0.5548 j 0.5578 i 0.5553 j P17 Udin Bakkanik 06 0.5462 gh 0.5432 k 0.5432 j 0.5442 k P19 Mbulus Nga 07 0.5114 k 0.5124 n 0.5124 m 0.5121 o P21 Peng Madu 03 0.7126 b 0.7124 e 0.7234 b 0.7161 cd P22 Sala Gyel 01 0.5786 e 0.5764 h 0.5764 g 0.5771 h P23 Bwut Madu 03 0.7543 a 0.7564 a 0.7567 a 0.7558 a P24 Chwu Zashi 08 0.7234 b 0.7224 cd 0.7254 b 0.7237 b P25 Jan Buru Chiga 06 0.7163 b 0.7175 de 0.7123 c 0.7154 d P26 Chikarai Jwakras 08 0.6213 c 0.6123 g 0.6201 e 0.6179 f P27 Gindiri Vwang 06 0.5563 fg 0.5557 j 0.5576 i 0.5565 j K1 Ziyan 08 0.5770 e 0.5768 h 0.5743 g 0.5760 hi K2 Wuwam 08 0.6243 c 0.6324 f 0.6398 d 0.6322 e K3 Zealt 06 0.6054 d 0.6104 g 0.6046 f 0.6068 g K4 Halat Jaba 08 0.7254 b 0.7243 c 0.7221 b 0.7239 b B1 Wyandat 08 0.5762 e 0.5676 i 0.5676 h 0.5705 i B3 Wyant 06 0.5143 k 0.5201 m 0.5231 l 0.5192 mno B4 Hlad 06 0.5543 fgh 0.5564 j 0.5565 i 0.5557 j B5 Chid Kusung 06 0.6123 cd 0.6124 g 0.6056 f 0.6101 g

SE (±) 0.0043 0.0016 0.0001 0.7 CV (%) 1.24 0.45 0.04 0.00638 Means followed by the same Letter(s) in a column are not significantly different at 5% level of probability (Duncan‟s new multiple range test)

57

4.2 CORRELATION

A positive and highly significant correlation existed between plant height (PH) and stem girth (SG) in the first year (2012), implying that the taller the plant, the wider the stem girth (Table 9). This trend was observed between plant height and leaf length (LL), leaf width (LW), number of days to maturity (DM) and a thousand seed weight, with the corresponding values of 0.875, 0.808, 0.940 and 0.899 respectively.

This trend was also observed between stem girth (SG) and leaf length (LL), leaf width (LW), number of days to maturity and a thousand seed weight, with the corresponding values of 0.823, 0.780, 0.894 and 0.870. Again, this observation was exibited between leaf length (LL) and leaf width, number of days to maturity (DM) and a thousand seed weight; and, between leaf width (LW) and number of days to maturity (DM), and a thousand seed weight (1000S). There was also, a highly significant and positive correlation between number of days to maturity and a thousand seed weight.

In the second year (2013), all the yield components were positively correlated.

However, such correlations were not highly significant between plant height (PH) and stem girth (SG) and between stem girth (SG) and leaf length (LL), leaf width (LW), number of days to maturity (DM) and a thousand seed weight (Table 10).

In the third year, 2014, all the yield components were highly significant and positively correlated. The highest significant value was observed between plant height

(PH) and stem girth with a value of 0.985, while the least significant being 0.789, between a thousand seed weight and leaf width (LW) (Table 11).

58

Table 9 Correlation Coefficient Between Pairs of Morphological Traits 2012: PH(cm), SG(cm), LL(cm), LW(cm), DM, 1000S(g).

_PH(cm) SG(cm) LL(cm) LW(cm) DM______

SG(cm) 0.913 0.000

LL(cm) 0.875 0.823 0.000 0.000

LW (cm) 0.808 0.780 0.749 0.000 0.000 0.000

DM 0.940 0.894 0.867 0.781 0.000 0.000 0.000 0.000

1000S (g) 0.899 0.870 0.793 0.778 0.883 0.000 0.000 0.000 0.000 0.000 Cell contents: Pearson correlation P-Value

Key

PH (cm) = Plant Height(cm) SG (cm) = Stem Girth(cm) LL (cm) = Leaf Length(cm) LW (cm) = Leaf width(cm) DM = Number of days to maturity 1000S (g) = 1000 Seed Weight(g)

59

Table 10 Correlation Coefficient Between Pairs of Morphological Traits 2013: PH(cm),SG(cm),LL(cm), LW(cm), DM, 1000S(g).

PH(cm) SG(cm) LL(cm) LW(cm) DM______

SG(cm) 0.319 0.086

LL(cm) 0.939 0.329 0.000 0.076

LW (cm) 0.884 0.196 0.845 0.000 0.300 0.000

DM 0.951 0.339 0.938 0.892 0.000 0.067 0.000 0.000

1000S(g) 0.909 0.312 0.855 0.823 0.900 0.000 0.093 0.000 0.000 0.000 Cell contents: Pearson correlationP-Value

Key

PH (cm) = Plant Height(cm) SG (cm) = Stem Girth(cm) LL (cm) = Leaf Length(cm) LW (cm) = Leaf width(cm) DM = Number of days to maturity 1000S (g) = 1000 Seed Weight(g)

60

Table 11 Correlation Coefficient Between Pairs of Morphological Traits 2014: PH(cm), SG(cm), LL(cm), LW(cm), DM, 1000S(g)

PH(cm) SG(cm) LL(cm) LW(cm) DM______

SG(cm) 0.985 0.000

LL(cm) 0.948 0.952 0.000 0.000

LW (cm) 0.840 0.856 0.819 0.000 0.000 0.000

DM 0.952 0.970 0.940 0.836 0.000 0.000 0.000 0.000

1000S (g) 0.899 0.921 0.848 0.789 0.900 0.000 0.000 0.000 0.000 0.000 Cell contents: Pearson correlation P-Value

Key:

PH (cm) =Plant Height(cm) SG (cm) =Stem Girth(cm) LL (cm) = Leaf Length(cm) LW (cm) = Leaf width(cm) DM = Number of days to maturity 1000S (g) = 1000 Seed Weight(g)

61

4.3 PRINCIPAL COMPONENT ANALYSIS (PCA)

The results of the Principal Component Analysis (PCA) showed that 95.4% of the total variation was contributed by the first three principal component axes. It was found that Principal Component I (PC1) contributed 87.1% whereas PC2 and PC3 contributed 4.7 and 3.6% respectively of the total variation. The traits which contributed more to PC1 wereplant height (PH), stem girth (SG), number of days to maturity (DM) and 1000 seed weight (1000S). PC2 was dominated by leaf width

(LW) while the PC3 was dominated by leaf length (LL) and 1000 seed weight. The first three principal components (PCs) explained 95.4% of the total variation in the six variables data set. The PC1, PC2 and PC3 showed relatively large variation with eigenvalues 5.2234, 0.2833 and 0.2155 respectively. The eigenvalues of PC1 greater than one and represented exact linear dependency but the rest had small variance

(eigenvalues less than 1) as seen in Table 12.

In the 2013 cropping season, the principal component analysis showed that

96% of the total variation was contributed by the first three principal components. It was observed that PC1 contributed 78.3% whereas PC2 and PC3 contributed 14.9 and

2.8% respectively. The traits which contributed more to PC1 were number of days to maturity (DM), plant height (PH), leaf length (LL), a thousand seed weight (1000S) and Leaf width (LW) in that order. PC2 was dominated by stem girth (SG) while PC3 was dominated by Leaf width (LW) and a thousand seed weght (1000S). The first three PCs explained 96% of the total variation in the six variable data set. PC1, PC2 and PC3 showed relatively large variation with the eigenvalues 4.6983, 0.8937 and

0.1694 respectively. The eigenvalue of PC1 is greater than 1 and represented exact linear dependency but the rest had small variance, with eigenvalues less than 1(Table

13).

The trend was similar in the third year (2014) as in the previous two. The principal component analysis showed that 98.2% of the total variation was contributed 62

Table 12 Eigen analysis of the Correlation Matrix of Morphological traits for 2012 Rainy Season

Eigenvalue 5.2234 0.2833 0.2155 0.1262 0.0958 0.0558

Proportion 0.871 0.047 0.036 0.021 0.016 0.009

Cumulative 0.871 0.918 0.954 0.975 0.991 1.000_____

Variables PC1 PC2 PC3 PC4 PC5 PC6

PH(cm) -0.425 -0.146 -0.048 -0.043 0.307 -0.836

SG(cm) -0.413 -0.145 -0.266 -0.742 -0.409 0.138

LL(cm) -0.399 -0.246 0.790 0.188 -0.341 0.066

LW (cm) -0.381 0.915 0.112 -0.021 0.042 0.044

DM -0.420 -0.230 -0.018 -0.010 0.710 0.516

1000S (g) -0.409 -0.079 -0.539 0.641 -0.341 0.094

Key PH (cm) = Plant Height(cm) SG (cm) = Stem Girth(cm) LL (cm) = Leaf Length(cm) LW (cm) = Leaf width(cm) DM = Number of days to maturity 1000S (g) = 1000 Seed Weight(g) PC = Principal Component

63

Table 13 Eigen analysis of the Correlation Matrix of Morphological traits for 2013 Rainy Season ______

Eigenvalue 4.6983 0.8937 0.1694 0.1441 0.0482 0.0463

Proportion 0.783 0.149 0.028 0.024 0.008 0.008

Cumulative 0.783 0.932 0.960 0.984 0.992 1.000______

Variable PC1 PC2 PC3 PC4 PC5 PC6

PH(cm) -0.451 -0.064 0.097 -0.157 0.483 0.724

SG(cm) -0.178 0.974 -0.113 0.078 0.033 -0.015

LL(cm) -0.442 -0.039 0.018 -0.682 0.185 -0.552

LW(cm) -0.423 -0.205 -0.730 0.445 0.133 -0.176

DM -0.452 -0.042 -0.024 -0.137 -0.844 0.251

1000S (g) -0.432 -0.050 0.667 0.536 0.045 -0.276

PH (cm) = Plant Height(cm) SG (cm) = Stem Girth(cm) LL (cm) = Leaf Length(cm) LW (cm) = Leaf width(cm) DM = Number of days to maturity 1000S (g) = 1000 Seed Weight(g) PC = Principal Component

64 by the first three principal components. It was observed that PC1 contributed 91.5% while PC2 and PC3 contributed 4% and 2.7% respectively. The traits which contributed more to PC1 were stem girth (SG), plant height (PH), number of Days to maturity (DM) and leaf length in that order. PC2 was dominated by leaf width (LW) and a thousand seed weight (1000S) while PC3 was dominated by a thousand seed weight (1000S) and leaf length (LL). The first three PCs accounted for 98.2% of the total variation in the six variable data set. Again, as in the other two years, PC1, PC2 and PC3 showed relatively large variation with eigenvalues of 5.4916, 0.2378 and

0.1604 respectively. The eigenvalue of PC1 is greater than1 and represented exact linear dependency but the rest had small variances, with eigenvalues less than 1

(Table 14).

65

Table 14 Eigen analysis of the Correlation Matrix of Morphological Traits for 2014 Rainy Season Eigenvalue 5.4916 0.2378 0.1604 0.0512 0.0485 0.0105

Proportion 0.915 0.040 0.027 0.009 0.008 0.002

Cumulative 0.915 0.955 0.982 0.990 0.998 1.000______

Variable PC1 PC2 PC3 PC4 PC5 PC6__

PH(cm) -0.419 -0.150 -0.165 0.270 0.654 0.524

SG(cm) -0.423 -0.127 -0.060 0.008 0.343 -0.827

LL(cm) -0.410 -0.152 -0.534 0.389 -0.610 0.022

LW(cm) -0.381 0.918 0.096 0.021 -0.042 0.030

DM -0.417 -0.154 -0.114 -0.860 -0.124 0.186

1000S (g) -0.398 -0.269 0.814 0.188 -0.255 0.081

PH (cm) = Plant Height(cm) SG (cm) = Stem Girth(cm) LL (cm) = Leaf Length(cm) LW (cm) = Leaf width(cm) DM = Number of days to maturity 1000S (g) = 1000 Seed Weight(g) PC = Principal Component

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4.4. GENETIC RELATEDNESS OF ACCESSIONS

The results presented in Figure 5 in the dendrogram showed that the 30 accessions that were clustered together indicated that they shared similar traits. All the accessions are separated along the 100% baseline. On the other hand,accessions 3 and

18 both formed separate clusters implying that they are distinct from each other, while

5, 28, and 30 were grouped together, showing that they have similar grain and traits.

At about 90% similarity, the accessions 1, 3, 9, 18, 26,16, 19 and 20 form one group, and 2, 22, 5, 28, 30, 12, 27, 29, 23, 25, 14, 24, 11, 15, 6, 10, 17, 4, 8, 21, 13 and 7 from a second group. The dendrogram suggests that accession 1, 3, 9, 18, 26, 16, 19, and 20 are similar measures for one main component and 2, 22, 5, 28, 30, 12, 27, 29,

23, 25, 14, 24, 11, 15, 6, 10, 17, 4, 8, 21, 13 and 7 are similar measure for a second main component. Cluster group two has higher similarity degree than cluster group one. Sub cluster groups also exist, that is, 2 and 22, 5 and 28, 12 and 27. On the other hand, 28 and 30 clusters have higher similarity percentage than all cluster groups

(almost 100%). Clusters 3 and 9 have higher similarity (90 – 95%) in the first group.

All accessions also, form one cluster group at about 44.06% similarity (Fig. 5)

All the variables in the 2012 sowing season are separated between the 96.79 to

93.59% indicating that there are no variables that are 100% similar. Leaf width is clustered separately as a unique cluster at 90.38% similarity level from other clusters

(Figure 6).

In the 2013 analysis, the dendrogram again revealed two major clusters as in

2012 among the 30 accessions. It was observed that the two major clusters had accessions which shared similar features and were very closely related. In the first cluster comprising of accessions 1, 3, 9, 20, 16, 19, 18 and 26, accessions 3, 9, and 20 were very similar in all respects while 16 and 19 were also similar in all respects and related to the former set. These were again separated from 18 and 26, which formed another sub cluster with accession 1. On the other hand, the most closely related are 67

Dendrogram with Single Linkage and Euclidean Distance

44.06

62.71

y

t

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r

a

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81.35

100.00

1 3 9 8 6 6 9 0 2 2 5 8 0 2 7 9 3 5 4 4 1 5 6 0 7 4 8 1 3 7

1 2 1 1 2 2 2 3 1 2 2 2 2 1 2 1 1 1 1 2 1 Observations

Figure 5 Dendrogram of Morphological Characters Showing the Linkage Among Thirty Accessions of Acha for 2012 Cropping Season.

68

Dendrogram with Single Linkage and Correlation Coefficient Distance 90.38

y 93.59 it ar ilm Si

96.79

100.00 PH(cm) DM SG(cm) 1000S(g) LL(cm) LW(cm) Variables

Figure 6 Cluster Analysis of Morphological Variables for 2012

69

12 with 14, 5 with 24, 6 with 25 and 2 with 22, forming separate sub clusters. Other such similarities existed as can be seen in Figure 7. The first group (cluster) comprised of the Digitaria iburua. Again, as in the first year, the first cluster had 8 accessions of Digitaria iburua while the second cluster had 22 accessions comprising

21 accessions of Digitaria exilis and one accession of D. barbinodis (Nding Zawan, accession 4). Eventhough related with other D. exilis accessions, D. barbinodis has clearly formed a distinct cluster and stands out alone (accession 4) as seen in Figure 7.

A higher degree of similarity exists between members of the second cluster than between members of the first cluster. Sub-cluster groups also exist and are observed with accessions 2 and 22, 5 and 24, 12 and 14 and, 6 with 25. Among these, 12 and 14 show a higher similarity percentage than all the clusters and sub cluster groups

(almost 100%). Sub cluster 18 and 26 appear to have a higher similarity (90 – 95%) in the first cluster group. All accessions form one cluster group at about 40.04% similarity (Figure 7).

In the 2013 rainy season, all the variables were separated between the 100 and

94.62% similarity index. Even though there was no 100% similarity, the variables clusters were very tight, within the 5.4% range of the similarity index (Figure 8).

Clearly, the stem girth (SG) was clustered separately as a unique cluster at 66.96% of the similarity level index from the others.

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Dendrogram with Single Linkage and Euclidean Distance

40.04

60.03

y

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80.01

100.00 1 3 9 20 16 19 18 26 2 22 4 5 24 30 12 14 27 11 28 6 25 15 29 23 17 7 8 21 13 10 Observations

Figure 7 Dendrogram of Morphological Characters Showing the Linkage Among Thirty Accessions of Acha for 2013 Cropping Season

71

Dendrogram with Single Linkage and Correlation Coefficient Distance 66.96

y 77.97 it ar ilm Si

88.99

100.00 PH(cm) DM LL(cm) 1000S(g) LW(cm) SG(cm) Variables

Figure 8 Cluster Analysis of Morphological Variables for 2013

72

In the third year (2014), another D. iburua group is clearly separated from the

D. exilis with D. barbinodis group. In this case unlike in the second year, accession 1 and 19 are the most closely related in all features, in the D. iburua cluster group.

Accession 16 is standing alone forming its distinct cluster even though closely related to 20 and 3. Again, in the D. exilis cluster, accessions 2 and 22 are very closely related. Accessions 12 and 30 along with accession 28 are also very closely related at

99.9% similarity level (Figure 9). Other closely related clusters within the major clusters are obvious. In this year as in the previous, accession 4 is standing on its cluster separate from others indicating some level of distance in their relationship.

In the third year, all the variables were separated between the 99 and 96% similarity index, indicating that there were no variables that were 100% similar. Leaf width is clustered separatedly and uniquely at 92.8% (Figure 10). In this case as in the first year (Figure 6), the leaf width (LW) clustered separately as a unique cluster at

92.8% similarity level index from the others.

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Dendrogram with Single Linkage and Euclidean Distance

38.19

58.79

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79.40

100.00 1 19 9 20 16 3 18 26 2 22 4 5 12 30 28 27 14 23 29 15 25 6 10 11 24 17 7 8 13 21 Observations

Figure 9Dendrogram of Morphological Characters Showing the Linkage Among Thirty Accessions of Acha for 2014 Cropping Season

74

Dendrogram with Single Linkage and Correlation Coefficient Distance

92.80

y 95.20 it ar ilm Si

97.60

100.00 PH(cm) SG(cm) DM LL(cm) 1000S(g) LW(cm) Variables

Figure 10 Cluster Analysis of Morphological Variables for 2014

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The output of clustering the 30 accessions to PH Plant Height (cm), SG Stem

Girth (cm), LL Leaf Length (cm), LW Leaf width (cm), Number of days to maturity and 1000 Seed Weight (g) is given in Table 15 below. The first step, number 29 joins accessions 28 and 30 into a cluster called CL29 with distance level 0.1721 at similarity of 99.6531. The next step, number 28, joins accession 5 and 28 into CL28 with distance level 0.3163 at similarity 99.3622. The procedure continues until all accessions have been grouped together into the last cluster CL1, which is a combination of accession1 and accession 2 with distance level 27.7435 at similarity

44.0590. The similarity measure decreases little during the first 19 steps, where 11 clusters remain. Then it decreases somewhat more until step 28, where we are left with 2 clusters. At step 29, a larger drop occurs with 1 cluster joining accession 1 and

2 (Table 15).The relationships between accessions are expressed in the dendrogram in

Figure 5.

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Table 15 Cluster Analysis of Observations of Morphological Characters for 2012 Euclidean Distance, Single LinkageAmalgamation Steps

No. of obs. No. of Similarity Distance Clusters New in new Step clusters level level joined cluster cluster 1 29 99.6531 0.1721 28 30 28 2 2 28 99.3622 0.3163 5 28 5 3 3 27 99.1445 0.4243 5 12 5 4 4 26 99.0888 0.4519 5 27 5 5 5 25 98.1704 0.9074 5 29 5 6 6 24 97.9432 1.0201 5 23 5 7 7 23 97.7995 1.0913 11 15 11 2 8 22 97.7253 1.1281 5 25 5 8 9 21 97.7002 1.1406 5 14 5 9 10 20 97.5041 1.2378 5 24 5 10 11 19 97.4419 1.2687 2 22 2 2 12 18 97.1060 1.4353 5 11 5 12 13 17 97.0673 1.4544 5 6 5 13 14 16 96.0178 1.9749 2 5 2 15 15 15 95.6317 2.1664 2 10 2 16 16 14 95.5731 2.1955 3 9 3 2 17 13 95.3005 2.3307 2 17 2 17 18 12 94.4883 2.7335 2 4 2 18 19 11 93.9071 3.0217 18 26 18 2 20 10 91.9890 3.9730 2 8 2 19 21 9 90.7178 4.6034 2 21 2 20 22 8 90.5522 4.6856 2 13 2 21 23 7 90.3004 4.8104 19 20 19 2 24 6 90.0595 4.9299 2 7 2 22 25 5 89.6810 5.1176 3 18 3 4 26 4 89.5246 5.1952 16 19 16 3 27 3 88.8862 5.5118 3 16 3 7 28 2 88.8114 5.5489 1 3 1 8 29 1 44.0590 27.7435 1 2 1 30

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In the 2013 cropping season, the output of clustering the 30 accessions to plant height, stem girth, leaf length, leaf width, number of days to maturity and a 1000 seed weight is given in Table 16.

In the first step, number 29 joins accessions 12 and 14 into a cluster, CL 29 with distance level 0.2468 at similarity level of 99.5041. The next step 28 joins accessions 5 and 24 into CL 28 with a distance level 0.3316 at similarity level of

99.3336. This procedure and trend continues until all the accessions have been grouped together into cluster CL 1, which is a combination of accessions 1 and 2 with distance level of 29.8360 at similarity level of 40.0435. The similarity measures decreases little during the first 19 steps where 11 clusters remain. Then it decreases somewhat more, until step 28 where 2 clusters are left. At step 29, a larger drop occurs with 1 cluster joining accessions 1 and 2 (Table 17). The relationships between the accessions are expressed in the dendrogram in Figure 7.

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Table 16 Cluster Analysis of Observations of Morphological Characters for 2013 Euclidean Distance, Single LinkageAmalgamation Steps

No. of obs. No. of Similarity Distance Clusters New in new Step clusters level level joined cluster cluster 1 29 99.5041 0.2468 12 14 12 2 2 28 99.3336 0.3316 5 24 5 2 3 27 99.1058 0.4450 5 30 5 3 4 26 98.7565 0.6188 12 27 12 3 5 25 98.4722 0.7603 6 25 6 2 6 24 98.3527 0.8198 5 12 5 6 7 23 98.3300 0.8310 5 11 5 7 8 22 97.9586 1.0158 15 29 15 2 9 21 97.9577 1.0163 6 15 6 4 10 20 97.8243 1.0827 5 28 5 8 11 19 97.7751 1.1072 5 6 5 12 12 18 97.7433 1.1230 2 22 2 2 13 17 96.8143 1.5853 5 23 5 13 14 16 96.0253 1.9779 4 5 4 14 15 15 95.9553 2.0128 2 4 2 16 16 14 94.3586 2.8073 2 17 2 17 17 13 93.5224 3.2234 2 7 2 18 18 12 93.3774 3.2956 18 26 18 2 19 11 93.3126 3.3278 8 21 8 2 20 10 93.0475 3.4598 3 9 3 2 21 9 92.9535 3.5065 8 13 8 3 22 8 92.9414 3.5126 3 20 3 3 23 7 92.6852 3.6401 16 19 16 2 24 6 91.7585 4.1012 3 16 3 5 25 5 91.3221 4.3184 1 3 1 6 26 4 91.2015 4.3784 1 18 1 8 27 3 89.5732 5.1887 2 8 2 21 28 2 83.7338 8.0945 2 10 2 22 29 1 40.0435 29.8360 1 2 1 30

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Again, as in the previous years, two distinct cluster groups are observed, that is, one with accessions 1, 19, 9, 20, 16, 3, 18, and 26 forming one major cluster group while accessions 2, 22, 4, 5, 12, 30, 28, 27, 14, 23, 29, 15, 25, 6, 10, 11, 24, 17, 7, 8,

13 and 21 forming the second major cluster group. The same accessions form the two clusters but with differences in the subcluster group combinations. Cluster group 2 has exhibited a higher level of similarity than that which is found in cluster group 1.

Sub cluster groups also exists as is seen between 2 and 22, 12 and 30, 23 and 29, 15 and 25 in the second major cluster group while sub cluster between 1 and 19 is seen in the first major cluster. Sub cluster 12 and 30 have the highest similarity percentage than all the cluster/ sub cluster groups (almost 100%). Cluster 1 and 19 has the highest similarity (95 – 98%) in the first group. All accessions also form one cluster group at about 38.19% similarity (Figure 9).

The output of clustering the 30 accessions to PH Plant Height (cm), SG Stem

Girth (cm), LL Leaf Length (cm), LW Leaf width (cm), Number of days to maturity and 1000 Seed Weight (g) is given in table 17 below. The first step, number 29 joins accessions 12 and 30 into a cluster CL 29, with a distance level at 0.1647 at similarity level at 99.6678. The next step, number 28, joins accessions 12 and 28 into CL 28 with distance level 0.2236 at similarity level at 99.3622. The procedure continues until all accessions have been grouped together into the last cluster CL1, which is a combination of accessions 1 and 2 with distance level of 30.6403 at similarity level of

38.1864. The similarity measures, decreases little during the first 19 steps, where 11 clusters remains. Then it decreases somewhat more until step 28, where we are left with 2 clusters. At step 29, a larger drop occurs with one cluster joining accessions 1 and 2 (Table 20). The relationship between the accessions is expressed in the dendrogram in figure 9.

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Table 17 Cluster Analysis of Observations of Morphological Characters for 2014 Euclidean Distance, Single LinkageAmalgamation Steps

No. of obs. No. of Similarity Distance Clusters New in new Step clusters level level joined cluster cluster

1 29 99.6678 0.1647 12 30 12 2 2 28 99.5489 0.2236 12 28 12 3 3 27 99.2157 0.3887 2 22 2 2 4 26 98.7705 0.6095 12 27 12 4 5 25 98.6018 0.6930 23 29 23 2 6 24 97.9306 1.0258 5 12 5 5 7 23 97.8007 1.0902 15 25 15 2 8 22 97.7260 1.1272 5 14 5 6 9 21 97.6128 1.1833 1 19 1 2 10 20 97.5284 1.2251 4 5 4 7 11 19 97.5030 1.2377 4 23 4 9 12 18 97.4562 1.2610 4 15 4 11 13 17 97.2555 1.3604 6 10 6 2 14 16 97.2189 1.3786 4 6 4 13 15 15 97.1321 1.4216 4 11 4 14 16 14 97.1302 1.4225 4 24 4 15 17 13 96.3371 1.8157 2 4 2 17 18 12 96.0808 1.9427 2 17 2 18 19 11 94.9371 2.5096 1 9 1 3 20 10 94.5526 2.7002 18 26 18 2 21 9 94.0529 2.9479 1 20 1 4 22 8 93.7962 3.0751 3 18 3 3 23 7 92.7139 3.6116 13 21 13 2 24 6 90.6305 4.6444 7 8 7 2 25 5 89.6533 5.1288 2 7 2 20 26 4 89.4740 5.2176 2 13 2 22 27 3 89.2850 5.3113 1 16 1 5 28 2 89.0725 5.4166 1 3 1 8 29 1 38.1864 30.6403 1 2 1 30

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Digitaria iburua seeds Digitaria exilisseeds

Digitaria barbinodis seeds

Plate 2 Showing the seeds of the three species of Digitaria

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4.5 MOLECULAR ANALYSIS

Plates 3 to 12 show the electrophoregram of the eleven (11) selected accessions‟ genomic DNA (Table 18). The accessions genomic DNA were loaded as follows: GeneRuler 100 bp DNA Ladder Lane 1, P1 (D. iburua) Lane 2, P4 (D. barbinodis ) Lane 3, P6 (D. exilis) Lane 4, P15 (D. exilis) Lane 5, P24 (D. iburua)

Lane 6, P27 (D. exilis) Lane 7, K1 (D. exilis) Lane 8, K2 (D. exilis) Lane 9, K4 (D. iburua) Lane 10, T3 (D. exilis) Lane 11, and T4 (D. exilis) Lane 12.

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Table 18 List of Selected Accessions Used for the Molecular Analyses

Gel Accession Location Accession Name Botanical Name Wells Code LANE 1 GeneRuler 100 bp DNA Ladder LANE 2 P1 PLATEAU Chwu Kperie 08 Digitaria iburua LANE 3 P4 PLATEAU Nding Zawan Digitaria barbinodis LANE 4 P6 PLATEAU Chwe Rihwe Ansie 08 Digitaria exilis LANE 5 P15 PLATEAU Chwe Rikwi Kpa Guriea 08 Digitaria exilis LANE 6 P24 PLATEAU Chwu Zashi 08 Digitaria iburua LANE 7 P27 PLATEAU Gindiri Vwang 06 Digitaria exilis LANE 8 K1 KADUNA Ziyan 08 Digitaria exilis LANE 9 K2 KADUNA Wuwam 08 Digitaria exilis LANE 10 K4 KADUNA Halad Jaba 08 Digitaria iburua LANE 11 T3 BAUCHI Wyant 06 Digitaria exilis LANE 12 T4 BAUCHI Hlad 06 Digitaria exilis

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The pattern observed from Plate 3 below,primer De 01, showed that accession P1 in

Lane 2 (D. iburua), accession P4 Lane 3 (D. barbinodis) and accessionT4 Lane 12 (D. exilis) did not amplify using the primer. The samples, based on their morphology were identified as Digitaria iburua Lane 1 D barbinodis Lane 2 and D. exilis Lane

11.Lanes 3 to 10 were however, amplified. The 257 bp fragment was obtained. The bands in Lanes 5 P24 (D. iburua) and 10 were lesser than the others amplified. This was to be expected as the primers were designed to amplify microsatellite fragments in D. exilis. Even though Lane 11 is D. exilis, there was no evidence of amplification.

For De 04 primer of Plate 4, Lane 2 was not amplified while Lanes 3 to 12 were amplified. For lane 6, P 24 (D. iburua) another band of approximately 1Kb size was observed suggesting a slight genetic variation in the accessions.

For primer De 06 in Plate 5, Lanes 2 (D. iburua), 6 P24 (D. iburua), and 10 were not amplified. This suggests that they do not have the target regions for the primer to amplify. Lanes 3 P4 (D. barbinodis), 4 P6 (D. exilis), 5 P15 (D. exilis), 7

P27 (D. exilis), 8 K1 (D. exilis), 9 K2 (D. exilis), and 11 T 3 (D. exilis) were all amplified. Again, even though lane 3 was (D. barbinodis), it nevertheless was amplified. Interestingly, lanes 3 and 9 had double bands. This suggests that the „gene‟ is double copy (single copy genes amplify single bands while multicopy gene amplify more than one band) because the target area/gene on the genome is not one. Lane 6

P24 (D. iburua) has a band with an approximate weight of 850Kbp.

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1 2 3 4 5 6 7 8 9 10 11 12 13

1000 500

100

Plate 3 Gel Electrophoregram of Primer De 01 on the 11 Accessions Lane 1: 100 bp marker; Lane 2: P1(Digitaria iburua); Lane 3 P4 (Digitaria barbnodis); Lane 4 P6 (Digitaria exilis); Lane 5 P15 (Digitaria exilis); Lane 6: P 24 (Digitaria iburua); Lane 7: P27 (Digitaria exilis); Lane 8: K1(Digitaria exilis); Lane 9: K2 (Digitaria exilis); Lane 10: K4 (Digitaria iburua); Lane 11: T3 (Digitaria exilis); Lane 12: T4 (Digitaria exilis); Lane 13: negative control (using nuclease free water)

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12 3 4 5 6 7 8 9 10 11 12 13

1000 500

Plate 4 Gel Electrophoregram of Primer De 04 on the 11Accessions. Lane 1: 100 bp marker; Lane 2: P1(Digitaria iburua); Lane 3 P4 (Digitaria barbnodis); Lane 4 P6 (Digitaria exilis); Lane 5 P15 (Digitaria exilis); Lane 6: P 24 (Digitaria iburua); Lane 7: P27 (Digitaria exilis); Lane 8: K1(Digitaria exilis); Lane 9: K2 (Digitaria exilis); Lane 10: K4 (Digitaria iburua); Lane 11: T3 (Digitaria exilis); Lane 12: T4 (Digitaria exilis)

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1 2 3 4 5 6 7 8 9 10 11 12 13

1000 500

100

Plate 5 Gel Electrophoregram of Primer De 06 on the 11 Accessions Lane 1: 100 bp marker; Lane 2: P1(Digitaria iburua); Lane 3 P4 (Digitaria barbnodis); Lane 4 P6 (Digitaria exilis); Lane 5 P15 (Digitaria exilis); Lane 6: P 24 (Digitaria iburua); Lane 7: P27 (Digitaria exilis); Lane 8: K1(Digitaria exilis); Lane 9: K2 (Digitaria exilis); Lane 10: K4 (Digitaria iburua); Lane 11: T3 (Digitaria exilis); Lane 12: T4 (Digitaria exilis)

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For De 08 in plate 6, all lanes had similar band sizes across the accessions except for lanes 10 K4 (D. iburua) and 12 T4 (D. exilis). This suggests that lanes 2 to 9 and 11 had the target area while lanes 10 and 12 did not. This primer amplified Lane 2, (D. iburua) and Lane 3 (D. barbinodis) even though it was designed to amplify sections in D. exilis.

For De 10 primer plate 7, Lanes 2 P1 (D. iburua), 6P24 (D. iburua) and 9 K2

(D. exilis) did not amplify. Very faint bands were observed for Lanes 3 P4 (D. barbinodis), 4 P6 (D. exilis), 5 P15 (D. exilis), 7 P27 (D. exilis) and 8 K1 (D. exilis) and then 10 K4 (D. iburua) and 11 T3 (D. exilis) at a 100bp level.

In De 14 plate 8, no definitive fragments were observed in all the lanes, even though lanes 5 P15 (D. exilis) and 6 P24 (D. iburua) were observed to have very faint bands at the 100bp or even slightly lower. However, lanes 3 P4 (D. barbinodis), 4P6

(D. exilis), and 7 P27 (D. exilis) gave double bands of unequal sizes greater or equal to 1Kb.

1 2 3 4 5 6 7 8 9 10 11 12 89

1000 500

100

Plate 6 Gel Electrophoregram of Primer De 08 on the 11 Accessions Lane 1: 100 bp marker; Lane 2: P1(Digitaria iburua); Lane 3 P4 (Digitaria barbnodis); Lane 4 P6 (Digitaria exilis); Lane 5 P15 (Digitaria exilis); Lane 6: P 24 (Digitaria iburua); Lane 7: P27 (Digitaria exilis); Lane 8: K1(Digitaria exilis); Lane 9: K2 (Digitaria exilis); Lane 10: K4 (Digitaria iburua); Lane 11: T3 (Digitaria exilis); Lane 12: T4 (Digitaria exilis)

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1 2 3 4 5 6 7 8 9 10 11 12

1000 500

100

Plate 7Gel Electrophoregram of Primer De 10 on the 11 Accessions Lane 1: 100 bp marker; Lane 2: P1(Digitaria iburua); Lane 3 P4 (Digitaria barbnodis); Lane 4 P6 (Digitaria exilis); Lane 5 P15 (Digitaria exilis); Lane 6: P 24 (Digitaria iburua); Lane 7: P27 (Digitaria exilis); Lane 8: K1(Digitaria exilis); Lane 9: K2 (Digitaria exilis); Lane 10: K4 (Digitaria iburua); Lane 11: T3 (Digitaria exilis); Lane 12: T4 (Digitaria exilis)

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1 2 3 4 5 6 7 8 9 10 11 12

1000 500

100

Plate 8 Gel Electrophoregram of Primer De 14 on the 11 Accessions Lane 1: 100 bp marker; Lane 2: P1(Digitaria iburua); Lane 3 P4 (Digitaria barbnodis); Lane 4 P6 (Digitaria exilis); Lane 5 P15 (Digitaria exilis); Lane 6: P 24 (Digitaria iburua); Lane 7: P27 (Digitaria exilis); Lane 8: K1(Digitaria exilis); Lane 9: K2 (Digitaria exilis); Lane 10: K4 (Digitaria iburua); Lane 11: T3 (Digitaria exilis); Lane 12: T4 (Digitaria exilis)

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De 17 primer amplified all the lanes, with the exception of lane 2P1 (D. iburua) and 12, suggesting that the target region is found in all the accessions studied.

Lanes 3 P4 (D. barbinodis), 6 P24 (D. iburua), and 10 were slightly different from the rest because they had more than once of the target region which was evident from the number of band sizes. A 201 bp and greater than 1 Kb were observed as shown in

Plate 9.

Primer De19 (plate 10) amplified all lanes except lane 4. Lanes 2 and 12 which were not amplified with De 01 to De 18 were amplified by this primer. Double bands were observed for lanes 2P1 (D. iburua), 5 P15 (D. exilis), 7 P27 (D. exilis), 8

K1 (D. exilis), 9K2 (D. exilis), 10K4 (D. iburua), 11 T3 (D. exilis) and 12T4 (D. exilis), even though lanes 9 and 11 were not so definitive.

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1 2 3 4 5 6 7 8 9 10 11 12

1000

500

100

Plate 9 Gel Electrophoregram of Primer De 17 on the 11 Accessions Lane 1: 100 bp marker; Lane 2: P1(Digitaria iburua); Lane 3 P4 (Digitaria barbnodis); Lane 4 P6 (Digitaria exilis); Lane 5 P15 (Digitaria exilis); Lane 6: P 24 (Digitaria iburua); Lane 7: P27 (Digitaria exilis); Lane 8: K1(Digitaria exilis); Lane 9: K2 (Digitaria exilis); Lane 10: K4 (Digitaria iburua); Lane 11: T3 (Digitaria exilis); Lane 12: T4 (Digitaria exilis)

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1 2 3 4 5 6 7 8 9 10 11 12

1000

500

100

Plate 10 Gel Electrophoregram of Primer De 19 on the 11 Accessions Lane 1: 100 bp marker; Lane 2: P1(Digitaria iburua); Lane 3 P4 (Digitaria barbnodis); Lane 4 P6 (Digitaria exilis); Lane 5 P15 (Digitaria exilis); Lane 6: P 24 (Digitaria iburua); Lane 7: P27 (Digitaria exilis); Lane 8: K1(Digitaria exilis); Lane 9: K2 (Digitaria exilis); Lane 10: K4 (Digitaria iburua); Lane 11: T3 (Digitaria exilis); Lane 12: T4 (Digitaria exilis)

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For primer De 22 (plate 11), lanes 2 P1 (D. iburua), 6 P24 (D. iburua), and K4

(D. iburua) were not amplified. Lanes 3 P4 (D. barbinodis ), 4 P6 (D. exilis), 5 P15

(D. exilis), 7 P27 (D. exilis), 8 K1 (D. exilis), 9 K2 (D. exilis), T3 (D. exilis), and T 4

(D. exilis) were all amplified, with double bands observed in lanes 3 P4 (D. barbinodis ), 8 K1 (D. exilis), 9 K2 (D. exilis), and 11 T3 (D. exilis).

For primer De 26 (plate 12), amplification was observed in lanes 3 P4 (D. barbinodis), 4 P6 (D. exilis), 5 P15 (D. exilis), 7 P27 (D. exilis), 8 K1 (D. exilis), 9 K2

(D. exilis), 10 K4 (D. iburua) and 11 T 3 (D. exilis), with double bands in 4 P6 (D. exilis), 7, and 10. This primer failed to amplify in lanes 2 P1 (D. iburua), Lane 6 P24

(D. iburua), and Lane 12 T4 (D. exilis).

The pattern observed from the Plates above showed that samples P1, P24, K4 and T4 did not amplify using the primers available. The samples, based on their morphology were identified as Digitaria iburua with the exception of accessionT4 which was identified as D. exilis. This is not unusual as P1, P24 and K4 are D. iburuaand the primers were designed to amplify microsatellite fragments in D. exilis.

The total amplified fragment length polymorphs of the accessions ranged between 6 and 16, with accession P4 (D. barbinodis) and accession P15 (D. exilis) from Plateau State, having the highest number of polymorphs while accession P1 (D. iburua) also from Plateau State and B4 (D. exilis) from Bauchi state, having the least number of polymorphs. Other accessions found between these extremes included B3

(D. exilis) and K4 (D. iburua), K2 (D. exilis) and P6 (D. exilis), K1 (D. exilis) and

P24 (D.iburua) and P27 (D. exilis) with the corresponding number of polymorphs as

11, 12, 13, and 14 respectively.

Out of the ten (10) primers used for the RAPD and microsatellite PCR of the acha genomic DNA, Primer De 01with molecular weight of 257 base pairs (bp) appeared to have the highest percentage polymorphism of 16.92 (Table 23). This was 96

1 2 3 4 5 6 7 8 9 10 11 12

1000

500

100

Plate 11 Gel Electrophoregram of Primer De 22 on the 11Plant Accessions Lane 1: 100 bp marker; Lane 2: P1(Digitaria iburua); Lane 3 P4 (Digitaria barbnodis); Lane 4 P6 (Digitaria exilis); Lane 5 P15 (Digitaria exilis); Lane 6: P 24 (Digitaria iburua); Lane 7: P27 (Digitaria exilis); Lane 8: K1(Digitaria exilis); Lane 9: K2 (Digitaria exilis); Lane 10: K4 (Digitaria iburua); Lane 11: T3 (Digitaria exilis); Lane 12: T4 (Digitaria exilis)

97

1 2 3 4 5 6 7 8 9 10 11 12

1000

500

100

Plate 12Gel Electrophoregram of Primer De 26 on the 11Accessions Lane 1: 100 bp marker; Lane 2: P1(Digitaria iburua); Lane 3 P4 (Digitaria barbnodis); Lane 4 P6 (Digitaria exilis); Lane 5 P15 (Digitaria exilis); Lane 6: P 24 (Digitaria iburua); Lane 7: P27 (Digitaria exilis); Lane 8: K1(Digitaria exilis); Lane 9: K2 (Digitaria exilis); Lane 10: K4 (Digitaria iburua); Lane 11: T3 (Digitaria exilis); Lane 12: T4 (Digitaria exilis)

followed by De 19 and De 26 with molecular weights of 164 and 181 base pairs

respectively, and a percentage of 12.31 each. De 04 with a molecular weight of 258bp 98 had 11.53%. De 17 and De 22 had 10% each while De 14 had 7.69. De 08 has a percentage polymorphism of 6.92 while De 06 and De 10 had the lowest percentage polymorphism of 6.15 (Table 20).

The results of the molecular analysis of the selected acha accessions and the number of amplified fragment length polymorphs (AFLP) with the estimated molecular weights (bp) are presented in Table 19.

The 10 selected RAPD and microsatellite primer pairs yielded a total of 130 scorable bands whose molecular weights ranged in size between 100 and 400 bp. Even though no single primer amplified microsatellite sections in all the accessions, all the primers amplified microsatellite sections in P4 (D. barbinodis), P15 (D. exilis), and P27 (D. exilis) (Table 20). Primer De 01 amplified sections in all the accessions except for B4

(D. exilis). Primer De 04 amplified microsatellite sections in all the accessions tested except P1 (D. iburua), and K1 (D. exilis). Primer De 06 did not amplify any sections in P1, P24, K4 and B4, the first 3 being D. iburua. The same B4 which was identified as D. exilis, was also, not amplified by primers De 08, De 10, De 14, De 17 and De

26. These primers were expected to amplify fragments in D. exilis. Accession P6 (D. exilis), was also amplified except by primer De 19. Accession P24 (D. iburua), was not amplified by De 06 and De 22. Whereas accession K1 (D. exilis) was not amplified by primer De 04, it was amplified by all the other primers. Accession K2

(D. exilis) and accession K4 (D. iburua), were not amplified by Primers De 10 and De

14. Accessoion B3 (D. exilis) also not amplified by De 14 and De 17 (Table 18).

Variations exist in the number of polymorphic bands generated by each primer combination. All the RAPD primer combinations used were suitable to fingerprint the 99

11 accessions as most of the bands appeared to be present in all the accessions (Table

19). 100

Table 19 Sequences of Primers Used for RAPD and Microsatellite PCR of Acha (Digitaria sp) genomic DNA and the Polymorphism Obtained

Mol. Wt. S/N Primers /Size Locus Fwd Primers 5′ - 3′ P1 P4 P6 P15 P24 P27 K1 K2 K4 B3 B4 Total (bp) 1 De 01 257 CTAACTCCTTCTCCCTCACC 2 1 2 2 2 3 3 3 2 2 0 22 2 De 04 258 CATTTTCCCGAAGACAGAGG 0 1 1 1 3 1 0 1 2 2 3 15 3 De 06 207 AGGAATGGCCTCAATACAT 0 2 1 1 0 1 1 1 0 1 0 8 4 De 08 206 TTGGTGGATATTGGAATTATG 1 1 1 1 1 1 1 1 0 1 0 9 5 De 10 203 TCTTTTGTTTCTGGGATG 0 1 1 1 1 1 1 0 1 1 0 8 6 De 14 199 CGAGACCTGATTTGTTTAGC 0 3 2 1 1 2 1 0 0 0 0 10 7 De 17 201 GTAACGAACATCGGGTGA 1 2 1 3 2 1 1 1 1 0 0 13 8 De 19 164 CATCTTCGAGGTTCTTGGT 2 1 0 1 1 2 2 2 2 1 2 16 9 De 22 193 ATCGAGAGTTCAGTGAGTCC 0 3 1 1 0 1 2 2 0 2 1 13 10 De 26 181 AATACATTTTCCCCTTCGTC 0 1 2 4 2 1 1 1 3 1 0 16 TOTAL 6 16 12 16 13 14 13 12 11 11 6 130

Expected Molecular Weight 100 - 200 100 -400 150 - 300 150 – 300 150 – 200 180 – 300 180 – 310 180 – 300 180 – 220 250 – 300 190 - 240 101

Table 20 Sequences of Primers Used for RAPD and Microsatellite PCR of Acha (Digitaria sp) genomic DNA and the Percentage Polymorphism.

Mol. Wt. Percentage S/N Primers Locus Forward Primers 5′ - 3′ Reverse primers (5 ′ – 3 ′) Total /Size (bp) Polymorphism 1 De 01 257 CTAACTCCTTCTCCCTCACC TGGCACTGACACAGTAAC 22 16.92% 2 De 04 258 CATTTTCCCGAAGACAGAGG GACCTTGTGGCACCCATC 15 11.53% 3 De 06 207 AGGAATGGCCTCAATACAT AGAAAGCAGTTGGATTGGT 8 6.15% 4 De 08 206 TTGGTGGATATTGGAATTATG TTTACCCAACGCATAGGTAG 9 6.92% 5 De 10 203 TCTTTTGTTTCTGGGATG ACTTGAGACCTGCAAAGA 8 6.15% 6 De 14 199 CGAGACCTGATTTGTTTAGC CAAGTCTTTGATTTCCGTCT 10 7.69% 7 De 17 201 GTAACGAACATCGGGTGA CTGATGGCAAGGATGTGT 13 10.00% 8 De 19 164 CATCTTCGAGGTTCTTGGT AGCAGTGATTCGGTAGGAC 16 12.31% 9 De 22 193 ATCGAGAGTTCAGTGAGTCC GATCATCAAACCATTTACCC 13 10.00% 10 De 26 181 AATACATTTTCCCCTTCGTC GGATCTCGTTCATGTGCTAT 16 12.31%

TOTAL 130

102

CHAPTER FIVE DISCUSSION

5.1 MORPHOLOGICAL CHARACTERS

Variations have been reported to exist in the expression of morphological traits in some cereals (Reddy et al., 2007 and Mnyenyenbe and Gupta, 1988). Such variations in quantitative traits are influenced greatly by environmental factors.

Evaluation of the phenotypic and genotypic characters for the different accessions in this study shows that the genotypes expressed more variability in genetic diversity for plant height, leaf length, and days to maturity and 1000 seed weight in the three year field trials. Tables 4, 6, and 8 show the range of variability expressed in the 30 accessions used in these trials. Garavandi and Kabrizi (2010) and

Shahryari et al. (2011) had established the existence of variability in genetic diversity for plant height and 1000 seed weight in bread wheat genotypes and similar crops.

The effect and or importance of genetic variability in plant improvement or breeding is well documented. Ghazanfar et al. (2013) reported the considerable genetic variability that exists in wheat genotypes for yield improvement, stressing that both additive and non additive gene actions were involved in the expression of yield components. Govindaraj et al. (2015) reported the importance of genetic diversity in conservation of crop plants.

5.1.1 Plant height

Grain yield and grain attributes are parameters used in identifying yield in cereal crops. This premise has shown that positive correlations between grain yield and yield attributes exists with respect to plant height, grains/ear, 1000 seed weight and days to maturity.

Plant height has been reported to be an important yield trait (Ghazanfar et al.,

2013) that directly affects plant yield. It is also the most dependable variable trait such that after the green revolution, short stature plants were preferred in varietal 103

development, because short stature plants have been found to be resistant to lodging and are more responsive to fertilizers. However, in this study, plant height was positively correlated to all yield attributes, suggesting that the taller accessions had a yield advantage over the shorter ones. It will imply from these results that lodging was inconsequential among the taller plants. Plant height and seed yield have been reported to be positively correlated in some studies of wheat (Knott and Kumar, 1975;

Busch and Rauch, 1993). In this study, the principal component analysis showed that plant height contributed very highly to the total variation with respect to yield. In the three years of analyses, plant height appeared among the first three principal components, indicating that plant height was crucial in the determination of yield in the acha. Even though short stature plants have been reported in other cereals such as wheat to be preferred in varietal development since they are resistant to lodging and more responsive to fertilizers as observed by Ghazanfar et al. (2013), this does not seem to be so with acha as the accessions with the tallest plants gave the best grain yields. It is likely that lodging is not a problem among the acha accessions evaluated

Mashiringwani and Schweppenhauser (1992), have observed that the significant positive correlations between 1000 seed weight and grain yield with days to maturity, indicate the benefit of a long duration of grain fill. According to them, the rates of grain fill and grain weights per ear have explained the variation in yield among wheat lines under different conditions. Also, Aliero and Morakinyo (2002) have reported that positive correlation exists between number of spikelets per culm with grain weight and spike length. Kempanna and Thirumalachar (1968), Goud and

Lakshmi (1977) and Abraham et al. (1989) have reported a significant variation for grain yield and number of productive tillers per plant. Josh and Mchra (1988), also reported a significant variation for days to flowering, plant height, finger length and number of fingers in finger millet accessions. The phenotypic diversity/plasticity has 104

been reported by Reddy et al. (2007) in pearl millet germplasm especially in terms of days to flowering, plant height, total tillers and 1000 seed weight. Garavandi and

Kabrizi (2010) and Shahryari et al. (2011) have also reported the genetic diversity for plant height, 1000 seed weight, seed number, spikelet etc in bread wheat genotypes.

Results from this study have confirmed that plant height consistently was particularly correlated with other traits maintaining a specific trend of genetic diversity as reported by other workers.

5.1.2 Stem girth

The accessions with thewideststem girth appeared to have the heaviest 1000

Seed weight, implying the highest grain yield. Baba (1954), Hayashi and Ito (1962),

Jennings (1964) and Ito and Hayashi (1969) have suggested that this character should assist in lodging resistance. Short stiff culm has been considered the most important morphological character that has contributed to breeding high yielding rice and wheat varieties by giving lodging resistance. This is true of rice in Japan, Shigemura (1966),

Matsumoto (1968) and Porter et al. (1964) and tropical rice, Chandler (1968).

Lodging has been described as the process by which the shoots of small grained cereals are permanently displaced from their vertical stance (Pete, 2013). He further explained that lodging limits yield potential and reduces grower profits, but that it is difficult to control because it is a complex process that is influenced by many factors including wind, rain, topography, soil type, previous crop, crop management, and disease. Significant progress was made during the 1950s, 1960s, and 1970s to reduce lodging risk by the introduction of semi-dwarf varieties. The reduced lodging risk of these shorter varieties enabled them to respond to greater amounts of fertilizers and this was a significant reason for the steady improvement in global cereal grain yields starting in the late 1960s. However, he noted that lodging is still a major problem in 105

many countries and there is an urgent need to improve lodging resistance to further increase the yield of cereal species.

5.1.3 Leaf length

Leaf length varied significantly among the accessions. The genetic variability and norm of reaction which exists between these accessions would account largely, for this. Environmental factors could also play a very important role in the expression of the genotypes in the phenotypes. Even though reports have shown that short and small leaves are associated with erect and even distribution of leaves in a canopy and so, are suggested to be desirable for high yielding varieties (Jennings, 1964; Kariya and Sakamoto, 1963). This does not seem to agree with the findings of this work as accessions with longest leaf lengths had the heaviest 1000 seed weights, suggesting better grain yields.

5.1.4 Leaf width

Genetic variation also existed among the accessions with respect to leaf width.

Gardener (1966), Hayashi and Ito (1962) and Tanner et al. (1966) have shown the extreme usefulness of wider leaf width for selection of high yielding varieties. Leaf width correlated positively with 1000 seed weight in all the years thereby agreeing with the results of their findings. This also agrees with the work of Sahin and Metin

(2006), where flag leaf width was positively correlated with grain yield per plant and suggested that successful selection could be practiced for this trait. They observed also, that the flag leaf width and grain yield per plant traits were significant.

5.1.5 Number of days to 75% maturity

The maturity class varied considerably among the different accessions and across the years. Ghazanfar et al. (2013) have reported that days to maturity is an important trait in wheat breeding programme. According to them, early and late maturing varieties have a great impact on yield of the crop, and genotypes with early 106

maturing ability are desirable for breeders. However, in this study, accessions with late maturing ability appeared to have done better than the early maturing types. The low yields of the early maturing types are not unrelated to the humid weather conditions prevalent in the study area at the time of maturity.

5.1.6 1000 seed weight

Variability existed among the accessions with respect to 1000 seed weight in all the the years, indicating suitability for sorting out genotypes with superior grain weight. Ghazanfar et al. (2013) reported that a significant variability in seed index/grain weight is essential for the breeder to sort out genotypes with superior grain weight that result in higher grain yield.

5.2 CORRELATION ANALYSES

Plant height was positively correlated to all yield attributes suggesting that the taller accessions had a yield advantage over the shorter ones. Plant height and seed yield have been reported to be positively correlated in some studies of wheat (Knott and Kumar, 1975; Busch and Rauch, 1993). This has been speculated to be due to the greater aerial biomass of taller plants (this could be a selection index for crops in this group), thus providing a lager source to contribute to the final sink of seed yield.

Sokoto et al. (2012) reported that total phytomass yield, grain yield, harvest index and 1000 grain weight are major contributors towards grain yield since these characters had high correlations, thus direct selection for these characters should be major concern for plant breeder for increased grain yield and grain quality of wheat.

5.3 PRINCIPAL COMPONENT ANALYSIS (PCA)

It was observed that principal component 1(PC1) contributed 85.9% whereas

PC2 and PC3 contributed 6.7 and 2.8% respectively of the total variation.This is a confirmation of the extensive variability existing among these phenotypic characters.One of the traits which contributed more to PC1 was leaf length. Among 107

several leaf characters associated with high yielding ability, erect habit seems the most important. Studies have shown that leaf angle has been closely correlated with nitrogen response in cereal crops. Hayashi and Ito (1962), Kariya and Sakamoto

(1963), Gardener (1966) and Tanner etal. (1966) have all shown the extreme usefulness of leaf angle and leaf width for selection of high yielding varieties.

In this study, it was observed that stem girth and leaf width contributed 6.7% of PC2. This suggests that stem girth is not a major trait contributing to grain yield.

This has also corroborated the work of Aliero and Morakinyo (2002), which has shown that stem girth was negatively correlated with grain weight.

Accessions P1, P3, P11, P21, P22, P25 and K4 were all identified to be

Digitaria iburua. These took the longest number of days to maturity and had the highest 1000 seed weight with an average at 0.72g. Accessions P26, K2, K3 and T5 which were identified to be Digitaria exilis, despite its early maturity date of an average at 132 days had an average of 0.62g. This trend could be said to be similar to the former set, perhaps because it flowered earlier than the other accessions. P4, identified as Digitaria barbinodis, was the earliest to mature, with an average number of days at 130 even though the seeds had the least weight. Traditionally, this accession is usually the first to be harvested, followed by the Digitaria exilis and finally with D. iburua. These findings give credence to the work of Aliero and

Morakinyo (2001), who worked with 10 accessions of Digitaria consisting of 8 D. exilis and 2 D. iburua which were characterized using vegetative, floral and spikelet characters and evaluated them for yield. They showed that the accessions differed significantly with respect to qualitative and quantitative attributes.

This work has shown the phenotypic plasticity of Digitaria species which were collected from different geographical locations. This was observed in Bwut

Madu 03 and Halat Jaba 08 which were very closely related even though the 108

accessions were collected from different locations. This agrees with the work of

Dachi and Gana (2008), who have shown that even though differences existed in

Digitaria species with respect to adaptability and yield, such differences were however, not significant.

5.4 MOLECUAR ANALYSIS

Ji Qi et al. (2014) in their work on the Detection of genomic variations, DNA polymorphisms and impact on analysis of meiotic recombination and genetic mapping, observed that DNA polymorphisms are important markers in genetic analyses andare increasingly detected by using genome resequencing. Accordingly, these DNA polymorphisms are ubiquitous genetic variations amongindividuals and include single nucleotide polymorphisms(SNPs), insertions and deletions (indels), and other larger rearrangements (Feuk et al., 2006, Sharp et al., 2006 and Mitchell-Olds and Schmitt, 2006). They can have phenotypicconsequences and also serve as molecular markers for geneticanalyses, facilitating linkage and association studies of geneticdiseases, and other traits in humans (Stankiewicz and Lupski, 2002, Hurles et al., 2008 and Stankiewicz and Lupski, 2010), animals, plants (Johanson et al., 2000,

Michaels et al., 2003, Koornneef et al., 2004 and Krieger et al., 2010)and other organisms. Using DNA polymorphisms for moderngenetic applications requires low- error, high-throughput analyticalstrategies.

Even though Digitaria exilis and D. iburua resemble each other in some remarkable ways, as reported by Porteres (1976), and Haq and Ogbe (1995),

Digitaria iburua appeared to have taller plant height, wider stem girth, wider leaf width and longer leaf length. It also took longer days to maturity. The seeds of D. iburua are larger and heavier. Digitaria barbinodis on the other hand, appeared slightly lower with respect to these morphological traits. The seeds in D. barbinodis were evidently the least in weight and oblong in shape. The traits used in this study do 109

not seem to completely agree with the observations of Haq and Ogbe (1995). The major difference between the three species is mainly related to their inflorescence and spikelet as reported by Stapf (1915) and Henrard (1950).

RFLPs appear to suggest a clear separation of the the 3 species (Digitaria iburua, D. exilis and D. barbinodis) demonstrating their genetic differences at the molecular/DNA level. Hilu et al. (1997) had reported an AFLP data demonstrating a high genetic differentiation at the DNA level between Digitaria iburua and D. exilis, confirming the results of a molecular investigation where RAPD markers were used to confrm the botanical distinction of the two species. In this study, some of the RFLPs were shared by the 3 species, showing their relatedness even though some morphological variations were visible. This level of shared genome may suggest some form of evolutionary separation between them. For instance, all the microsatellite primers for the D. exilis used in this study were amplified in D. barbinodis. This was followed by D. exilis and the least amplification was in, D. iburua, suggesting that eventhough the primers were developed for D. exilis, it was more of a generic than of species problem/consideration.

Adoukonu-Sagbaja et al. (2007a) rightly observed that farmers and perhaps traders would have played an important role in the gene flow between the growing areas. The resemblance of D. exilis and D. iburua species within the three states may not be unconnected with this. Furthermore, because of the large number of accessions and growing areas on the Plateau, it would appear that the spread to Bauchi (North ward) and Kaduna (South-West ward), must have been from the Jos Plateau.

Otherwise, multiple domestication events associated with the different diversity areas may have ocured in the species (accessions collected). The distribution of genetic diversity as observed in this study suggests the origin from Plateau State. It is apparent that the separation of the species into two major clusters with sub-clusters 110

within the different species is indicative of a secondary diversification. It can therefore, be deduced that the groups identified are likely to represent the major evolutionary groups developed over time during the cultivation and dispersal of the crop. D. barbinodis may well be the earliest cultivated species, perhaps because of its early maturity period. From this species, may have evolved D. exilis, from which D. iburua must have evolved.

The combined effects of many factors such as selection and adaptation, mutation, mating systems and dispersal mechanism, and drift determine population genetic structures (Hamrick and Godt, 1989). It is not unlikely that the variation between D. exilis and D. iburua may be due to selection that is associated with differences in traditional agricultural practices and the adaptation of acha landraces to the different environmental conditions where they have been cultivated since time immemorial. This agrees with Porteres (1976), Hilu et al. (1997) and Adoukounu-

Sagbaja, et al. (2006). Phenotypically, D. iburua shows divergent traits from D. exilis and D. barbinodis. Phenotypic and /or genotypic distances between genotpes are expected to provide predictive indices for high heterosis effects and performance of their hybrids. Although, discrepancy between molecular and phenotypic distances does seem to be a wide spread phenomenon in plants as observed by Gerdes and

Tracy (1994) and Portis et al. (2004), this is expected to to be related to the environmental effects on morphological traits (Adoukounu-Sagbaja, et al., 2007b).

Furthermore, molecular markers such as AFLPs are neutral and not necessarily linked to genes underlying morphological traits.

111

CHAPTER SIX SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS

6.1 SUMMARY OF FINDINGS

1. Sixty germplasm were assembled from representative locations in three states

of Nigeria, namely Bauchi, Plateau and Kaduna.

2. Three phylogenetic species of Digitaria were identified in the accessions

namely; Digitaria iburua, D. exilis and D.barbinodis.

3. Digitaria iburua and D. exilis were identified in all the collections except D.

barbinodis which was identified only from the samples collected from Jos

South Local Government Area of Plateau State.

4. Accessions P1 and P21 (D. iburua) appeared the tallest across the three years

of study while accessions P5 (D. exilis) and P13 (D. exilis) recorded the

shortest plant height.

5. Plant height was positively correlated to all yield attributes suggesting that the

taller accessions had a yield advantage over the shorter ones.

6. Accessions P23 (D. iburua) and P25 (D. iburua) produced the widest stem

girth while accession P4 (D. barbinodis) had the narrowest stem girth.

7. Accession P25 produced the longest leaf length while accession P4 (D.

barbinodis) had the shortest.

8. The widest mean leaf width of 1.3cm was observed in accession P23 (D.

iburua) while the narrowest of 0.6cm was recorded in accessionP22 (D.

exilis).

9. The accession with the highest number of days to maturity was P23 (D.

iburua), taking 160 days while the accession with the least number of days to

mature was P4 (D. barbinodis), having 130 days.

10. Accession P23 (D. iburua) had the heaviest seed weight of 0.7567g and

accession P15 (D. exilis) was the least heavy with a value of 0.5124g. 112

11. A positive and highly significant correlation existed between morphological

traits and yield. The number of days to maturity was highly and positively

correlated to yield, implying that the longer the number of days to mature, the

heavier the seeds.

12. The principal component analysis (PCA) showed that 95.4% of the total

variation was contributed by the first three principal components. Principal

component 1(PC1) contributed 87.1% wheareas, PC2 and PC3 contributed 4.7

and 3.6% respectively of the total variation.

13. The traits which contributed more to PC1 were plant height (PH), stem girth

(SG), number of days to maturity (DM) and 1000 seed weight (1000S). PC2

was dominated by leaf width (LW), while PC3 was dominated by leaf length

(LL) and 1000 seed weight (1000S).

14. At about 90% similarity, the accessions 1, 3, 9, 18, 26,16, 19 and 20 form one

group, and 2, 22, 5, 28, 30, 12, 27, 29, 23, 25, 14, 24, 11, 15, 6, 10, 17, 4, 8,

21, 13 and 7 from a second group. The dendrogram suggests that accession 1,

3, 9, 18, 26, 16, 19, and 20 are similar measures for one main component and

2, 22, 5, 28, 30, 12, 27, 29, 23, 25, 14, 24, 11, 15, 6, 10, 17, 4, 8, 21, 13 and 7

are similar measure for a second main component.

15. Cluster group two has higher similarity degree than cluster group one. Sub

cluster groups also exist, that is, 2 and 22, 5 and 28, 12 and 27. On the other

hand, 28 and 30 clusters have higher similarity percentage than all cluster

groups (almost 100%). Clusters 3 and 9 have higher similarity (90 – 95%) in

the first group. All accessions also, form one cluster group at about 44.06%

similarity

16. Although intra-species differences existed in Digitaria species with respect to

adaptability and yield, such differences where however, not significant. 113

17. The major difference between the three species is mainly related to their

inflorescence and spikelet as reported by Stapf, (1915) and Henrard, (1950).

18. RFLPs appear to suggest a clear separation of the the 3 species (D. iburua, D.

exilis and D. barbinodis) demonstrating their genetic differences at the

molecular/DNA level.

19. In this study, some of the RFLPs were shared by the 3 species, showing their

relatedness even though some morphological variations were visible.

20. This level of shared genome may suggest some form of evolutionary

separation between them. For instance, all the microsatellite primers for the D.

exilis used in this study were amplified in D. barbinodis. This was followed by

D. exilis and the least amplification was in, D. iburua, suggesting that though

the primers were developed for D. exilis, it was more of a generic than of

species problem/consideration.

21. Phenotypically, D. iburua shows divergent traits from D. exilis and D.

barbinodis.

6.2 CONCLUSION

In this study, plant height was positively correlated to all yield attributes, suggesting that the taller accessions had a yield advantage over the shorter ones, implying that lodging was inconsequential among the taller plants. The accessions (1,

3, 9, 18, 26,16, 19 and 20) with thewideststem girth, appeared to have the heaviest

1000 seed weight.

Accessions with longest leaf lengths had the heaviest 1000 seed weights, indicating better grain yield. Variation also existed among the accessions with respect to leaf width. Leaf width correlated positively with 1000 seed weight in all the years thereby agreeing with the results of their findings. Flag leaf width has been reported 114

to have been positively correlated with grain yield per plant and suggestion made that successful selection could be practiced for this trait.

Accessions with late maturing ability appeared to have done better than the early maturing types. The low yields of the early maturing types may be related to the humid weather conditions prevalent in the study area at the time of maturity.

Variability existed among the accessions with respect to 1000 seed weight in all the the years, indicating suitability for sorting out genotypes with superior grain weight/yield.

Evaluation of the phenotypic and genotypic characters for the different accessions in this study shows that the genotype had more variability in genetic diversity for plant height, leaf length, days to maturity and 1000 seed weight in the three year field trials. RFLPs appear to suggest a clear separation of the the 3 species

(D. iburua, D. exilis and D. barbinodis) demonstrating their genetic differences at the molecular/DNA level.

6.3 RECOMMENDATIONS

1. Further studies of this nature should incorporate samplesfrom the diverse acha

producing areas of Nigeria.

2. Also, the promising accessions in terms of yield and yield components should be

incorporated in multilocational and advanced yield trials with the aim of having

uniform yielding accessions that can be further employed in future breeding and

selection work for this crop.

3. Part of the AFLP should be sequenced and data used to establish similarity and /or

dissimilarity among the accessions. 115

6.4 LIMITATIONS OF THE STUDY Finance has been the major constraint of this work. This had greatly affected the ease with which the expeditions were made for the collections of the accessions in the three states, namely; Bauchi, Plateau and Kaduna states, thereby restricting the collections of accessions for this study to the three states.

Another limitation is the fact that even though the accessions were collected from different areas, the field trial was conducted in only one location, the Jos

Plateau. Ideally, the field trial should have been multilocational.

The cost of the molecular analyses was quiet high and so reduced the number of accessions that were analysed in the laboratory. Only the morphological traits were used to establish the degree of relatedness of the accessions with the dendrogram. The molecular analysis was used to amplify microsatellite fragments in only eleven (11) accessions using primer combinations specially designed for Digitaria exilis.

6.5 SUGGESTIONS FOR FURTHER STUDY

Since gene mapping was not part of this study, it is suggested that mapping studies and/or experiments should be conducted as this would be ideal to identify specific genes or genomic regions that have an influence on the phenotypic variations so far observed. The germplasm (accessions) was collected from different ecological areas and evaluated in only one location, the Jos Plateau. A better view of the plastic responses and adaptation of the germplasm (accessions) should be developed.

6.6 CONTRIBUTION TO KNOWLEDGE

1. Assemblage of acha germplasm to the University of Jos.

2. Microsatelite primer combinations specially designed for Digitaria exilis

amplifiedmicrosatellite fragments in the other species of Digitaria namely;

Digitaria barbinodis and Digitaria iburua. This level of shared genome may

suggest some form of evolutionary separation between them. For instance, all the

microsatellite primers for the D. exilis used in this study were amplified in D. 116

barbinodis. This was followed by D. exilis and the least amplification was in, D.

iburua, suggesting that eventhough the primers were developed for D. exilis, it

was more of a generic than of species problem/consideration.

3. Evaluation of the phenotypic and genotypic characters for the different accessions

in this study shows that the genotype had more variability in genetic diversity for

plant height, leaf length, days to maturity and 1000 seed weight in the three year

field trials. RFLPs appear to suggest a clear separation of the the 3 species (D.

iburua, D. exilis and D. barbinodis) demonstrating the extent of their genetic

differences at the molecular/DNA level.

117

REFERENCES

Abraham, M. J., Gupta, A. S. & Sarma, B. K. (1989). Genetic variability and character association of yield and its components in finger millet (Eleucine corocana L. Gaertn) in acidic soils of Meghalaya. Indian Journal of Agricultural Science, 59: 579-581.

Adoukonou-Sagbadja, H. (2010). Genetic characterization of traditional fonio millets (Digitaria exilis,D. iburua Stapf) landraces from West-Africa: Implications for conservation and breeding. Phd Dissertation. Justus-Liebig University Giessen.

Adoukonou-Sagbadja, H., Dansi, A., Vodouhè, R. & Akpagana, K. (2006). Indigenous and traditional conservation of fonio millet. (Digitaria exilisStapf,Digitaria iburuaStapf) in Togo.Biodiversity Conservation,15, 2379-2395.

Adoukonou-Sagbadja, H., Dansi, A., Vodouhè, R. & Akpagana, K. (2004). Collecting fonio (Digitaria exilisStapf, Digitaria iburuaStapf)landraces in Togo.Plant Genetic Resources Newsletter,139, 63–67.

Adoukonou-Sagbadja, H., Schubert, V., Dansi, A., Jovtchev, G., Meister, A., Pistrick, K., Akpagana, K. & Friedt, W. (2007a). Flow cytometric analysis reveals different nuclear DNA contents in cultivated fonio (Digitariasp.) and some wild relatives. Plant Systematics andEvolution,267, 163-176.

Adoukonou-Sagbadja, H., Wagner, C., Dansi, A., Ahlemeyer, J., Daïnou, O., Akpagana, K., Ordon, F. & Friedt, W. (2007b). Genetic diversity and population differentiation oftraditional fonio millet (Digitaria spp.) landraces from different agro-ecologicalzones of West-Africa.Theoretical and Applied Genetics, 115, 917-931.

Agrama, H. A. & Tuinstra, M. R. (2003). Phylogenetic diversity and relationships among sorghum accessions using SSRs and RAPDs. African Journal of Biotechnology, 2, 334 – 340.

Ahlquist, J. E. & Charles G. Sibley (1999). A commentary on 30 years of collaboration. The Auk, 116, 3 (July 1999). http://elibrary.unm. edu/sora/Auk/v116n03/ index.php.

Akkaya, M. S., Bhagwat, A. A. & Gregan, P. B. (1992). Length polymorphism of simple sequence repeat DNA in soybean. Genetics, 132, 1131-1139.

Aldrich, P. R., Doebley, J., Schertz, K. F. & Stec, A. (1992). Patterns of allozyme variation in cultivated and wild Sorghum bicolor.Theoretical and Applied genetics,85, 293-302.

Aliero, A. A. & Morakinyo, J. A. (2001). Phenotypic correlation between vegetative and floral characteristics in acha varieties. Moor Journal of Agriculture,3:130- 136.

Aliero, A. A. & Morakinyo, J. A. (2002). Characterization of Digitaria exilis (Kipp) Stapf and D. iburua Stapf accessions. Nigerian Journal of Genetics, 16, 10-21. 118

Anand, P., Samit, R. & Amit, R. (2014). Molecular markers in phylogenetic studies-A review. Retrieved June 25, 2016, from http://dx.doi.org/10.4172/2329- 9002.1000131.

Anonymous, (1995). A small cereal with great promise. Spore Bulletinof the CTA no. 55, 5.

Appa-Rao, S., Prasada-Rao, K. E., Mendesha, M. H. & Gopal-Reddy, V. (1996). Morphological diversity in sorghum germplasm from India. Genetic Resources and Crop Evolution,43, 559-567.

Archak, S., Gaikwad, A. B., Gautam, D., Rao, E. V. V. B., Swamy, K. R. M& Karihaloo, J. L. (2003). Comparative assessment of DNA fingerprinting techniques (RAPD, ISSR and AFLP) for genetic analysis of cashew (Anacardium occidentale L.) accessions ofIndia. Genome, 46, 362-369.

Aslafy, J. H. (2003). Organic fonio to woo Europe. Spore 106.

Avise, J. C. (2004). Molecular markers, natural history and evolution.(2nd edition) Chapman &Hall Inc., New York, 541.

Ayana, A., Bryngelsson, T. & Bekele, E. (2000). Genetic variation of Ethiopian and Eritrean sorghum [Sorghum bicolor(L.) Moench.] germplasm assessed by random amplified polymorphic DNA (RAPD). Genetic Resources and Crop Evolution, 47, 471-482.

Ayele, M., Tefera, H., Assefa, K. & Nguyen, H. T. (1999). Genetic characterization of two Eragrostis species using AFLP and morphologicaltraits.Heriditas,130, 33–40.

Baba, I. (1954). Studies on rice breeding (A separate volume of Journal of breeding). 43, 167-184.

Baltensberger, D. D. (1996). Foxtail and proso millet. In: Janick, J. (eds) Progress in new crops. ASHS Press, Alexandria, VA, 182-190.

Bänfer, G., Fiala, B., Weising, K. (2004). AFLP analysis of phylogenetic relationships among myrmecophytic of Macaranga (Euphorbiaceae) and their allies.Plant Systematics and Evolution,249, 213-231.

Banjo, A. (1988). Keynote address delivered at the Proceeding of an International Conference on crop Genetic Resources of Africa. 11th - 20th October. Genetics, 4, 237-24.

Barnaud, A., Vignes, H., Risterucci, A., Noyer, J., Pham, J., Blay, C., Buiron, M., Vigouroux, Y. & Billot, C. (2012). Development of Nuclear Microsatellite Markers for the Fonio, Digitaria exilis (Poaceae), an understudied West African Cereal. Retrieved February 16, 2014, from e105-e107 doi:10.3732/ajb.1100423.

Barrett, B. A. & Kidwell, K. K. (1998). AFLP-based genetic diversity assessment among wheat cultivars from the Pacific Northwest. Crop Science,38, 1261- 1271. 119

Barry, M. B., Pham, J. L., Noyer, J. L., Billot, C., Courtois, B. & Ahmadi, N. (2006). Geneticdiversity of the two cultivated rice species (O. sativa &O. glaberrima) in Maritime Guinea: evidences for interspecific recombination. Euphytica,154, 127-137.

Beckmann, J. S. & Soller, M. (1990). Toward a unified approach to genetic mapping of eukaryotes based on sequence tagged microsatellite sites. Biotechnology, 8, 930-932.

Benito O. de Lumen, Robert, B. & Pilar, S. R. (1986). Nutritional analyses of some underutilised crops. Journal of Agriculture and Food Chemistry. 34 (2).

Bezpaly, I. (1984). Les plantes cultivées en Afrique Occidentale.Ouvrage sous la direction de Oustimenko.Bakoumovski. Editions MIR. Moscow, 84-87.

Bogdan, A. V. (1977). Tropical Pasture and Fodder Plants (Grasses and Legumes). Longman London.

Bonin, A., Pompanon, F. & Taberlet, P. (2005).Use of Amplified Fragment Length Polymorphism (AFLP) markers in surveys of vertebrate diversity.Methods inEnzymology, 395, 145-161.

Broun, P. & Tanksley, S. D. (1996).Characterization and genetic mapping of simple repeat sequences in the tomato genome. Molecular Genetics, 250, 39-49.

Brown, S. M., Hopkins, M. S., Mitchell, S. E., Senior, M. L., Wang, T. Y., Duncan, R. R., Gonzalez-Candelas, F. & Kresovich, S. (1996). Multiple methods for the identification of polymorphic simple sequence repeats (SSRs) in sorghum [Sorghum bicolor(L.)Moench].Theoretical and Apllied genetics93, 190-198.

Burr, B. (1994). Some concepts and new methods for molecular mapping in plants. In: Philips, R. L. &Vasil, I. K. (eds.) DNA-based Markers in Plants. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp 1-7.

Busch, R. H. & Rauch, T. L. (1993). Agronomic performance of tall versus short semi dwarf lines of spring wheat. Crop Science, 33, 941-943.

Bustos, A., Soler, C. & Jouve, N. (1999). Analysis by PCR-based markers using designed primers to study relationships between species of Hordeum (Poaceae). Genome,42, 129-138.

Cao, W., Scoles, G., Hucl, P. & Chibbar, R. N. (1999).The use of RAPD analysis to classify Triticum accessions.Theoretical and Applied genetics,98, 602-607.

Carr, J., Xu, M., Dudley, J. W. & Korban, S. S. (2003). AFLP analysis of genetic variability in New Guinea impatiens.Theoretical and Applied genetics,106, 1509-1516.

Cervera, M. T., Cabezas, J. A., Sancha, J. C., Martínez de Toda, F. & Martínez- Zapater, J. M. (1998). Application of AFLPs to the characterization of grapevine (Vitis viniferaL.) genetic resources. A case study with accessions from Rioja (Spain). Theoretical and Applied Genetics,97, 51-59.

Central Bank of Nigeria, CBN (1998). Annual report and statement of account pp. 84 120

Chandler, R. F. Jr. (1968). Science for better living. U.S. department of Agriculture Handbook.

Chowdari, K. V., Davierwala, A. P., Gupta, V. S., Ranjekar, P. K. & Govila, O. P. (1998a). Genotype identification and assessment of genetic relationships in pearl millet [Pennisetumglaucum(L.) R. Br.] using microsatellites and RAPDs.Theoretical and Applied genetics,97, 154-162.

Chowdari, K. V., Venkatachalam, S. R., Davierwala, A. P., Gupta, V. S., Ranjekar, P. K. & Govila, O. P. (1998b). Hybrid performance and genetic distance as revealed by the GATA microsatellite and RAPD markers in pearl millet. Theoretical and Applied genetics,97, 163-169.

Clayton, W. D. & Renvoize, S. A. (1986). GeneraGraminum, Grasses of the world. Kew Bulletin. Additional Series XIII.

Colombo, C., Second, G., Valle, T. L. & Charrier A (1998).Genetic diversity characterization of cassava cultivars (Manihot esculentaCrantz.) I. RAPD markers. Genetics and Molecular Biology,21, 69-84.

Cook, J. R. (1998). Towards a successful multinational crop plant genome initiative. Proceedings of the National Academy95, 1993-1995.

Crawford, D. J. (1989). Enzyme electrophoresis and plant systematics. In: Soltis, E. D. &Soltis, P. S. (eds) Isozymes in plant biology. Dioscor. Press, Portland, pp. 146-164.

Cruz, J. F. (2004). Fonio: A small grain with potential. In: Magazine on LEISA. Vol. 201, March, 2004, 16-17. (Low external input and sustainable) Agriculture.

Dachi, S. N. & Gana, A. S. (2008). Adaptability and yield evaluation of some acha (Digitaria exilisand D. iburua Kippist Stapf) accessions at Kunsogi – Bida, Niger State, Nigeria. African Journal of General Agriculture,4, 73-77.

Dachi, S. N. & Omueti, J. A. I. (2000). The effects of different rates of NPK and organo-mineral fertilizers on the growth and yield of “acha” in southwestern Nigeria. Journal of Arts, Science and Technology. (NIJASAT), Bida, 3, (1), 23-30.

Dalziel, J. M. (1937). The Useful plants of west tropical Africa. In: Hutchinson, J. &Dalziel, J. M. (eds) An Appendix to The Flora of West Tropical Africa. The Crown Agents for the Colonies, London, 612.

Danna, K. & Nathans, D. (1971). Specific changes of simian virus 40 DNA by restriction endonuclease of Hemophilus influenza.Proceedings of National Academy of Science,68, 2913-2917.

Dauda, A. & Luka, D. (2003). Status of acha (Digitaria exilis) production in Bauchi State, Nigeria. In: proceedings of the first National acha stakeholders workshop at PADP, Jos (9-11th March 2003). Kwon-Ndung, E. H., Bright, E. O. & Vodouhe, R. (eds). 121

de Oliveira, A. C., Richter, T. & Bennetzen, J. L. (1996). Regional and racial specifications in sorghum germplasm assessed with DNA markers. Genome,39, 579-587.

Dean, R. E., Dahlberg, J. A., Hopkins, M. S., Mitchell, S.E., & Kresovich, S. (1999). Genetic Redundancy and diversity among „Orange‟ accessions in the U.S. national sorghum collection as assessed with simple sequence repeat (SSR) markers. Crop Science,39, 1215-1221.

Depeige, A., Goubely, C., Lenior, A., Cocherel, S., Picard, G., Raynal, M., Grellet, F. & Delseny, M. (1995). Identification of the most represented repeated motifs in Arabidopsis thalianamicrosatellite loci. Theoretical and Applied Genetics,91, 160-168.

Diallo, T. A. (2003). Diversité génétique de Digitaria exilis en Guinée et mesure de préservation. In: Vodouhè, S. R., Zannou A et Achigan Dako E (eds) Actes du premier atelier sur la diversité génétique du fonio (Digitaria exilis Stapf.) en Afrique de l‟Ouest IPGRI, Rome, pp 32-35

Dice, L. R. (1945). Measures of the amount of ecologic association between species.

Djè Y, Ater M, Lefèbvre C. & Vekemans, X. (1998). Patterns of morphological and allozyme variation in sorghum landraces of Northwestern Morocco. Genetic Resources and Crop Evolution. 45, 541-548.

Edna, S. & Victor, H. A. (2008). History, objectivity, and the construction of molecular phylogenies. Stud. Hist. Phil. Biol. and Biomedical Science, 39, 451 – 468.

Excoffier, L., Smouse, P. E. & Quattro, J. M. (1992). Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics, 131, 479-491.

Fahima, T., Sun, G.L., Beharav, A., Krugman, T., Beiles, A. & Nevo, E. (1999).RAPD polymorphism of wild emmer wheat populations, Triticum dicoccoides, in Israel.Theoretical and Applied genetics,98, 434-447.

Felsenstein, J. (2004). Inferring phylogenies.Sinauer Associates Incorporated. ISBN 0-87893-177-5.

Feuk,L., Carson, A. R. & Scherer, S. W. (2006). Structural variation in the human genome. Nature Review of Genetics,7, (2).85–97

Fogg, W. H. (1976).Setaria italiaca: its origin and process of cereal domestication in Asia. PhD Dissertation, University of Oregon,USA.

Food andAgriculture Organisation (2009). Production year Book, Rome, Italy.

Foster, W. H. & Mundy, E. J. (1961). Forage Species in Northern Nigeria. Tropical agriculture, Trinidad, 38, 39-47.

Gani, A. M. (1988). Mineral nutrition and morphological studies as approaches to development of ideotypes for Digitaria iburua Stapf. and D. exilis Stapf. MSc. Desssertation. Wye College, University of London. 122

Gao, L. Z. & Hong, S. G. D. (2000). Allozyme variation and population genetic structure of common wild rice Oryza rufipogon Griff. in China. Theoretical and Applied genetics,101, 494-502.

Garavandi, M. & Kabrizi, M. (2010). The 11th Crop Science and Plant Breeding Congress, Iran, pp. 537-541.

Gardener, C. J. (1966). The physiological basis for yield differences in three high and three low yielding varieties of barley. PhDThesis.University of Guelph, Ontario, Canada.

Gerdes, J. T. & Tracy, W. F. (1994). Diversity of historically important sweet corn inbreds as estimated by RFLPs, morphology, isozymes and pedigree. Crop Science, 34: 26 -33.

Gerber, S., Mariette, S., Streiff, R. & Kremer, A. (2000). Comparison of microsatellites and AFLP markers for parental analysis. Molecular Ecology,9, 1037-1048.

Ghazanfar, H., Muhammad, K. M. M., Usman, I., Muhammad, M. R., Muhammad, S. & Abdullah. (2013). Genetic analysis of quantitative yield related traits in spring wheat (Triticum aestivum L.). American-Eurasian Journal of Agriculture and Environmental Science, 13, (9), 1239-1245.

Glew, R. H., Emmanuel, P. L., Jack, M. P., John, S., Ronnee, A.,Yuan-Chen, W., Yu- Chen, C. & Lu-Te, C. (2013). Fatty acid, amino acid, mineral and antioxidantcontents of acha (Digitaria exilis) grown on the JosPlateau, Nigeria. International Journal of Nutrition and Metabolism5, (1) 1-8. doi: 10.5897/IJNAM13.0137.

Goud, J.V.& Lakshmi, P.V. (1977). Morphological and genetic variabilities for quantitative characters in ragi (Eleucine corocana L. Gaertn). Mysore Journal of Agricultural Sciences, 11 (4). pp. 438-443.

Govindaraj, M., Vetriventhan, M. & Srinivasan, M. (2015). Importance of genetic diversity assessment in crop land and its recent advances: An Overview and its Analytical Perspectives. Genetic Research International, http://dx.doi.org/10. 1155/2015/431487.

Griaule, M. & Dieterlen, G. (1950). Un système soudanais de Sirius. Journal de la Societe des Africanistes.20, 273-294.

Hamrick, J. L. (1989). Isozymes and the analysis of genetic structure in plantpopulations. In: Soltis, E. D., Soltis, P. S. (eds) Isozymes in plant biology. Dioscor. Press, Portland, pp 87-105.

Hamrick. J. L. & Godt, M. J. W. (1989). Allozyme diversity in plant species. In: Brown, A. H. D., Clegg, M. T., Kahler, A. L., & Weir, B. S. (eds.) Plant populationgenetics, breeding and genetic resources. Sinauer Sunderland,Massachusetts, pp 43–63.

Haq, N. & Ogbe, F. D. (1995). Fonio (Digitaria exilisand Digitaria iburua). In: Cereals and pseudocereals, boundary row, Chapman & Hall, London, Chap 5, 2–6. 123

Harlan, J. R. &DeWet, J. M. J. (1972). A simplified classification of cultivated sorghum. Crop Science,12, 172-176.

Hayashi, K. & Ito, H. (1962). Studies on the Form of Plant in Rice varieties with Particular reference to the efficiency in utilizing Sunlight. Proceedings of Crop Science society. Japan 30, 329 – 334.

Henrard, J. T. (1950). Monograph of the genusDigitaria. Leiden Univ. Press, Leiden.

Hillis, D. M. & Moritz, C. (1996). Molecular systematics. 2nd ed. Sinauer Associates Incorporated. ISBN 0-87893-282-8.

Hilu, K. W. (1994). Evidence from RAPD markers in the evolution of Echinochloamillets (Poaceae). Plant Systematics and Evolution,189, 247-257.

Hilu, K. W., M‟Ribu K., Liang, H. & Mandelbaum, C.(1997). Fonio millets: Ethnobotany, genetic diversity and evolution. South African Journal of Botany, 63, (4): 185–190.

Holm, L. G., Plucknett, D. L., Pancho, J. V. &Herberger, J. P. (1977). The World’s worst Weeds: Distribution Biology. East-West Center/University of Hawaii, Honolulu. P.690

Hongtrakul, V., Huestis, G. M.,&Knapp, S. J. (1997). Amplified fragment length polymorphisms as a tool for DNA fingerprinting sunflower germplasm: Genetic diversity among oilseed inbred lines. 95, 400-407.

Hurles, M. E., Dermitzakis, E. T. & Tyler-Smith, C. (2008). The functional impact of structuralvariation in humans. Trends in Genetics 24,(5). 238–245.

Ibrahim, A. (2001). Hungry rice (Fonio): A neglected cereal crop. NAQAS Newsletter, Vol. No. 4 – 5.

Irvin, F. R. (1974). West African Crops. Oxford University Press London. 148 – 149.

Ito, H. & Hayashi, K., (1969). The changes in paddy rice varieties in Japan. Proceedings of symposium on optimization of fertilizer effect in rice cultivation. Agriculture and fisheries Research Council, Tokyo, Japan. 13-23.

Jennings, P. R. (1964). Breeding high yielding varieties in rice. Crop Science, 4, 13- 15.

Jideani, I. A. (1990). Acha (Digitaria exilis), theneglected cereal. Agriculture International,42, (5), 132–134.

Jideani, I. A. (1999). Traditional and possible technological uses of Digitaria exilis(acha) and Digitaria iburua(iburu): a review. Plant Foods for Human Nutrition,54, 363-374.

Ji Qi, Yamao Chen, Gregory P. copenhaver & Hong Ma (2014). Detection of genomic variations and DNA polymorphisms and impact on analysis of meiotic recombination and genetic mapping. Proceedings of the National Academy of Science, USA. 111(27): 10007-10012. doi: 10.1073/pnas.1321897111 124

Johanson, U., West, J., Lister, C., Michaels, S., Amasino, R. & Dean, C. (2000). Molecular analysis of FRIGIDA, a major determinant ofnatural variation in Arabidopsis flowering time. Science, 290,(5490). 344–347.

Jones, C. J., Edwards, K. J., Castiglione, S., Winfield, M. O., Sala, F., Van-der-Weil, C., Vosman, B. L., Matthes, M., Daly, A., Brettschneider, R., Bettini, P., Buiatti, M., Maestri, E., Marmiroli, N., Aert, R. L., Volckaert, G., Rueda, J., Vazquez, A. & Karp, A. (1998). Reproducibility testing of RAPDs by a network of European laboratories. In: Karp A, Isaac P. G., Ingram, D. S. (eds.) Molecular tools for screening biodiversity, Chapman & Hall, London, UK, pp 176-179.

Jordan, D. R., Tao, Y. Z., Godwin, I. D., Henzel, R. G., Cooper, M. & McIntyre, C. L. (1998). Loss of genetic diversity associated with selection for resistance to sorghum midge in Australia sorghum. Euphytica,102, 1-7.

Karam, D., Westra, P., Nissen, S. J., Ward, S. M. & Figueiredo, J. E. F. (2004). Genetic diversity among proso millet (Panicum miliaceum) biotypes assessed by AFLP technique.Planta Daninha,22, 167-174.

Kariya, K. & Sakamoto, S. (1963). Effect of nitrogen on leaf angle. Bulletin of Chugoku Agriculture Experiment Station, A9, 17 – 30.

Kempanna, C. & Thirumalachar, D. K. (1968). Studies on the genotypic variation in ragi (Eleucine coracana). Mysore Journal of Agricultural Sciences,2, 29-34.

Keriann McGoogan, Tracy Kivell, Matthew Hutchison, Hilary Young, Sean Blanchard, Margaret Keeth and Shawn M. Lehman (2007). Phylogenetic Diversity and the Conservation Biogeography of African Primates. Journal of Biogeography Vol. 34, No. 11pp. 1962-1974

Kimberling, D. N., Ferreira, A. R., Shuster, S. M. & Keim, P. (1996). RAPD marker estimation of genetic structure among isolated northern leopard frog populations in southwestern USA. Molecular Ecology, 5, 521-529.

Knott, D. R. & Kumar, J. (1975). Comparism of early generation yield testing and a single seed descent procedure in wheat breeding. Crop Science, 15, 295-299.

Koornneef, M., Alonso-Blanco, C. & Vreugdenhil, D. (2004). Naturally occurring geneticvariation in Arabidopsis thaliana. Annual Review of Plant Biology. 55, 141–172.

Kremer, A., Caron, H., Cavers, S., Colpaert, N., Gheysen, L. & Gribel, R. (2005). Monitoring genetic diversity in tropical trees with multilocus dominant markers. Heredity,95, 274-280.

Krieger, U., Lippman, Z. B. &Zamir, D. (2010). The flowering gene SINGLE FLOWER TRUSSdrives heterosis for yield in tomato. Nature Genetics, 42, (5). 459–463.

Kwon-Ndung, E. H. & Misari, S. M. (1999). Overview of research and development of fonio (Digitaria exilis Kippist Stapf) and prospect for improvement in Nigeria. In: Genetics and food security in Nigeria. GSN Publication, Nigeria, P. 71 – 76. 125

Kwon-Ndung, E. H., Misari, S. M.& Dachi, S. N.(1998). Collecting germplasm of acha, Digitaria exilis(Kipp.) Stapf, accessions in Nigeria. Plant Genetic Resources Newsletter,116, 30–31.

Kwon-Ndung, E. H., Misari, S. M. & Dachi, S. N. (2001). Study on the production practices of acha (Digitaria exilis Kippist Stapf) in Nigeria. Science Forum: Journal of Pure and Applied Science, 4, (1), 11 -19.

Le Thierry d‟Ennequin, M., Panaud, O., Toupance, B. & Sarr, A. (2000). Assessment of genetic relationships between Setaria italica and its wild relative S. viridis using AFLP markers. Theoretical and Applied genetics,100, 1061-1066.

Lewontin, R. C. (1991). Electrophoresis in the development of evolutionary genetics: milestone or millstone? Genetics,128, 657-662.

Lewontin, R. C. & Hubby, J. L. (1966). A molecular approach to the study of genetic heterozygosity in natural populations. II. Amount of variation and degree ofheterozygosity in natural populations of Drosophila pseudoobscura. Genetics,54, 595-609.

Liu, X. C. &Wu, J. L. (1998).SSR heterogenic patterns of parents for marking and predicting heterosis in rice breeding. Molecular Breeding 4: 263-268.

Lynch, M. &Milligan, B. G. (1994). Analysis of population genetic structure with RAPD markers. Molecular Ecology 3: 91-99.

Markert, C. L. & Möller, F. (1959). Multiple forms of enzymes, tissue, ontogenetic andspecies specific pattern. Proceedings of National Academy of Science, USA 45, 753-763.

Mashiringwani, N. A. & Schweppenhauser, M. A. (1992). Phenotypic characters associated with yield and adaptation of wheat to a range of temperature conditions. Field Crops Research, 29: 69-77

Matsumoto, T. (1968). Morphological characters associated with high yielding rice and wheat varieties. Japanese Agriculture Research Quarterly,4, 22-26

Menkir, A., Goldsbrough, P.& Ejeta, G. (1997). RAPD based assessment of genetic diversity in cultivated races of sorghum. Crop Science,37, 564-569.

Michaels, S. D., He, Y., Scortecci, K. C. & Amasino, R. M. (2003). Attenuation of FLOWERINGLOCUS C activity as a mechanism for the evolution of summer-annual flowering behaviorin Arabidopsis. Proceedings of the National Academy of Science, USA . 100,(17). 10102–10107.

Milla, S. R., Isleib, T. G. & Stalker, H. T. (2005). Taxonomic relationships among Arachis sect.Arachis species as revealed by AFLP markers. Genome,48, 1-11.

Mitchell-Olds, T. & Schmitt, J. (2006). Genetic mechanisms and evolutionary significance ofnatural variation in Arabidopsis. Nature,441,(7096). 947–952.

Mnyenyembe, P. H. & Gupta, S. C., (1988). Variability for grain yield and related traits in finger millet germplasm accessions from Malawi. Africa Crop Science Journal,6, (3). 317-322. 126

Moncada, P., Martinez, C. P., Borrero, J., Chatel, M., Gauch, H., Guimaraes, E., Tohme, J. & McCouch, S. (2001). Quantitative trait loci (QTL) for yield and yield components in an Oryza sativa × Oryza rufipogon BC 2 F 2 population evaluated in an upland environment.Theoretical and Applied Genetics,102, 41– 52.

Morales-Payan, J. P., Ortiz, J. R., Cicero, J. & Taveras, F. (2002).Digitaria exilisas a crop in the Dominican Republic. In: Janick J & Whipkey A (eds), Supplement toTrends in new crops and new uses. ASHS Press, Alexandria, VA, S1-S3.

Morden, C. W., Doebley, J. F. & Schertz, K. F. (1989). Allozyme variation in old world races of Sorghum bicolor (Poaceae). American Journal of Botany,76, 247-255.

Morgante, M. & Olivieri, A. M. (1993). PCR-amplified microsatellites as markers in plant genetics. Plant Journal,3, 175-182.

Mullis, K. B., Faloona, F., Scharf, S., Saiki, R., Horn, G. & Erlich, H. A. (1986). Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. Cold Spring Harbor Symp. Quantitative Biology 51: 263-273

Murray, R. Grant, Silvia Forcat, Mark, H. Bennett and John W. Mansfield (2008). A rapid and robust method for simultaneously measuring changes in the phytohormones ABA, JA and SA in plants following biotic and abiotic stress. Plant Methods4:16. https://doi.org/10.1186/1746-4811-4-16

Ndoye, M. &Nwasike, C. C. (1993).Fonio millet (Digitaria exilisStapf) in West Africa. In: Riley, K.W., Gupta, S.C., Seetharam, A. & Mushonga, J. N. (eds) Advances in smallmillets. Oxford & IBH publishing Co. Pvt. Ltd, New Delhi, Chap. 9, pp. 85-94

Nesbitt, M. (2005). Grains. In: Prance, S. G. & Nesbitt, M. (eds) The cultural history of plants. Routledge, New York-London, Chap. 4, 45-60.

Nissan-Azzouz F, Graner A, Friedt W, Ordon F. (2005). Fine-mapping of the BaMMV, BaYMV-1 and BaYMV-2 resistance of barley (Hordeum vulgare) accession PI1963. Theoretical and Applied Genetics2005;110:212–218. doi: 10.1007/s00122-004-1802-x.

NRC. (1996). Lost crops of Africa volume 1: Grains national Research Council. National Academy Press. Washington, DC, pp 59-75.

Oates, A. J., Barnes, R. M. & Skob, O. (1959). Pangola grass in the U.S. Virgin Islands.Tropical Agriculture, Trinidad,36, 130-137.

Obilana, A. B. &Manyasa, E. (2002). Millets.In: Belton PS, Taylor JRN (eds) Pseudocereals and less common cereals: grain properties and utilizationpotential. Springer-Verlag, Berlin Heidelberg New York, pp 177- 217.

Pablo, J. M., Richard, J. O., Ciero, J. & Taveras, F. (2003). Digitaria exilis as a crop in the Dominican Republic. Trends in new crops and uses. J. Janick & A. whipkey (eds). Ashs press, Alexandria, V. A. 127

Paul, S., Wachira, F. N., Powell, W. & Waugh, R. (1997). Diversity and genetic differentiation among populations of Indian and Kenyan tea (Camellia sinensis \L.) O. Kuntze) revealed by AFLP markers. Theoretical and Applied Genetics,94, 255-263.

Paulis, J. W. (1982). Recent developments in corn protein research. Journal of Agriculture and Food Chemistry, 30 14-20

Pejic, I., Ajmone-Marsan, P., Morgante, M., Kozumplick, V., Castiglioni, P., Taramino, G. & Motto, M. (1998). Comparative analysis of genetic similarity among maize inbred lines detected by RFLPs, RAPDs, SSRs, and AFLPs. Theoretical and Applied Genetics,97, 1248-1255.

Pete, M. Berry, (2013). Lodging Resistance in Cereals, Sustenable food Production. pp 1096-1110. Springer, Doi 10.1007/978-1-4614-5797-8_228.

Porter, K. B., Atkins, I. M., Gilmore, E. C. & Lahr, K. A. (1964). Morphological characters associated with high yielding wheat varieties. Agronomy Journal,56, 393-396.

Portères, R. (1946). L‟aire culturale du Digitaria iburuaStapf, cereal mineure de l‟ouest-Africain.L‟Agronomie Tropicale 1:589–592.

Portères, R. (1955). African Cereals: Eleucine, Fonio, Black Fonio, Teff, Brachiaria,Paspalum, Pennisetum and African Rice. In: Harlan, J. R, de Wet J. M. J., Stemler, A. B. L. (eds) Origins of African plant domestication. Mouton Publishers, The Hague, pp 498.

Portères, R. (1976). African Cereals: Eleucine, Fonio, Black Fonio, Teff, Brachiaria, Paspalum, Pennisetum and African Rice. In: Harlan, J. R., de Wet, J.M.J., Stemler, A. B. L. (eds) Origin of African plant domestication. Mouton Publishers, The Hague,pp 409-451

Portis E., Acquadro A., Cominio C. and Lanteri S. (2004).Analysis of DNA methylation during germination of pepper (Capsicum annuum L.) seeds using methylation-sensitive amplification polymorphism (MSAP). Plant Science166: 169–178

Poulsen, G. B., Hahl, G. & Weissing, K. (1993).Abundance and polymorphism of simple repetitive DNA sequences in Brassica napusL.Theoretical and Apllied genetics 85: 994-1000.

Powell, W., Morgante, M., Andre, C., Hanafey, M., Vogel, J., Tingey, S. & Rafalski, A. (1996).The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Molecular Breeding, 2, 225-238.

Purseglove, J. W. (1972). Tropical Crops, Monocotyledon, Longman group Ltd, England.

Purseglove, J. W. (1985). Tropical Crops, Monocotyledon, Longman group Ltd, England.

Reddy,V. G., Upadhyaya, H. D. and & Gowda, C. L. L. (2007). Morphological characterization of world‟s proso millet germplasm. International Crops 128

Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Andhra Pradesh, India. 3: 4

Romney, D. H. (1961). Productivity of pasture in British Honduras. Tropical Agriculture, Trinidad.

Russell, J. R., Fuller, J. D., Macaulay, M., Hatz, B. G., Jahoor, A., Powell, W. & Waugh, R. (1997). Direct comparison of levels of genetic variation among barley accessions detected by RFLPs, AFLPs, SSRs and RAPDs.Theoretical and Applied genetics,95, 714-722.

Saghai-Maroof, M. A., Biyashev, R. M., Yang, G. P., Zhang, Q. & Allard, R. W. (1994). Extraordinarily polymorphic microsatellite DNA in barley: Species diversity, chromosomal locations, and population dynamics. Proceedings of National Academy of Science, (USA) 91, 5466-5470.

Sahin, D. & Metin, B. Y. (2006). Inheritance of grain yield per plant, Flag leaf width and length in an 8x8 Diallel cross population of Bread Wheat (T. aestivum L.). Turkish Journal of Agriculture, 30, 339- 345.

Sanou, J. (1993). Evaluation de la variabilité au sein d‟une collection de fonio (Digitaria exilisStapf), Structuration, Potentialités agronomiques. Mémoire d‟Ingénieur, Option agronomie. Université Ouagadougou, Burkina Faso, 78p

Sarker, S., Tyagi, D. V. S. & Islam, M. H. (1993). Induced mutagenesis: an important tool for breeding small millets-a review. Agric Rev Karnal, 14, 61-65.

Seehalak, W., Tomooka, N., Waranyuwat, A., Thipyapong, P., Laosuwan, P., Kaga, A. &Vaughan, D. A. (2006). Genetic diversity of the Vigna germplasm from Thailand and neighboring regions revealed by AFLP analysis. Genetic Resources Crop Evolution, 53, 1043-1059

Semagn, K., Bjørnstad, Å. & Ndjiondjop, M. N. (2006).An overview of molecular marker methods for plants. African Journal of Biotechnology,5, 2540-2568.

Senior, M. L. & Heun, M. (1993).Mapping maize microsatellites and polymerase- chain-reaction confirmation of targeted repeats using a CT primer.Genome,36, 884-889.

Senior, M. L., Murphy, J. P., Goodman, M. M. &Stuber, C.W. (1998).Utility of SSRs for determining genetic similarities and relationships in maize using an agarose gel system. Crop Science,38, 1088-1098.

Shahryari, R., Behnam, M., Vahid, M. & Majid, k. (2011). Genetic diversity in bread wheat for phonological and Morphological traits under terminal drought stress condition. Advances in Environmental Biology, 5 (1): 169-172.

Shannon, C. E. & Weaver, W. (1949). The mathematical theory of communication. University of Illinois Press, Urbana

Sharma, S. K., Knox, M. R. & Ellis, T. H. N. (1996). AFLP analysis of the diversity andphylogeny of Lens and its comparison with RAPD analysis. Theoretical and Applied genetics,93, 751-758. 129

Sharp, A. J., Cheng, Z. & Eichler, E. E. (2006). Structural variation of the human genome. Annual Review of Genomics Human Genetics, 7, 407–442.

Shatuck-Eidens, D. M., Bell, R. N., Neuhausen, S. & Helentjaris, T. (1990). DNA sequence variation within maize and melon: Observations from polymerase chain reaction amplification and direct sequencing. Genetics,126, 207-217.

Shigemura, S. (1966). Breeding high yielding rice. Japanese Agriculture Research Quarterly, 1, 1-6.

Smith, S. & Helentjaris, T. (1996). DNA fingerprinting and plant variety protection. In: Paterson, A. H. (Ed.) Genome mapping in plants, Academic Press, San Diego, CA USA, pp 95-110.

Snedecor, G. W. & Cochran, W. G. (1969). Statistical methods (6th edition). Iowa State University Press, Iowa, USA. 607pp.

Sokoto, M. B. Abubakar, I. U. & Dikko, A. U. (2012). Correlation analysis of some growth, yield components and grain quality of wheat (Triticum aestivum L.). Nigerian Journal of Basic and AppliedScience, 20, (4): 349-356.

Soltis, P. S., Soltis, D. E. & Doyle, J. J. (1992). Molecular systematics of plants. Chapman and Hall, New York. ISBN-0-41202-231-1

Sonnate, G., A., Marangi, G. Venora & D. Pignone. (1997). Using RAPD markers to investigate genetic variation in chickpea. Genetic Journal of Breeding, 51: 303- 307.

Stankiewicz, P. & Lupski, J. R. (2002). Genome architecture, rearrangements and genomicdisorders. Trends in Genetics 18,(2). 74–82.

Stankiewicz, P. & Lupski, J. R. (2010). Structural variation in the human genome and its rolein disease. Annual Review of Medicine, 61, 437–455.

Stapf, O. (1915). Iburu and Fundi, two cereals of Upper Guinea (Digitaria iburua, D. exilis). Kew Bulletin,8, 381-386.

Steele, W. M. (1976). The Botany of Tropical Crops. 2nd edition. Longman. NY.

Struss, D. & Plieske, J. (1998).The use of microsatellite markers for detection of genetic diversity inbarley populations.Theoretical and Applied genetics,97, 308-315.

Tanner, J. W., Gardener, C. J., Stoskopf, N. C. & Reinbergo, E. (1966). Selection Criteria for High Yielding Rice Varieties. Canadian Journal of Plant Science,46, 690.

Taramino, G. & Tingey, S. (1996). Simple sequence repeats for germplasm analysis and mapping in maize. Genome,39, 277-287.

Taramino, G., Tarchini, R., Ferrario, S., Lee, M. & Pe‟, M. E. (1997).Characterization and mapping of simple sequence repeats (SSRs) in Sorghum bicolor.Theoretical and Applied genetics,95, 66-72. 130

Temple, V. J. & Bassa, J. D. (1991). Proximate chemical composition of fonio (Digitaria exilis) grain. Journal of Science, Food and Agriculture,56, 561-564.

Tessier, C., David, J., This, P., Boursiqot, J. M. & Charrier, A. (1999).Optimization of the choice of molecular markers for varietal identification in Vitis vinferaL.Theoretical and Applied genetics,98, 171-177.

Tsumura, Y., Suyama, Y., Yoshimura, Y. & Mukai, Y. (1997).Sequence-Tagged-Sites (STSs) of cDNA clones in Cryptomeria japonica and their evaluation as molecular markers in conifers.Theoretical and Applied genetics,94, 764-772.

Uptmoor, R., Wenzel, W., Friedt, W., Donaldson, G., Ayisi, K. & Ordon, F. (2003). Comparative analysis on the genetic relatedness of Sorghum bicolor accessions from Southern Africa by RAPDs, FLPs and SSRs. Theoretical and Applied genetics,106, 1316-1325.

Vane-Wright, R. I., Humphries, C. J. & Williams, P. H. (1991). What to protect – systematics and the agony of choice. Biological Conservation, 55, 235-54.

Vendramin, G. G. & Hansen, O. K. (2005). Molecular markers for characterizing diversity in forest trees. In: Geburek, T. & Turok, J. (eds.) Conservation and Management of Forest Genetic Resources in Europe. Arbora Publishers, Zvolen, 337-368.

Vodouhè, S. R., Achigan, D. E., Dansi, A. and Adoukonou-Sagbadja, H. (2003). Fonio: A treasure for West Africa. Poster, International workshop on Underutilized PlantSpecies. Leipzig, Germany 6-8 May 2003. http://www.underutilized-species.org/events/ w_shop_leipzig_documents/posters/rv.pdf

Vos, P., Hogers, R., Bleeker, M., Reijans, M., Van de Lee, T., Hornes, M., Frijters, A., Pot, J., Peleman, J., Kuiper, M. & Zabeau, M. (1995). AFLP: A new technique for DNAfingerprinting. Nucleic Acids Researh,23, 4407-4414.

Wagner, C., Friedt, W., Marquard, R. A. & Ordon, F. (2005). Molecular analysis on the genetic diversity and inheritance of (-)-alpha-bisabolol and chamazulene content in tetraploid chamomile (Chamomilla recutita (L) Raush.). Plant Science,169, 917-927.

Watson, L. &Dallwitz, M. J. (1992).DigitariaHaller. In: The grass genera of the world. Retrieved October, 3rd 2015, from http://delta- intkey.com/grass/www/dig‟ia.htm.

Welsh, J. & McClelland, A. (1990). Fingerprinting genomes using PCR with arbitrary primers. Nucleic Acids Research, 18, 7213 – 7218.

Wendorf, F., Close, A. E., Schild, R., Wasylikowa, K.,Housley, R. A., Harlan, J. R. & Królik, H. (1992). Saharan exploitation of plants 8,000 years BP. Nature,359, 721-724.

Wikipedia: Retrieved April, 21st 2010 from http://en.wikipedia.org/wiki/Molecular_phylogeny. 131

Williams, J. G. K., Kubelik, A. R., Livak, K. J., Reafalski, J. A. & Tingey, S. V. (1990). DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Research, 18, 6531 – 6535.

Wu, K. S. &Tanksley, S. D. (1993). Abundance, polymorphism and genetic mapping of microsatellites in rice. Mol. Gen. Genet. 241: 225-235.

Yin, D., Liu, H., Zhang, X. & Cui, D. (2011). A protocol for extractionof high-quality RNA and DNA from peanut plant tissues. Molecular Biotechnology, 49:187– 191. doi: 10.1007/s12033-011-9391-9.

Zeidler, M. (2000). Electrophoresis of plant isozymes.Biologica,38, 7-14.

Zhang, Q., Liu, K. D., Yang, G. P., Saghai-Maroof, M. A., Xu, C. G. & Zhou, Z. Q. (1997). Molecular marker diversity and hybrid sterility in indica-japonica rice crosses. Theoretical and Apllied genetics,95, 112-118.

Zhivotovsky, L. A. (1999). Estimating population structure in diploids with multilocusdominant DNA markers. Molecular Ecology, 8, 907-913.

Zhu, J., Gale, M. D., Quarrie, S., Jackson, M. T. & Bryan, G. J. (1998).AFLP markers for the study of rice biodiversity.Theoretical and Applied genetics,96, 602-61.

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APPENDIX A: INSTRUMENTS USED FOR THE STUDIES

Appendix A1: Field Plan for 2012 Cropping Season

2m

P1 P1 P7 K3 P2 P1 T3 P1 P2 T5 K2 K P1 P8 P4 P2 K1 P2 P2 P3 P2 P2 P1 P5 T1 P6 P2 T4 P1 P1

3m 7 1 4 9 6 6 4 2 5 3 2 7 1 3 2

1m

P2 P2 T4 P1 T1 P3 P1 K3 P2 T5 P1 P5 K2 K1 P1 K4 P1 T P2 P1 P8 P4 P2 P2 P7 P2 P6 P2 P1 P1

11 3 1 5 2 2 1 6 3 3 6 5 4 7 7 9 m

P2 T4 K P5 P6 P1 P3 P1 P1 K P1 P7 P2 P2 T3 P2 P4 T P1 K T5 P2 P2 P2 K P8 P1 P1 P2 P1 60m 5 2 9 3 5 1 1 4 2 3 1 6 4 6 7 3 7 2 1

133

134

Appendix A1: Field Plan for 2013 Cropping Season 2m

P17 P11 P7 K3 P24 P19 T3 P16 P26 T5 K2 K4 P12 P8 P4 P25 K1 P2 P23 P3 P22 P27 P1 P5 T1 P6 P21 T4 P13 P12

3m

1m

P25 T4 K2 P5 P6 P19 P3 P13 P15 K1 P11 P7 P24 P22 T3 P24 P4 T1 P16 K4 T5 P26 P2 P27 K3 P8 P17 P12 P21 P1

11m P23 P21 T4 P15 T1 P3 P12 K3 P22 T5 P11 P5 K2 K1 P16 K4 P13 T3 P2 P1 P8 P4 P26 P25 P7 P24 P6 P27 P17 P19

60m 135

Appendix A1: Field Plan for 2014 Cropping Season 2m

P2 T4 K2 P5 P6 P1 P3 P1 P1 K1 P1 P7 P2 P2 T3 P2 P4 T P1 K T5 P2 P2 P2 K P8 P1 P1 P2 P1 3m 5 9 3 5 1 4 2 3 1 6 4 6 7 3 7 2 1

1m

P1 P1 P7 K3 P2 P1 T3 P1 P2 T5 K2 K P1 P8 P4 P2 K1 P2 P2 P3 P2 P2 P1 P5 T1 P6 P2 T4 P1 P1 7 1 4 9 6 6 4 2 5 3 2 7 1 3 2

P2 P2 T4 P1 T1 P3 P1 K3 P2 T5 P1 P5 K2 K1 P1 K4 P1 T P2 P1 P8 P4 P2 P2 P7 P2 P6 P2 P1 P1 11 3 1 5 2 2 1 6 3 3 6 5 4 7 7 9 m

60m i

Appendix A2: Analysis of Variance for Plant Height in 2012 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 0.207 0.103 2.5864 0.0840 2 Factor A 29 19048.091 656.831 16422.3251 0.0000 -3 Error 58 2.320 0.040 Total 89 19050.618

Appendix A2: Analysis of Variance for Plant Height in 2013 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 0.281 0.140 0.2587 2 Factor A 29 20161.885 695.237 1280.6974 0.0000 -3 Error 58 31.486 0.543 Total 89 20193.652

Appendix A2: Analysis of Variance for Plant Height in 2014 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 1.301 0.650 0.5091 2 Factor A 29 20110.102 693.452 542.8365 0.0000 -3 Error 58 74.093 1.277 Total 89 20185.496

Appendix A2: Analysis of Variance for Stem Girth in 2012 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 0.078 0.039 1.9348 0.1537

ii

2 Factor A 29 34.969 1.206 60.1644 0.0000 -3 Error 58 1.162 0.020 Total 89 36.209

Appendix A2: Analysis of Variance for Stem Girth in 2013 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 0.009 0.005 0.3186 2 Factor A 29 36.889 1.272 88.4512 0.0000 -3 Error 58 0.834 0.014 Total 89 37.732

Appendix A2: Analysis of Variance for Stem Girth in 2014 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 0.018 0.009 0.6876 2 Factor A 29 38.425 1.325 100.7488 0.0000 -3 Error 58 0.763 0.013 Total 89 39.205

iii

Appendix A2: Analysis of Variance for Leaf Length in 2012 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 0.047 0.023 0.9629 2 Factor A 29 1177.517 40.604 1679.1703 0.0000 -3 Error 58 1.402 0.024 Total 89 1178.966

Appendix A2: Analysis of Variance for Leaf Length in 2013 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 0.362 0.181 7.7950 0.0010 2 Factor A 29 1153.890 39.789 1715.6814 0.0000 -3 Error 58 1.345 0.023 Total 89 1155.597

Appendix A2: Analysis of Variance for Leaf Length in 2014 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 0.162 0.081 4.4404 0.0161 2 Factor A 29 1171.569 40.399 2214.6874 0.0000 -3 Error 58 1.058 0.018 Total 89 1172.789

Appendix A2: Analysis of Variance for Leaf Width in 2012 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob

iv

1 Replication 2 0.031 0.015 1.6118 0.2083 2 Factor A 29 2.649 0.091 9.5343 0.0000 -3 Error 58 0.556 0.010 Total 89 3.236

Appendix A2: Analysis of Variance for Leaf Width in 2013 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 0.106 0.053 8.0307 0.0008 2 Factor A 29 2.466 0.085 12.8757 0.0000 -3 Error 58 0.383 0.007 Total 89 2.955

Appendix A2: Analysis of Variance for Leaf Width in 2014 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 0.062 0.031 3.3153 0.0433 2 Factor A 29 1.818 0.063 6.7536 0.0000 -3 Error 58 0.538 0.009 Total 89 2.418

v

Appendix A2: Analysis of Variance for Days to 75% Maturity in2012 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 6.822 3.411 2.5415 0.0875 2 Factor A 29 8175.956 281.930 210.0588 0.0000 -3 Error 58 77.844 1.342 Total 89 8260.622

Appendix A2: Analysis of Variance for Days to 75% Maturity in 2013 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 8.067 4.033 1.5197 0.2274

2 Factor A 29 8762.900 302.169 113.8532 0.0000 -3 Error 58 153.933 2.654

Total 89 8924.900

Appendix A2: Analysis of Variance for Days to 75% Maturity in 2014

K Degrees of Sum of Mean F

Value Source Freedom Squares Square Value Prob 1 Replication 2 2.022 1.011 0.6566 2 Factor A 29 9101.956 313.861 203.8258 0.0000

-3 Error 58 89.311 1.540 Total 89 9193.289

vi

Appendix A2: Analysis of Variance for 1000 Seed Weight in 2012 K Degrees of Sum of Mean F Value Source Freedom Squares Square Valu Prob 1 Replication 2 0.000 0.000 0.5811

2 Factor A 29 0.600 0.021 376.5328 0.0000

-3 Error 58 0.003 0.000

Total 89 0.603

Appendix A2: Analysis of Variance for 1000 Seed Weight in 2013 K Degrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 0.000 0.000 0.0083 2 Factor A 29 0.609 0.021 2836.5419 0.0000 -3 Error 58 0.000 0.000 Total 89 0.609

Appendix A2: Analysis of Variance for 1000 Seed Weight in 2014 KDegrees of Sum of Mean F Value Source Freedom Squares Square Value Prob 1 Replication 2 0.000 0.000 1.2963 0.2814 2 Factor A 29 0.599 0.021 354721.6965 0.0000 -3 Error 58 0.000 0.000 Total 89 0.599

vii

Appendix A 3: Correlation Coefficient Distance, Single Linkage Amalgamation Steps for Cluster Analysis of Variables for 2012.

Number Number of obs. of Similarity Distance Clusters New in new Step clusters level level joined cluster cluster 1 5 96.9990 0.060020 1 5 1 2 2 4 95.6294 0.087412 1 2 1 3 3 3 94.9439 0.101121 1 6 1 4 4 2 93.7455 0.125090 1 3 1 5 5 1 90.3824 0.192351 1 4 1 6

Appendix A 3: Correlation Coefficient Distance, Single Linkage Amalgamation Steps for Cluster Analysis of Variables for 2013.

Number Number of obs. of Similarity Distance Clusters New in new Step clusters level level joined cluster cluster 1 5 97.5447 0.049107 1 5 1 2 2 4 96.9641 0.060719 1 3 1 3 3 3 95.4549 0.090901 1 6 1 4 4 2 94.6236 0.107527 1 4 1 5 5 1 66.9619 0.660761 1 2 1 6

Appendix A 3: Correlation Coefficient Distance, Single LinkageAmalgamation Steps for Cluster Analysis of Variables for 2014.

Number Number of obs. of Similarity Distance Clusters New in new Step clusters level level joined cluster cluster 1 5 99.2673 0.014655 1 2 1 2 2 4 98.4805 0.030391 1 5 1 3 3 3 97.5974 0.048052 1 3 1 4 4 2 96.0406 0.079189 1 6 1 5 5 1 92.8014 0.143973 1 4 1 6