GENETIC DIVERSITY, PATH COEFFICIENT ANALYSIS, CLASSIFICATION AND EVALUATION OF YAMS (Dioscorea spp.) IN SOUTHWEST ETHIOPIA
PhD DISSERTATION
TEWODROS MULUALEM BEYENE
NOVEMBER 2016 HARAMAYA UNIVERSITY, HARAMAYA
ii Genetic Diversity, Path Coefficient Analysis, Classification and Evaluation of Yams (Dioscorea spp.) in Southwest Ethiopia
A Dissertation Submitted to the School of Plant Science, Postgraduate Program Directorate, Haramaya University
In Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY IN AGRICULTURE (PLANT BREEDING)
Tewodros Mulualem Beyene
November 2016 Haramaya University, Haramaya
i SCHOOL OF GRADUATE STUDIES
HARAMAYA UNIVERSITY
As PhD research advisory committee members, we hereby certify that we have read and evaluated this dissertation, which has been prepared under our guidance by Tewodros Mulualem Beyene, entitled “Genetic Diversity, Path Coefficient Analysis, Classification and Evaluation of Yams (Dioscorea spp.) in Southwest Ethiopia”. We recommend that the dissertation be submitted as it fulfils the requirements.
1. Firew Mekbib (PhD) 22/07/2016 Chairman of the Advisory Committee Signature Date 2.Shimelis Hussien (Professor) Member of the Advisory Committee Signature Date 3. Endale Gebre (PhD) ______Member of the Advisory Committee Signature Date
As members of the Board of Examiners of the PhD Dissertation Open Defense Examination, we certify that we have read, evaluated the Dissertation prepared by Tewodros Mulualem, and examined the candidate. We recommend that the Dissertation be accepted as fulfilling the Dissertation requirement for the Degree of Doctor of Philosophy in Agriculture (Plant Breeding). 1. Nigussie Dechassa (Professor) ______Chairperson Signature Date 2. Wassu Mohammed(PhD) ______Internal Examiner Signature Date 3. Derebew Belew (Professor) ______External Examiner Signature Date
Final approval and acceptance of the Dissertation is contingent upon the submission of the final copy to the Council of Graduate Studies through the School Graduate Committee of the Candidates major Department.
ii DEDICATION
I dedicate this manuscript to my beloved mother, Kebebush Abebe Gebre Medihen; who passed away without seeing this achievement.
i STATEMENT OF THE AUTHOR
I declare that this dissertation is the result of my own work and that all sources of materials used for its preparation have been duly acknowledged. This dissertation has been submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy at Haramaya University and is deposited at the library of the University to be made available to borrowers under the rules and regulations of the library. I solemnly declare that this dissertation has not been submitted to any other institution anywhere for the award of any academic degree, diploma, or certificate.
Brief quotations from this dissertation are allowed without requiring special permission provided that an accurate acknowledgement of source is made. Requests for permission for extended quotations from or reproduction of the manuscript in whole or in part may be granted by the Head of the Department of Plant Sciences or the Dean of the School of Graduate Studies when in his or her judgment the proposed use of the material is for a scholarly interest. In all other instances, however, permission must be obtained from the author.
Name: Tewodros Mulualem Signature: ______Date: November, 2016 School/Department: Plant Sciences/Plant Breeding
ii BIOGRAPHICAL SKETCH
The author was born on September 26, 1975 in Hawassa, Sidama Zone, Southern Nations, Nationalities and Peoples Regional State of Ethiopia. He attended elementary education at Hawassa Tabor Elementary School from 1982 to 1989 and secondary education at Hawassa Senior Secondary School from 1990 to 1993. He took the Ethiopian School Leaving Certificate Examination in 1994, and then joined Debub University, Faculty of Agriculture to pursue a study leading to diploma in Plant Science Technology. After graduation, he worked at Tum district Agriculture and rural development office of Bench Maji zone as agricultural development agent. He, then rejoined Debub University now Hawassa University College of Agriculture and graduated with BSc degree in Plant Production and Dry land Farming in 2003.
He began his career as a researcher in 2004 at South Agricultural Research Institute, Areka Agricultural Research Center. After serving for three years, he rejoined Hawassa University to study Master of Science in Plant Breeding. After completing his MSc study, the author was reinstated at Areka Agricultural Research Center and served as Assistant Researcher until September, 2011. During 2012, the author was transferred to Ethiopian Institute of Agricultural Research, Jimma Agricultural Research Center (JARC) serving as Assistant Researcher. He served the JARC for two years and promoted to associate researcher II. He joined Haramaya University in 2013 to pursue his Doctor of Philosophy studies in Plant Breeding.
iii ACKNOWLEDGEMENTS
My deepest gratitude goes to Dr. Firew Mekbib (chairman of advisory committee), for the meticulous academic and research guidance, for sharing his deepest knowledge and skills and providing valuable comments. Without his close and professional guidance, this work would not have been in its present shape. I will never forget his overall support and the vital contributions he made towards my success; working with him was a wonderful learning opportunity in many ways. I would also like to express my special heart felt thanks and appreciation to Professor Shimelis Hussien (member of advisory committee) for full support for molecular studies, for his close supervision, constructive ideas, valuable comments and hospitality. His overall support and motivation from the beginning to the end of my dissertation is sincerely appreciated. I would also like to express my special thanks and appreciation to Dr. Endale Gebre (member of advisory committee) for his guidance and valuable comments, sustained interest, stimulating views and encouragement during the writing up of this dissertation.
I would like to extend special gratitude to my former instructors Dr. Muluneh Tamiru from Iwate Biotechnology Institute/Iwate Biotechnology Research, Japan, Dr. Hussein Mohammed from Hawassa Univesity, Dr. Wassu Mohammed and Professor Habtamu Zeleke of Haramaya University, and Dr. Gemechu Keneni and Dr. Dagne Wegari of Ethiopian Institute of Agricultural Research (EIAR). Thank you for providing me a foundation knowledge and skill in the area of plant breeding. I would like to extend my sincere gratitude to Mr. Lemma Ayele, Dr. Asnake Fikre, Dr. Amelewerk Beyene, Dr. Mohammed Abate, Dr. Asmare Dagnew, Dr. Wendawek Abebe and Dr. Eshetu Derso for their advice, constructive ideas and technical support during my study period.
I would like to convey my sincere gratitude, appreciation, and thanks to various institutions and individuals who assisted me and contributed to the successful completion of this study. A special gratitude is due to EIAR and JARC for sponsoring the research work and for offering a scholarship opportunity, for providing field facilities and technical support for my PhD studies. The School of Plant Sciences and School of Graduate Studies of Haramaya University deserve a special appreciation for providing me with the necessary facilities at the campus and offering me the PhD courses timely.
iv I would like to express my sincere gratitude to my colleagues Solomon Abate, Gelila Asamenaw, Henok Nahusenay, Dinka Mulugata, Aytenewu Samuel, Mesfine Abita and Yibrah Amare from EIAR and Abyot Hundea, from Jimma University, College of Agriculture and Veterinary Medicine (JUCAVM) for their support during biochemical analysis. I am also grateful to yam growers in Southwest Ethiopia who kindly provided me yam planting materials and shared their invaluable knowledge during the survey work. I also thank to zonal and district agricultural officers for their help during the survey work.
I would like to express my sincere gratitude to my colleagues, Kifle Belachew, Ashenafi Ayana, Dr. Abush Tesfaye, Tadess Eshetu, Gebreselassie Hailu, Getachew Weldemichael, Demelash Teferi, Mesfin Seyum, Werku Demisse of the staff of horticultural crops research case team, and finance department particularly, Hailu Urga, Emiru Gebremariam, Siraje Farise and Ahmedin Haji for their unlimited support, financial assistance rendered during experimental setup and data collection. I am indebted to my colleagues from Haramaya University, Tesfaye Walle, Tesfaye Molla, Zelalem Bekeko, Eprem Gunchi, Fisseha Negash, Yared Dagne, Belay Yebo and Addisu Asrat for their encouragement and friendly support during my study period.
It gives me a pleasure to forward my humble gratitude and appreciation to my wife Meseret Molla for her understanding and patience of taking the sole responsibility of our son (Dagmawi Tewodros) and for bearing all other family burden during my absence. I would like to offer my heartfelt thank to my father Mulualem Beyene, my sisters Tsige Gebreanania and Youbdar Mulualem and brothers, Wondwossen Mulualem, Alemayehu Mulualem and Mandefro Mulualem and Ashnafi Mulualem for their encouragements.
Finally, Thanks to God for every events in my life!
v LIST OF ABBREVIATIONS AND ACRONYMS
AFLP Amplified Fragment Length Polymorphism AMOVA Analysis of Molecular Variance ANOVA Analysis of Variance CSA Central Statistical Agency DA Development Agent EIAR Ethiopian Institute of Agricultural Research FAO Food and Agriculture Organization FTA Flinders Technology Associates GAM Genetic Advance as percent of Mean GCV Genotypic Coefficient of Variation IPGRI International Plant Genetic Resources Institute EBI Ethiopian Biodiversity Institute IKS Indigenous Knowledge System JARC Jimma Agricultural Research Center LSD Least Significant Difference MAP Months After Planting PCA Principal Component Analysis PCoA Principal Coordinate Analysis PCV Phenotypic Coefficient of Variation PIC Polymorphic Information Content RAPD Random Amplified Polymorphic DNA RLL Rate of Landrace Loss SAS Statistical Analysis System . SSRs Simple Sequence Repeats SNNPRS Southern Nation, Nationality, and People Regional State SPSS Statistical Package for Social Sciences UPGMA Un-weighted Pair Group Method Arithmetic average
vi TABLE OF CONTENTS
STATEMENT OF THE AUTHOR ii BIOGRAPHICAL SKETCH iii ACKNOWLEDGEMENTS iv LIST OF ABBREVIATIONS AND ACRONYMS vi TABLE OF CONTENTS vii LIST OF TABLES xii LIST OF FIGURES xv LIST OF APPENDIX xvi ABSTRACT xvii 1. GENERAL INTRODUCTION 1 1.1. Origion and distribution of yams 1 1.2. Taxonomy of Yam 4 1.3. Morphology of yam 5 1.4. Farmers’ perceptions for agro-morphological traits 6 1.5. Genotype characterization and evaluation 8 1.6. Genetic diversity 10 1.7. Production status of yam in Ethiopia 11 1.8. Genetic erosion of yam in Ethiopia 10 1.9. Nutritional value of yam 12 1.10. Rationale of the study 13 1.11. Objectives 14 1.11.1. General objective 14 1.11.2. Specific objectives 14 1.12. Dissertation structure 14 1.13. References 16 2. ANALYSIS OF INDIGENOUS KNOWLEDGE, ON FARM DIVERSITY, DISTRIBUTION AND MANAGEMENT OF YAM (Dioscorea spp.) LANDRACE COLLECTIONS IN SOUTHWEST ETHIOPIA 23 ABSTRACT 24 2.1. INTRODUCTION 25 2.2. MATERIALS AND METHODS 26 2.2.1. Description of the study areas 26 2.2.2. Sampling and methods of data collection 29 2.2.3. Data analysis 30 2.3. RESULTS AND DISCUSSION 33
vii TABLE OF CONTENTS (Continued) 2.3.1. The level of landrace diversity 33 2.3.2. Distribution of the landraces 36 2.3.3. Selection, cultivation and management of yam landraces 39 2.4. CONCLUSION 46 2.5. REFERENCES 47 3. FARMERS’ PERCEPTION FOR CLASSIFICATION, SPATIAL DISTRIBUTION AND GENETIC EROSION OF YAM (Dioscorea spp.) LANDRACE COLLECTIONS OF SOUTHWEST ETHIOPIA 51 ABSTRACT 52 3.1. INTRODUCTION 53 3.2. MATERIALS AND METHODS 55 3.2.1. Selection of study sites and farmers 55 3.2.2. Data collection 56 3.2.3. Data analysis 58 3.3. RESULTS AND DISCUSSION 60 3.3.1. Socio economic characteristics of surveyed yam farmers 60 3.3.2. Identification, naming and classification of landraces 60 3.3.2.1. Identification of yam 61 3.3.2.2. Naming of yam 61 3.3.2.3. Classification of yam 63 3.3.3. Assessment of spatial distribution and diversity of yam 66 3.3.4. Distribution and causes of genetic erosion 73 3.3.4.1. Distribution of genetic erosion 76 3.3.4.2. Causes of genetic erosion 75 3.3.5. Conservation of yam genetic resources 83 3.4. CONCLUSION 84 3.5. REFERENCES 85 4. MORPHOLOGICAL CHARACTERIZATION AND DIVERGENCE ANALYSIS OF YAM (Dioscorea spp.) LANDRACE COLLECTIONS OF SOUTHWEST ETHIOPIA 90 ABSTRACT 91 4.1. INTRODUCTION 92 4.2. MATERIALS AND METHODS 94 4.2.1. Description of the study area 94 4.2.2. Experimental materials 94 4.2.3. Experimental design and management 94 4.2.4. Morphological data collection 96 4.2.4.1. Qualitative characters 96
viii TABLE OF CONTENTS (Continued)
4.2.4.2. Quantitative characters 97 4.2.4.3. Data collected on individual plant basis 97 4.2.4.4. Data collected on plot basis 98 4.2.5. Data analysis 99 4.2.5.1. Cluster analysis based on qualitative traits 99 4.2.5.2. Frequency distribution and Shannon-Weaver diversity index (H’) 99 4.2.5.3. Principal component analysis 99 4.2.5.4. Analysis of variance based on quantitative traits 100 4.2.5.5. Cluster analysis using quantitative traits 100 4.2.5.6. Genetic distance 100 4.3. RESULTS AND DISCUSSION 101 4.3.1. Morphological diversity analysis based on qualitative traits 101 4.3.1.1. Cluster analysis 101 4.3.1.2. Qualitative traits distribution 106 4.3.1.3. The Shannon-Weaver Diversity Index (H') 109 4.3.1.4. Principal component analysis 112 4.3.2. Morphological diversity analysis based on quantitative characters 114 4.3.2.1. Analysis of Variance 114 4.3.2.2. Mean performance of yam 116 4.3.2.3. Cluster analysis based on quantitative characters 117 4.3.2.4. Distance between clusters 119 4.4. CONCLUSION 122 4.5. REFERENCES 123 5. GENETIC VARIABILITY, CORRELATION AND PATH ANALYSIS OF TUBER YIELD AND YIELD COMPONENTS OF YAM (Dioscorea spp.) IN SOUTHWEST ETHIOPIA 127 ABSTRACT 128 5.1. INTRODUCTION 129 5.2. MATERIALS AND METHODS 131 5.2.1. Description of the study area 131 5.2.2. Experimental materials, design and management 131 5.2.3. Data collection 131 5.2.4. Data analysis 132 5.3. RESULTS AND DISCUSSION 134 5.3.1. Estimates of variability 134 5.3.1.1. Phenotype and genotype variation 134 5.3.1.2. Estimates of heritability and expected genetic advance 135
ix TABLE OF CONTENTS (Continued)
5.3.2. Association between characters 137 5.3.2.1. Genotype correlation coefficients 137 5.3.2.2. Phenotype correlation coefficients 140 5.3.3. Path coefficient 141 5.4. CONCLUSION 144 5.5. REFERENCES 145 6. INFLUENCE OF MATURITY STAGES OF YAM (Dioscorea spp.) LANDRACES ON YIELD AND QUALITY TRAITS 150 ABSTRACT 151 6.1. INTRODUCTION 152 6.2. MATERIALS AND METHODS 154 6.2.1. Description of the study area 154 6.2.2. Experimental materials 154 6.2.3. Experimental design and management 154 6.2.4. Data collection 155 6.2.4.1. Agronomic data 155 6.2.4.2. Biochemical data 155 6.2.5. Data Analysis 155 6.3. RESULTS AND DISCUSSION 157 6.3.1. Analysis of variance 157 6.3.2. The effects of maturity stages on the quality of yams 161 6.3.2.1. Analysis of variance 161 6.3.2.2. Biochemical composition 162 6.4. CONCLUSION 170 6.5. REFERENCES 171 7. BIOCHEMICAL COMPOSITION OF YAM (Doscorea spp.) LANDRACE COLLECTIONS OF SOUTHWEST ETHIOPIA 176 ABSTRACT 177 7.1. INTRODUCTION 178 7.2. MATERIALS AND METHODS 180 7.2.1. Samples collection and preparation 180 7.2.2. Biochemical analysis 180 7.2.3. Data analysis 183 7.3. RESULTS AND DISCUSSION 184 7.3.1. Analysis of variance 184 7.3.2. Mean response to biochemical traits 185 7.3.3. Principal component analysis 188
x TABLE OF CONTENTS (Continued)
7.3.4. Cluster analysis 190 7.3.5. Distance between clusters 192 7.4. CONCLUSION 194 7.5. REFERENCES 195 8. GENETIC DIVERSITY OF YAM (Dioscorea spp.) LANDRACE COLLECTIONS OF SOUTHWEST ETHIOPIA USING SSR MARKERS 200 ABSTRACT 201 8.1. INTRODUCTION 202 8.2. MATERIALS AND METHODS 204 8.2.1. Plant materials and study sites 204 8.2.2. DNA extraction 204 8.2.3. FTA processing for PCR 214 8.2.4. PCR amplification and product analysis 205 8.2.5. Data analysis 207 8.3. RESULTS AND DISCUSSION 209 8.3.1. Polymorphism and allelic diversity of SSR markers 209 8.3.2. Analysis of molecular variance and partitioning of genetic diversity 213 8.3.3. Principal coordinate analysis 215 8.3.4. Genetic dissimilarity and distance analysis among landraces 216 8.3.5. Cluster analysis 218 8.4. CONCLUSIONS 221 8.5. REFERENCES 222 9. SUMMARY, CONCLUSION AND RECOMMENDATIONS 228 9.1. SUMMARY AND CONCLUSION 228 9.2. RECOMMENDATIONS 231 10. APPENDIX 231
xi LIST OF TABLES
Table 2.1. Description of study districts in Jimma, Sheka and Bench maji Zones of Southwest Ethiopia 32 Table 2.2.Variation in the number of yam landraces planted per farm in study districts. 33 Table 2.3. Yam landraces recorded in the study districts of Jimma, Sheka and Bench maji Zones 34 Table 2.4. Yam landrace diversity in the various districts of Jimma, Sheka and Bench maji Zones of Southwest Ethiopia, expressed as richness, Simpson (1-K) and Shannon (H’) diversity indices and Evenness 35 Table 2.5. Sørenson’s similarity estimates of yam landrace diversity between different districts of Jimma, Sheka and Bench maji Zones of Southwest Ethiopia 36 Table 2.6. Major sources of the planting materials for field planting of yam as reported by farmers in Jimma, Sheka and Bench maji Zones of Southwest Ethiopia 40 Table 2.7. Criteria used by farmers in the study areas for timing of harvesting 44 Table 3.1. Household characteristics of the surveyed districts 56 Table 3.2. Parameters used for the participatory evaluation for classification of yam landraces in Southwest Ethiopia 59 Table 3.3. The names and major attributes of yam landraces identified by farmers 62 Table 3.4. Folk classification of yam landraces in Southwest Ethiopia 65 Table 3.5. Distribution, extent of analysis and the rate of landrace loss in the study districts and Kebeles 72 Table 3.6. Causes of genetic erosion of yam in Southwest Ethiopia 78 Table 4.1. Descriptions of the 36 yam landraces used for the study 95 Table 4.2. Clusters of Dioscorea spp. landraces based on qualitative traits 103 Table 4.3. Cluster means of 19 quantitative traits of 36 Dioscorea spp. based on quantitative Characters 105 Table 4.4. Frequency distribution and Shannon-Weaver diversity indices (‘H’) of 32 qualitative traits of Dioscorea grown at Jimma, 2015 110 Table 4.5. Eigen values, proportion, cumulative variance and component scores of the first seven principal components for qualitative traits in 36 yam from Southwest Ethiopia 113 Table 4.6. Analysis of variance for quantitative characters 115 Table 4.7. Mean standard deviation and ranges of 19 quantitative traits of Dioscorea spp 116 Table 4.8. Distribution of 36 Dioscorea spp. into five clusters 118 Table 4.9. Cluster means for 13 quantitative traits of Dioscorea spp. grown at Jimma 119
xii LIST OF TABLES (Continued)
Table 4.10. Pair wise generalized squared distances between five clusters of Dioscorea spp. collected from Southwest Ethiopia 120 Table 5.1. Estimates of components of variance, PCV, GCV, heritability and genetic advance for 13 quantitative characters of Dioscorea spp. grown at Jimma 135 Table 5.2. Genotypic (above diagonal) and phenotype (below diagonal) correlation coefficient among 13 traits in 36 Dioscorea spp landraces grown at Jimma 139 Table 5.3. Genotypic direct (bold and underlined) and indirect effects of some characters on tuber fresh weight of Dioscorea spp 142 Table 6.1. Analysis of variance for growth and yield related traits of two Dioscorea spp. landraces grown at Jimma 2015 158 Table 6.2. The mean value of growth and yield related traits harvested at different maturity stages of yam 160 Table 6.3. Analysis of variance of 14 biochemical traits from storage tuber and bulbils of aerial yam landraces grown at Jimma, 2015 162 Table 6.4. Biochemical composition from bulbils with different maturity stages 167 Table 6.5. Biochemical composition of storage tuber with different maturity stages 168 Table 7.1. Analysis of variance of different biochemical traits of yams 184 Table 7.2. Mean, standard deviation and ranges of 14 biochemical traits of Dioscorea spp. 187 Table 7.3. Eigen values, proportion, cumulative variance and component scores of the first six principal components for quality traits in 36 landraces of yams. 189 Table 7.4. Distribution of 36 Dioscorea spp. into eight clusters based on biochemical traits 190 Table 7.5. Cluster means of nine biochemical traits of Dioscorea spp. grown at Jimma 193 Table 7.6. Pair wise generalized squared distances between eight clusters of Dioscorea spp. collected from Southwest Ethiopia 193 Table 8.1. List of 36 yam landraces and their areas of collection 206 Table 8.2. Selected SSR primers for yam genetic diversity study 207 Table 8.3. Genetic diversity of 33 yam landraces based on 10 microsatellites markers 211 Table 8.4. Genetic diversity within and among the 33 yam landraces classified by areas of collection 212 Table 8.5. Analysis of Molecular Variance (AMOVA) among 33 yam landraces collected from seven districts using 10 SSR markers 214
Table 8.6. Pair-wise estimates of gene flow (Nm) (above diagonal) and genetic differentiation (FST) (lower diagonal) 214 Table 8.7. A similarity matrix among 33 yam landraces using Euclidian measure 217
xiii LIST OF FIGURES
Figure 1.1. Predictable areas of origin and distribution of Dioscorea species. . 3 Figure 2.1 Location of the study areas in Southwest Ethiopia 29 Figure 2.2. Number of landraces in different districts 37 Figure 2.3. Percent of distribution and abundance of 38 yam landraces in seven districts 38 Figure 2.4. Critical time of yam planting and percentage of households in Jimma, Sheka and Bench maji Zones of Southwest Ethiopia. 40 Figure 3.1. The spatial distribution of 38 landraces in seven districts 68 Figure 3.2. The spatial distribution of 38 landraces at 22 Kebele 71 Figure 3.3. UPGMA based clustering of 38 yam landraces based on 12 farmers’ identified causes of genetic erosion 81 Figure 3.4. The association of 36 yam landraces and 12 causes of genetic erosion in the study areas 82 Figure 4.1. Dendrogram showing hierarchical clustering patterns of 36 Dioscorea spp. landraces (UPGMA) based on 32 qualitative characters 106 Figure 4.2. Vegetative, reproductive and storage organ (tubers) of yam of different landrace collections from Southwest Ethiopia 108 . Figure 4.3. The Bi-plot diagram of PCA I and PCA II of 36 yam landraces based on 29 qualitative traits 114 Figure 4.4. Dendrogram showing hierarchical clustering patterns of 36 Dioscorea spp. landraces (UPGMA) based on 13 quantitative traits. 117 Figure 7.1. The Bi-plot diagram of PCA I and PCA II of 36 yam landraces based on nine biochemical traits 189 Figure 7.2. Dendrogram showing hierarchical clustering patterns of 36 Dioscorea spp. landraces (UPGMA) based on nine biochemical traits 191 Figure 8.1. The Bi plot analysis of 33 yam landraces using 10 SSR markers 215 Figure 8.2. Dendrogram revealing genetic relationships among 33 yam landraces from Southwest Ethiopia based on ten SSR analysis of Euclidian similarity coefficients with UPGMA clustering 220
xiv LIST OF APPENDIX
Appendix Table 3.1. Questionnaire 233 Appendix Table 4.1. Average performance value of 19 quantitative characters of 36 Dioscorea spp. landraces evaluated at Jimma, in 2015 239 Appendix Table 6.1. Summery of climate data on the research site during the growth period 241 Appendix Table 6.2. The mean values of landraces and respective quantitative traits 242 Appendix Table 6.3. The mean value of 14 biochemical traits of two Dioscorea spp. landraces collected from bulbils of aerial yam 243 Appendix Table 6.4. The mean value of 14 biochemical traits of two Dioscorea spp. landraces collected from storage tuber of aerial yam 244 Appendix Table 7.1. Mean performance value of 14 bio-chemical traits of 36 Dioscorea spp. landraces evaluated at Jimma, in 2015 245
xv Genetic Diversity, Path Coefficient Analysis, Classification and Evaluation of Yams (Dioscorea spp.) in Southwest Ethiopia
By: Tewodros Mulualem (MSc): Department of Plant Breeding, Hawassa University, Ethiopia. Firew Mekbib (PhD: Department of Plant and Environmental Sciences, Norwegian University of Life Sciences, Norway. Shimeles Hussein (PhD): Department of Plant Breeding, The University of Free state, South Africa. Endale Gebre (PhD): Department of Plant Science, Faculty of Agriculture, University of Putra, Malaysia.
ABSTRACT
Yams (Dioscorea spp.) are food security and economic crops, serving a wide range of smallholder households in sub-Saharan Africa. In Ethiopia, it has been cultivated in a densely populated and high rainfall area for food, medicine and market values. Despite its food and economic importance, the genetic diversity of yams in different agro ecological zones of the country have never been adequately assessed; the ethno-botanical data, agronomic and culinary attributes of the landraces have never been documented for use by research and development programs in the country. Thus, the study was conducted to assess the diversity, distribution, management, farmers’ classification system, the rate of genetic erosion and major contributing factors based on farmers’ indigenous knowledge, estimate the genetic diversity based on morphological, biochemical and molecular (SSRs) markers and to determine appropriate maturity stages of yams for yield and quality. To this end, a field survey was conducted on 240 households from seven districts of Jimma, Sheka and Bench maji zones. Questionnaire was used to collect primary data from an average of 34 farmers who are potentially rich sources of information on yam at district level. Additional data were collected through group discussions and key informant interviews. A total of 38 farmer named landraces were identified through on farm. The number of landraces maintained by a farmer varied from one to six with a mean and a standard deviation of 2.78 and 1.08, respectively. Farmers’ classification system of the landraces varied and highly depended on the domestication status, sex type, use value and time of maturity. The distribution of the landraces per district varied from 30 to 42 with a mean of 34.28. At Kebele level, the number varied from 6 to 21 with a mean of 10.9. The rate of genetic erosion at district and Kebele levels varied from 28.80% in Yeki to 57.93% in Kersa districts and 0% in Gubea muleta to 25% in Mehal sheko Kebeles with a mean rate of 44.48% and 14.1%, respectively. In all study districts, the number of farmers growing landraces drastically decreased in the past decades. Low attention given to the value of crop (95%), drought at early stage (93%), damage by wild animals (90%), shortage of farm land (74%), displacement of landraces by high value crops (72%) were the prominent factors for reduced interest in landrace cultivation. Moreover, farmers’ preference for cash crops with high
xvi yield potential subsequently reduced the chance of maintaining landraces. Field evaluations were conducted at Jimma Agricultural Research Center by using 36 landraces in a 6x6 lattice design with two replications. Morphological, biochemical and molecular data were collected and subjected to analysis of variance, correlation, path analysis, cluster and principal component analysis. Variance analysis, estimate of phenotypic and genotypic coefficients of variation showed significant differences (p<0.01) among the landraces. Comparatively high genotypic (14.87, 8.36 and 5.38%) and phenotypic coefficients of variation (40.11, 24.03 and 15.17%) were observed for tuber fresh weight, petiole length and number of vine hill -1 in the order of magnitudes. Heritability (13.70% and 12.10%) coupled with genetic advance as percent of mean (11.56% and 5.94%) were recorded for tuber fresh weight and petiole length, respectively. Correlation study between different quantitative characters showed highly significant associations among the characters. Analysis of path coefficients at genotypic level revealed that tuber length had maximum positive direct effect on tuber fresh weight (p=48.292) followed by vine length (p=2.089) and days to maturity, suggesting that simultaneous selection of the two traits may improve genetic gain of tuber yield in yam breeding. This result was also confirmed harvesting stage determination, revealed, selection of later maturing landraces may improve genetic gain in storage tuber and bulbils yield in yam. The genetic diversity of yam in this study hardly showed consistency with their areas of collection. This allowed through genetic clustering based on morphological, biochemical and molecular markers. The overall studies in this dissertation demonstrated the existence of high genetic diversity and variability of yams in Southwest Ethiopia providing better breeding opportunity and conservation.
Keywords: Biochemical traits, Dioscorea, farmers, genetic diversity, heritability, landrace, microsatellites, morphological traits, path analysis, yams
xvii 1. GENERAL INTRODUCTION
Yam is a multi species crop that belongs to the genus Dioscorea and family Dioscoreaceae (Coursey, 1967; Tamiru et al., 2007). It is found in Africa, India, Southeast Asia, Australia and South America comprising of 600 species (Jayasurya, 1984; Wilkin, 1998; Mignouna et al., 2002; Loko et al., 2015). All species are tropical origin and cultivated for their edible starchy tubers (enlarged, fleshy, usually underground storage stems). About ten yam species are cultivated as food staples serving millions of people in the tropics (Hahn, 1993; Sesay et al., 2013). West Africa is the predominant yam producing region globally. This region contributes 95% of the world’s yam produce with considerable varietal and genetic diversity (Hamadina et al., 2009; Dansi et al., 2013).
Guinea yam (D. cayenensis and D. rotundata complex) is the most important species and represents more than 97% of the total yam production in West Africa (Mignouna and Dansi, 2003; Abebe et al., 2013; Demuyakor et al., 2013). There is considerable varietal and genetic diversity of the Dioscorea spp. due to the continuous process of domestication from related and wild species of D. abyssinica. Dioscorea abyssinica is native to Ethiopia and currently grown in tropical Africa (Rehim and Espig, 1991). Dioscorea abyssinica Hochst and D. praehensilis Benth are among the wild species which are the progenitors of cultivated yam species in Africa (Hahn, 1995; Dansi et al., 2013). Ethiopia is an important center of origin and diversity of yam making the country a strategic source of genetic materials for breeding and conservation (Abdissa, 2000). According to Miege and Demissew (1997, about eleven Dioscorea species cultivated in Ethiopia. Besides, four Dioscorea species such as, D. bulbifera (aerial yam), D. alata (water yam), D. cayenensis and D. rotundata Complex (Dioscoreaceae) are widely distributed in major growing areas of the country for different purposes (Vavilov, 1951; Coursey 1967).
1.1. Origin and distribution of yams
The English term “yam” is most likely derived from the Portuguese word, ‘ynhame’, found in early documents, it being the transcription of niam, the word used in the Malinke language spoken widely through the Guineas, Sierra Leone and Ivory Coast (Coursey, 1976). It is
1 originating in the far East, the genus had spread worldwide by the end of the cretaceous period (approximately 75 million years ago) (Alexander and Coursey, 1969). The suitability of yams as medical ingredients and food stores on ships significantly facilitated their distribution throughout the world (Coursey, 1967). Currently, Dioscorea is a pan tropical genus and different species have been independently domesticated across the continent (Coursey, 1967; Tamiru et al., 2007). Dioscorea alata (greater yam or water yam) is the most widespread species (Njoh et al., 2015). It is believed to be originated in Southern Asia, but current genetic studies have confirmed New Guinea, or in Melanesia as the centre of origin of yams (Lebot, 1999; Malapa et al., 2005). This geographic region is also the centre of diversity of the species (Martin and Rhodes, 1977; Lebot, 1992; Lebot et al., 1998) and different morpho types existed.
From its domestication, yam arrived in northern Australia due to human introduction before the continent was separated from New Guinea by sea level rise 10,000 years ago. It is believed to have been among several Asian crops introduced to Madagascar by Austronesians some 2,000 years ago, and from there it spread into mainland East Africa (Lebot, 2009). It is not clear whether D. alata was established in West Africa before European contact, but it has since come to rival African species both there and in the Caribbean. In West Africa, D.cayenensis (yellow Guinea yam) and D. rotundata (white Guinea yam) are endemic, and the main cultivated species. The distinction between these species are unclear, and some researchers prefer to refer to them as the D. cayenensis–rotundata species complex (Hahn, 1995), while others argue that they should be recognized as separate taxa (Mignouna et al., 2005; Dansi et al., 2013). Among non geneticists, the difference is usually retained for practical morphological reasons. The species status of West African yams is poorly defined in any case, as landraces of both yellow yam and white yam may have arisen through independent hybridization of wild parents, including D. abyssinica and D. praehensilis with little genetic exchange occurring naturally among members of either group (Chair et al., 2005).
Yam agriculture in Africa is believed to have started at least 7,000 years ago, and domestication of wild types continues today (Lebot, 2009). In Central America, D.trifida was domesticated by Amerindians (Hahn, 1993). It has since been widely dispersed in Asia and the Pacific region, but remains a minor crop in all areas in which it is grown. However, most of scientists believed that
2 yam is amongst the earliest angiosperm that originated from Southeast Asia, but followed a divergent evolution in three continents separated by the formation of the Atlantic Ocean and desiccation of the Middle East (Coursey, 1967; Degras, 1993). As a result, the major food species occur in three isolated centers: West Africa, Southeast Asia and Tropical America. These centers are also considered areas for independent yam domestication (Asiedu et al., 1997; Tamiru, 2006).
Figure 1.1. Predictable areas of origin and distribution of Dioscorea species (adopted from Degras, 1993)
Yams spread by migrating people from Asia to the South Pacific as early as 3500 BC (Coursey, 1976). Yams are the third most important crop in West Africa on the basis of production volume, exceeded by cassava and sweet potato (FAOSTAT, 2010). The remaining 3% is grown in the Americas particularly the Caribbean, having been introduced from West Africa during slave trade, the Pacific region and Asia (Lebot, 2009). Moreover, significant quantities of Dioscorea species are also grown in the Caribbean, and virtually all humid or sub-humid parts of Asia with some morphological differences (Burkill, 1960; Miege, 1982). In Ethiopia there is no exact information when yam is introduced; however, about eleven species of yams have been described (Miege and Demessew, 1997). Although, Ethiopia is a center of origin and diversity of yam (Rehim and Espig, 1991), there are no reports so far done with regard to, the evolutionary process, species taxonomy and identity of Ethiopian yams. However, some species have grown as cultivated and wild forms in South, Southwestern and Western parts of the country without taxonomic classification.
3 1.2. Taxonomy of Yam The genus name Dioscorea was chosen by Linné in honor of the Greek medico and herbalist Dioscorides, who lived in the first centaury AC (Judith, 2004). Plants of the genus Dioscorea are angiosperms that belong to the monocotyledon class Liliopsidea, the subclass Liliidae comprising of the order Asparagales, Orchidales, Pandanales, Liliales and Dioscoreales. The order Dioscoreales characterized by some dicotyledonous features, i.e reticulate veining, staking, net nerving leaves; circular arranged vascular bundles in the stem cross section and lateral position of the pistil. Based on morphological character, the genus Dioscorea are classified into six different sections Enantiophyllum, Lasiophyton, Opsophyton, Combilium, Macroura and Macrgynodium (Alexander and Coursey, 1969). However, the morphological traits do not allow the distinction between species such as D. cayenensis and D. rotundata, and also between landraces. Dioscorea rotundata may in fact be a subspecies of Dioscorea cayenensis or alternatively may have originated from D. praehensilis (Coursey, 1967).
About 600 species have been described under the genus Dioscorea (Burkill, 1960; Asiedu et al., 1997; Dansi et al., 1999; Loko et al., 2015). The taxonomy of few species in this genus is considered to be problematic (Velayudhan et al., 1998; Marcos et al., 2014), which is attributed to their extensive variability within the species, especially aerial and subterranean plant parts; as the consequence, its taxonomy is confusing. However, the communities who depend on wild Dioscorea species for their food classify each member based on characters of its edibility and other uses and applications. Such classification of taxa based on use value and application by ordinary men and women is often called folk taxonomy (Brush, 1981). Several authors have discussed folk taxonomy and their relation to the genetics of crops and it has been shown that inter-specific folk taxonomies are often biologically accurate (Quiros et al., 1990). Folk identification and classification to a certain extent also helps in estimating genetic diversity (Mekbib, 2007). Folk taxonomists depend on a wide range of information including not only characteristics of the taxa in question, but also location in certain habitats and seasonal periods (Boster, 1985; Kwik, 2008).
The taxonomical approach can help botanists to define polymorphic taxa with continuous variation, mainly in the case of species with use values. This type of classification system covers
4 a wide range of categories, which could be referred to as ‘use classes’ according to the uses of the species and ‘locality classes’ referring to the characters of the locality (Peter and Lister, 2001; Marcos et al., 2014). The folk system relies chiefly on the use classes of tubers such as edibility, taste, flesh color, tuber size, fiber content, cooking properties and occasionally tuber and bulbils number. For classifying Dioscorea species, the Linnaean system uses character classes pertaining to that of floral, fruit and seed (reproductive) characters as well as the direction of twine of the vine. The size, shape and nature of tubers and bulbils are also used as important taxonomic characters.
The ploidy level is also vary among Dioscorea species ranges from 2n=20 up to 140 (Asiedu et al., 1997). The most frequently reported differences among yam landraces have been variations for qualitative morphological characters such as tuber/bulbils color and surface texture of the plant (Tanimato and Matsumoto, 1986; Loko et al., 2015). Moreover, the disparity of chromosome number of Dioscorea species may be due to the fact that yam chromosomes are liable to unpredictable behavior during cell division (Onwume and Charles, 1994). The number of chromosome varies by the type of species. For example, the most commonly reported chromosome numbers of aerial yam (D. bulbifera) are 2n=30, 40, 50, 60, 70, 80 and 100 (Coursey, 1967; Alexander and Coursey, 1969; Purseglove, 1972 and Rehim and Espig, 1991). D. alata has variable chromosome numbers: 2n=20, 30, 40, 50, 60, 70 and 80 (Onwume and Charles, 1994; Asiedu et al., 1997). Landraces of the large tuber types were mostly diploid, while those of small tuber types are mostly tetraploids (Zhang and Johanson, 1992). Such view is in contrast to the works of Karamura (1998) on banana; diploid banana landraces have small pseudo stem size, while tetraploids banana landraces have big pseudo stem. Therefore, to clarify the taxonomy of this crop, a comprehensive approach is needed to assess characters when delimiting species and varieties, perhaps by integrating the Linnaean, folk and modern taxonomic systems.
1.3. Morphology of yam Yams are monocotyledonous, herbal, climbing with tuberous and non tuberous carbohydrate rich storage organs. They have perennial vines with strongly marked annual cycle of growth (Coursey, 1967; Lebot, 2009). Typical Dioscorea species produce annual vines, which climb by
5 twining through the trees/poles to the left (clockwise) and right (anti-clockwise) depending on the species. The leaves are borne on long petioles, usually simple and chordate or acuminate, petioles sometimes slightly winged, but stems not so. The flowers are small and borne in long racemes, with male and female always being separate and usually borne on separate plants. Male flowers generally appear before female’s flowers, the male buds open in 15 days, and the female buds open in a range of 21-30 days after emergence of flower. However, the reverse is true in the case of the Dioscorea alata (Rene and Admasu, 1994).
The genus Dioscorea is dioeceous with an extremely irregular production of male and female flowers and became partially or often highly sexually sterile as a result of centuries of vegetative propagation (Coursey, 1983; Onwume and Charles, 1994). The fruiting and seed production of yams has been observed in many floras, although these organs are not always available and information on the sexual reproduction of this specie is very superficial (Lebot, 1999; Himanshu et al., 2016). Some species produced flat seed, has a wing like structure, and usually goes through a dormancy period of three to four months before germination can occur. The aerial storage organ of Dioscoreacea is the bulbils, which forms on the base of the petioles (Lebot et al., 1998; Himanshu et al., 2016). Economically, the most important parts of the plant are the tuber and bulbils, used as a food, medicine and propagation material (Malapa et al., 2005; Lebot, 2009). The root apparatus consists of root hairs and the adventitious roots arising from the base of the stem to absorb water and mineral nutrients.
Most landraces of yam mature 6-10 months and produced tuber, which is 20 to 40 cm long and weighs from two to several dozen kilograms, depending on cultivars and growing conditions. The body can be elongated or spherical with a white, yellow or purple flesh color. The bulbils, which are a characteristic of some species of Dioscorea bulbifera, are produced on the leaf axis, and weigh about 20 to 100 g (Degras, 1993).
1.4. Farmers’ perceptions for agro-morphological traits Farmers recognize many of the agro-morphological features for identification, classification and selection of yams (Dansi et al., 2013). The name of the material may be related to the original source, the morphology, culinary characteristics, agronomic performance of the plant and
6 adaptation to particular environmental factors such as type of soil or resistance to certain diseases (Coursey, 1967). The description may be related to the use of the cultivar, such as for food (fast cooking time, taste, use for straw or other parts of the plant), medicine and commercial. The traits used to explain the material may differ from those characters that the farmers are selecting for or against (Shepherd, 2010; Loko et al., 2015). Information of farmers’ evaluation criteria and farmers' preferred and non-preferred characters are valuable to rank the magnitude of agro- morphological criteria; because these traits are the basis by which farmers will recognize new genetic diversity within a population (by hybridization, introgression or mutation) or outside a population (by migration as farmers introduce new tubers/bulbils). This information can be validated by agro-morphological characterization and evaluation data from the field (Kwik, 2008). However, this type of information with regard to yam in Ethiopia is scarce (Tewodros, 2008). Preferred agro-morphological criteria may be considerably different according to different members of the community, depending on the age, sex type, ethnic group and socioeconomic status of the individual. Even for one individual, the preferred agro-morphological traits can change over time. As local environments change, whether due to natural processes or the ability of farmers to manipulate the agro-ecosystem, preferences in specific agro-morphological traits are likely to shift. Similarly, socio-cultural dynamics are likely to change with time, and as they do, different crop traits may become more or less valued.
Over the past decades, increased studies on indigenous knowledge system (IKS) in yams (Dansi et al., 2013; Loko et al., 2015), the system cover wide ranging concept which includes, but it is not limited to the botanical knowledge, traditional food knowledge, nearby environmental and biological knowledge that enable farming communities to lead stable livelihoods in their environments (Mekbib,2007). IKS of a farming society, expressed in the form of folklores and transmitted orally from one generation to next, is embedded in the food cultures, yams planted by the farmers, and the environment and ecological setting the community lives in. It is well established fact that documenting and deploying the local knowledge of farmers' management and use system of yams is a crucial starting point for improving farming systems as well as for fending-off the loss of bio cultural diversity (Tamiru, 2006). The generation and sustained preservation of local yam diversity is supported by farmers' indigenous knowledge and practices. However, recent studies have noted that the agricultural system of Ethiopia is changing or has
7 changed in its social, biological and environmental context (Tenaye and Geta, 2009). Yam and its farming system are hardly supported by formal research, and its resources are underutilized as compared to its potential (Tamiru, 2008; FAO, 2010). This limited effort has meant that the potential of yam is underexploited, and its genetic resources and associated IKS are put at threat of continuous erosion.
Besides, the result of different studies revealed that social attributes of human communities, such as local knowledge, experiences and cultural values, play a substantial role in the sustainable management, conservation and utilization of yam genetic resources and restoration of agro- ecosystems (Shepherd, 2010; FAO, 2011; Dansi et al., 2013). Indigenous knowledge is important not only for the society owing it, but also for planners, policy makers and scholars for designing conservation and agro-ecosystem restoration strategies.
1.5. Genotype characterization and evaluation Characterization may be defined as the scoring of characters that can be easily detected and have high heritability based on the form and structure of the organism, especially their external form (Karamura, 1998; Garedew, 2006). The character can be morphological, agronomic and bio chemical. It is also very useful to study the genetic variability present in the collection, especially within samples and develop the most appropriate techniques and strategies for maintaining the genetic integrity of such diversity. A character is a feature of an organism that can be measured, counted or otherwise assessed (Heywood, 1967). Characters forms the central theme of any study concerned with identification and classification of the organisms. Characters may not always be of equal value for the purpose of comparison; for example, characters which do not vary in a group under the study are not useful because they are unable to separate them, while characters which are logically correlated are neither acceptable because they provide similar information i.e. different ways of expressing the same thing (Pankhurst, 1991). Characters are chosen on criteria such as ease of observation, availability and usefulness in classifying and identifying organisms. Good characters are not modified by environmental factors and have a genetic basis such that they are unlikely to change readily (Jeffry, 1996; Karamura, 1998). These may be referred to as constant characters and are highly heritable. On the contrary, bad
8 characters are easily modified, to a greater or lesser extent, by the environment. Their phenotypic expression is the product of the combined effect of the environment and the landrace.
According to Hawkes (1983), the purpose of characterization is to study genetic variability of certain characters in relation to their geographical distribution in order to develop new and more adequate collecting strategies for further collection of useful landraces in the same or similar areas; to study the genetic variability present in the collections, especially within samples and develop the most appropriate techniques and strategies to maintaining the genetic integrity of such diversity; to understand the genetic diversity of crops through inter-specific, intra-specific and inter-generic hybridization and mutation (Emmanuel et al., 2015). Furthermore, it is essential to screen the collection for traits which, from time to time, are considered important for breeding programs aiming to improve agriculture in a given country, region or geographical area. In this regards, few reports were done on characterization of aerial yams collection from Southwest Ethiopia (Tewodros, 2008), Besides, Tamiru (2006), reported on yams collection from Gamo-gofa and Wolayita zones of Southern Ethiopia.
Characterization based on morphological or agronomic traits on yams hides’ important genetic information (Dansi et al., 1999). Apart from morphological traits, several reports were available on yam by using different markers, For example, isozymic techniques (Mignouna et al., 2002; Mignouna and Dansi, 2003) and flow cytometry (Dansi et al., 2000b), and molecular techniques, provide opportunities to obtain high amplification of genetic traits for the development of genetic maps, variety identification and for the analysis of important morphological and agronomic traits of yams (Dansi et al., 2000a; Tostain et al., 2003; Dumont et al., 2005; Loko et al., 2015). Molecular markers show a high level of polymorphism on plant materials (Sonnante et al., 1994; Akakaya et al., 1995; Marcos et al., 2014), RAPDs (Random Amplified Polymorphic DNA) (Williams et al., 1993; Dansi et al., 2000c), and AFLP (Vos et al., 1995; Malapa et al., 2005; Abebe et al., 2013). RAPD markers have been shown to be useful in assessing inter-specific genetic diversity in many crop plant species incuding yam (Williams et al., 1993; Emmanuel et al., 2015).
9 1.6. Genetic diversity Genetic diversity is expressed as genetic differences between species, sub species, varieties, populations or individuals (Brown, 1983; Jarvis et al., 2000; Ramanatha and Hodgkin, 2002). Thus, genetic diversity needs to be described and measured if it is to be effectively incorporated into breeding strategies through efficient management of plant genetic resources (Agong et al., 2000). In agriculture, genetic diversity can enhance production as several varieties can be bred to maximize production and adapt to adverse environmental conditions (McNaught, 1988; Khlestkina et al., 2004). The knowledge of genetic diversity plays a crucial role in selection, conservation and utilization of the available genetic resources (Ramanatha and Hodgkin, 2002; Dansi et al., 2013). Breeders are recognized the importance of genetic diversity in crop improvement program. It is generally assumed that landraces originating from widely separated geographical regions of the world are more likely to be genetically different. On this analogy, such landraces are considered in the breeding approach with the hope that their presumed genetic diversity will be of great promise. In applied plant breeding, however, the origin of lines is hardly known; selection of parents based on the geographical diversity alone may not be enough.
Different approaches to measure genetic distance have been proposed over the past few decades to suit various objectives (Mahalanobis, 1936). D2 statistics is used in the identification of genetically different landraces so that, grouping and characterization for their morphological characteristics can be carried out accordingly. This technique has been widely used to assess genetic diversity in crop plants. In this regard, few reports were available on the Mahalanobis’ D2 statistics on aerial yam from Southwest Ethiopian collection. Tewodros (2008), evaluated the phenotypic variability in 47 aerial yam landraces using 17 qualitative and 15 quantitative traits in Southwest Ethiopia. A considerable level of variation was recorded for a number of morphologic and agronomic traits, while limited diversity was observed among the landraces for qualitative traits. Besides, Tamiru (2006) assessed the level of diversity based on morphology and AFLP marker in 84 landraces collected from Gamo-gofa and Wolayita zones.
1.7. Production status of yam in Ethiopia Yam production is an ancient phenomenon in Ethiopia. Although, there is no precise information when yam is introduced, different yam species are cultivated and widely grown as subsistence
10 farming in Southern, Southwestern and Western parts of the country (Hildebrand, 2002). Miege and Demissew (1997) have described eleven Dioscorea species cultivated in the country. Besides, 23 indigenous yam types belonging to four Dioscorea species such as, D. bulbifera (aerial yam), D. alata (water yam), D. cayenensis and D. rotundata Complex (Dioscoreaceae) are widely distributed in major growing areas of the country for food, medicinal use and to fill economic gaps during the off season (Vavilov, 1951; Coursey 1967). Furthermore, various Dioscorea species growing in the complex farming system as cultivated and wild forms with cereals and other root and tuber crops (Edwards, 1991). According to the report of Tamiru (2006) the total annual production of yam in Ethiopia was estimated at about 277,000 metric tons from an area of 68,000 ha, corresponding to a yield of about 4 tons per hectare. Although, the most yam production potential area in Ethiopia lies in the Southern and Southwestern parts mainly on in Sheka, Keffa, Bench Maji, Jimma, Illubabor, Wolaita, Gamogofa, Wellega and Dauro zones and Yem and Konta special districts for food, medicinal use and to fill economic gaps during the off season (Vavilov, 1951; Coursey 1967; Tewodros, 2012). There is few efforts so far done in Wolayta and Gamo-gofa zones Southern Ethiopia (Tamiru, 2006) and information of yam from Southwestern parts of the country is missed.
1.8. Genetic erosion of yam in Ethiopia Genetic erosion is defined as the loss of variability from crop populations in diversity centers, i.e. areas of domestication and secondary diversification (Brush, 1999). It implies that the normal addition and disappearance of genetic variability in a population is altered so that the net change in diversity is negative (Tsegaye and Berg, 2007).
Ethiopia is considered to be the centre of origin and diversity of many crop species, such as Eragrostis tef (tef), Guizotia abyssinica (nuge), Rhamnus prinoides (geeshoo), Ensete ventricosum (enset), Catha edulis (chat) and Coffea arabica (buna) and are distributed over a wide range of agro-ecological zones in the country (Vavilov and Chester, 1951; Harlan, 1969; Worede, 1991). These diverse genetic resources are used and managed in various ways by farmers’ communities. Currently, the indigenous crop genetic resources in general and yam genetic diversity in particular are becoming seriously endangered owing to the high rate of genetic erosion resulting from natural calamities, market preferences, agricultural modernizations, urbanization, high pest and disease pressures and changing of cropping patterns
11 due to climate change and environmental degradation have largely affected the magnitude of the crop genetic diversity in the country. If this trend continues, the gene pool of crop genetic resources could be lost in the near future. Moreover, on farm genetic resource conservation receives less attention and agricultural extension in the country has focused on the improved varieties to maximize yield (Yifru and Karl, 2006). Furthermore, indigenous genetic resources in the country are exposed for serious genetic erosion due to displacement by introduced varieties (Friis-Hansen, 1999; Nabhan, 2007) and market preferences. For many decades the government agricultural policy did not adequately address the role and contribution that indigenous genetic resources could play (Tamiru, 2006). This is partly due to low attention to the value of indigenous genetic resources and partly because of the ambition to fill gaps in food security (Ford-Lloyd, 2006; Loko et al., 2015).
Besides, information on traditional farming system on yam in Ethiopia is scanty. The rate of genetic erosion of crops and their wild relatives is accelerating at an alarming rate due to human interventions (Megersa, 2014). The frequent drought in the past decades has eroded considerable amount of biodiversity in the country. In addition, the causes, effects and the degree of genetic erosion on local landraces or list of varieties/species lost in various parts of the country are unknown. In this regard, it is difficult to quantify the rate of genetic erosion and to apply conservation measures on different crop landraces including yams.
1.9. Nutritional value of yam Yam is considered to be the most nutritious tropical tuber crop (Wanasundera and Ravindran, 1994; Abera, 2011). It contains about four times protein than cassava, and it is the only major tuber crop that exceeds rice in protein content in amount to digestible energy. The amino acid composition of yam protein is suboptimal in sulfur containing amino acids (cysteine and methionine), but the overall rating for essential amino acids is high and superior than sweet potato (Bhandari et al., 2003). Yam is also a good source of vitamins A and C, fiber and minerals. It’s relatively low calcium content is related to low concentrations of calcium oxalate, and other anti nutritional factor. Many authors (Agbor and Treche, 1995; Baah et al., 2009) are evaluated on the variability of protein content within yam species, indicated potential for selection for high protein and carbohydrate contents. However, some of these variabilities would be due to varying degrees of nitrogen and carbon deficiency in the tubers sampled (Baah et al.,
12 2009). Improving the nitrogen content of yams will increase the protein content. However, the relative contribution of nitrogen content and landrace to the observed range of protein content has not been determined. Even though, Ethiopia is the center of origin and diversity of yams, there is no effort so far done with regard to the nutritional value of Ethiopian yam, as a result, large amount of yam genetic resources are eroded.
1.10. Rationale of the study
Yam is a food security crop in developing countries of tropical Africa. In Ethiopia, although reliable statistical information on the production and distribution of yam is deficient, the crop has been cultivated, in major growing areas of Southern, Southwestern and Western parts of the country by small-holder farmers as fill seasonal food and economic gaps. Even though, yam is one of the most important food and economical crop in Ethiopia, the production and productivity is threatened by different socio-economic factors, such as lack of adapted and improved technologies, land shortage, lack of knowledge for utilization and management of the crop, lack of collection strategy to minimize genetic erosion and conservation of yam genetic resources for different purposes. Furthermore, yam in Ethiopia is less known by scientific community and farmers are the sources of information on diversity and management based on their conventional knowledge. Besides, yams in Ethiopia are considered as less valuable crop within the society. As a result, the indigenous yam genetic resources are eroded due mainly by poor farmers’ management practices before characterization, conservation and documentation. Therefore, studies on genetic diversity are imperative through morphological, biochemical and molecular traits in association with farmers’ traditional knowledge to assess and sustainably utilize the existing genetic diversity for further conservation of yams in the country. Therefore, the present study was undertaken with the following premises: Information on indigenous classification system, on farm based diversity, management, distribution and estimates of the rate of landrace loss, analyze the cause and its variation across major growing areas of yam in Ethiopia are limited, thus, estimates of the rate of genetic erosion per species and landraces in major growing areas of yam is difficult. Besides, the ethno-botanical data, the name of farmers identified yam landraces with its agronomic and culinary attributes have also not been documented for further breeding program of the crop; this in turn hinders researchers to access yam genetic resources in the country. Hence, this research was conducted to assess farmers’ indigenous knowledge on
13 diversity, classification, distribution and management system on yam, to generate base line information in Southwest Ethiopia. A total of 38 yam landraces were identified on farm and collected from Southwest Ethiopia for comprehensive study, such as, to estimate the extent of genetic variability; quantify genetic diversity, heritability and association of economically important traits of the local landraces using agro-morphological traits, biochemical and molecular markers for a better understanding of factors underlying yield and quality traits for breeding and genetic diversity of the crop. Maturity on yams vary between species and the yield and quality of yams are affected by time of harvest, consequently, assessment of appropriate time of harvest and analyze the morphological and biochemical composition have valuable impact for livelihood security and sustainable utilization of yams in Ethiopia.
1.11. Objectives
1.11.1. General objective To assess the pattern and extent of genetic diversity, distribution and variability in yam landraces collection of Southwest Ethiopia using farmers’ perception, agro - morphological, biochemical traits and molecular markers to generate information for future breeding programme.
1.11.2. Specific objectives: Assess the diversity, classification, distribution and management of yam landraces based on farmer’s indigenous knowledge. Estimate the rate of landrace loss (genetic erosion) and analyze its variation across district and Kebele level. Estimate the extent of genetic diversity and variability based on key morphological descriptors. Determine the heritability, correlation and path coefficient analysis of different traits. Identify appropriate harvesting stages of yams for yield and quality traits. Evaluate the biochemical compositional variation within yams from Southwest Ethiopia Quantify the genetic diversity of yam landraces using SSR molecular markers.
14 1.12. Dissertation structure The first chapter of the dissertation introduces the origin and distribution, taxonomical classification, morphology, genotypic characterization and evaluation and genetic erosion of yams. It also presents the research motivation and justification for the study. In connection with this, the general and specific objectives of the study are given. The remaining chapters covered wide range of research topics on the study. Chapter 2 and 3 present the assessment of on farm based indigenous knowledge in major yam growing areas of Jimma (Manna, Shebe sombo, Seka chekorsa, Dedo and Kersa), Sheka (Yeki) and Bench-maji (Sheko) zones of Southwest Ethiopia. Results obtained from the survey are used to generate base line information on farmers’ indigenous classification system, on-farm based genetic diversity, management, distribution and estimate of the rate of landrace loss, major factors contributing to genetic erosion and analysis of variation across district and Kebele levels, which was in fact, not previously studied in the areas for future conservation of yam genetic resources intervention.
The field study was conducted under two components: the first component focuses on morphological characterization of yam landraces from Southwest Ethiopia so as to assess the diversity within the collection of landraces of yams based on key morphological descriptors; evaluate the extent of variability, correlation and path coefficient analysis of storage tuber yield and yield components of yams. The harvested yield and quality of yam are affected by the time of harvest, therefore, the second field component entirely focused on the effect of different maturity stages on two dominantly used yam landraces in the region for yield and biochemical traits. The result of the study is vital to assess the yield and quality of yams in Southwest Ethiopia.
The bio chemical composition of yams was analyzed to assess the proximate, mineral and anti nutritional composition of different yam landraces. The analysis was conducted at Ethiopian Institute of Agricultural Research (EIAR) food and nutrition laboratory and Jimma University, College of Agriculture and Veterinary Medicine (JUCAVM) Animal Sciences Nutrition Laboratory. The results from this study are important to estimate the proximate composition and additional use of yam landraces from Southwest Ethiopia. The last chapter focuses on the assessment of genetic diversity of yams based on ten SSRs molecular markers, which was conducted at Incotec Biotechnology laboratory, South Africa, in 2016.
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22 II
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ANALYSIS OF INDIGENOUS KNOWLEDGE, ON FARM DIVERSITY, DISTRIBUTION AND MANAGEMENT OF YAM (Dioscorea spp.) LANDRACE COLLECTIONS IN SOUTHWEST ETHIOPIA ______
23 Analysis of indigenous Knowledge, On-farm Diversity, Distribution and Management of Yam (Dioscorea spp.) Landrace Collections in Southwest Ethiopia
ABSTRACT
Yam (Dioscorea spp.) is one of the most important food security and socio economic crop in Africa. In Ethiopia, it has been cultivated in densely populated and high rainfall areas to fill seasonal food and economic gaps. To assess the diversity, management and spatial distribution of yam landraces in major growing areas, a survey was conducted on 240 households from seven districts of Southwest Ethiopia. Data were collected from different sites through the application of participatory research appraisal tools and techniques, such as individual household and key informant interviews, direct observation, field visits and group discussion with farmers using a well prepared questionnaire. A total of 38 farmers named landraces were identified on farm. The number of landraces maintained on individual farmers’ varies from one to six with mean and standard deviation of 2.78 and 1.08, respectively. From the household interviews, 76(31.71%), 49 (20.40%) and 29 (12.10%) of farmers’ replied that they select and collect materials from their family, local market and own gardens, respectively. The remaining 86(35.83%) of the farmers’ collected planting material from different sources. Regarding the type and the number of landraces to plant, farmers’ decision on the selection of landraces is mainly affected by environmental factors, the knowledge of the crop, market and stake demands. Most of the landraces (60.53%) had limited abundance and uneven distribution and only a few (39.47%) grew dominantly. There was a considerable variation amongst the districts with respect to diversity, management, distribution and abundance of the landraces.
Keywords: Folk biology, indigenous knowledge, landrace diversity, seed sources, survey
24 2.1. INTRODUCTION
Yam (Dioscorea spp.) is a crop of major economic and socio cultural importance for a wide range of smallholder households in sub-Saharan Africa (Mignouna et al., 2002). It is the fourth most important tuber crop in the world after potatoes (Solanum tuberosum L.), cassava (Manihot esculenta Crantz) and sweet potatoes [Ipomoea batatas (L.) pori.] (Loko et al., 2013). The genus Dioscorea comprises over 600 species (Jayasurya, 1984; Wilkin, 1998; Himanshu et al., 2016). Of these, only ten of them are cultivated for human food for millions of people in tropical and sub-tropical regions (Hahn, 1993; Dansi et al., 2013; Sesay et al., 2013).
West Africa is the predominant yam producing region globally. The region contributes 95% of the world’s yam produce with considerable varietal and genetic diversity (FAO, 2010; Dansi et al., 2013). Guinea yam (D. cayenensis and D.rotundata complex) is the most important species and represents about 97% of the total yam production in West Africa (Mignouna and Dansi, 2003; Demuyakor et al., 2013). There is considerable varietal and genetic diversity of the Dioscorea spp. due to the continuous process of domestication from related and wild species of D. abyssinica. Dioscorea abyssinica is native to Ethiopia and currently grown in tropical Africa (Rehim and Espig, 1991; Hildebrand, 2003; Tamiru et al. 2011). Edwards (1991) reported that Dioscorea species are widely adapted in Ethiopia as cultivated and wild relatives. In line with this, D. abyssinica Hochst and D. praehensilis Benth are believed to be among the wild species that are ancestors of cultivated African species (Terauchi et al., 1992; Hanh, 1995; Mignouna and Dansi, 2003; Dumont et al., 2005; Sesay et al., 2013). Besides, 23 indigenous yam types belonging to at least four Dioscorea species are widely distributed in Southwest Ethiopia (Nashriyah et al., 2011). In this regard, Ethiopia may be an important center of origin of yam diversity that can constitute a useful source of genetic materials for breeding and conservation (Zeven and De Wet, 1982; Abebe et al., 2013).
In Ethiopia, yams are hardly known by researchers before 1984 famine, however, different yam species are grown and widely distributed in major growing areas of Southern, Southwestern and Western parts in composite farming systems and there has no study on diversity, distribution and management of the crop (Tamiru et al. 2011). Miege and Demissew (1997) have described
25 eleven Dioscorea species cultivated in the country. Furthermore, several Dioscorea species might have their origin in Ethiopia as well (Vavilov, 1951; Harlan, 1969). Edwards (1991) also reported D. bulbifera (aerial yam), D. alata (water yam), D. cayenensis and D. rotundata Complex (Dioscoreaceae) are grown in Ethiopia for food, medicinal use and to fill economic gaps during the off season. These reports further confirmed that yam is widely cultivated as subsistence farming in different areas of the country. Moreover, there is a large pool of landraces which are expected to be found within the existing yam diversity in Southwestern and Western Ethiopia (Norman et al., 1995; Hildebrand, 2003), which is yet to be studied before. Besides, the diversity, distribution and management of yam landraces throughout agro ecological zones have never been assessed; the ethno botanical data, agronomic and culinary attributes of landraces have also not been documented for use by scientific research and development programs in the country; and that limited researchers’ access to yam genetic resources in the country (Hildebrand et al., 2002). Previous study on-farm diversity, distribution and management of yam in Southern Ethiopia (Tamiru, 2006) confirmed the presence of significant genetic diversity and distribution of yam in Wolayita and Gamo-gofa zones. Nevertheless, the report did not fully cover the other major yam growing areas of the country. Cognizant of these facts, strategic use of existing genetic diversity and management of yams at community level is expected to have significant importance to develop conservation plan and to address the existing problems on yam genetic resources in an affordable and sustainable way. Hence, this study was designed to assess the diversity, distribution, and management of yam landraces based on farmers’ indigenous knowledge in Southwestern Ethiopia.
2.2. MATERIALS AND METHODS
2.2.1. Description of the study areas The study was conducted in seven districts from major yam growing areas of Jimma (Manna, Shebe sombo, Dedo, Seka chekorsa and Kersa), Sheka (Yeki) and Bench-maji (Sheko) zones of Southwestern Ethiopia. These areas were selected for study based on strong tradition in cultivating and domesticating various yam landraces with wide genetic base (Hildebrand, 2003; Demissew et al., 2003; Abebe et al., 2013), high production potential and long history on production and management system of yam with farmers’ traditional knowledge (Miege and Demissew, 1997).
26 Jimma zone is located in Oromia National Regional State. The administrative center of the zone is Jimma town and is located 350 km Southwest of Addis Ababa. The zone has approximately 18,412.54 km square, and an estimated population of 2.6 million, of which 11% are urban and 89% are rural dwellers and engaged in agriculture (CSA, 2010). From the total population, 1,326,527 (51.0%) are male headed and 1,274,628 (49.0%) are female headed. The residents of the Jimma zone are primarily the Oromo ethno linguistic communities speaking the Omotic Oromiffa language. Agro ecologically, Jimma zone is classified as: 15% highlands, 67% midlands and 18% lowlands. It receives mean annual rainfall ranging from 1,200–2,800 mm. In normal years, the rainy season extends from June to October. The zone is one of the major coffee growing areas of Oromia region and endowed with natural resources contributing significantly to the national economy of the country. The zone is largely agriculturalists, practicing mixed crop livestock production and living in permanent settlements. Within their landholdings, community members preserve coffee orchards, root and tuber crops (enset, yams, taro, cassava and potato), fruit trees (avocado, mango and guava), cereal (maize, tef, sorghum and barley) and pulses (beans and pea), vegetables, spices and rearing domestic animals. Although, Jimma zone has seventeen districts, this study entirely focused on major yam growing areas of Dedo, Seka chekorsa, Kersa, Shebe sombo and Manna districts and contributed 12.60%, 8.32%, 5.59%, 5.31% and 3.76% of the population, respectively, and estimated 35.58% of the total zonal population. In these districts, yam is a strategic crop and substantially contributing to the food security and as a medicine from June to November.
Sheka zone is one of the major yam growing areas in Ethiopia. It is located in Southern Nations, Nationality, and People Regional State (SNNPRS) of Southwest Ethiopia. The zone covers about 2387.54km2. The administrative center of Sheka is Masha, located 676km Southwest of Addis Ababa. Geographically, the zone lies between 07024’52N latitude and 035013’35E longitude and consisted three districts, namely: Yeki, Anderacha and Masha. The zone is bordered by Bench maji, the Gambela region, the oromia region and Keffa zone, on Southwest, North and East respectively. A total of 45 rural and two urban Kebeles (Kebele is the least administrative hierarchy in Ethiopia) are found in Sheka zone. According to the Central Statistical Agency (CSA, 2009), the total zonal population is estimated about 226,090, of which 114,661 are men and 111,429 women; 38,819 (17.17%) are urban inhabitants.
27 About 187,270 (82.83%) of the total population resides in rural areas and engaged in agriculture (CSA, 2009). The ethnic composition of Sheka zone is Shekicho (34.70%), Kafficho (20.50%), Amhara (20.50%), Oromo (9.60%), Sheko (5.0%), Bench (4.80%), Majangir (2.00%) and all other ethnic groups made up 2.90% of the population. These people do have their own culture, language and life styles. Even though, Sheka zone has been classified into three districts, the survey was conducted in 32 farmers’ fields from Yeki district. Based on the 2007 census conducted by the central statistics agency (CSA), the total population of the district estimated about 134,519, of which 68,895 are men and 65,624 women; 24,832 or 18.46% of its population is urban dwellers. The majority of the inhabitants practiced Ethiopian Orthodox Christianity, with 44.85% of the population reporting that belief, 29.8% were Evangelical, 21.66% were Muslim, and 1.99% practiced traditional beliefs.
Bench-maji zone is administered under the Southern Nations, Nationalities, and Peoples' Regional States (SNNPRS) and have eleven districts. The total area of the zone is about 19,252.00 km2 and bordered on the West by South Sudan, on the Northwest by the Gambela region, on the North by Sheka zone, on the Northeast by Keffa, and on the East by Debub Omo zone. The administrative center of the zone is Mizan teferi, is located 520km from Southwest of Addis Ababa. The total zonal population estimated about 652,531, of which 323,348 are men and 329,183 women. The seven largest ethnic group was reported in this zone were Bench (45.11%), Menit (21.36%), Amhara (8.23%), Kafficho (6.55%), Dizi (5.17%), Sheko (4.21%), and the Suri (3.88%); all other ethnic groups made up 5.49% of the population. From the total population, 49.27%, 26.34%, 18.12% and 3.47% practiced Evangelical, traditional beliefs, Ethiopian Orthodox Christianity and Muslim, respectively.
Although, Bench maji zone have been fragmented into eleven districts, the present study was conducted in 32 farmers’ fields from different corner of Sheko district. The name of the district was derived from Sheko people, whose homeland lies in this district and covered about 4.2% of zonal population (CSA, 2009). Sheko is bordered on the South by Debub bench, on the West by Guraferda district, on the Northwest by the Gambela region, on the North by the Sheka zone, and on the East by Semen bench district. The capital town of the district is Sheko is similar to the name of the district Sheko.
28 Figure 2.1. Location of the study areas in Southwest Ethiopia
2.2.2. Sampling and methods of data collection For this study, purposive and stratified random sampling methods were followed to describe the sampling units. Three zones are selected purposively and the district stratified first based on elevation and agro-ecology to wrap appropriate ecological range of yam and kebeles were elected and judge as main yam growing areas. The present study was conducted in 22 Kebeles belonging to seven administrative districts from Jimma, Sheka and Bench maj zones from April to December 2015. Three Kebeles were selected from each district based on consultation with district agricultural experts on the basis of yam growing potential and agro-ecological variation (Table 2.1). To undertake an exhaustive yam diversity inventory, a total of 240 households were selected. The representative households were selected from each district based on farmers owning larger farm size of yam and growing diversified species by consulting Kebele leaders and key informants that are knowledgeable about the crop.
Data were collected from different sites through the application of participatory research appraisal tools and techniques, such as individual household interviews, direct observation, field visits, group discussion and key informant interviews using a well prepared questionnaire and
29 checklists. It is vital to identify and document the diversity and naming of yam landraces at Kebele and district level. On average thirty four individuals were randomly selected from each district for individual interviews with the help of local translators. Besides, about 15 to 20 yam producers of different social groups, who identified and assemble with the help of key informants involved in the study to facilitate the group meetings and data collection. Before starting the discussion, all farmers were requested to bring different samples of yam landraces that they were cultivating and knew within the district. Information on the specific location (name of district, Kebele and ethnic group) were collected after a detailed presentation of the research objectives to the farmers. Then, farmers listed (by providing vernacular names, type of uses, distribution, management and cultural importance) and exhibit different landraces of yam they grew in their district/Kebele. Through discussions, the detailed agronomic, sex type, maturity time and culinary characteristics of the listed landraces were identified and documented.
The identified landraces were assessed against different agronomic and culinary traits of economic importance using participatory method and on group basis with the two level (0, 1) scoring evaluation method. Hence, for a given trait like palatability (powderness of tuber when cooked), or those clearly known as "palatable yam" was given a score of 1, or otherwise given a score of 0. In individual and group level, the discussions were free and open. Some of the identified yam landraces were collected with the permission of farmers from each district and maintained at Jimma Agricultural Research Center (JARC) for conservation. Furthermore, ten farmers and five development agents (DAs) were considered as key informants in each district. Selected key informants and DAs based on who grew different yam landraces for long period of time and have deep knowledge about the crop. Information obtained from key informants is valuable to cross check and clear contradictory ideas on the existing yam diversity, management and distribution from individual interview and group discussion with farmers’ throughout the tested districts.
2.2.3. Data analysis For the purpose of this study, landraces refer to a dynamic population of a cultivated plant with a historical origin, morphologically distinct, named, genetically diverse, locally adapted and managed by farmers.
30 Accordingly, ethno-botanical data were analyzed through descriptive statistics (frequencies, percentages, means, etc.) to generate summaries and tables at different (districts and zones) levels using Statistical Package for Social Sciences (SPSS, 1996, Ver. 16). Simpson’s diversity index was computed to estimate diversity of landraces (evenness and richness) in all the districts. Simpson’s index (K) mainly measures the probability that two individuals randomly selected from a single belong to the same category (Simpson, 1949) and hence, as K increases, the diversity decreases. Therefore, it transformed as 1-K with values ranging from 0 to 1. The index was computed for all districts using the function:
(1-k)= ∑(n/N) 2 =∑(n(n-1)/(N(N-1) Where (1-k) = Simpson’s diversity index N= the total number of households assessed in each district n= the number of households where a landraces was found. Shannon weaver diversity index (H’) was considered to assess the diversity of landraces by using the number and evenness of the landraces (Shannon and Wiener, 1949). The index is defined as:
Where S= the numbe= r− of∑ landracespi ln pi, p= the proportion of landraces i relative to the total number of landraces (S/N) and ln= logarithm to base e.
The Shannon weaver index values (H’) can range from 0 to ~ 4.6 using the natural log (versus log10). A value near 0 indicated that every species in the sample are the same. Conversely, a value near 4.6 indicated the numbers of individuals are evenly distributed between the species (Hennink and Zevan, 1991). Although, Shannon’s index takes into account evenness of the abundance of landraces can be calculated separately as a measure of the observed diversity to the maximum diversity. For calculating the evenness of the landraces, the Pielou’s evenness index (E) was used (Pielou, 1966). It is defined by the function: E=H’/lnS, where, H’ is Shannon diversity index, S referring to the total number of landraces described in each district and ln= natural logarithm (Vikrant and Pawan, 2014). High evenness resulting from all landraces having similar abundance and normally is equated with high diversity (Magurran, 1988).
Differentiation of diversity estimates how different and similar habitats in terms of diversity of category under consideration. This can be achieved by doing similarity measure of pair of sites,
31 as is the case with Sørenson’s similarity index (Mohan et al., 2007). In this study, the index was computed based on the presence and or absence of landraces (qualitative data) to estimate landrace similarity between pairs of districts as follow:
Sørenson’s Similarity index =
Where, S= the number of landraces( common) to both districts, Sa= the number of landraces in district A and Sb= the number of landraces in district B.
Table 2.1. Description of study districts in Jimma, Sheka and Bench maji Zones of Southwest Ethiopia
Districts Zone Elevation (m) Number of Mean family No. of house-hold Ranges Kebele size interviewed Dedo Jimma 1683-2130 3 8.00 38 Kersa Jimma 1702-1829 3 6.19 42 Manna Jimma 1650-1980 4 6.08 35 Seka chekorsa Jimma 1556-1980 3 6.80 30 Shebe sombo Jimma 1469-1908 3 6.90 31 Sheko Bench maji 1429-2009 3 6.43 32 Yeki Sheka 1171-1591 3 6.80 32 Total 22 240
32 2.3. RESULTS AND DISCUSSION
2.3.1. The level of landrace diversity Farmers identify different landraces by their own descriptors with different names for management decisions. They described their landraces in various ways and characters used to separate landraces. The number of farmer named landraces in the farming communities are a preliminary stage for measurement of on farm crop diversity at a given location (Bhuwon et al., 2012). In this study, farmers identified 38 known yam landraces (Table 2.3). Of these, four landraces, (liyan, offea, welmeka and woko) belong to well definite species of aerial yam and are recognized based on the differences of bulbils shape, color, size and surface texture. Moreover, one species called badaye belongs to Dioscorea alata basically identified by early maturity and a square stem with tubers varying in shape, and flesh color (white, creamy yellow or purple). Nevertheless, these characters could not give the impression to provide reliable means of identification, as they tend to differ within a landrace. It was difficult to identify as a species or group of species. Furthermore, wild yams widely named as sasa and karakachi were identified in forest areas of Manna and Sheko districts having typically big thorn on the surface of vine and underground tuber. In all districts, the number of landraces on individual farmer’s fields ranged from one to six with mean and standard deviation of 2.78 and 1.08, respectively. The number of yam landraces per districts was summarized in Table 2.2. From all districts, relatively large numbers of farms having five or more landraces were found in Shebe sombo, Manna and Seka chekorsa districts. Most of the farmers visited in these districts were residing relatively in lower altitudes (Table 2.1).
Table 2.2.Variation in the number of yam landraces planted per farm in study districts. No. of Dedo Kersa Manna Seka Shebe Sheko Yeki Total Landraces chekorsa sombo 1 3.0 0.0 5.0 5.0 3.0 2.0 8.0 26.0 2 22.0 6.0 16.0 6.0 14.0 7.0 6.0 77.0 3 10.0 27.0 7.0 13.0 5.0 15.0 12.0 89.0 4 3.0 8.0 3.0 4.0 5.0 8.0 6.0 37.0 5 0.0 1.0 4.0 0.0 0.0 0.0 0.0 5.0 6 0.0 0.0 0.0 2.0 4.0 0.0 0.0 6.0 Total 38.0 42.0 35.0 30.0 31.0 32.0 32.0 240 Mean 9.5 10.5 7.0 6.0 6.2 8.0 8.0 34.28
33 Table 2.3. Yam landraces recorded in the study districts of Jimma, Sheka and Bench maji Zones
Districts Total No Name of Dedo Kersa Manna Seka Shebe Sheko Yeki landraces chekorsa sombo 1 Afra - 2 2 - 1 3 - 8 2 Anchiro 13 18 8 8 8 - - 55 3 Badaye 1 - - - - 18 21 40 4 Badenseye ------2 2 5 Baki boye 2 - 1 - - 1 2 6 6 Bambuche 1 - 4 1 - - 6 7 Banda - - - - - 4 3 7 8 Bola boye - - - - 2 - - 2 9 Bori boye - - - - - 3 - 3 10 Chebesha - - - - - 5 1 6 11 Dakuy - - - - - 2 4 6 12 Dapo - 2 - - - - 2 13 Dartho 13 6 1 5 7 - - 32 14 Doni - - - - - 4 20 24 15 Erkabea ------2 2 16 Feda 4 - - 2 - - - 6 17 Geano boye 2 17 8 7 7 - - 41 18 Gesa boye - - - - 2 - - 2 19 Goshitea 12 7 4 6 3 - - 32 20 Gurshume 14 6 2 - 5 - - 27 21 Hati boye - 10 2 1 1 - - 14 22 Karakachi** - - - - - 7 - 7 23 Kerta boye 6 - - 2 1 - - 9 24 Liyan* - - - - - 9 9 25 Mecha boye 3 - 2 - - - - 5 26 Offea* - 18 4 6 4 - - 32 27 Pada 2 - 3 2 - - - 7 28 Sesa** 1 - 1 - - - - 2 29 Torebea - - - - - 4 - 4 30 Tsedeboye - - - - - 2 - 2 31 Wadela boye - 2 2 1 3 - - 8 32 Woko* - - - - - 3 13 16 33 Washinea ------7 7 34 Wayera 6 ------6 35 Welmeka* - 3 7 - 2 - 12 36 Zankur - - - - - 6 - 6 37 Zatemera - - 3 - - - - 3 38 Zawera 4 ------4
* = Aerial yams, ** = Wild yams
34 Table 2.4. Yam landrace diversity in the various districts of Jimma, Sheka and Bench maji Zones of Southwest Ethiopia, expressed as richness, Simpson (1-K) and Shannon (H’) diversity indices and Evenness. Districts Richness % of the No. of unique 1-K ‘H’ Evenness total* landrace Dedo 15 39.47 2 0.90 1.71 0.631 Kersa 10 26.31 0 0.84 2.30 0.998 Manna 17 44.73 2 0.84 1.66 0.585 Seka chekorsa 10 26.31 0 0.74 1.57 0.681 Shebe sombo 14 36.84 2 0.74 1.61 0.610 Sheko 13 34.21 5 0.80 1.69 0.658 Yeki 11 28.94 4 0.84 1.48 0.617 *Calculated on the basis of 38 yam landraces throughout the study districts
From all tested districts, Manna and Dedo exhibited high richness, and have less diversity due to comparatively lower number of unique landraces. The lowest number of landraces, least diverse and none of which was unique were observed in Kersa and Seka chekorsa districts.
To analyze the similarity and associations between districts, Sørenson’s similarity index was intended for all possible pairs of districts combinations based on the presence and absence of yam landraces. The overall similarity of districts ranges from 0.00 to 0.39. Shebe sombo, Seka chekorsa and Kersa were similar districts. Furthermore, Dedo and Sheko, Yeki and Manna were also similar districts. On other hands, Yeki and Seka chekorsa, Sheko and Manna and Sheko and Kersa were the dissimilarly districts. The results highly expressed the geographical differences between the districts. This is true for similar and dissimilar districts. Similar districts belong to the same boundary and have possibilities to share genetic materials between farmers; however, districts not bounded by the same boundary had different landraces. However, the association hardly follow similar tendency, as the most similar districts of Dedo and Sheko, Yeki and Manna were also among those located far apart. In agreement with this, similar trends were observed in regard to similarity and dissimilarity of yam landraces from results of Tamiru (2006) on yams in Southern Ethiopia.
35 Table 2.5. Sørenson’s similarity estimates of yam landrace diversity between different districts of Jimma, Sheka and Bench maji Zones of Southwest Ethiopia
Districts Dedo Kersa Manna Seka Shebe Sheko Yeki chekorsa sombo Dedo 1.00 Kersa 0.20 1.00 Manna 0.31 0.37 1.00 Seka chekorsa 0.28 0.35 0.27 1.00 Shebe sombo 0.25 0.39 0.33 0.35 1.00 Sheko 0.07 0.04 0.03 0.00 0.00 1.00 Yeki 0.07 0.05 0.07 0.05 0.00 0.29 1.00
2.3.2. Distribution of the landraces The distribution of yam landraces in conventional farms is determined by environmental condition and farmers’ preferences. In the present study, fifteen (39.47%) landraces had a narrow distribution and specified a single district. The remaining 23(60.53%) were found in more than one district. In the whole areas surveyed, no common landrace was found in all districts. This confirms the real picture with regard to the abundance and distribution of yams in Southwest Ethiopia. From all the surveyed districts, 5(13.16%) of the landraces (anchiro, dartho, geano boye, goshitea and gurshume) were found in all districts of Jimma zone. However, five (13.16%) of the landraces such as banda, chebesha, dakuy, doni and woko were found only in Sheko and Yeki districts of Southwest Ethiopia. Besides, there were considerable differences with regard to the number of farms where the landrace were found. Anchiro, badaye, geano boye, goshitea, dartho and offea are the most abundant landraces as they covered 50.65% of the total landrace of the surveyed districts.
As indicated in Table 2.3, landrace abundance varied across the tested districts. Few landraces were well represented in some districts, but almost missing in the others. For example, doni was found more than 62.5% of the farms visited in Yeki. Outside of this district, it was only established in Sheko with a very low abundance (about 12.5%). Similarly, offea was found in most districts from Jimma zone and its abundance is high about 42.85% of the farms in Kersa
36 and was narrow in out of this district, for example, 11.42%, 20.0% and 12.90% in Manna, Seka chekorsa and Shebe sombo respectively. Nevertheless, the distribution of offea in Sheko and Yeki was nil. This confirmed that, the abundance and distribution of some landraces were regional and district specific and was found in one region or district and missing in other districts and their distribution is highly associated with geographical variation.
The trends of distribution and abundance of landraces in the districts of Jimma zone was comparatively diverse. The landraces described in these districts were local (78.75%) and rare (21.25%). In most diverse districts of Dedo and Manna, the majority of the landraces were quite distributed with a relatively lower but comparable abundance. This reflected higher evenness of landrace abundance in these districts (Table 2.2). The least diverse district of Sheko was similar to Yeki. The landraces described in Sheko and Yeki are rare, unique and most of the landrace hardly exist in the districts of the Jimma zone. In general, there was a significant and positive correlation between distribution and abundance of the landraces (r=0.78, p<0.01). The variation among districts with respect to the distribution and abundance of landraces were also evident from the number of farms surveyed and corresponding number of landraces recorded.
15(39.47%) 16 14 12
10 8(21.05%) 8 6(15.79%) 5(13.16%) 6 4(10.53%) 4
Number of landraces found landraces of Number 2 0 1 2 3 4 5 6
Figure 2.2. Number of landraces in different districts
37 Yeki Dedo (12.36%) (16.85%)
Sheko (14.6%) Kersa 11.24%)
Shebe sombo (14.6%) Manna (19.1%) Seka chekorsa (11.24%) Figure 2.3. Percent of distribution and abundance of 38 yam landraces in seven districts
Furthermore, variation between zones with regard to the distribution and abundance of landraces also varied. From the total surveyed study areas, 21(55.26%), 5(13.16%) and 3(7.91%) of landraces were found in Jimma, Bench maji and Sheka zones respectively. Besides, some landraces were found between zones of Southwest Ethiopia. For example, 13.16% of the landraces found in areas between Sheko and Yeki, while 7.90% of the landraces in areas between Jimma and Sheko. Additionally, only two landraces were found in some districts of Jimma, Bench maji and Sheka zones of Southwest Ethiopia.
In addition to those described on their farms, farmers orally reported some vernacular names of landraces that were no longer found in their districts/Kebeles and thought to be lost. About 34 of such vernacular names were reported throughout the tested districts. Of these, 11(32.35%) of these names correspond to those landraces encountered on farms of the other households surveyed. The widely distributed landraces, such as, anchiro, gurshume, hati boye, offea and wadela boye were also among those frequently reported orally. The remaining 23(67.65%) of vernacular names were new and never encountered on farmers’ fields of the surveyed districts.
38 2.3.3. Selection, cultivation and management of yam landraces
In the present study, the result of farmers’ evaluation indicated that there are many important practices carried out by farmers’ concerning selection, cultivation and management of yam landraces. Although, selection of landraces can go on throughout the year by observation, intensive selection and planting of the selected material was done during the main rainy season. From the household interviews, 76(31.71%), 49(20.40%) and 29(12.10%) of farmers’ replied, they select and collect planting materials from their family, local market and neighbours, respectively (Table 2.6).
In all tested districts, cultivated on annual cycle of planting in the farmers’ field. It also varied between districts and zones. For example, about 45.45% and 22.72% of farmers’ in Jimma zone planting was done in October and November respectively. Likewise, there were the same trends was follow in Sheko and Yeki districts. In these districts, 96.87% and 85.00% of farmers’ were planted in November and October (Figure 2.4). There is similar tendency with respect to the time of planting of yams in other tested districts of Southwest Ethiopia. The result of this study was similar to the work of Tamiru (2006) who reported, the main time of planting of yams in Southern Ethiopia is in October. Although, in South and Southwest Ethiopia, yam planting is done in October and November (dry season), the seedling actively grow up at the onset of rainy season (March and April). Farmers have their own reason to plant yams in October and November; mainly, to break dormancy of the tuber or bulbils, to solve their storage facility problem after harvest and reduce the cost of planting material at critical time of planting.
Production of yam is severely constrained by the cost of planting material and availability of healthy seed during critical time of planting. In all surveyed districts, there was no formal seed supply system for yam nor do farmers specialize in the production of yam planting materials. Besides, there are no extension services to support farmers to produce yam tuber seed. In the present study, it was observed that farmers get planting material from different sources (Table 2.6) and planted yam on ridges along the rows.
39 Table 2.6. Major sources of the planting materials for field planting of yam as reported by farmers in Jimma, Sheka and Bench maji Zones of Southwest Ethiopia
Source Number of Proportion of households farmers’ (%) Gift from family 76 31.70 Local market 49 20.40 Neighbors 29 12.10 Own harvest + Gift from family 28 11.70 Local market + Neighbors 22 9.20 Own harvest 14 5.80 Neighbors + Gift from family 6 2.50 Local market + Neighbors + Exchange of seeds 4 1.70 Local market + Neighbors + Own harvest+ Gift from family 4 1.70 Local market + Exchange of seeds 3 1.30 Local market + Gift from family 2 0.80 Exchange of seeds 1 0.40 Neighbors + Local market + Gift from family 1 0.40 Exchange of seeds + Own harvest + Gift from family 1 0.40
98.0% 100.00 85.0%
80.00
60.00
32.0% 40.00 13.0% House holds (%) holds House 20.00 11.0% 1.0% 0.00 September October November December March April Time of Planting
Figure 2.4: Critical time of yam planting and percentage of households in Jimma, Sheka and Bench maji Zones of Southwest Ethiopia
40 From all surveyed areas, it was found that 97.8% of the farms, yam established monocropping. Most farmers in the area believed that, intercropping reduces the tuber yield and difficult to apply different management practices during the entire growing stages of yam. Besides, in Southwest Ethiopia, farmers have similar belief that recurrent visit on yam fields are not appreciated due to reduce the growth of tuber. This might be due to, farmers’ in the study areas have long tradition on regularity entrance to yam fields reduce the tuber yield severely, thus, monocropping is the appropriate option for yam cultivation in the areas. On the contrary, in some districts, farmers intercrop yams with cereals and high value crops such as maize [Zea mays (L.)], sweet potato [Ipomoea batatas (L.) Lam.] and coffee [Coffea arabica (L.)]. Farmers explained that intercropping of different species/varieties of field crops helped them to minimize damage caused by pest and diseases. The mix of species and varieties served as a buffer for certain pest and disease problems. Most of the respondents agreed that such constraints to crop production such as frost, weeds, insect pest and disease did not damage all varieties when planted as intercrop. Increasing diversity did not reduce all diseases and pests, but promoted diversity thereby reducing risks and resulting in yield stability. Based on the above results, traditional management of yam genetic resources based on use and preference values could be useful for choosing conservation strategies pertinent to target yam species in a given agro- ecological conditions.
Yam is mainly cultivated along rows of stakes, except the wild yam, where the tubers are brought from surrounding forests. Yam staking significantly increases yield ranged between 33 and 85% (Obiazi, 1995; Afio, 2006; Tewodros, 2012). In the present study, about 57.9% of the farmers used any materials as a stake to support yam during the entire growing period. In some locality, however, farmers used dried coffee as stake, the rest of famers used a combination of different materials to support yam for production. Stacking commences after one months of planting when the vine attains 15-30cm height. Early staking is important to get high tuber yield and highly correlated with the time of planting. In the present study, 47.6% of the farmers use stake at planting and the remaining 52.4% used stake after two months of planting. This result is similar with the work of Ogah (2013) who reported that staking at planting gave the highest grain and tuber yields on African yam beans. Farmers applied different staking methods, in the present study, 86.20%, 2.10% and 8.30% of the farmers used a single stake, fence and combination of
41 both, respectively, however, few farmers (3.4%) used frame as a stake to support many plants simultaneously.
Farmers in the study areas and Sesay et al. (2013) described, producing diversity of yam landraces at household level is seem as a risk minimizing strategy. The available lands for exploitation are not homogeneous (landscape, fertility, moisture content, etc.) and landraces also differ in terms of adaptability to climate variability. In the present study, farmers planted different landraces on their farms. The early, medium and late maturing types usually occupy the same farm as a mixture. On average, 72.51% of the farmers preferred the late maturing landraces, 21.40% selected medium maturing and the remaining 6.09% of the farmers selected early maturing landraces, based on organoleptic taste of the boiled yam. According to farmers who were interviewed, early maturing landraces had high amount of water after boiling, no taste, poor color and quality after cooking. With regard to the total yield, 61.2%, 24.30% and 14.50% of the farmers selected the late, medium and early maturing yam landraces, respectively. Farmers preferred early maturing landraces to get early harvest during seasonal food shortage in the area when other crops are still in the field. Thus, the early maturing landraces are used to fill seasonal gap in food supply. Besides, the first harvest of the early maturing landraces satisfies farmers’ food needs after the long period of scarcity, the second harvest serves to get yield and collect tuber (seed) for the next planting period. So it is necessary for farmers to plant the late maturing landraces to ensure food security during the dry season and reduce the cost of the seed during the critical time of planting. Besides, late maturing landraces are only harvested once, with the large storage tubers being used for consumption and the small ones as seed tuber sources for the next cropping season. Farmers choose their landraces in taking into account factors that may significantly influence not only the yield, but also their management practices (time for planting, conditions and duration of the storage, seed practices, the availability of seed tuber and tubers for consumption and sales) over the whole year. Use of inorganic fertilizer reduces the organoleptic quality of the pounded yam and contributes to the loss of quality. Taking this into consideration, 15.0% and 10.0% of the farmers used manure and compost for yam production and the remaining 75.0% of the farmers hardly used any available fertilizer for yam production. Based on harvesting of yams, 112(46.67%) of the farmers selected early maturing landraces for ease of harvest.
42 In the late maturing landraces, the tuber penetrates deep into the soil and harvesting became tedious, as a result, only 27(11.25%) of the farmers preferred late maturing landraces. The remaining 101(42.20%) farmers selected medium maturing type. There are similar trends in respect to the times of harvest in the study areas. The late maturing landraces are harvested once at maturity (full of senescence), where as the early and medium maturing types are harvested twice and in some cases three times. In the present study, 85% and 98% of farmers planted in October and November, double harvest involved in between the second week of June to end of July. This is achieved by careful digging and removing of the soil to free the tubers, which are then cut 15cm from their point of attachment of the vine. The tubers are then, covered with the soil and the plant is left to form more tubers in different directions. Single harvest requires less effort as tubers are harvested at the end of the growing season. In all surveyed areas, farmers are experienced with regard to minimizing the load of harvesting operation by managing or bending up the tuber at early growth stage.
43 Table 2.7. Criteria used by farmers in the study areas for timing of harvesting
Harvest criteria Proportion of farmers’ (%) Yellowing of leaves, flowering and seed development 15.10 Yellowing of leaves and soil cracking 13.80 Flowering, seed development and soil cracking 10.90 Yellowing of leaves 9.20 Yellowing of leaves and count from the time of planting 8.40 Yellowing of leaves and digging and checking the tuber 5.00 Wilting of vine 4.20 Count from the time of planting, flowering and seed development 4.00 Count from the time of planting 3.80 Flowering and seed development 3.80 Yellowing of leaves and wilting of vine 3.80 Wilting of vine, flowering, seed development and soil cracking 3.30 Digging and checking the tuber 2.50 Soil cracking 2.50 Digging and checking the tuber and count from the time of planting 2.50 Digging and checking the tuber, flowering and seed development 1.70 Count from the time of planting, yellowing of leaves and soil cracking 1.30 Flowering, seed development, yellowing of leaves and soil cracking 0.80 Count from the time of planting + soil cracking 0.80 Wilting of vine, flowering and seed development 0.80 Wilting of vine, digging and checking the tuber, flowering and seed 0.80 development Wilting of vine and count from the time of planting 0.40 Yellowing of leaves, digging and checking the tuber and flowering and seed 0.40 development
In some districts, farmers used plastic sheet with hole and level under the surface of the tuber to grow horizontally. The lateral roots of the tubers easily penetrate into the soil through the hole of plastic sheet to absorb mineral nutrients from the soil for the functioning of the plant.
44 This is also important to reduce the labour cost during the time of harvesting. Farmers are knowledgeable about maturity indices of yam. In most districts, farmers used flower development, soil cracking and counting days from the time of planting are the main signals to estimate the time of harvesting (once, twice and three times) (Table 2.7). The aim is to get more yield and quality tuber in first and second harvest, respectively.
During the entire growing period of the crop, no diseases and pests were observed; as a result farmers did not evaluate the susceptibility and resistance of landraces to diseases and insect pests. However, during the dry season and onset of rainy season, some larvae of yam beetle were observed in the area (personal communication with farmers in the study areas). In all districts, farmers’ selection criterion varied and highly depended on the needs of individual farmers and the availability of planting material. In general, farming communities in major yam producing areas of Southwest Ethiopia managed their cropping systems employing a range of indigenous skills (personal observation).
Weed reduces the quality and the quantity of yield of yam, especially when the plant is at early growth stage. In the present study, Biden pilosa Tufo, Cuscuta, Kelo (aday ababa), Dobi, Zaban, Muja, Serdo and Amarantus are the major weed species that affect the yield of yams in Southwest Ethiopia. This taken into account 51(21.2%), 127(52.9%) and 55(22.9%) of farmers weed their farm lands twice, three times and four times respectively. Hand weeding, early land preparation, plough the land during the dry season, soil burning, application of mulch and combine application of these practices are the best options that used by farmers to reduce the effect of weeds in the area. Generally, the socio-cultural contexts shape the roles of different individuals or groups within a household or community (Arua, 1981; Brydon, 1981; Uzozie, 1981; Bellon, 2001). These socially determined roles affect farmers’ knowledge, actions and access to resources regarding the maintenance of crop diversity (Jarvis et al., 2000). Thus, study on the relevance of socio cultural factors on on-farm crop diversity is important to understand how the social maintenance mechanism of yam diversity in Ethiopia might be enhanced.
45 2.4. CONCLUSION
From the result of the present study, a total of 38 farmers named landraces were identified on farm. Twenty three (60.52%) landraces had narrow abundance and unbalanced distribution and only fifteen (39.47%) of them grew dominantly. Besides, Manna and Dedo exhibited high richness, and have less diversity due to comparatively lower number of unique landraces. Conversely, the least diversity were observed in Kersa and Seka chekorsa districts of Southwest Ethiopia.
Management of yam diversity is mainly done by farmers, based on their indigeneous knowledge. Consequently, analyses of different socio cultural, economic and agronomic factors could play a significant role in yam diversity management in Southwest Ethiopia.
The use of local landraces has valuable impact to study the diversity of the crop in traditional farming system. Hence, the indigenous knowledge of yam and local landraces must be collected, analyzed and properly documented for use to enhance the research and development program in the country.
Conservation and use of yam genetic resources are an important aspect for sustainable utilization of genetic diversity. To do so, understanding the extent and distribution of diversity within and between species through molecular and bio- chemical characterizations are vital for clarification of synonymies and identification of duplicates for conservation and development of a participatory varietal selection of yam in Southwest Ethiopia.
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50 III
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FARMERS’ PERCEPTION FOR CLASSIFICATION, SPATIAL DISTRIBUTION AND GENETIC EROSION OF YAM (Dioscorea spp.) LANDRACE COLLECTIONS OF SOUTHWEST ETHIOPIA ______
51 Farmers’ Perception for Classification, Spatial Distribution and Genetic Erosion of Yam (Dioscorea spp.) Landrace Collections of Southwest Ethiopia
ABSTRACT
The study was conducted to assess farmers’ classification system, spatial distribution, quantify the rate of genetic erosion and identify major causes of genetic erosion on yam landraces and its distribution in Southwest Ethiopia. To this end, a field survey was conducted on 240 households from seven districts of Jimma, Sheka and Bench maji zones from April to December 2015. Questionnaire was used to collect primary data from an average of 34 farmers who are potentially rich sources of information on yam at district level. Additional data were collected through group discussions and key informant interview. The results of the study revealed that, farmers’ classification of the landraces varied and depended on the domestication status (wild and cultivated), sex type (male and female), use value (food, medicine and market value) and time of maturity (early, medium and late). The distribution of the landraces per district varied from 30 to 42 with 34.28 on average. The lowest distribution was observed in Seka chekorsa and the highest in Kersa districts in the Jimma zone of Oromia region. At Kebele level, the number of landraces was varied from 6 to 21 with 10.90 on average. The lowest diversity was observed in Gube muleta (Manna) and the highest in Boye kecema (Seka chekorsa) in the Jimma zone. The rate of genetic erosion at district and Kebele levels varied from 28.80% in Yeki to 57.93% in Kersa districts and 0% in Gubea muleta to 25% in Mehal sheko Kebeles with an average rate of 44.48% and 14.1% respectively. Number of farmers growing yam landraces decreased drastically in all surveyed areas in the past decades. Low attention given to the crop (95%), drought at early stage (93%), porcupine attack (90%), shortage of farm land (74%), displacement of landraces by high value crops (72%), were the prominent factors for ending landrace cultivation. Moreover, farmers’ preference to yield potential and cash crops subsequently reduced the chance of maintaining landraces. Accordingly, in this study, the conceptual framework for analyzing farmers’ classification system, spatial distribution of yam and factors that causes of genetic erosion was assessed in a systematic way.
Keywords: Conservation, districts, genetic erosion, households, survey.
52 3.1. INTRODUCTION
Yams (Dioscorea spp.) constitute a diverse group of plant species widely distributed throughout the humid and semi-humid tropics (Alexander and Coursey, 1969; FAO, 2012). Most Dioscorea species exhibit considerable morphological variability both in the aerial and underground tuber. Thus, morphological descriptors and farmers’ indigenous knowledge have been widely used in the study of yam, specifically to determine the relationships, classification and spatial distribution of the various species and landraces (Loko et al., 2013; Dansi et al., 2013a). Landraces are the result of selection from centuries by farmers and are a major source of genetic diversity in agriculture providing much of the genetic resources for plant breeding (Mashilo et al., 2015). However, study on landraces and their use by farmers are problematic in that the vernacular names used for landraces vary greatly and are not consistent (Demuyakor et al., 2013). Popular landraces can have several names even within the same district/Kebele and different landraces may have the same name (Mekbib, 2007). Thus, assessing the classification and spatial distribution of landraces in relation to vernacular names are imperative. Besides, determination of vernacular names of landraces with the phenotypic and genetic characterization with farmers’ indigenous management system of their plant genetic resources is of paramount importance (Mignouna et al., 2002; Siqueira et al., 2014).
In Ethiopia, there is large pool of yams that are widely distributed in major growing areas in complex cropping system with wide genetic base (Edward, 1991; Miege and Demessew, 1997; Hildebrand, 2003). Farmers have their own descriptor for classification and management of their landraces. Like other indigenous technical knowledge, folk taxonomy might have a problem of consistency. Hence, consistency of the naming system was the key issue for validation (Mekbib, 2007). In the present study, the existing local classification system is consistent to some extent with conventional botanical classification. As traditional farmers’ numbering in the millions have turned away from their traditional landraces, the knowledge of how to maintain the selected landraces that performed well in particular habitats and conditions has fallen victim to even greater erosion than the landrace itself. While plant collectors have managed to salvage some of the abandoned genetic diversity, the knowledge that produced and maintained the diversity over many generations remains on site and has only rarely been recorded in connection with the collection of landrace for ex situ storage (Friis Hanse and Guarino, 1995; Loko et al., 2015). The
53 erosion of indigenous knowledge which accompanies genetic erosion may be as damaging to the local community as the loss of the genetic material itself.
In Ethiopia, it is generally accepted that the indigenous yam genetic resources are becoming seriously endangered owing to the high rate of genetic erosion resulting from natural calamities, displacement of yams by high value crops, changes in production systems and markets preferences (Hildebrand et al., 2002; Megersa, 2014). Besides, climate change and the availability of very limited funds for conservation have largely increased the genetic vulnerability of yam in the country. Moreover, limited attention has been given to assess the diversity and conservation of indigenous yam genetic resources and research is a rudimentary stage for identification, classification, description and evaluation of the available genetic resources for different utilization options. Furthermore, the causes and effects of the genetic erosion of plant genetic resources are poorly understood in Ethiopia (Megersa, 2014). As a result, some of yam genetic resources in Ethiopia are in danger of extinction, and unless urgent efforts are taken to characterization, evaluation and conservation, they may be lost even before they described and documented. In addition, the majority of yam genetic resource diversity found in the country, where documentation is scarce and risk of extinction is the highest and increasing. Besides, genetic erosion of agricultural crops on farmers' fields receives less media attention even though, it is of far greater importance to the livelihood of millions of farmers, however, within the international community concerned with conservation and use of plant genetic resources, the causes and effects of the genetic erosion of agricultural crops and possible ways of limiting such erosion have been heatedly discussed during the UNCED conference in 1992 (in the preamble to the Convention of Biological Diversity (UNCED, 1992) and in particular loss and threat to crop species has received much attention in recent years and there are often widely differing views on the issues involved.
To address these constraints, genetic control through use of tolerant landrace are necessary (Yifru and Karl, 2006). Such landraces are expected to be found within the existing yam diversity in Ethiopia, which are yet to be studied. Moreover, the diversity of yam landraces maintained per districts and per households throughout agro-ecology and administrative zones have never been assessed and the rate of landrace loss at national and regional levels is still unknown. The name of the existing landraces hardly recognized; the ethno botanical, farmers’
54 classification system and the spatial distribution of landraces per growing district by scientific research hardly assessed and it limits producers and researchers to access yam genetic resources in Ethiopia. Consequently, estimation of spatial distribution, diversity of yam, ethno botany and management of the existing diversity is crucial for conservation and sustainable utilization of yams in Ethiopia. Accordingly, the objectives of this study were: to assess farmers’ classification system, estimate the rate of landrace loss (genetic erosion), analyze its spatial distribution and variation of yams across study districts and Kebeles and identify major factors that causes of genetic erosion in Southwest Ethiopia.
3.2. MATERIALS AND METHODS
3.2.1. Selection of study sites and farmers For this study, a total of seven districts were assessed from the major yam growing areas of Jimma, Sheka and Bench maji zones of Oromia and Southern Nation Nationalities and People Regional State (SNNPRS) from April to December 2015. Accordingly, five districts namely Manna, Dedo, Shebe sombo, Seka chekorsa and Kersa from Jimma zone and two districts namely Sheko and Yeki from Bench-maji and Sheka zones, respectively, were selected. From each district, on average thirty four farmers, 15 to 20 yam producers, ten key informants and five DAs were sampled from different social groups for individual interviews, group and key informants’ discussions. The key informants were selected in order to conduct in depth interview and discussion. They were selected from household heads of both sexes and different age groups based on their availability, willingness and practical knowledge on yam genetic resources in the areas.
The local administrators and agricultural extension workers helped in identifying the names of the focus groups. Field visits (home gardens, cultivated fields) were conducted to perceive some of the species under cultivation. The household characteristics of the surveyed districts are presented in Table 3.1.
55 Table 3.1. Household characteristics of the surveyed districts
Districts No. of Religion Sex Mean Mean Mean farmer Ortho Mus Eva Male Female age of family farm s farmers size size(ha) Dedo 38.0 20.0 8.0 10.0 28.0 10.0 55.08 8.00 1.63 Kersa 42.0 22.0 13.0 7.0 30.0 12.0 47.83 6.19 0.86 Mana 35.0 9.0 22.0 4.0 28.0 7.0 47.46 6.08 1.47 Seka chekorsa 30.0 10.0 17.0 3.0 27.0 3.0 51.67 6.80 1.44 Shebe sombo 31.0 19.0 7.0 5.0 23.0 8.0 52.35 6.90 1.40 Sheko 32.0 8.0 0.0 24.0 22.0 10.0 45.62 6.43 1.13 Yeki 32.0 15.0 2.0 15.0 24.0 8.0 50.11 6.80 1.51 Total 240.0 103.0 69.0 68.0 182.0 58.0 350.12 47.20 9.44 Mean 34.28 14.71 9.85 9.71 26.0 8.28 50.01 6.74 1.35 Ortho= Orthodox, Mus =Muslims, Eva= Evangelical Christian
3.2.2. Data collection Data were collected from the different areas and institutions through the application of Participatory Research Appraisal (PRA) tools and techniques such as direct observation, individual interviews, key informants and focus group discussions using a well prepared questionnaire and checklists. Different households from each district were selected and interviews were conducted with the help of local translators. From each district, the selected farmers were requested to bring samples of the yam landraces they produce or knew. Through discussion, some key information was recorded on each of the landraces identified. These information includes, local vernacular names, adaptability, use value, time of planting and maturity of the landraces. Each landrace was properly evaluated based on, extent of the production, distribution, degree of consumption, perceived nutritional value, cultural importance, sex type, medicinal properties, market preferences, market value and contribution to household income.
The distribution and extent of diversity of the landraces were assessed using the four cell sometimes called four squares analysis approach described by Loko et al. (2013) at
56 district/Kebele level in the participatory way, to classify and assessed the distribution and extent of diversity of the identified landraces into four groups based on area apportioned to the landraces and the relative number of households cultivating it. These were landraces cultivated by many households on large areas; landraces cultivated by many households on small areas; landraces cultivated by few households on large areas and landraces cultivated by few households on small areas. Besides, reasons of that explanation for each landrace by many or few households and on large or small areas were discussed and documented. Later on, the names of the landraces that have completely moved out from the areas were recorded and the causes of their abandonment are identified. It is the most important way to reduce further genetic erosion of yam in the areas. In addition, the distribution and association of factors that causes of genetic erosion at district/Kebele level were assessed through four cell analysis in the participatory way to apply conservation measure.
For farmers’ classification system, the identified yam landraces were assessed against domestication status, sex type, agronomic characteristics, use value, maturity and culinary traits of economic importance (Table 3.2) using participatory approach. Additionally, data on indigenous knowledge and experiences of local farmers those considered to be more knowledgeable on the distribution, variability within the landraces and on genetic erosion at the landrace level were collected. Information such as change in cropping systems and reasons for change, adoption of improved technologies of yams, assessment of the yam seed supply systems, change with regard to the use of landraces, trends of yam cultivation in the area, availability of extension services on yam and farmers’ perception about comparative advantages of landraces were assessed. At individual, group and key informant levels, the discussions were free and open ended, without a time limit being set, following the method described by Dansi et al. (2013a). Information obtained from key informants are valuable to cross check and clear contradictory ideas on the existing yam diversity, farmers’ classification system and distribution of yam throughout the study districts. Furthermore, data were collected from secondary sources like district agricultural offices, reports of extension and Institute of Biodiversity Conservation (IBC) at Jimma zone, to examine the extent of possible genetic erosion occurred in the last decades temporal comparison was used.
57 3.2.3. Data analysis
The collected data were analyzed through descriptive statistics (frequencies, percentages, means, etc.) to generate summaries and tables at different (Kebeles and districts) level using Statistical Analysis System (SAS) package (version 9.0 of SAS Institute Inc, 2000) and SPSS (1996) version 16 (Statistical Packages for Social Sciences). The rates of landraces loss (RLL) were calculated by using the formula described by Kombo et al. (2012) with some modification to assess the loss per study district and Kebele level.
RLL = ( ) Where n= numberx 100 of landrace cultivated by few households on small areas with in a district/kebele, k= number of newly introduced landraces in kebele/district and N= total number of landraces recorded in the district/Kebele level.
To analyze the spatial distribution of the 38 identified yam landraces at districts and Kebele level, the principal component analysis tool was adopted by using Minitab statistical software (Minitab, 2010 version 16). Similarly, the degree of similarity and association between explored yam landraces and 12 farmers identified causes of genetic erosion, the principal component analysis was also adopted based on likert scale method (Likert, 1932) (ranked 1-3, where, 1= factor that have low contribution to genetic erosion, 2= the causes that have medium contribution to genetic erosion and 3= the causes that have higher contribution to landrace erosion) and clustering of identified landraces computed by Minitab statistical software (Minitab, 2010 version 16) using the simple matching coefficient of similarity and a dendrogram was constructed with Un-weighted Pair group Method with Arithmetic average (UPGMA) to examine the relationships between identified landraces with factors of genetic erosion for conservation.
58 Table 3.2. Parameters used for the participatory evaluation for classification of yam landraces in Southwest Ethiopia.
Variables Parameters Scoring Domestication status Cultivated 0 (cultivated) Wild 1 (wild) Sex type Male 0 (Male) Female 1(Female) Agronomic Tolerance to drought 1 (tolerant) – 0 (susceptible) Productivity 0 (low )-1(high) Seeds production rate 0 (low)-1(high) Adaptability of wider environment 0 (non adapted)-1 (adapted) Staking demanding 0 (low ) 1(high) Tolerance to weeds 0(susceptible) 1(resistant) Use value For food 0 (no) - 1(yes) For medicine 0 (no) - 1(yes) For market 0 (no) - 1(yes) For food and medicine 0 (no) - 1(yes) For food and market 0 (no) -1(yes) For food, medicine and market 0 (no) -1(yes) Maturity Early 0 (Early) Medium 1 (Medium) Late 2 (Late)
59 3.3. RESULTS AND DISCUSSION
3.3.1. Socio economic characteristics of surveyed yam farmers The results of the present study indicated that, yam production is a male (75.83%) dominated activity. The high percentage of male farmers may be due to their access to farmland and their position as head of family. In this regard, similar study was conducted by Olweny et al. (2013) who concluded that farming is a male dominated profession. In all surveyed area, about 99% of the farmers were married; may be as a result of the belief of the areas that, married people are more accountable to control and manage farming. The mean age distribution of the farmers between districts revealed that the highest (55.08) age was recorded in Dedo and the lowest (45.62) age in Sheko. In this study, 42.91%, 28.75% and 28.33% of the farmers were found to be Orthodox, Muslim and Evangelical Christian, respectively. The educational background of the farmers showed, 102 (42.5%) of them are illiterate with no formal education, 116 (48.33%) of the farmers had basic education (primary school level), whereas 22 farmers (9.17%) received secondary education. In each district, almost all farmers’ have the same knowledge with regard to the name of landraces cultivated in their Kebele. This provides information for a good setting to access the diversity, distribution, management, classification, selection and evaluation of yam in traditional agriculture using naming landraces with a minimum influence of language polymorphism within the farmers. More than half of the surveyed producers (73.2%) had 8-25 years of experience in yam production and 26.8% had between 26 and 43 years of experience. In a cross tabulation in Table 3.1 it is shown that 82.5% of the farmers hold average farm size more than one hectare. Very few (17.5%) of the farmers in Kersa had 0.86 ha. Based on household family size, in all surveyed areas almost all the farmers had similar average household family size (Table 3.1).
3.3.2. Identification, naming and classification of landraces In the present study, farmers distinguish each landrace from the other based on three main categories namely; landrace identification, naming of the landraces and classify them.
60 3.3.2.1. Identification of yam For identification, farmers used their own local descriptors (Table 3.4). Those descriptors are related to: morphological characteristics (vine color and length, twinge direction, tuber color, shape and string on flesh tuber), agronomic characteristics (tolerant to drought, disease and pests, maturity time) and use value (food, medicine and market). Besides, farmers also use, tuber size and shape and tuber surface color for identification. The local farmers sometimes used the combinations of descriptors for identification characteristics; they referred first to the morphological characters of a landrace. In most cases the descriptors that are related to the use value, culinary quality and agronomic characteristics came only after morphological characteristics (Brush et al., 1981; Dansi et al., 2013a). In the present study, 64%, 52% and 42% of the farmers mentioned descriptors for identification of landraces for tuber color, times of harvesting and twing direction respectively. Some descriptors (tuber flesh and surface color) were used for the identification of a limited number of landraces. For example, sharp and square vine with white tuber flesh, as descriptor was used specifically for landrace badaye to distinguish from the other landraces.
3.3.2.2. Naming of yam
In all study districts, famers’ give separate vernacular name for each landrace they grew. The names are often descriptive and reflect the variations of landraces in places of geographical origin, morphology, agronomic and culinary characteristics. Most of the time, farmers’ tie up the names of places in neighboring districts to the names of the landrace; for example, in the present study, the name of landrace pada was originally collected from Dauro zone of Southern region by Dedo farmers and had white flesh color, therefore, pada sometimes called Dauro white in Dedo district. Moreover, the naming may include the indication of physical entities. In other instances, farmers exactly used words to describe the specific morphological, agronomic and cooking quality attributes of the particular landraces (Magule et al., 2014). From all surveyed districts, a total of 38 farmers’ named yam landraces were identified and the attributes of each landraces are presented in Table 3.3.
61 Table 3.3. The names and major attributes of yam landraces identified by farmers
Name of Major attributes Name of Major attributes landraces landraces Afra White and red mixture flesh Gurshume Deep purple flesh color Thorney tuber and vine Used as a medicine Used as food and medicine Late maturing type Medium maturing type Resistant to drought Anchiro White tuber flesh color Hati boye White tuber flesh color More palatable and broad More palatable and used as a leaves food Early maturing and female Early maturing and female type Badaye Early maturing type Karakachi Vigorous growth and large tuber White, purple and white Thorns on vine and tuber purple flesh color White yellowish tuber flesh Thick/sharp and a square Wild type vine Badenseye White and black mixture Kerta Resistant to drought flesh type boye Big tuber size Testy and female type Medium maturing type Baki boye Thick vine Liyan White aerial bulbils flesh Used as food and medicine Big tuber and testy Medium maturing type Used as food and medicine Bambuche Long dark green leaves Mecha White tuber flesh color Big tuber size boye More palatable and used as a Female type food Early maturing and female type Banda Mixed black and white flesh Offea Purple aerial bulbils flesh color Used as a food and medicine Small tuber and testy Bola boye Highly branched tuber Pada Early maturing type Big tuber size White tuber flesh color Female type Tasty and female type Bori boye Large dark green leaves Sesa Wild type Early maturing Thorny on vine and tuber White tuber flesh color White tuber flesh color Chebesha Used as a food & more Torebea Late maturing type palatable Used as food and medicine Early maturing and female Female type Low water holding content after boiling Dakuy Deep red tuber flesh color Tsedeboye Black tuber flesh color Big tuber size and used as Big tuber size medicine Male type Medium maturing type Late maturing type
62 Table 3.3. Continued
Name of Major attributes Name of Major attributes landraces landraces Dapo White yellowish tuber flesh Wadela Deep purple tuber flesh color Female type boye Used as a medicine Dartho Purple and white purple flesh Woko White bulbils flesh color color Big tuber and testy Resistant to drought Early maturing type Female type Used as food and medicine Doni Variegated tuber flesh color Washinea Large and black flesh yam (white and red) Thorns on tuber surface Early type Resistant to drought Used as a food Male type Erkabea Outer deep purple and inner Wayera Vigorous growth and spiny white flesh vine Used as food and medicine White flesh color Female type Feda White yellowish flesh color Welmeka Variegated bulbils flesh color Big tuber size (white and red) Female type Big tuber and testy Geano Large and deep red tuber flesh Zankur Deep purple flesh color boye Used as a medicine Used as a medicine Male yam Resistant to drought Late maturing type Male type Gesa boye Variegated tuber flesh color Zatemera Small dark green leaves (purple and white) Resistant to drought Goshitea Small leaves and high density Zawera Outer surface of the tuber Late maturing deep purple and inner white Female type flesh Big tuber and testy Female type
3.3.2.3. Classification of yam Farmers use folk classification systems in order to group their landraces into different categories such as, domestication status, sex type, use value and time of maturity to classify the landrace (Table 3.4). Farmers distinguish two yam types on the basis of their domestication status used wild or cultivated. During in the key informants discussion and agricultural expert elicitation sessions, participants indicated that it is very unlikely to find wild yam in most study districts.
63 However, wild yams only grow naturally in forest areas of Sheko (karakachi) and Manna districts (sesa) of Southwest Ethiopia. Due to its dioecious nature, classification of landraces as ‘female and male’ is an interesting aspect of the local classification system. Careful scrutiny of farmers’ account of the two categories indicated that this grouping reflects more than mere differences in agro-morphological traits and ecological adaptation. It appears that the system has also a bearing on the society’s perception of sex type and its role. This sex type related categorization of crop in the study area is peculiar to yam. A similar system also exists for enset in Sidama and Wolaita, where local landraces are separated as ‘male and female’ partly based on the shape and size of pseudo stem (Tsegaye, 2002; Magule et al., 2014). Tsegaye (2002) reported sex type based classification of enset landraces in Kefa shaka and Sidama zones of South and Southwest Ethiopia. The detail description of the major attributes of the two groups, pointed out that such category reflected the fact that men and women prefer different qualities in their landraces. Moreover, the female characters are highly related to consumption qualities. These findings further reveal the complex nature of folk taxonomy and support the view that crops are biological as well as cultural entities, which makes the study of both evolution and society possible through diversity (Brush, 2004). In this study, farmers classified yam as 'female' yam (hati boye) and 'male' yam (geano boye). The distinction as 'male' and 'female' is not related to the biological reproduction of the landraces. Of the total 38 landraces identified, 9(23.68%) were classified as 'male', 16(42.11%) as 'female' and the remaining 13(34.21%) landrace had unclear sex designation, some farmers claiming them 'male' and others claiming them 'female'. Based on use value, farmers classified the landraces in two comprehensive use groups: food use and medicinal value. Although most landraces can be used as both for food and medicine, there are preferences for specific landrace among the societies for scrupulous purposes. In all districts, farmers planted the landraces primarily for food and others for medicinal uses. In some districts, farmers used yam as a source of fill economic gaps during the off season.
Mature yam is generally distinguishable by cessation of vegetative growth, yellowing of leaves, seed and flower development. The period from planting or field emergence to maturity is the cause of variability of yield and highly dependent on the species. The most frequently reported measures were done from the period of planting to harvest (growing period), but it has been suggested that the time from emergence to maturity provides a better measure of growing period
64 (Onwueme and Charles, 1994). In this study, yam landraces were categorized into three maturity groups: early, medium and late maturing groups. Based on farmers’ recognition, early maturing landraces mature within a short period (four to five months after emergence) of time. Farmers’ used these landraces to fill their seasonal food and economic gaps. Medium and late maturing landraces mature 6-8 months and more than 8 months after emergence, respectively. Selection based on maturity also varies within yam species. For example, D. Cayenensis matures within 280-350 days (9-11) after planting and D. rotundata matures within a range of 200-330 days (6- 11) after planting (Onwueme and Charles, 1994). The yield (tuber and bulbils) of the landraces varied depending on their maturity group. For example, early maturing landraces produced relatively smaller yield than those of late maturing yam landraces.
Table 3.4. Folk classification of yam landraces in Southwest Ethiopia
Folk classification Categories Characteristics of landrace in each Landraces in each bases category category (%) (N =38) Domestication status Wild Sexually reproduced; occurring 2(5.26%) naturally in forest areas; Cultivated Vegetative propagated: it occurs 36(94.74%) in home garden under farmers' management condition Sex type Female Early maturing, more tender, with 16 (42.11%) edible storage tuber Male Late maturing, vigorous, drought 9(23.68%) tolerant, with medicinal tubers Un clear Some of them are medium 13(34.21%) maturing yams used to fill seasonal food and economic gaps Use-value of Food use Mainly used for yam based foods NA landrace Medicine Mainly used as medicine NA Both Mainly used as a food and NA medicine Maturity Early Mature within 4-5 months 18(47.37%) Medium Mature within 6-8 months 11(28.95%) Late Mature more than 8 months 9(23.68%) NA- Not available
65 In this study, 18(47.37%), 11(28.95%) and 9(23.68%) of the landraces were identified as early, medium and late maturing yam landraces, respectively. For the sake of times of harvest, both early and medium types harvesting was done twice while for the late type only one harvesting was done once.
3.3.3. Assessment of spatial distribution and diversity of yam At Kebele level, the number of landraces varied from 6 to 21 with an average of 10.90. The lowest diversity was observed in Gube muleta (Manna) and the highest in Boye kecema (Seka chekorsa) in Jimma zone of Oromia region (Table 3.5). The Principal Component Analysis (PCA) was also carried out to assess the association and distribution of the identified landraces in the surveyed districts for conservation. The results clearly showed that the amount and distribution of landraces in study districts of Southwest Ethiopia.
The first two principal components scores 37.4% (PCA-1) and 57.5% (PCA-2) of the total variation are presented in (Figure 3.1). The distribution of 38 yam landraces resulted in partitioing of chebsah, afra, banda, dakuy, woko and tsedeboy, from other landraces, mecha boye, gurshumea, bambuche, hati boye, bola boye, anchiro and zatemera assigned them into the negative and positive direction of the first component respectively. Being a negative or positive direction has nothing to do with values, it does show their association i.e. contribution to the distribution of respective districts (Figure 3.1). District such as Yeki showed higher score in the negative direction of the first components showing their strong association with early and the white flesh landraces of badaye. Attributes such as use value for food and medicine and yield and taste, was also associated with chebsha and woko and liyan. Farmers described woko for its quality food and medicine. They also mentioned that woko and liyan grows commonly in the low and mid altitude areas and had better adaptation to moistures stress and produce bulbils after five to six months of planting. This result was supported by earlier study by Ketema (1997) who indicated that local cultivars of tef such as gea-lamie, dabi, shewa-gimira, beten and bunign, to be early maturing varieties (<85 days), and are widely used in areas that have a short growing period due to low moisture stress or high temperature.
In the positive and negative direction of the first component covers 71.5% of the total distribution of the landraces that adapted to Dedo, Shebe sombo, Kersa, Manna and Seka
66 chekorsa districts of Jimma zone (Figure 3.1). Landraces distributed in this axis indicates high associated with low and mid altitudes, white, red and combination of both flesh color and have double harvest. Most of the landraces are used as food, have high market value and resistant to disease and pests. This result is in agreement with the findings of Gizachew (2000) and Addis (2005) who indicated that landraces nobo, mezya and henewa are tolerant to enset bacterial wilt and mealy bug and adapted variable environments.
The second component departed landraces such as badaye, dakuy, woko, feda, goshitea, mecha boye, gurshumea, bola boye, banda, and tsedeboy in the negative direction from chebsah, afra, mecha boye, gurshumea, hati boye, anchiro, beki, bambuche, geno boye and zatemera (assigned in the positive direction). Landraces that are found in the positive direction of the biplot highly adapted variable environments of the districts especially, Dedo, Shebe sombo, Seka chekorsa, Kersa and Sheko. According to the farmers’ attributes such as earliness, yield, drought tolerance, different tuber flesh color, multipurpose use, good taste and market value are contributed more to the distribution of the landraces. Whereas, wildness, single and double harvest, use for food, medicine and market contributed more to the negative direction of the second component.
The spatial distribution of landraces indicated specific interactions between landraces and their areas of collection. Pada, anchiro, bambuchea, sesa and bola boye were the landraces with stable for variable environments, as the points for these landraces were close to the origin of the biplot. On the other hand, landraces liyan, badensey, erkabea, baki boye, washinea, badaye and woko had adapted specific environments especially Sheko and Yeki districts of Southern Ethiopia (Figure 3.1). This result is in line with the report of Hildebrand et al. (2002) who confirmed four Dioscorea species which were found in Sheko district. Besides, Miege and Demessew (1997) reported eleven Dioscorea species, both wild and cultivated in Ethiopia. Based on the above results, it is possible to predict the extinction of the existing landraces in Southwest Ethiopia.
67 0 3 Sheko Afra 2 Seka Chekorsa OffeaWadelaGeano boyeboye TsedeboyeKarakachiBoriTorebeaZankur boye HatiWelmeka boye Chebesha 1 ZatemeraDapo GesaBola boyeBambucheShebe Sombo Banda Dedo Anchiro 0 0 0 5 Pada
. Badaye
7 Dakuy Sesa 5 Goshitea GurshumeDartho = -1 WokoDoni Mecha boye I Baki boye I BadenseyeWashineaErkabeaLiyan Feda WayeraZaweraKerta boye C
P -2
-3 Yeki Manna -4
-5 -5.0 -2.5 0.0 2.5 5.0 PC I=37.40 Figure 3.1: The spatial distribution of 38 landraces in seven districts
The present study showed that yam landraces grew in few districts and distributed in small areas exposed to erosion unless urgent control measure are applied. This result was also supported by Loko et al. (2013) who reported that yam cultivars produced by few households and distributed on small areas considered as threatened last generally only five years and most often, effectively disappear after this period from the villages where they identified. If this trend continues, the indigenous yam diversity could be lost in the near future. This threat is also in line with the FAO report (FAO, 1999), which states that genetic resources in developing countries in general, are being eroded through the rapid transformation of the agricultural system, in which the main cause of the loss of genetic resources is the indiscriminate introduction of exotic genetic resources, before proper characterization, utilization and conservation. Besides, changes in production systems, market preferences, environmental hazards, natural catastrophes and the availability of very limited funds for conservation activities have reduced the diversity of yams genetic resources in the country (Loko et al., 2015).
In order to address these constraints, there is a need to establish an integrated and concerted conservation program through the use of yam diversity that includes, but not limited to, landrace exchanges (genetic enrichment) among districts and Kebeles, diversity fairs, community gene banks and introduction into certain districts of good performing cultivars identified within the
68 existing diversity through participatory evaluation. Furthermore, there is a need to enlarge the knowledge base of the crop through studies on diversity and use of the available landraces.
In the present study, all landraces may be locally bred and a few imported from elsewhere meaning there has not been any cross breeding with foreign landraces. This might be due to, crops that were originally domesticated outside of the East African exhibit extreme secondary diversification in Ethiopia (Harlan, 1969; Vavilov, 1951). Besides, Zeven and De Wet (1982) and Tamiru et al. (2011) further reported that Ethiopia as a centre of origin and diversity of many cultivated species including yams. Furthermore, since evolution outside the centers of origin has resulted in different genetic constitution of the materials, it can be argued that these materials originated from the farms where they were further shaped and maintained. Scarcelli et al. (2011) reported that yam reproduce sexually and have evolved by mutation. Sexual reproduction involves the combination of genes, and results in more diverse population and able to adapt variable environmental conditions (Himanshu et al., 2016). When plants reproduce sexually some of the offspring might possess genes that give them resistance to a disease and adaptation to environmental extremes (e.g. drought, high soil salinity, etc.). Through natural selection that acts entirely by the preservation and buildup of variations, these plants are more likely to survive such conditions to reproduce and to pass on their genes to the offspring for fitness(Dumont et al., 2005). Sexual reproduction nature of yam enables to fasten favorable combinations of important traits, and superior genetic variance interactions. It also maintains high levels of heterozygosity necessary for high hybrid vigor. Apart from the environment, and evolutionary processes, the genetic variation found within yam landraces in Ethiopia has been also shaped over time by farmers. Gene flow through seed exchange between and within farmers enhances novel variation and recombination, as well as differentiation among landraces (Dumont et al., 2005; Dansi et al., 2013a). The combination of human mediated and natural selection continues as farmers select for traits of interest, while the environment selects for traits that boost fitness.
Principal component analysis was also used to understand the distribution of landraces at Kebele level. It is evident from previous descriptions that changes were observed for certain landraces when analyzed individually. Figure 3.2 reveals, distribution of landraces and their distant positions from their area of collection in the biplot. Most of the landraces were distributed from the origin. The first principal component had a variation value (Eigen value) of 6.2643 and
69 explained the 39.2% of difference among the total landrace distribution. The coefficients listed under PC1 showed the relation of the landraces in regarding to distribution. The landraces distribution of afra, badaye badenseye, baki boye, banda, boriboye and chebsha etc. are limited on six Kebeles (Figure 3.2). On the contrary, the negative coefficients expressed the more dispersed landraces in different Kebeles. The 2nd principal component consisted of variance values (Eigen value) greater than 2.7168 and accounted 17.0% of the difference among the total landrace distribution at Kebele level. Together, two principal components represented 56.20% of the total variability among the total landrace distribution. Thus, most of the data structure can be captured in some underlying variations. The remaining principal components account 43.80% of the variability.
Within all study districts and Kebeles, some yam landraces had early maturing or double harvest that produced two or more large sized tubers and some late maturing or single harvest landraces that generally produced big tuber size in relatively lower numbers per mound. This result disagrees with the study reported by Dumont in Togo (Dumont et al., 2005). In all surveyed districts, early maturing landraces significantly outnumbered than late maturing ones. At Kebele level, there were on average 2.5 late maturing landraces against 8.4 early maturing landraces. However, this proportion varied across districts and Kebeles (Table 3.5). In some Kebeles like Ankaso for example, there were late maturing and early maturing landraces (9 against 12). In contrast, in most districts, early maturing landraces that out numbered than the late maturing ones while, in Sheni qoche, both landrace types appeared almost the same in numbers (3 and 4). In all the districts, the dominance of early maturing landraces over the late maturing ones was observed (personal observation). Labor shortage during the time of planting and harvesting are the common phenomena in yam production in most surveyed districts. Due to such circumstances, some farmers enforced to harvest yams only once and considered this as the best solution. In some surveyed districts, farmers’ who characterized late maturing yams for local markets (seed) and for medicinal use. Therefore, the late maturing yams in some districts may be linked to a cultural preference coupled with commercial reasons.
The study revealed a differential distribution of the two classes (late maturing or single-harvest; early maturing or double harvest) of yam across the surveyed districts and Kebeles.
70 Figure 3.2: The spatial distribution of 38 landraces at 22 Kebele Unlike the Sheni qoche, Addis alem, Afolea dawea and Beda buna, the two classes appeared in the same proportions; however, in most study Kebeles (Marewa, Bilida, Kishea, Gaziqa and Addis berhan) landraces are dominated by the early maturing (double harvested) yams. In Kishea (Shebe sombo) are seriously exposed to drought, the result obtained was expected as cultivation of late-maturing landraces is somehow risky. Farmers in Yeki district had some recognized cultural and long standing expertise in processing tubers into dried yam chips for local markets and better chips are obtained from tubers of the single-harvest landraces. In most districts, predominance of early maturing yam has no other explanations than food security and market oriented (being sold in the major towns). At harvest, the first yams that appear on the markets such as landrace badaye is always more expensive and contribute more to household incomes. In this regard, similar results reported on cassava in Congo (Kombo et al., 2012) and on yam in Togo (Dansi et al., 2013b). The distribution and extent analysis also revealed that most of the existing landraces are being disappearing and the efforts on production at Kebele level seems to be concentrated on small numbers of economically important landraces such as: high yielding, tolerance to diseases and pests, good culinary characteristics, cultivated by many households and on large areas.
71 Table 3.5. Distribution, extent of analysis and the rate of landrace loss in the study districts and Kebeles
District Kebele TNL SH DH DET NIL RLL ML MS FL FS % Dedo Afolea dawea 14.0 2 12 3 4 2 4 1 21.42 Billo adicho 13.0 3 10 4 5 2 2 0 15.38 Keta kedida 11.0 2 9 4 3 1 2 1 9.09 Total 38.0 7 31 11 12 5 8 2 45.89 Kersa Ankaso 21.0 9 12 5 7 3 5 1 19.04 Beda buna 12.0 2 10 4 4 2 2 0 16.67 Marewa 9.0 2 7 2 4 1 2 0 22.22 Total 42 13 29 11 15 6 9 1 57.93 Manna Bilida 9.0 2 7 2 4 1 2 0 2.22 Gubea muleta 6.0 1 5 1 3 0 1 1 0.00 Meati 10.0 1 9 3 4 1 2 0 20.00 Somodo 10.0 3 7 2 4 1 2 1 10.00 Total 35 7 28 8 15 3 7 2 32.22 Seka chekorsa Boye kecema 17.0 5 12 3 6 4 4 0 23.52 Gibe boso 6.0 2 4 1 3 1 1 0 16.67 Sheni qoche 7.0 3 4 3 2 1 1 0 14.28 Total 30 10 20 7 11 6 6 0 54.47 Shebe sombo Kishea 9.0 2 7 2 3 1 2 1 11.11 Sebeka dabeye 14.0 4 10 3 5 2 3 1 14.28 Sebeka wala 8.0 2 6 1 4 1 1 0 12.50 Total 31 8 23 6 12 4 6 2 37.89 Sheko Gaziqa 9.0 2 7 1 3 2 2 1 11.11 Mehal sheko 12.0 2 10 3 5 1 3 0 25.00 Shami 11.0 2 9 2 3 1 3 1 18.08 Total 32 6 26 6 11 4 8 2 54.19 Yeki Addis alem 13.0 1 12 3 5 1 2 1 7.69 Addis berhan 9.0 2 7 2 3 1 2 1 11.11 Selam ber 10.0 2 8 2 3 2 2 1 10.00 Total 32 5 27 7 11 4 6 3 28.80 Mean 10.9 2.5 8.4 2.5 3.9 1.4 2.3 0.5 14.1
TNL= Total number of landraces; SH= Single harvest landraces; DH= Double harvest landraces; DET= Distribution and extent; RLL=Rate of landraces loss; NIL= Newly introduced landraces; ML=Many households and large area; MS = Many households and small area; FL= Few households and large area; FS = Few households and small area.
72 Therefore, it is necessary to develop strategies to preserve the actual diversity and distribution of yam for sustainable use for present and future generation. For on farm conservation, Kebeles showing large number of landraces, low rate of landraces loss and substantial numbers of landraces per household are the best to be selected.
3.3.4. Distribution and causes of genetic erosion 3.3.4.1. Distribution of genetic erosion
At district level, the rate of genetic erosion varied from 28.80% in Yeki to 57.93% in Kersa with a mean rate of 44.48% (Table 3.5). Genetic erosion is a complex process and several factors that involved either directly or indirectly on existed landraces. Some of these factors are related to socio-economic factors in general, while others are related to biotic and a biotic factor.
In the present study, four cell analyses were used to assess the abundance and distribution of local landraces diversity, to identify common, unique and rare/endangered landraces or species and to identify types of intervention for conservation of yam at district/Kebele level. The results revealed that, only a few landraces (2.5) on average per Kebele were cultivated by many households and on large areas. According to the producers, these landraces were found to have good agronomic (high productivity, high multiplication rate, etc.), utilization and culinary characteristics and therefore their production are economically profitable (Table 3.5). Landraces cultivated by many households but on small areas (3.9) on average per Kebele had exceptional culinary characteristics (good taste, good quality for medicine) but presenting a lot of weaknesses. They had low productivity, high staking demand, poor post harvest storage and post maturity conservation in the mounds, high susceptibility to poor soils fertility, low multiplication rate, etc. making their production economically unprofitable. Some landraces (1.4) on average per Kebele were cultivated by few households on large areas. According to the farmers, these landraces had good agronomic and culinary qualities but presenting some particularity: difficulty to harvest, soil selectivity and long dormancy. Finally, some landraces (2.3) on average per Kebele were cultivated by a few households and on small areas indicating that, landraces had high growth performance and were threatened or being disappeared (Table 3.5).
73 According to Loko et al. (2013) who reported that yam cultivars produced by few households and distributed on small areas considered as threatened and most often, effectively disappear after this period from the villages where they identified. From the present observation, diversity and distribution of landraces in each identified district/Kebele can be predicted for the next five years by using the survey data and the ordinary Kriging method, which is a geo-spatial modeling. Based on the above result of genetic erosion in Southwest Ethiopia will increase and high degradation of landraces diversity in the study areas. In line with this, Diehi (1982), Manyong and Nokea (2003) and Ashiedu and Alieu (2010) predicted a future decline yam production based on economic and agronomic consideration. For example, according to Hildebrand et al. (2002), who described five farmers’ landraces namely, erkabea, don-babu, don-bai, chebesha and kuchi-kundi to be threatened in Sheko district. In the present survey, except erkabea and chebesha, three landraces were lost and not described by farmers in the same district. Moreover, changes in production systems, market preferences, environmental hazards and the availability of very limited funds for conservation activities have reduced the diversity of yams genetic resources in the country (Sesay et al., 2013).
With regard to the conservation of genetic resources, landraces of the first classes were not threatened and could simply be monitored in situ conservation while those considered as the last class and were found to be threatened should be collected and conserved ex situ. In fact, the disappearance of landraces inevitably goes with a set of genes that could be used for breeding purposes (Hammer and Laghetti, 2005; Tsegaye and Berg, 2007; Dansi et al., 2010). From the 22 Kebeles assessed in Southwest Ethiopia, the rate of genetic erosion (loss of landraces diversity) varied from 0% in Gubea muleta to 25% in Mehal sheko with an average rate of 14.1% (Table 3.5). The zero rate of diversity loss recorded in Gubea muleta is not an indication of a better preserver, but rather a maximum threshold of landraces abandonment reached. Similar results were obtained on yam (Dansi et al., 2010) and cassava (Kombo et al., 2012).
Genetic erosion in cultivated species is a complex process, and although it does seem very likely that genetic erosion occurred as agriculture developed, sound scientific evidence supporting this hypothesis is difficult to find (Mark van et al., 2009). In the present study, the reasons for abandoning landraces identified throughout the Kebeles were diverse and varied from one Kebele to another (Table 3.6). To prevent such a situation, developments of conservation
74 strategies (in situ and ex situ) have remarkable impact for sustainable yam production in the areas (Gao et al., 2012; Jianzhan et al., 2012). The strategy should be cost effective, highly integrated to the culture of the society and effective utilization to meet the needs of present and future generations. Besides, the crop must have high contribution to the national food security program and have high commercial value. In most cases it is important for sustainable utilization of yams in the country. In all surveyed districts, yam production is mainly affected by genetic erosion leading to serious devastation of landraces diversity, farmers’ indigenous knowledge and yield losses. Based on the overall farmers’ assessment, 12 different causes of genetic erosion was identified and ranked based on its importance (Table 3.6).
3.3.4.2. Causes of genetic erosion
Farmers’ named landraces that had decreased cultivation areas or which entirely disappeared and were no longer cultivated by farmers in the study areas. For example, in the present survey, farmers verbally reported some vernacular names of landraces that were no longer found in their districts/Kebeles and thought to be lost. Some other landraces had undergone notable reductions during recent years (Megersa, 2014). The loss of diversity on yam in study districts of Southwest Ethiopia could be attributed to several reasons.
Based on farmers’ assessment, low attention to the value of the crop is one of the main factors that caused genetic erosion and it accounts 95% of the total farmers (Table 3.6). Although, the most frequently reported about 95% of the cause of crop genetic erosion is dilution of the crop by improved technologies, in this study, it accounts 72% of the total farmers replace the yam plant by coffee, chat, turmeric and maize. In all study districts, many farmers’ confirmed, young people today have less interest to yams as compared to grains. Elder farmers’ allege that maize varieties with shorter maturation time had been introduced in the past 30 years, making maize harvested twice within a year. In addition, more productive maize varieties have been and are being introduced by agricultural extension workers, who also encourage intensive cultivation practices and compete area and labor force from yam production. For example, the main planting time requires field preparation in February and early March, and planting during the small rains in March. These times correspond to the ideal period for preparing the fields of stake rows, during which maize and stake yams compete directly for labor.
75 Changes from tuber crops to cereals based farming system is also another cause of genetic erosion. Agricultural extension workers in different districts are more knowledgeable and enthusiastic about grains, especially maize, and less familiar to root and tuber crops (Hildebrand et al., 2002). Furthermore, many people from northern Ethiopia had settled in all surveyed districts, often achieving majority status over the indigenous people. Having grown grains in their former region, most northerners despise root and tube crops and eat them only when absolutely necessary. Thus, root and tuber crops have come to be regarded as low status relative to the grains sown by extension workers and new comers. In addition, in some farming community, for example, in Sheko and Yeki, cultivation of cereal crops considered as modernization of living standards. In this regards, similar findings are reported by Zimmerer (1992), Kiambi (1998), Charles and Weiss (1999) and FAO (1999) who described farmers preferences to maintain their desires in satisfying different yam foods and income generating in time, in addition to socio-cultural value that farmers preserve. In some cases, however, farmers express some contrary needs and make different choices, due to other factors of economic or market importance. Furthermore, due to the superior qualities of modern varieties (especially higher yields and higher prices), farmers are increasingly replacing yam landraces by modern varieties in many fields.
The second most important cause of genetic erosion was occurrence of drought at early stages of the crop and it accounted 93.0% of the total farmers. Most of the interviewed farmers indicated that, yam planting was done in October and November, and during this period moisture stress was found to occur at emergence and subsequent months, thus, the plant became stunted and finally die. Porcupine (90.0%) and mole rat (60.0%) attacks were the other prominent factors contributing more to genetic erosion of yams. Some landraces used as medicine are less preferred by porcupine and mole rats. This might be due to medicinal yams had high polyphenols or tannin like compound and not favorite by wild animals. Arunachalam (1999) who reported, natural disaster such as floods, drought and wild animal attack are more contributing to genetic erosion. Shortage of farm land (74.0%) and labour (23.0%) were also another factors mentioned by farmers as causes of genetic erosion. According to the farmers’, high population pressure and city expansion in different districts are the main factor to cause land shortage. This result in line with the work done by Cebolla et al. (2007) who described city expansion is the main causes of
76 genetic erosion in city of Valencia. Besides, the high rate of urban migration, especially among younger people, has reduced the labour force, resulting in the abandonment of landraces requiring high amounts of labour. This finding also similar with the result of Charles and Weiss (1999) and Zimmerer (1992) who described land and labour shortage are the main causes of genetic erosion in many crops. Changing climatic conditions are also resulted in the loss of adaptation of some formerly high yielding landraces, forcing farmers to shift to new and better adapted landraces.
Although, climate change is the main factor to the development of new diseases and pests, in the present study, no disease and pest were observed throughout the study districts. Decline soil fertility, as a result of frequent cultivation of the land without furrowing was evident and some landraces have therefore been abandoned due to low productivity. The present finding was similar with the work of Worede (1997) who stated the Ethiopian crop genetic resources are increasingly threatened by genetic erosion and extinction mainly due to habitat fragmentation and over exploitation of natural resources. Staking is one of the most common agronomic practice in yam production. In the present study, 49.0% of the total farmers confirmed stake shortage is the cause of landraces loss in the areas. This is highly associated with high cost of the stake and reduced the forest coverage in the area (Table 3.6).
In all surveyed areas, some local experts consider yams as a less valuable crop and low yielding ones. Similar study was conducted by Tsegaye and Berg (2007) who stated in tetraploid wheat, that farmers’ varieties have not been part of the agricultural extension package in Ethiopia. Inadequate attention has been given to the improvement of farmers’ varieties as they have often been regarded as low yielding. Besides, some socio economic parameters related to the households surveyed such as the age of the households and years of experience in yam production, the size of the family of the household, the size of the yam field cultivated and labour supply give the impression to affect the farmers’ decision making in the number of landraces to maintain (Table 3.1). Moreover, about 98.7% of the farmers recognized decreasing the trend of yam diversity in the areas. In line with this, recollection of one elder farmer in Sheko district gives a more specific picture of decline in household yam production. During his childhood and adolescence (40 years ago), each household had a field of yams containing 15-25 rows of stakes. Such a field would have 260-350 individual plants with multiple harvests per
77 season, would have yielded approximately 100 family evening meals with leftovers for breakfast. Today, in the same district most farmers plant 1-6 rows of stake yams across a narrower area (Hildebrand et al., 2002). This result is similar with the work done by Megersa (2014) who reported that the tendency of barley genetic resources in North Shewa zone of Oromia National Regional State region deteriorated through decades without any measure. To increase diversity at district/Kebele level farmers mentioned three basic strategies: training on the diversity, use, production and postharvest utilization on yams (30.2% of the farmers); exchange of landraces (genetic enrichment) between farmers, Kebeles (52.6% of farmers) and collect different yam landraces from the market (15.2% of farmers). The remaining 2.2% of the farmers had no suggestion with regard to the solution of landrace loss. Besides, about 82.6% of farmers use the combination of one or more strategies to increase the diversity of yams.
Table 3.6. Causes of genetic erosion of yam in Southwest Ethiopia.
Causes Dedo Kersa Manna Seka Shebe Sheko Yeki Total (38) (42) (35) chekorsa sombo (32) (32) (30) (31) Low attention to the crop 11.0 20.0 18.0 17.0 16.0 8.0 5.0 95.0 Drought at early season 31.0 3.0 7.0 2.0 9.0 18.0 23.0 93.0 Porcupine attack 17.0 7.0 5.0 25.0 13.0 15.0 8.0 90.0 Need more management 4.0 20.0 14.0 10.0 6.0 8.0 20.0 82.0 Shortage of farm land 8.0 17.0 13.0 4.0 2.0 8.0 22.0 74.0 Replaced by high value 16.0 4.0 7.0 2.0 2.0 17.0 24.0 72.0 crop Attacked by mole rat 0.0 14.0 3.0 10.0 18.0 7.0 8.0 60.0 Shortage of stake 11.0 0.0 13.0 10.0 12.0 1.0 2.0 49.0 Labour shortage 5.0 0.0 0.0 3.0 2.0 13.0 0.0 23.0 Low market value 0.0 2.0 2.0 0.0 0.0 0.0 0.0 4.0 Low soil fertility 0.0 2.0 0.0 1.0 0.0 0.0 0.0 3.0 Lack of extension service 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 Total 103.0 89.0 83.0 84.0 80.0 95.0 112.0 646.0 Figures in parenthesis refer to number of farmers surveyed in each district. Source: own survey result, 2015; Sum greater than 100 is due to double counting.
78 Assessment of the degree of similarity and association between factors that cause genetic erosion is valuable to develop conservation measures. In the present study, the clustering approach was adopted to cluster the landraces into six different groups by cut the dendrogram at 60.13% of similarity (Figure 3.3). This is important to examine the relationships between explored yam landraces based on the degree and similarity of the 12 farmers identified factors of genetic erosion by using likert scale method (Likert, 1932). The degree of different yam landraces with different factors of erosion in the study areas were presented in Table 3.7. Based on the results, the clustering of landraces classified into six distinct groups with different sizes. A dendrogram summarizing similarity among 38 landraces of yam is given in Figure 3.3.
The clustering pattern showed that the amount of landraces in each cluster varied from one in cluster VI to eighteen in cluster II. Cluster II, consisted the maximum number and accounted 47.36%) % of the total landraces. Landraces in this cluster were mainly identified by 80.07% of similarity. Conversely, landraces in this cluster have low contribution to drought and wild animal attacks particularly of porcupine and mole rat. Cluster I had seven (18.42%) of the total and landraces grouped in this cluster had 73.49% similarity and high contribution to most of the causes of genetic erosion that were identified by farmers. Cluster III and V had had three entries (7.89%) from each, all of them had medium contribution to most of the causes of erosion on yams. Similarly, cluster IV and VI had seven landraces (18.42%). Landraces in these clusters had 70.32% and 40.20% similarity, respectively and high contribution to drought, shortage of farm land, management and mole rat attack.
The results obtained from biplot principal component analysis evidently showed that genetic erosion is an important constraint associated with yam production in Ethiopia. The first two principal components contributed 25.40% (PCA-I) of the total variation and the second component contributed 13.60% (PCA-II) showed a clear interaction between yam landraces and causes of genetic erosion (Figure 3.4). In the positive direction of the first component, attributes such as low attention to the crop, porcupine attack, presence of drought at early stage of the crop, replaced the yams by high value crops especially chat and coffee and availability of low extension services contributed more and are the most important sources of genetic erosion of yam in Southwest Ethiopia. About 72.0 % of the identified landraces were eroded by these factors.
79 Table 3.7. Landraces ranking by farmers to causes of genetic erosion in study districts
Landraces A B C D E F G H I J K L Afra 3.0 2.0 2.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 Anchiro 2.0 2.0 1.0 1.0 2.0 2.0 1.0 3.0 2.0 1.0 1.0 1.0 Badaye 2.0 2.0 2.0 1.0 1.0 2.0 1.0 2.0 1.0 2.0 1.0 1.0 Badenseye 2.0 2.0 3.0 2.0 1.0 2.0 1.0 2.0 1.0 2.0 1.0 2.0 Baki boye 3.0 2.0 2.0 2.0 1.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 Bambuche 3.0 3.0 2.0 2.0 2.0 2.0 1.0 3.0 2.0 2.0 1.0 2.0 Banda 3.0 2.0 2.0 1.0 2.0 1.0 1.0 2.0 2.0 1.0 1.0 2.0 Bola boye 2.0 3.0 1.0 1.0 2.0 3.0 1.0 1.0 1.0 1.0 1.0 1.0 Bori boye 2.0 2.0 3.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 2.0 Chebesha 2.0 3.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 Dakuy 3.0 2.0 2.0 1.0 2.0 2.0 1.0 2.0 2.0 1.0 2.0 1.0 Dapo 2.0 3.0 2.0 1.0 2.0 2.0 1.0 2.0 2.0 2.0 2.0 1.0 Dartho 2.0 1.0 2.0 2.0 3.0 2.0 2.0 2.0 2.0 2.0 2.0 1.0 Doni 2.0 3.0 2.0 2.0 3.0 2.0 1.0 2.0 1.0 1.0 2.0 1.0 Erkabea 3.0 2.0 2.0 3.0 3.0 2.0 2.0 3.0 1.0 2.0 3.0 2.0 Feda 2.0 2.0 3.0 2.0 3.0 2.0 1.0 1.0 1.0 2.0 2.0 2.0 Geano boye 2.0 1.0 1.0 2.0 2.0 1.0 2.0 2.0 2.0 1.0 2.0 1.0 Gesa boye 3.0 2.0 2.0 2.0 2.0 3.0 2.0 1.0 2.0 2.0 1.0 1.0 Goshitea 1.0 2.0 1.0 1.0 2.0 2.0 2.0 3.0 2.0 1.0 1.0 1.0 Gurshume 2.0 2.0 3.0 2.0 2.0 3.0 1.0 2.0 2.0 3.0 1.0 1.0 Hati boye 2.0 1.0 2.0 3.0 2.0 2.0 2.0 3.0 2.0 2.0 2.0 1.0 Karakachi 2.0 2.0 1.0 2.0 2.0 1.0 3.0 2.0 1.0 1.0 1.0 1.0 Kerta boye 2.0 1.0 2.0 2.0 3.0 2.0 2.0 2.0 1.0 2.0 1.0 2.0 Liyan 3.0 2.0 3.0 2.0 2.0 1.0 2.0 2.0 2.0 3.0 1.0 2.0 Mecha boye 2.0 2.0 2.0 1.0 2.0 3.0 2.0 2.0 2.0 2.0 2.0 2.0 Offea 2.0 2.0 2.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 1.0 1.0 Pada 2.0 3.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 Sesa 2.0 2.0 3.0 2.0 2.0 1.0 2.0 2.0 1.0 3.0 2.0 2.0 Torebea 2.0 3.0 2.0 2.0 1.0 2.0 2.0 3.0 2.0 3.0 2.0 2.0 Tsedeboye 2.0 3.0 1.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 1.0 2.0 Wadela boye 3.0 2.0 2.0 1.0 2.0 2.0 1.0 2.0 2.0 2.0 1.0 2.0 Woko 2.0 3.0 2.0 1.0 2.0 2.0 1.0 2.0 3.0 2.0 2.0 2.0 Washinea 2.0 1.0 2.0 2.0 3.0 2.0 2.0 2.0 3.0 2.0 1.0 1.0 Wayera 2.0 3.0 2.0 2.0 3.0 2.0 1.0 2.0 1.0 2.0 2.0 1.0 Welmeka 3.0 2.0 2.0 3.0 3.0 2.0 2.0 2.0 3.0 2.0 2.0 2.0 Zankur 2.0 2.0 3.0 2.0 3.0 2.0 1.0 3.0 2.0 2.0 3.0 1.0 Zatemera 2.0 1.0 1.0 2.0 2.0 1.0 2.0 2.0 2.0 3.0 2.0 3.0 Zawera 2.0 2.0 2.0 1.0 1.0 2.0 1.0 2.0 1.0 1.0 2.0 2.0 1=low cause, 2=Medium cause and 3= Higher cause for landrace erosion A= Low attention to the crop, B= Drought at early stage, C= Porcupine attack, D= Need more management, E= Shortage of farm land, F= Replaced by high value crop, G= Attacked by mole rat, H= Shortage of stake, I= Labor shortage, J= Low market value, K= Low soil fertility and L= Lack of extension service.
80 Similarity 1 0 8 6 4 0 0 0 0 . . . . 0 0 1 2 0 7 3 0
Afra Baki boye Gesa boye Bambuche Banda Wadela boye Dakuy Badaye Gurshume Badenseye Chebesha Dapo Woko C=II Offea Torebea Tsedeboye
L Mecha boye a Zawera n
d Bola boye r
a Doni c
e Wayera s Pada Feda Erkabea C=I Zankur Bori boye Liyan Sesa C=III Anchiro Goshitea Karakachi Dartho C=V Washinea Geano boye Welmeka Hati boye C=IV Kerta boye Zatemera C=VI
Figure 3.3. UPGMA based clustering of 38 yam landraces based on 12 farmers’ identified causes of genetic erosion
While, factors such as, shortage of stake to support yam and poor soil fertility showed higher score in the negative direction of the first component. Landraces such as, geano boye, karakachi, dartho and erkabea are sensitive to these factors regarding genetic erosion. In most of surveyed districts, farmers destroy yam genetic resources and replace the yam farmland by coffee and chat in Jimma zone and spice crops particularly turmeric and black pepper in Yeki. Although, mole
81 rat attack, low market value and labor shortage to manage the yam field are the main contributing factors of genetic erosion in the second component of the biplot, they exhibited lower value to erode yam genetic resources in the areas and only landrace zatemara highly vulnerable to these factors.
0 5.0 Drought
2.5 Stake shortage Dartho Low attention Erk abea Doni W elmek a Zank urW ay era 0 GeanHoKabetirotbyaoebyoey e 6 F eda Dak uy . Karak achi A nchiro
3 BoPlaadbaoy e Lack of extension Low soil fertility Banda Dapo 1 W ashinea BGaukrisbhouymeBeambuche GoshGiteeasa boy eCWhaedbelsahba oy e = Sesa Liy an Baday e I 0.0 M echaBbaodZyeaenWwsAeofyrOrkaeaoffea 0 I Zatemera Bori boy e Tsedeboy e A Mole rat attack Torebea C P Low market value Replaced by crop -2.5 Porcupine attack
Need more management -5.0 -5.0 -2.5 0.0 2.5 5.0 PCA I= 25.40
Figure 3.4. The association of 38 yam landraces and 12 causes of genetic erosion in the study areas
Furthermore, the biplot principal component also clearly indicated the relationships among yam landraces and resistant to the factors that cause genetic erosion. In this study, landraces torebea, baki boye, badaye and tsedeboye were quite similar in terms of resistance early drought and porcupine attack. Projection of landraces points to genetic erosion revealed specific interactions between yam landraces and the causes. Landraces, gesa boye, goshitea, gurshume, hati boye, karakachi, kerta boye, liyan, mecha boye, offea, pada and sesa were found to have stable resistance to most of the factors to causes of genetic erosion, as the points for these landraces were close to the origin of the biplot. On the other hand, landraces zatemera, washinea and geano boye are sensitive to management, poor soil fertility and staking demand. Thus, the knowledge of genetic erosion and interaction with the landraces are significantly important to apply conservation measure.
82 3.3.5. Conservation of yam genetic resources The present study, clearly flagged out the factors that determine the place of yam landraces in the production system and the interest that farmers have in them. These factors are important to develop sustainable conservation strategies and similar agreement with the report of Brush and Meng (1998). Yam production system in Southwest Ethiopia, conservation through use approach needs to be sustained with a number of strategic actions such as i) collection of yam genetic resources, ii) morphological and genetic characterization of these resources, iii) promotion of yam diversity with emphasis on production locations where the diversity is high and iv) training and capacity building, particularly on cultivation and post harvest practices (personal discussion with farmers).
Establishment of a core collection of yam genetic resources at regional level in major growing areas is also of paramount importance. Although yam is vegetative propagated, it cannot only rely on farmers to maintain all the diversity that might be available particularly when the diversity is unknown. That is why in addition to landrace collection, the morphological and genetic characterization of yam landraces should be carried out. Based on the results of the present study, the main causes of yam genetic erosion in Southwest Ethiopia was identified. Consequently, the associations between the causes and the landraces based on Socio-economic factor of the house hold, morphological, biochemical, molecular traits and farmers’ indigenous knowledge have marvelous impact to build up conservation plan, genetic enhancement and efficiently utilization of the crop. Another strategic action includes promotion of yam diversity with emphasis on production areas have valuable impact. Furthermore, the analysis on the extent and distribution of the genetic diversity in a species, and of the way in which its structure is an essential prerequisite to determine what to conserve, and where and how to conserve it.
83 3.4. CONCLUSION
Farmers in the study area have substantial awareness on yams. Due to the past research neglect, farmers are often the only sources of information concerning yams in Southwest region of Ethiopia. Consequently, through the analysis of indigenous knowledge system, farmers’ perception in designing conservation and improvement programs are critical so as to bring practical solutions to the identified problems of the farmers in the study areas immediate concern to them.
Farmers’ classification system are mainly based on very specific needs, preferences and socio- cultural aspects; thus, research in cooperation with farmers becomes necessary to establish new ways of a dialogue between researchers and farmers in evaluating the characteristics of the landraces farmers maintained in their systems.
In Southwest Ethiopia, different numbers of yam landraces are found with uneven distribution, and are exposed to severe threat of disappearance (genetic erosion) mainly due to low attention given to the value of the crop, drought at early stage, wild animal attack, shortage of farm land, displacement of landraces by high value crops. Besides, late maturing landraces are subjected to replacement by early maturing once, which in turn results in shortage of planting materials at peak season. Therefore, urgent intervention is required to develop conservation plan to maintain and enhance the existing diversity for use by present and future generation.
In the present study, the major causes of genetic erosion and its distribution on yam was identified, thus, training to farmers, diversity fair, diversity block, genetic enrichment/diversity kits across districts and Kebeles are very important for conservation and sustainable utilization of yam genetic resources in Southwest Ethiopia.
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89 IV
______- MORPHOLOGICAL CHARACTERIZATION AND DIVERGENCE ANALYSIS OF YAM (Dioscorea spp.) LANDRACE COLLECTIONS OF SOUTHWEST ETHIOPIA ______
90 Morphological Characterization and Divergence Analysis of Yam (Dioscorea spp.) Landrace Collections of Southwest Ethiopia
ABSTRACT
The study was conducted at Jimma Agricultural Research Center during 2015. The objectives of the study were to characterize and assess the level of diversity within farmers’ and reference collection of yams from Southwest Ethiopia. Thirty six landraces of yam were tested by using 6x6 simple lattice design with two replications. Data on 32 qualitative and 19 quantitative traits were collected and subjected to various data analyses. Cluster analysis based on qualitative characters revealed six distinct clusters with varying sizes and presence of variability, based on their foliar and subterranean traits which will be highly useful in the genetic improvement of the crop. The result of Shannon-Weaver diversity index (H`) revealed, existence of high levels of diversity among Dioscorea spp. landraces based on the frequency distribution of phenotypic characters that were considered. The results of Principal Component Analysis (PCA) indicated traits that have good contribution to the variability. The first seven components explained 88.4% of the total variation, while PC-I and PC-II accounted 55.30% of the total variability. Highly significant differences (p<0.01) was detected among collections for the studied quantitative traits. The cluster and distance analysis showed the existence of six divergent groups. The widest inter cluster distance was found between cluster III and V (380.09) followed by cluster IV and VI (261.5). Maximum genetic divergence between the cluster points to the fact that hybridization among the landraces included with them would produce potential and meaningful hybrids and desirable segregants.
Keywords: Cluster, food security, genetic distance, hybridization, PCA, traits,
91 4.1. INTRODUCTION
Yams (Dioscorea spp.) are important crop plants widely distributed throughout the humid and sub humid tropics (Coursey, 1967; Tamiru et al., 2007). The major food species are originated from three isolated regions: Southeast Asia, West Africa, and Tropical America, which are also considered the main centers of yam domestication and diversity (Asiedu et al., 1997; Himanshu et al., 2016). It is particularly imperative for food security since many tropical areas often experience for unfavorable environmental conditions (Loko et al., 2015). In Ethiopia, yam has been cultivated extensively in dense populated and high rainfall areas of South and Southwestern parts to fill seasonal food and economic gaps when other crops are absent in the field (Amsalu, 2003; Tewodros and Yared, 2014).
Farmers in Ethiopia, give many reasons why they cultivate yams. According to Edosa (1996), yams are cultivated, because they produce reasonable amount of yield when other crops hardly grow, they are resistant to disease and pests, because of ease of ecological adaptation and their being utilized for different purposes (Dansi et al., 1999; Loko et al., 2015). For instance, different yam landraces cultivated by farmers were distinguished one from the other on the bases of morphological characters. Besides, most Dioscorea species exhibit considerable morphological variability in both the aerial and underground plant parts (Dansi et al., 1999; Mignouna et al., 2002; Demuyakor et al., 2013). Furthermore, the existence of different vernacular names for the same cultivar of the species, or vice versa has created problems to classify landraces while avoiding duplicates for conservation (Karamura, 1998; Marcos et al., 2014). In Southwest Ethiopia, there is large pool of yam landraces found in farmers’ field and forest areas mixed with cereals (Hildebrand, 2003). Nevertheless, the landraces are being lost due to low attention given to the value of the crop; replacement of the yam by high value crops (coffee, chat and spices), occurence of long season drought, human interference such as deforestation and urbanization. Thus, there is a need to collect, characterize, evaluate and maintain the landraces so as to make them serve as a source of desired landraces for further improvement of the crop and also to reduce their further genetic erosion (Edward, 1991). For evaluation, the collection of landraces consisted traits with highly heritable, expressed in different environments and can be easily seen by naked eye. Information from characterization along with passport data provides an indication of the range of diversity in the collections, and is
92 of significant use for the breeders to narrow the selection of potential breeding stocks (Zannou et al., 2004). Currently, there are few efforts so far done with regard to yam landrace collection and conservation works by the Ethiopian Institute of Agricultural Research (EIAR) and Institute of Biodiversity Conservation (IBC). Nevertheless, yams are distributed across diverse agro- ecological areas of the country that have not been adequately represented. Moreover, the collected landraces have not been properly evaluated and their attribute remains unknown by the breeders. Thus, detailed descriptions of the landraces based on agro-morphological characters have remarkable impact for the classification, conservation and genetic improvement of the crop (Ross et al., 2003). According to Din et al. (2010) scientific classification of the plant still relies on morphological traits, this technique is much easier, cost effective, simple to score, requires less time and no need of any technical knowledge. Besides, it is also powerful taxonomic tool that has been utilized for the preliminary grouping of landraces prior to their characterization using more precise marker technologies (Zannou et al., 2004).
Furthermore, for an effective yam breeding program, variety development through hybridization and the analysis of genetic diversity is a key instrument and plays a significant role in identification and selection of best landraces as parents (Rhman and Munsur, 2009). In agreement with this, Kwon et al. (2002) suggested, identification of parents based on divergence study to be more effective and also allowing superior recombinants that are essential in any variety development and management scheme (Mazid et al., 2013). Nevertheless, until now, there is little awareness on how farmers manage this diversity on yam in Ethiopia and on how they make decisions related to this issue. New varieties from research centers often are not adapted to the local conditions and also do not satisfy farmers’ needs. This might be due to, the perceptions of various stakeholders, including farmers, having largely been excluded from the process of variety development. To overcome this problem, an innovative research approach has to be designed so as to include all actors and stakeholders who are expected to plan a crucial role in promoting technology and knowledge on various aspects of yam. This includes detailed analysis of the characterization and genetic divergence of yam, good understanding of farmers’ perception for classification and management of the landraces. Prior to this study, there has been inadequate information on these aspects that are supposed to be useful to plan an effective improvement program in association with farmers’ knowledge and skills on yam in the country.
93 Accordingly, this study was initiated to characterize and assess the level of diversity within farmers and reference collection of yam landraces that were collected from major growing areas of Southwest Ethiopia.
4.2. MATERIALS AND METHODS
4.2.1. Description of the study area The experiment was conducted at Jimma Agricultural Research Center (JARC). The center is located at latitude 7o 40.00' N and longitude 36o 47’.00’ E with an altitude of 1753 meters above sea level (m.a.s.l.). The area receives mean annual rainfall of 124.6 mm with mean maximum and minimum temperatures of 26.5 0C and 12.0 0C, respectively. The soil of the study site is Eutric Nitosol (reddish brown) with pH of 5.3.
4.2.2. Experimental materials A total of 36 yam landraces which were grown and managed by farmers was collected during the survey from selected districts of Jimma, Sheka and Bench-maji zones of Southwest Ethiopia and some previously collected yam landraces (not aerial yam) which were found at JARC used for this study.
4.2.3. Experimental design and management
The experiment was laid out in 6x6 simple lattice design with two replications. Plants were field established using a 7m long rows using inter-row spacing of 1.5m and intra-rows spacing of 1m. Tubers of the same size which started sprouting were used as planting material. One month after planting, seedlings were earthed up followed by frequent weeding. All other agronomic practices were followed according to the recommendations and farmers practices of the areas. For each yam plant, dried coffee stick of 3.5-4.5 meters long was used as supports so as to enable the plant develop good canopy and vine development. Five middle plants within a row were sampled and tagged for data collection and final harvest.
94 Table 4.1. Descriptions of the 36 yam landraces used for the study.
No Name of Region Zone District Latitude Longitude Altitud . landraces e 1 59/02 Oromia Jimma Mana 07040’37N 036049’10E 1718 2 68/01 Oromia Jimma Dedo 07030’63N 036053’45E 1784 3 6/02 SNNPRS Bench maji Sheko 06059’66N 035034’11E 1728 4 75/02 Oromia Jimma Kersa 07040’43N 036048’76E 1734 5 3/87 Oromia Jimma Manna 07040’58N 036048’75E 1731 6 56/76 Oromia Jimma Manna 07041’89N 036048’06E 1837 7 54/02 SNNPRS Bench maji Sheko 07002’03N 035032’77E 1892 8 46/83 Oromia Jimma Dedo 07031’28N 036053’59E 1771 9 08/02 Oromia Jimma Kersa 07040’46N 036048’79E 1740 10 116 Oromia Jimma Dedo 07031’28N 036053’63E 1683 11 01/75 SNNPRS Sheka Yeki 07011’30N 035026’22E 1171 12 06/83 Oromia Jimma Dedo 07031’32N 036053’64E 1692 13 17/02 SNNPRS Sheka Yeki 07011’27N 035026’26E 1176 14 07/03 Oromia Jimma Dedo 07031’50N 036053’60E 1733 15 45/03 Oromia Jimma Mana 07041’86N 036048’08E 1810 16 27/02 Oromia Jimma Seka chekorsa 07035’06N 036041’91E 1877 17 37/87 Oromia Jimma Mana 07041’87N 036048’13E 1940 18 10/002 SNNPRS Bench maji Sheko 07002’91N 035029’76E 1668 19 76/02 Oromia Jimma Kersa 07040’64N 036048’84E 1728 20 06/2000 Oromia Jimma Sekachekorsa 07035’43N 036041’86E 1850 21 7/83 Oromia Jimma Sekachekorsa 07035’06N 036041’91E 1898 22 58/02 SNNPRS Sheka Yeki 07011’22N 035026’25E 1192 23 39/87 Oromia Jimma Seka chekorsa 07035’42N 036042’94E 1885 24 32/83 Oromia Jimma Shebe sombo 07026’74N 036024’01E 1372 25 24/02 Oromia Jimma Shebe sombo 07026’75N 036024’07E 1379 26 2/87 Oromia Jimma Shebe sombo 07026’76N 036024’12E 1365 27 60/87 SNNPRS Sheka Yeki 07011’72N 035026’48E 1199 28 15/2000 SNNPRS Bench maji Sheko 07004’13N 035037’74E 1320 29 34/87 Oromia Jimma Dedo 07031’37N 036053’44E 1911 30 21/02 Oromia Jimma Seka chekorsa 07036’48N 036045’09E 1785 31 57/76 SNNPRS Bench maji Sheko 07002’88N 035029’74E 1654 32 0001/07 Oromia Jimma Shebe sombo 07026’74N 036024’12E 1367 33 0004/07 Oromia Jimma Kersa 07040’55N 036048’75E 1741 34 7/84 SNNPRS Bench maji Sheko 07002’88N 035029’74E 1661 35 7/85 SNNPRS Sheka Yeki 07014’30N 035026’17E 1173 36 06/2001 SNNPRS Bench maji Sheko 06059’69N 035034’09E 1387
95 4.2.4. Morphological data collection Descriptor of yam (Dioscorea spp.) developed by Bioversity International (IPGRI, 1999) was used for data collection. Two sets of data (qualitative and quantitative traits) were considered to assess the diversity of yam landraces based on foliar and subtraninnian plant parts during the entire growth and time of harvest.
4.2.4.1. Qualitative characters
A total of 32 qualitative characters were measured for this study. Data were recorded from the middle of five plants of the row and the average value was used for data analysis. Mounsell color chart was used for color identification. The lists of qualitative characters are presented as follow: Leaf color: 1= yellow green, 2= pale green, 3=dark green, 4= purplish green, 5=purple, 99=other Leaf vein color upper surface: 1= green, 2= yellow green, 3=pale purple, 4= purple, 99=other Leaf vein color lower surface: 1= green, 2= yellow green, 3= pale purple, 4= purple, 99= other Leaf margin: 1= .Entire 2= Serrate Leaf margin color: 1= green, 2= purple, 3= yellow green, 99= other Leaf shape: 1= ovate, 2= chordate 3= chordate long, 4=chordate broad, 5= sagittate Leaf apex shape: 1= obtuse 2= acute, 3=emarginated Hairiness of leaf surface: 0= present, 1= absent Leaf position: 1= alternate, 2= opposite Leaf size: 1= small, 2= medium, 3= large Leaf density: 1= low, 2= medium, 3= high Leaf lobations: 1= shallow, 2=deep Waxiness: 0= absent 1= present Tip color: 1= light green, 2= dark green, 3= purple, 4= red, 99=other Petiole color: 1= all green with purple base, 2= all green with purple leaf junction, 3= all green with purple at both ends, 4= all purplish green with purple base, 5= all purplish green with purple leaf junction, 6= all purplish green with purple at both ends, 7= green, 8= purple, 9= brownish green, 10= brown, 11= dark brown, 99=other. Petiole wing color: 1= green, 2= green with purple edge, 3= purple, 99= other Twinge direction: 1= clockwise, 2= anticlockwise Vine color: 1= yellowish, 2= green, 3= light green, 4=purple, 99=other
96 Wings on vine: 1=present, 2= absent Wing color: 1= green, 2= green with purple edge, 3= purple, 99= other Spines: 1= present, 2= absent Spine shape: 1= straight, 2= curved Color at spine base: 0= present, 1= absent Spine on the vine base: 0= none, 3= Few, 7=many Tuber shape: 1= round, 2= oval, 3= oval-oblong, 4= cylindrical, 5= flattened, 6= irregular, 99=other Tuber skin color: 1= grayish, 2= light brown, 3=dark brown, 4= 0ther Tuber branching: 0= none, 1= slightly branched, 2= branched, 3= highly branched Tuber surface texture: 1= smooth, 2= rough Tuber flesh color: 1= white, 2= yellowish white, 3= yellow, 4= orange, 5= light purple, 6= purple 7= purple with white, 8= white with purple, 9= outer purple/ inner white, 99= other Hairiness of tuber surface: 0= absent, 1=small, 2=medium, 3= large Flower: 0= present, 1 absent Flesh color at central transverse cross section: 1= white, 2= yellow white, 3= yellow, 4= orange, 5= light purple, 6= purple, 7= purple with white, 8= white with purple, 9= outer purple/inner yellowish, 99 = other
4.2.4.2. Quantitative characters A total of 19 quantitative traits was assessed for characterization. Data were collected on individual plant and the plot basis. The following are the list of quantitative characters examined and the detailed procedures followed for data recording.
4.2.4.3. Data collected on individual plant basis Data were collected from the middle five plants and the average value was used for data analysis. Leaf length and width were measured from five leaves of the same plant. Leaf length (cm): five fully expanded young leaves on the main vine were measured from collar to the tip of the leaves at maturity and the mean value was considered. Leaf width (cm): The mean widths of five fully expanded young leaves were measured on the main vine from widest part at maturity.
97 Length of leaf lobe (cm): the mean of five fully expanded leaf lobs from the main vine of leaves was measured from the middle at maturity. Petiole length (cm): the mean length of five petioles on the main vine was measured from the base to the point of insertion of the leaf lob at maturity. Distance between lobs (cm): the mean distance of five fully expanded leaf lobs from the main vine of leaves was measured from the base at maturity. Vine length (cm): the length of the longest vine was measured with a measuring tape from the ground level to the tip at the time of maturity. Number of tubers plant-1: counted from five plants and the mean calculated Tuber length (cm): measured from the lower to the upper tip using venire caliper at harvest. Tuber diameter (cm): measured at the middle using venire caliper at harvest. Internode length (cm): mean length was measured from the middle of vine at maturity. Number of internode vine-1: counted from five plants and the mean calculated Number of vine hill-1: counted at maturity. Tip length (cm): the mean length of tip was measured from the apex at maturity.
4.2.4.4. Data collected on plot basis
Vine fresh weight (t/ha): the vine of all the plants from each plot were bulked and weighed at harvest and the vine fresh weight/plot was then converted to tonnes per hectare (t/ha). Vine dry weight (t/ha): the total vine dry weight was estimated by drying 100 g of fresh vine in a forced air circulation oven at 700C for about 72 hours and vine fresh weight per plot was expressed in tonnes per hectare of the vine fresh weight. Tuber fresh weight (t/ha): the tuber of all the plants of each plot were bulked and weighed at harvest and the tuber fresh weight/plot was then converted to tonnes per hectare (t/ha). Tuber dry weight (%): the total tuber dry weight was estimated by drying 100 g of fresh tuber in a forced air circulation oven at 700C for about 72 hours and expressed in tonnes per hectare of the tuber fresh weight. Number of days to mature: was recorded from date of emergence to the date when the crop was ready for harvesting, i.e. tubers had become mature and the plant started drying. Harvest index (%): was calculated as a ratio of total marketable fresh tuber yield to the sum of biomass and total marketable tuber yield and expressed in percentage.
98 4.2.5. Data analysis
4.2.5.1. Cluster analysis based on qualitative traits Clustering was done using Statistical Analysis System (SAS) package (version 9.0 of SAS Institute Inc, 2000) based on Un-weighted Pair Group Methods with Arithmetic average (UPGMA). The number of cluster was determined by looking into three statistics namely, pseudo F, pseudo T2 and Cubic Clustering Criteria (CCC). The number of cluster was decided considering where the CCC and pseudo F statistics combined with small value of pseudo T2 and large pseudo T2 statistical for the next cluster fusion (Milligan and Cooper, 1985). Dendrogram was constructed by Un-weighted pair-group method based on arithmetic averages (UPGMA).
4.2.5.2. Frequency distribution and Shannon-Weaver diversity index (H’) Frequency distribution is a systematic way of ordering a set of data from the lowest to the highest value showing the number of occurrences (frequency) at each value or range of values. In the latter, the distribution is called a relative frequency distribution. The Shannon-Weaver diversity index was used to determine the diversity of the landraces collected from different districts of Southwest Ethiopia by using the frequency of distribution and the number of phenotypic classes (Hennink and Zevan, 1991). The index is defined as:
th Where pi is the proportion= − ∑ of the total number of individuals (landraces) in the i class and S is the number of phenotypic classes.
The Shannon weaver index values (H’) can ranges from 0 to ~ 4.6 using the natural log (versus log10). A value near 0 indicated that every species in the sample are the same. Conversely, a value near 4.6 indicated the numbers of individuals are evenly distributed between the species (Hennink and Zevan, 1991).
4.2.5.3. Principal component analysis Principal component analysis was performed by using correlation matrix by employing SAS version 9.0 (SAS, 2000). The objective of this analysis was to reduce the observed variables in to small number of principal components that were accounted for most of the variance in the observed variables (Mohammadi and Prasanna, 2003).
99 4.2.5.4. Analysis of variance based on quantitative traits Data collected from 19 quantitative characters subjected to analysis of variance (ANOVA) as suggested by Gomez and Gomez (1984) using Statistical Analysis System (SAS) package (version 9.0 of SAS Institute Inc, 2000). Means were separated using the Least Significant Difference (LSD) procedure at 1% and 5% levels of significance. The analysis of variance (ANOVA) was made using model for simple lattice design as follow: Yijklm = µ + ti + j + + yl + +
th th Where, Yijklm = response of Y trait from the i genotype, j replication, µ= Overall mean effects, ti= th th th Effects of i level of treatments, β= Effects of j level of replication, χk= Effects of K level of blocks th within replications (adjusted for treatments), yl = Effects of l level of intra block error, πm= Effects of the mth randomized complete block error and Σijklm= is a random error component.
The genotypic and environmental variances were estimated by the formula suggested by Singh and Chaudhary (1985) as follows: 2 g = (MSg-MSe)/r 2 e = MSe 2 2 Where, g = genotype variance components, e = error variance, r = number of replications, MSg = mean square of genotype and MSe = mean square of error.
4.2.5.5. Cluster analysis using quantitative traits Like qualitative characters, clustering based on quantitative traits was used to separate the landraces into different groups. The same procedures follow as qualitative traits to determine the numbers of clusters, their dendrogram and the distance between clusters.
4.2.5.6. Genetic distance The average intra and inter cluster distances were calculated using the generalized Mahalanobis's D2 statistics (Mahalanobis, 1936). The D2 value obtained for pairs of clusters were considered as the calculated value of Chi-square (2) was tested for significance at the required level of probability against the tabulated values of 2 for p-2 degrees of freedom, where p is the number of characters considered (Singh and Chaudhary, 1985). SAS software (version 9.0 of SAS Inistitute Inc, 2000) was employed for the analysis. The D2 is defined as: 2 = 1 -1 D ij (Xi-Xj) S (Xi-Xj) 2 Where, D ij = is the distance between two groups i and j; Xi and Xj are the two vector mean of the traits for ith and jth groups respectively, and S-1 is the inverse of the pooled covariance (Mahalanobis, 1936).
100 4.3. RESULTS AND DISCUSSION
4.3.1. Morphological diversity analysis based on qualitative traits
4.3.1.1. Cluster analysis Grouping of landraces based on their similarity is indispensable. In the present study, this approach was adopted to cluster the landraces into six different groups with different sizes by cut the dendrogram at 0.75 average distances between clusters (Table 4.2). A dendrogram summarizing genetic similarly among 36 Dioscorea landraces based on qualitative characters is given in Figure 4.1. The detail descriptions of the traits are presented in section 4.2.4.1. The number of landraces belonging to each cluster was diverse and varied from one in cluster VI to 14 in cluster I (Table 4.2).
Cluster I was the largest and consisted 14(38.89%) of the total landraces. Of these, twelve from Jimma zone (3 from Dedo, 2 each from Shebe sombo,Manna and Seka chekorsa and 3 Kersa from), and one landrace each from Bench maji and Sheka. Landraces grouped under this cluster have mainly identified by dark green leaf, green vein on upper and lower surface, entire and yellow green leaf margin, chordate leaf, acute leaf apex, opposite leaf position, medium leaf size, medium leaf density, light green tip and petiole, cylindrical tuber shape, light brown and slightly branched tuber, large hair rough tuber surface with white with purple flesh color at the central transverse.
Likewise, Cluster II also accommodated 11 entries (30.55%), five from Bench maji and five from Jimma collections. Landraces fall into this cluster different from cluster I by having yellow green vein on upper surface, obtuse leaf apex, small leaf size, all green with purple edge petiole, green vine, irregular and branched tuber with medium hair, rough tuber surface and light purple flesh color at the central transverse. Besides, four landraces (11.11%) were grouped under cluster III, all from Sheka and Jimma collections. They typically possessed yellowish and dark green leaf, yellowish and green leaf vein on upper surface, yellow green leaf margin and leaf vein on lower surface, all green with purple at both ends of petiole, obtuse leaf apex, purple tuber flesh, dark brown tuber skin and purple with white flesh color at the central transverse.
101 Similarly, four landraces (11.11%) grouped under cluster IV, three from Jimma (two from manna and one from Dedo) zone. Predominantly, landraces differ from the other two clusters by having shallow and deep leaf lobs, dark green tip color, all green with purple at both ends petiole color, clockwise and anti clockwise twining direction, light green vine, green wing and flattened tuber shape. Some landraces categorized under this cluster, produce flower at maturity. Finally, three landraces (8.33%) were grouped under cluster V and, VI two from Jimma, and one from Sheka zones collection. All landraces typically possess the combination of all characters described under the above four clusters. Landraces used to this study were collected from diverse agro- ecological areas of Southwest Ethiopia; nevertheless, landraces from the same or different areas fall into different clusters and showing different genetic make-up. Thus, it may be of considerable importance to enlarge the genetic bases of yams by sustainable and continual collection of landraces throughout the major growing areas of the country for further genetic enhancement of the crop. Dewey (1959) and Tewodros and Yared (2014) who further confirmed that while selecting landraces from a particular cluster, the inter cluster distance and cluster mean performance of traits should be taken into consideration.
The cluster means of 19 quantitative characters were assessed based on clusters formed by 32 qualitative characters. The result showed that cluster VI had the highest mean value in most of the characters considered in this study; for example, longest leaves, petiole length, internode length, tube length, widest leaves and vine fresh weight (Table 4.3). Cluster IV showed high performance among the characters that were considered. For example had highest length of vine, number of internode vine-1, tuber fresh weight and harvest index. Cluster I showed the highest number of tuber hill-1, distance between lobs, late maturing type, and tuber fresh weight; however, the rest of clusters had the average performance of the traits considered.
102 Table 4.2. Clusters of Dioscorea spp. landraces based on qualitative traits
Clust Number of Serial Name of Major characteristics ers landraces in number landraces in each cluster each cluster I 14 1,6, 32, 59/02,56/76, LC=dark green, LVCUS= green, LVCLS= green, 29,36,1 0001/07, LMC=yellow green, LM= entire, LS= chordate, LAS= 9,26,16 34/87, acute, LPO= opposite, LSi=medium, LD=medium, ,4, 8, 06/2001, LLO= shallow, Tic= light green, PC=light green with 30, 12 76/02, 2/87, purplish leaf junction, PWC=green with purple edge, Twdir=clockwise, VC= yellow green, Wic= green, ,21 and, 27/02,75/02, Tsh= cylindrical, Tsc= light brown, TFC=light 27 46/83,21/02, brown, TBr.= slightly branched, TStex= rough, 06/83,7/83 HoTs= large and FCCS= white with purple. and, 60/87
II 11 3, 34, 6/02, 7/84, LC=dark green, LVCUS= yellow green, LVCLS= 28, 15/2000, green,LMC=yellow green, LM= entire, LS= chordate, 31,7, 2, 57/76,54/02, LAS=obtuse, LPO=opposite,LSi=small,LD= medium, 5,18,25 68/01, LLO= shallow, Tic= light green, PC= all green with ,23,and 3/87,10/002, purple edge, PWC= green, Twdir= clockwise, VC= green, Wic= green, Tsh= irregular, TFC=light brown, 24 24/02,39/87 Tsc= light brown, TBr.= branched, TStex= rough, and 32/83 HoTs= medium and FCCS= light purple. III 4 22, 35, 58/02, 7/85, LC=yellowish and dark green, LVCUS=yellow and 10 and 116 and green, LVCLS= yellow green, LMC=yellow green, 20. 06/2000. LM= entire, LS= chordate, LAS= obtuse, LPO= . opposite, LSi=small, LD=medium, LLO= shallow, Tic= light green, PC= all green with purple at both ends, PWC=green, Twdir= clockwise, VC= light green, Wic= green, Tsh= oval oblong, TFC=purple, Tsc= dark brown, TBr.= branched, TStex= rough, HoTs= medium and FCCS= purple with white. IV 4 13, 14, 17/02,07/03, LC= green, LVCUS=yellow and green, LVCLS= 15 and 45/03 and yellow green, LMC= green, LM= entire, LS= 17. 37/87. chordate, LAS= acute, LPO= opposite, LSi=small, LD=high, LLO= shallow, Tic= green, PC= all green with purple at both ends, PWC=green with purple edge, Twdir= clockwise, VC= light green, Wic= green, Tsh= irregular, TFC=purple with white, Tsc= dark brown, TBr.= branched, TStex= rough, HoTs= small and FCCS= purple with white.
103 Table 4.2. Continued
Clust Number of Serial Name of Major characteristics ers landraces in number landraces each cluster in each cluster V 2 9 and 08/02 and LC=pale green, LVCUS=yellow green, LVCLS= 11 01/75 yellow green, LMC=green,LM= entire, LS= chordate, LAS=obtuse,LPO=opposite,LSi=medium,LD=mediu m, LLO= shallow and deep, PC= all green with purple at both ends, PWC=green, Twdir= clockwise and anti clockwise, VC= light green, Wic= green, Tsh= round and oval, TFC=light purple, Tsc= dark brown, TBr.= slightly branched, TStex= rough, HoTs= medium and FCCS= purple with white. VI 1 33 0004/07 LC= yellow green, LVCUS=yellow green, LVCLS = yellow green, LMC= green, LM= entire, LS= chordate, LAS= obtuse, LPO=opposite, LSi= medium, LD= medium, LLO= shallow, Tic= green, PC= all green with purple at both ends, PWC=green with purple edge, Twdir= clockwise, VC= green, Wic= green, Tsh= round, TFC=purple with white, Tsc= dark brown, TBr.= branched, TStex= rough, HoTs= large, and FCCS= purple with white.
LC=Leaf color, LVCUS= Leaf vein color upper surface, LVCLS= Leaf vein color lower surface, LMC= Leaf margin color, LM= Leaf margin, LS= Leaf shape, LAS = Leaf apex shape, LPO= Leaf position, LSi=Leaf size, LD= Leaf density, LLO= Leaf lobation, Tic= Tip color, PC= Petiole color, PWC= Petiole wing color, Twdir= Twing direction, VC= Vine color, Wic= Wing color, TSh=Tuber shape, Tsc=Tuber skin color, TBr= Tuber branching, TStex= Tuber texture, TFC= Tuber flesh color, HOTs= Hair on tuber surface, FCCS= Flesh color at central transverse.
104 Table 4.3. Cluster means of 19 quantitative traits of 36 Dioscorea spp. based on quantitative characters
Clust Characters er LL LW PL LLo DBL VL IL TiL NIPV NVP DM NTP TL TDi VFW VDW TFW TDW HI H H I 10.8 4.50 10.5 2.21 3.60 254.0 9.63 2.59 26.04 4.37 145.4 4.33 38.5 15.48 10.98 3.163 37.58 20.71 75.46 II 10.4 4.22 9.98 1.89 3.35 255.4 9.60 2.52 27.09 4.34 132.6 4.45 38.4 14.24 13.43 3.02 26.59 21.15 62.61 III 10.2 4.36 11.4 1.45 3.4 246.1 9.56 2.52 23.88 3.93 135.4 4.21 38.2 16.08 11.42 3.17 23.8 19.09 65.45 IV 9.87 3.94 9.28 1.54 3.24 265.3 9.8 2.32 28.88 4.58 142.5 4.21 39.2 15.62 12.89 2.91 35.3 20.4 72.51 V 10.9 4.08 10.3 2.6 3.55 255.1 10.0 2.63 22.9 3.89 145.4 4.33 40.2 15.69 12.28 2.81 37.6 21.35 75.48 VI 12.6 4.8 11.9 1.68 3.15 257 10.6 2.29 24.55 3.8 136.9 4.25 42.7 15.68 15.44 3.21 31.6 18.31 66.91 X- 10.8 4.31 10.5 1.89 3.38 255.5 9.88 2.47 25.55 4.15 139.7 4.29 39.5 15.46 12.74 3.047 32.07 20.16 69.73 S.di 0.98 0.31 0.98 0.44 0.17 6.16 0.43 0.14 2.21 0.32 5.49 0.09 1.72 0.63 1.60 0.16 5.83 1.21 5.49 LL= Leaf length (cm); LW= Leaf width (cm); PL= Petiole length (cm); LLo= Length of leaf lobe (cm); DBL= Distance between lobs (cm); VL= Vine length (cm); IL= Internode length (cm); TiL= Tip length (cm); NIPV= Number of internode vine-1; NVPH= Number of vine hill-1; DM= Days to maturity; NTPH= Number of tubers/hill; TL=Tuber length (cm); TDi= Tuber diameter (cm); VFW= Vine fresh weight (t/ha); VDW= Vine dry weight (t/ha); TFW= Tuber fresh weight (t/ha); TDW= Tuber dry weight (t/ha) and HI= Harvest index (%). X-= Mean and S.di= Standared deviation.
105 Average distance between clusters
II I V IV VI III
Serial number of landraces
Figure 4.1. Dendrogram showing hierarchical clustering patterns of 36 Dioscorea spp. landraces (UPGMA) based on 32 qualitative characters )
4.3.1.2. Qualitative traits distribution Individual characters differed in their patterns of distribution and amount of variation among the 36 landraces. Most of the landraces in this study was exhibited variation in foliar and subtraninnian plant parts are given in Fig. 4.2. ccession number
Leaf color varied between landracesI that were collected from the same and different districts. The dominant colors being dark green (61.11%), pale green (25.00%) and yellow green (13.89%). No landraces with purple leaves on both surfaces were observed. This may probably an adaptation for increasing the photosynthesis rate. A similar distribution was found in the color of leaf vein upper surface. However, (88.89 %) of the landraces had yellow green and (11.11%) had green leaf vein upper surface. Yellow green and green leaf vein lower surface were observed for 83.33 and 16.67% of the landraces respectively, with simple, entire and long petioles. Fifty percent of the landraces had green leaf margins, whereas, 27.78% had yellow green leaf margins with an opposite leaf arrangement, while, 66.67 and 33.33% of the landraces had acute and obtuse leaf apex shape (Table 4.4). Most landraces (55.55%) having medium leaf size with chordate shape (66.67%), high leaf density (77.78%), shallow (86.11%) leaf lobe, light green
106 (63.89%) tip color. Thirty-three percent of the landraces had purplish green petiole with purple base, whereas, 63.89% had green petiole with purple edge and petiole wing color (Table 4.4).
The predominant vine color was light green (58.33%), while the remaining (41.67%) of the landraces had green vines with a few purple spots. Most of the landraces (94.44%) having clockwise twining direction at emergence. Few landraces (25%) produce spine on their vine with variable shape; 22.22% had curved and 8.33% had straight shape, and highly associated with the wildness of the landraces. In wild type landraces spines distributed on the surface of vine and tuber in different amount and variable in size. Landraces collected from the same and different districts were showed differences in the above vegetative plant parts (Figure 4.2). Besides, variation in foliar before and after maturity were observed among landraces. This also indicated that there is wide range of variation in different traits of yams in Southwest Ethiopia. Thus, it needs strong attention in respect to conservation of yam genetic resources in Ethiopia.
Tuber shape of the landraces was varied from irregular (36.11%) to oval (8.33%). The predominant tuber flesh color was white with purple (25.00%), followed by outer purple/inner white (22.22%), purple (19.44%) and purple with white (13.89%) with dominant light and dark brown tuber skin color. Although, most of the landraces (44.44%), considered on this study exhibited branching tuber with rough (77.78%) and smooth (22.22%) surface. The flower of landraces predominantly produced spike type of inflorescent (25.0%) the other landraces hardly produced flower during the entire growing period. The predominant tuber flesh color at central transverse cross section was white (38.89%). Other flesh colors observed included white with purple (19.44%) and (13.89%) of landraces produced similar color, for example, light purple, purple and purple with white flesh color. This result consistent with the work of Tamiru et al. (2006) and Loko et al. (2015), who reported that there is a wide range of variability of tubers among Dioscorea species in south Ethiopia and Republic of Benin. Furthermore, similar result was reported by Nebeyu (2003) in cassava (Mannihot esculenta Cranz), Lebot et al. (2006) in yam and Dagne (2007) in taro (Colocasia esculenta).
107 A. Above ground plant part
Dedo-116 Yeki-60/87 B. The reproductive plant part
Sheko-6/02 Sheko-10/002 Manna-45/03
C. Tubers of different landraces
Sheko-54/02 Shebe sombo-32/83 Manna-37/87
Yeki-17/02 Yeki-58/02 Sheko-7/84
Figure 4.2. Vegetative, reproductive and storage organ (tubers) of yams of different landrace collections from Southwest Ethiopia.
108 4.3.1.3. The Shannon-Weaver Diversity Index (H') Assessment of genetic diversity is crucial in any crop improvement program to identify high yielding landraces (Rhman and Munsur, 2009). In the present study, Shannon-Weaver diversity index (H’) was adopted to compute the diversity of Dioscorea spp. based on the frequency distributions of 32 qualitative morphological characters and the number of phenotypic classes (Hennink and Zevan, 1991). The result of ‘H’ value for all observed phenotypic characters showed high level of diversity among 36 Dioscorea spp landraces, which ranged from 0.21 for vine twining direction to 1.64 for petiole color (Table 4.4). Besides, the overall mean of ‘H’ value of 0.72 confirmed the existence of phenotypic diversity among yam landraces from Southwest Ethiopia collections. This result was also in agreement with the works of Tamiru et al. (2011) who found an average level of diversity in yam collection from South Ethiopia and Loko et al. (2015) in yam collection from the Republic of Benin.
High ‘H’ value indicates relatively high level of diversity and even distribution of the landraces (Hennink and Zevan, 1991; Marcos et al., 2014). Furthermore, lower level of diversity was noticed on foliar qualitative traits (Table 4.4). This showed that subtraninian qualitative traits had greater influence on the phenotypic diversity among the Dioscorea spp landraces than the foliar traits. On the other hand, monomorphism was observed in some foliar characters; leaf margin, leaf position and wing on vine revealed the contribution of these characters to the diversity was low and omitted these characters for principal component analysis. The low level of diversity may also indicate the narrow genetic base of the plant and the low level of sexual reproduction in yam. These results indicate the need for further study based on genetics (molecular) investigation on yam.
109 Table 4.4. Frequency distribution and Shannon-Weaver diversity indices (‘H’) of 32 qualitative traits of Dioscorea grown at Jimma, 2015.
No. Qualitative character Index and description adopted Frequency H’ (%) 1 Leaf color Yellow green 13.89 0.92 Pale green 25.00 Dark green 61.11 2 Leaf vein upper Yellow green 88.89 0.35 color Green 11.11 3 Leaf vein lower Yellow green 83.33 0.45 color Green 16.67 4 Leaf margin color Green 50.00 1.03 Purple 22.22 Yellow green 27.78 5 Leaf margin Entire 100.00 0.00 6 Leaf shape Ovate 13.89 0.86 Chordate 66.67 Sagittate 19.44 7 Leaf apex shape Obtuse 33.33 0.63 Acute 66.67 8 Hair on the leaf Present 16.67 0.45 Absent 83.33 9 Leaf position Opposite 100.00 0.00 10 Leaf size Small 25.00 0.99 Medium 55.55 Large 19.44 11 Leaf density Intermediate 22.22 0.53 High 77.78 12 Leaf lobation Shallow 86.11 0.40 Deep 13.89 13 Waxiness on leaf Absent 83.33 0.45 Present 16.67 14 Tip color Light green 63.89 0.85 Dark green 27.78 Purple 8.33 15 Petiole color All green with purple base 13.89 1.64 All green with purple leaf junction 8.33 All purplish green with purple base 33.33 All purplish green with purple leaf junction 11.11 All purplish green with purple at both ends 25.00 Green 8.33
110 Table 4.4 continued
No. Qualitative character Index and description adopted Frequen H’ cy (%) Green 16.67 0.90 16 Petiole wing color Green with purple edge 63.89 Purple 19.44 17 Twinge direction Clockwise 94.44 0.21 Anticlockwise 5.56 18 Vine color Green 41.67 0.68 Light green 58.33 19 Wing on vine Present 100.00 0.00 20 Wing color Green 52.78 0.69 Green with purple edge 47.22 21 Present/absent of Present 30.55 0.61 wing Absent 69.45 Straight 8.33 0.79 22 Spine shape Curved 22.22 None 69.44 23 Color at spine base Absent 69.44 0.61 Present 30.56 24 Spine on vine Few 25.00 0.96 Many 16.67 None 58.33 25 Tuber shape Oval 8.33 1.39 Oval oblong 11.11 Cylindrical 36.11 Flattened 8.33 Irregular 36.11 White 19.44 1.59 26 Tuber flesh color Purple 19.44 Purple with white 13.89 White with purple 25.00 Outer purple/ inner white 22.22 27 Tuber skin color Light brown 50.00 0.69 Dark brown 50.00 28 Tuber branching Slightly branched 13.89 1.26 Branched 44.44 Highly branched 27.78 None 13.89 29 Tuber surface texture Smooth 22.22 0.53 Rough 77.78
111 Table 4.4 continued
No. Qualitative Index and description adopted Freque H’ character ncy (%) 30 Hairiness of tuber Small 22.22 0.53 surface Medium 77.78 31 Flower Present 25.00 0.56 Absent 75.00 32 Flesh color at White 38.89 1.50 central transverse Light purple 13.89 cross section Purple 13.89 Purple with white 13.89 White with purple 19.44 Overall Mean 0.72
4.3.1.4. Principal component analysis The patterns of variation and the relative importance of each trait in explaining the observed variability assessed through principal component analysis (PCA). In the present study, the principal component analysis was adopted based on 29 variables. The first seven principal components explained 88.4% of the total variation (Table 4.5). Principal component I (PC I) alone accounted for 32.5% of the total variation. Flesh color at central transverse, spine on vine base and tuber flesh color had the highest loadings on PC I. The second principal component (PC II), explaining 22.8% of the total variation, was highly correlated with presence of spines on vine base and tuber flesh color, while PC III was associated with the spine on vine base, flesh color at central transverse, petiole and leaf color explained 12.9% of the total variation. The remaining PCs accounted 20.2% of the total variation, and were mainly associated with petiole color, spine on vine base, tuber shape, flesh color at central transverse, tuber skin color and tuber branching. From all the characters, tuber flesh color was found to be the most discriminative parameter differentiating landraces collected from Southwest Ethiopia (Table 4.5). To evaluate the scores of solitary landraces, PC I and PC II was plotted (Figure 4.3). All landraces are distributed at the origin of the plot. The landraces 15/2000, 54/02 and 10/002 had the highest positive scores for both components and grouped into the top central corner of the plot. Conversely, landraces 75/02, 76/02, 27/02 and 59/02 had the lowest negative scores for PC- II and the highest positive values for PC I and grouped into the bottom right corner of the plot. This finding is consistent with the separation of landraces into two groups by UPGMA clustering (Figure 4.3).
112 Table 4.5. Eigen values, proportion, cumulative variance and component scores of the first seven principal components for qualitative traits in 36 yam from Southwest Ethiopia
Variable PC1 PC2 PC3 PC4 PC5 PC6 PC7 Eigen value 12.925 9.095 5.120 3.695 1.752 1.487 1.113 Proportion 32.50 22.80 12.90 9.30 4.40 3.70 2.80 Cumulative 32.50 55.30 68.20 77.50 81.90 85.60 88.40 Leaf color -0.005 0.066 0.157 -0.037 0.148 -0.290 -0.113 Leaf vein color upper surface 0.037 0.032 -0.034 -0.128 -0.013 0.239 0.097 Leaf vein color lower surface -0.013 0.029 -0.006 0.021 -0.009 0.018 -0.051 Leaf margin color 0.019 -0.024 -0.006 0.024 -0.134 -0.249 -0.466 Leaf shape 0.055 -0.075 -0.287 0.220 0.560 -0.535 0.060 Leaf apex shape -0.043 -0.006 -0.038 -0.020 -0.071 0.123 0.105 Hair on leaf surface 0.010 -0.021 -0.019 -0.004 -0.027 0.010 -0.067 Leaf size -0.003 -0.056 0.077 0.057 -0.091 0.128 -0.084 Leaf density 0.021 0.057 -0.034 -0.041 0.005 -0.023 -0.006 Leaf lobation 0.044 0.066 -0.012 0.021 -0.136 -0.043 0.014 Waxiness on leaf 0.008 0.050 0.007 0.057 0.105 -0.030 0.042 Tip color 0.046 0.066 -0.107 0.107 -0.039 -0.174 0.043 Petiole color 0.015 0.095 0.507 -0.710 0.220 -0.209 0.076 Petiole wing color 0.036 0.071 -0.049 0.091 0.024 -0.071 -0.247 Twining direction -0.014 -0.006 -0.007 0.075 0.066 0.016 0.030 Vine color -0.030 -0.045 0.018 0.002 0.010 0.063 0.129 Wing color -0.029 0.011 -0.026 0.103 -0.101 -0.094 0.152 Presence/absence of spine 0.070 0.039 0.084 -0.008 0.081 0.108 -0.031 Spine shape 0.131 0.054 0.137 -0.016 0.124 0.164 -0.089 Color at spine base 0.062 0.022 0.078 0.027 0.123 0.100 -0.024 Spine on vine base 0.482 0.498 0.428 0.410 -0.165 -0.144 0.076 Tuber shape 0.008 0.062 -0.255 -0.269 -0.578 -0.458 0.338 Tuber flesh color 0.461 -0.812 0.258 0.094 -0.114 -0.103 0.083 Tuber skin color 0.020 -0.078 -0.051 -0.090 0.238 -0.071 0.119 Tuber branching 0.010 0.067 0.052 0.159 0.207 0.087 0.669 Tuber surface texture -0.036 0.008 -0.042 0.010 -0.024 -0.013 -0.051 Hair on tuber surface 0.026 0.080 0.091 -0.040 -0.031 -0.213 -0.075 Flower 0.040 0.016 0.045 -0.016 0.073 -0.087 -0.096 Flesh color at central transverse 0.717 0.155 -0.496 -0.317 0.125 0.157 -0.061
113 Figure 4.3. The Bi-plot diagram of PCA I and PCA II of 36 yam landraces based on 29 qualitative traits.
4.3.2. Morphological diversity analysis based on quantitative characters
In the present study, the mean performance, cluster and divergence analysis were assessed based on quantitative characters in order to constitute the landraces into homogenous groups and measure the distance between the groups/clusters.
4.3.2.1. Analysis of Variance The analysis of variance of quantitative characters revealed significant difference at (p<0.01) among the landraces for 13 of the 19 characters suggested high degree of genetic variability in the materials evaluated and the existence of considerable genetic diversity among landraces for selection (Table 4.6). The characters that manifested significant difference among the landraces were leaf length, leaf width, petiole length, vine length, internode length, number of internode vine-1, number of vine hill-1, days to maturity, tuber length, tuber diameter, tuber fresh weight, tuber dry weight and harvest index. In contrary, non-significant difference was observed among the landraces for length of leaf lobe, distance between lobs, tip length, number of tubers hill-1, vine fresh weight and vine dry weight revealed that the contribution of these characters for the variability was low and therefore, it was discarded from further analysis. Baye et al. (2005) and
114 Kifle (2006) also reported similar results for the majority of the characters in potato, taro and aerial yam, respectively.
Table 4.6. Analysis of variance for quantitative characters
Trait Replica Mean square of Mean square Mean square of LSD Efficienc CV tion landraces of Blocks error y relative (%) (DF=1) (DF=35) within Reps to RCBD (Adj.) (%) Unadju Adjuste (DF=10) Intra RCBD 0.01 sted d block(25) (35) LL 1.11 3.75 3.15** 1.49 0.34 0.67 1.81 159.24 5.59 LW 0.10 0.49 0.46** 0.25 0.07 0.12 0.85 138.69 6.53 PL 1.04 8.59 7.56** 4.36 3.23 3.55 4.89 102.43 17.5 LLO 0.45 0.65 0.44 0.80 0.62 0.67 ns 101.64 39.8 DBL 0.17 0.35 0.35 0.34 0.56 0.45 ns 86.40 23.6 VL 950.4 339.38 192.7** 418.22 34.29 143.9 18.3 332.63 2.29 IL 0.03 0.51 0.32** 0.43 0.13 0.22 1.13 134.88 3.84 TiL 0.08 0.22 0.22 0.21 0.47 0.40 ns 84.20 27.3 NIPV 0.04 13.05 10.76** 3.27 2.22 2.52 4.05 104.02 5.73 NVPH 0.43 0.59 0.57** 0.32 0.21 0.24 1.26 104.14 10.9 DM 325.2 230.25 175.9** 83.52 64.13 69.67 21.8 101.88 5.78 NTPH 0.17 0.11 0.10 0.15 0.12 0.13 ns 100.93 8.12 TL 0.63 8.27 5.22** 6.97 2.22 3.58 4.54 134.85 3.84 TuDi 0.01 7.00 5.59** 1.57 1.96 1.85 3.82 94.36 9.40 VFW 76.50 10.11 13.21 20.15 17.43 18.20 ns 100.58 33.7 VDW 0.73 1.02 0.95 0.95 1.08 1.05 ns 96.44 33.5 TFW 13.66 333.03 219.1** 16.42 9.53 11.50 9.10 107.74 10.3 TDW 0.004 5.58 5.01** 1.09 1.04 1.06 2.78 100.06 4.93 HI 325.3 230.25 175.9** 83.52 64.13 69.67 21.8 101.88 11.7 ** = Highly significant at 0.01 level of probability. ns= non significant LL= Leaf length (cm), LW= Leaf width (cm), PL=Petiole length (cm), LLO= Length of leaf lobe (cm), DBL= Distance between lobs (cm), VL= Vine length (cm), IL= Internode length (cm), TiL= Tip length (cm),NIPV= Number of internodevine-1, NVPH= Number of vine hill-1, DM= Days to maturity, NTPH= Number of tubers hill-1,TL= Tuber length (cm), TuDi= Tuber diameter (cm), VFW= Vine fresh weight (t/ha), VDW= Vine dry weight (t/ha), TFW= Tuber fresh weight (t/ha),TDW= Tuber dry weight (t/ha) and HI= Harvest index (%). RCBD= Randomized complete block design, LSD= Least significant difference, CV=Coefficient of variation
115 4.3.2.2. Mean performance of yam The descriptive values of the landraces based on quantitative characters are shown in Table 4.7. The mean values of the landraces for various characters showed differences among the landraces. A wide range of variation in the characters studied was observed. The highest value was almost twice of the minimum value for most of the character that was considered in this study, for example, two fold for petiole length, vine fresh weight and vine dry weight; three fold for length of leaf lobs and harvest index and nine fold for tuber fresh weight (Table 4.7).
Table 4.7. Mean standard deviation and ranges of 19 quantitative traits of Dioscorea spp.
No Quantitative character Mean SE Range
1 Leaf length (cm) 10.55 0.17 8.28- 14.16
2 Leaf width (cm) 4.24 0.06 3.00-5.13
3 Petiole length (cm) 10.25 0.28 5.82 – 14.00 4 Length of leaf lobe (cm) 2.03 0.09 1.18 – 3.73
5 Distance between lobs (cm) 3.44 0.07 2.57 -4.29
6 Vine length (cm) 254.61 1.86 235.0 – 296.75 7 Internode length (cm) 9.70 0.07 8.89 – 10.96 8 Tip length (cm) 2.55 0.06 1.99 – 3.38 -1 9 Number of internode vine 25.97 1.32 21.40 – 33.00 -1 10 Number of vine hill 4.27 0.07 3.40 -6.10
11 Days to maturity 138.44 1.45 98.98 - 157.57 -1 12 Number of tubers hill 4.36 0.04 3.92 – 4.83 13 Tuber length (cm) 38.82 0.28 35.56 – 43.83
14 Tuber diameter (cm) 14.91 0.24 11.47 - 19.52
15 Vine fresh weight (t/ha) 12.42 0.45 8.00 - 19.83
16 Vine dry weight (t/ha) 3.13 0.11 2.58 - 7.05
17 Tuber fresh weight (t/ha) 29.90 1.53 6.80 -63.00
18 Tuber dry weight (t/ha) 20.70 0.21 16.93 – 23.57
19 Harvest index (%). 68.40 1.45 28.98 - 87.57
116 4.3.2.3. Cluster analysis based on quantitative characters Grouping of landraces based on their similarity is crucial (Franco and Ccosa, 1997). When the dendrograms cut at 0.75 average distances, the landraces were grouped into six clusters based on 13 characters (Table 4.8). The distribution of the landraces was evident from different clusters. Among the clusters, cluster I and II were the largest, having 30 (83.33%) landraces. Of these, 20(55.55%), from Jimma, six (16.67%) from Bench-maji and four (11.11%) from Sheka zone, indicated the overall genetic similarity among them. Cluster III and IV accommodated four (11.11%) landraces. Of these, three (8.33%) from Jimma and one (2.78%) from Bench maji zones. The remaining cluster V and VI contained two (5.55%) landrace collected each from Jimma and Bench maji zones of Southwest Ethiopia (Figure 4.4). Grouping pattern of landraces did not show relationships between genetic divergence and geographical diversity (Singh et al., 2005; Mengesha et al., 2010).
1.50 Average distance between clusters
1.25
1.00
IV VI 0.75 V I II 0.50 III
0.25
0.00 1 9 12 21 2 3 15 7 16 8 27 11 10 19 31 20 33 32 30 22 34 14 25 13 6 18 4 17 5 24 36 26 23 35 28 29
Name of Observation or Cluster Serial number of landraces
Figure 4.4. Dendrogram showing hierarchical clustering patterns of 36 Dioscorea spp landraces ) (UPGMA) based on 13 quantitative traits.
Usually, geographical diversity has been considered as a measure of genetic diversity. However, this is an inferential criterion andccession it may numbernot be so effective in quantifying different population. The present pattern of grouping of landracesI indicated that the genetic diversity was not fully related to the geographical diversity.
117 These results are in agreement with the findings of Satish et al. (2009). There are forces other than geographical separation which are responsible for diversity such as natural and artificial selection, genetic enrichment, genetic drift and environmental variation (Sirohi and Dar, 2009). Therefore, choice of the parents for hybridization should be decided on the basis of genetic diversity rather than geographic diversity (Rumesh et al., 2014).
Table 4.8. Distribution of 36 Dioscorea spp. into five clusters
Clusters Number of Serial Landraces % of landraces number contribution I 23 1,9,12,21,2,3,1 59/02, 08/02, 06/83, 7/83, 68/01, 63.89 5,7,16,8,27,11, 6/02, 45/03, 54/02, 27/02,46/83, 10,19,31,20,33 60/87, 01/75, 116, 76/02, 57/76, ,32,30,22, 34, 06/2000, 0004/07, 0004/07, 21/02, 14 and 25 58/02, 7/84,07/03 and 24/02 II 7 5,24,36,26,23, 3/87, 32/83, 06/2001, 2/87, 39/87, 19.44 35 and 28 7/85 and 15/2000 III 2 6 and 18 56/76 and 10/002 5.55 IV 2 4 and17 75/02 and 37/87 5.55 V 1 13 17/02 2.78 VI 1 29 34/87 2.78
The cluster mean for various quantitative traits revealed that considerable differences were noticed between the cluster means for different characters (Table 4.9). Maximum mean values for tuber fresh yield were observed in cluster III and V; for tuber dry weight in clusters II, IV; for leaf length and width in clusters III, VI and II; and for petiole length in clusters III and I. Thus, it was observed that landraces grouped under cluster III and IV showed ranked first by having the highest mean performance for most of characters considered in this study. For example, longest and widest leaves, longest tuber, late maturity, highest tuber fresh weight and harvest index. On the other hand, cluster VI, which consisted of one landraces, had the least in performance for the majority of quantitative characters (Table 4.9). For example, the landraces grouped under this cluster gave the least vine length, petiole length, internode length, tuber length and number of internode vine-1. Therefore use of landraces in cluster III, IV and V would be desirable to generate the variability in the desired direction. It is suggested that hybridization among the
118 landraces of above mentioned clusters would produce segregants for more than one economic character which can serve as parents of hybrids.
Table 4.9. Cluster means for 13 quantitative traits of Dioscorea spp. grown at Jimma
Cluste LL LW PL VL IL NIPV NVP DM TL TDi TF TD HI rs H W W I 10.1 4.0 10.1 256.9 9.80 27.10 4.30 139.9 39.0 14.8 29.9 21.0 69.9 II 10.8 4.2 9.92 246.9 9.50 25.10 4.03 121.8 38.0 14.0 13.6 21.2 51.8 III 11.0 4.4 10.6 265.5 9.97 26.85 4.60 154.8 39.9 16.0 58.4 19.4 84.8 IV 9.91 4.0 7.29 288.8 10.2 31.00 5.45 143.4 40.8 13.6 32.2 21.4 73.4 V 10.1 4.3 9.77 240.5 9.12 27.10 3.40 149.4 36.5 17.2 49.2 18.1 79.4 VI 10.7 4.1 8.17 235.0 9.01 22.50 4.20 98.98 36.0 12.6 6.80 20.5 28.9 Mean 10.4 4.2 9.3 255.6 9.6 26.6 4.3 134.7 38.4 14.7 31.7 20.3 64.7 S.div 0.5 0.2 1.3 19.6 0.5 2.8 0.7 20.8 1.9 1.7 19.9 1.3 20.8 LL= Leaf length (cm); LW= Leaf width (cm); PL= Petiole length (cm); VL= Vine length (cm); IL= Internode length (cm); NIPV= Number of internode vine-1; NVPH= Number of vine hill-1; DM= Days to maturity, TL= Tuber length (cm); TDi= Tuber diameter (cm); TFW= Tuber fresh weight (t/ha);TDW= Tuber dry weight (t/ha) and HI= Harvest index (%) and S.div= Standared deviation.
4.3.2.4. Distance between clusters The pair wise generalized square distances (D2) between the clusters was highly significant (p<0.01), suggesting high diversity among landraces grouped into different clusters. The inter cluster distance (D2) between six clusters are presented in Table 4.10. The nearest inter cluster distance was found between cluster I and IV (23.49) followed by cluster I and III (29.81). The widest inter cluster distance was found between cluster III and V (380.09), followed by cluster IV and VI (261.5). Maximum genetic divergence between the cluster points to the fact that hybridization among the landraces included with them would produce potential and meaningful hybrids and desirable segregants. Use of genetically distant landraces as parents to get the most promising breeding material had been suggested by Sakhti et al. (2009). Thus, inter mating between landraces grouped under these clusters may give high heterotic response and thereby better segregants in view of the genetic diversity. For instance, in this study, based on their tuber fresh yield, selecting and crossing landraces 56/76 (serial number 6) from cluster III with landraces 17/02 (serial number 13) from cluster V and 37/87 (serial number 17) from cluster IV with landraces 10/002 from cluster III may produce desirable recombinants for high tuber fresh
119 weight (Table 4.8). On the other hand, the highest intra cluster distance was observed for cluster VI (7.16), indicating that the landraces in this cluster were more divergent than any other groups. The lowest intra cluster distance was recorded for cluster I (1.38), showing that the landraces in this group were genetically closer than any other groups. Crossing between landraces belonging to the same or genetically related clusters might not be expected to yield of desirable segregants. Furthermore, intensive selection for agronomically important traits and similarity in parentage might be the cause of narrow genetic diversity and uniformity between these clusters. In agreement with this Keneni et al. (1997) reported that, during selection of parents, the special merits of each cluster and each landraces within a cluster should also be considered depending on the specific objective of hybridizations.
Table 4.10. Pair wise generalized squared distances between five clusters of Dioscorea spp. collected from Southwest Ethiopia
Cluster I II III IV V VI I 1.38 61.14 ** 29.81 ** 23.49 ** 231.48** 118.55** II 2.77 90.83 ** 53.04** 211.36** 133.83** III 5.71 57.80 ** 380.09** 257.10** IV 5.78 219.88** 261.50** V 5.78 149.17** VI 7.16 2 = *,**= Significant at 0.05 level of probability (x 11 18.31) 2 **= Highly significant at 0.01 level of probability (x 11 = 23.21)
In the present study, the clusters obtained from divergence analysis based on quantitative characters (Table 4.8) and grouping based on qualitative characters (Table 4.2) lacked correspondence in many landraces. For instance, landraces included into cluster I based on quantitative traits appear from cluster I, II and V of qualitative characters. This revealed that there was no close grouping of the landraces based on quantitative and qualitative characters. The reason might be due to the higher influence of the environmental factor on the quantitative traits as compared to qualitative traits. This finding is in line with the result of Ross et al. (2003) and Garedew (2006) who found low correspondence in the clustering of landraces for both types of variables in Colombian collections of Arracacha (Arracacia xanthorrhiza Bancroft) and
120 Jimma collection of Ethiopian potato (Plectranthus edulis L.). Conversely, a study was conducted on cassava (Manihot esculenta Crantz) and taro (Colocacia esculenta L.) that showed better degree of correspondence between clusters formed by both types of variables (Amsalu, 2003; Kifle, 2006; Sirohi and Dar, 2009). In this study there was no clear grouping of the landraces according to regions of collection. Landraces of one region were classified into different clusters although some of them belonged to the same cluster.
121 4.4. CONCLUSION
In the present study, cluster analysis based on morphological qualitative characters had nine distinct clusters with varying sizes, based on their foliar and subterranean traits which will be highly useful for genetic improvement.
The result of Shannon-Weaver diversity index also revealed existance of maximum diversity between yam landraces and hybridization among the landraces included with them would produce best hybrids and desirable segregants.
Based on quantitative traits, the analysis of variance indicated significant variation (p<0.01) among the landraces on leaf length, leaf width, petiole length, vine length, internode length, number of internode vine-1, number of vine hill-1, days to maturity, tuber length, tuber diameter, tuber fresh weight, tuber dry weight and harvest index. Thus, the contribution of these traits to the variability was high and selection based on these traits is more effective for genetic enhancement of the crop.
The cluster and distance analysis of quantitative characters also showed the existence of six divergent groups. Crossing between landraces grouped under cluster III and IV may give desirable recombinants for high tuber fresh weight due to widest inters cluster distance. As a result, the diversity and genetic variability of yam landraces in Southwest Ethiopia is high and the scope of its improvement is wide.
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126 V ______
GENETIC VARIABILITY, CORRELATION AND PATH ANALYSIS OF TUBER YIELD AND YIELD COMPONENTS OF YAM (Dioscorea spp.) IN SOUTHWEST ETHIOPIA ______
127 Genetic Variability, Correlation and Path Analysis of Tuber yield and Yield components of Yam (Dioscorea spp.) in Southwest Ethiopia
ABSTRACT
The study was conducted to estimate the magnitude of genetic variation, association between tuber yield and related traits and to identify the most influential character(s) involving 36 landrace collections of yam for effective selection and conservation. Field evaluations were conducted at Jimma Agricultural Research Center in Ethiopia using a 6x6 lattice design with two replications during 2015. Variance analysis of characters revealed significant differences (p<0.01) among the landraces. Estimate of phenotypic and genotypic coefficients of variation showed variability among the landraces. Comparatively high genotypic (14.87, 8.36 and 5.38%) and phenotypic coefficients of variation (40.11, 24.03 and 15.17%) were observed for tuber fresh weight, petiole length and number of vine hill -1 in the order of magnitudes. Heritability (13.70% and 12.10%) coupled with genetic advance as percent of mean (11.56% and 5.94%) were recorded for tuber fresh weight and petiole length respectively. Correlation study between different quantitative characters showed highly significant association among characters. At genotypic level, tuber fresh weight (t/ha) showed significant and positive correlation with tuber length (r=0.34*), vine length (r=0.39**), days to maturity (r=0.92**), internode length(r =0.34*), tuber diameter (r=0.45**) and harvest index (r=0.92**). Tuber fresh yield is a complex agronomic trait induced by many associated traits directly and indirectly and thus, selection of yams based on these traits will enhance tuber yield and may need high concern towards tuber yield improvement. Analysis of path coefficients at genotypic level revealed that tuber length (p=48.292) had maximum positive direct effect on tuber fresh weight followed by vine length (p=2.089). This study revealed that selection of landraces based on tuber length may improve genetic gain in storage tuber yield in yam breeding. Using the above results, the following collections such as: 27/02, 56/76, 08/02, 10/002, 39/87, 45/03, 6/02, 116, and 7/83 were selected for breeding and conservation. Keywords: Association, direct selection, diversity, genotype, indirect selection, landraces,
128 5.1. INTRODUCTION
Yam (Dioscorea species) belonging to the family Dioscoreaceae is cultivated in Africa, Asia, parts of South America, as well as Caribbean and the South Pacific islands for its storage tubers (Asiedu and Alieu, 2010). The genus comprises over 600 species (Dansi et al., 2013; Marcos et al., 2014), and only 10 of them are cultivated for human food. The economically significant yam species include; D. alata, D. esculenta, D. batatas or D. opposite, D. bulbifera, D. cayenensis, D. rotundata complex, D. dumetorum, D. trifida, D. nummularia and D. pentaphylla (Girma et al., 2012; Himanshu et al., 2016). Among all species, D. alata, D. bulbifera, D. cayenensis and, D. rotundata complex are the most widely cultivated species and have real economic significance in Africa (Lebot, 2009; Asiedu and Alieu, 2010; Norman et al., 2012). Ethiopia is considered to be the center of origin and diversity of most yam species (Coursey, 1967; Terauchi et al., 1992; Tamiru et al., 2011). In the country a large number of yam landraces are cultivated but no systematic genetic conservation has been done (Hildebrand et al., 2003). In South west Ethiopia, yams are predominantly grown by smallholder farmers for food, traditional medicine, and to earn cash incomes. Women farmers are the main producers and traders of yams in Ethiopia. Yam is increasingly showing high market value owing to its consumer demand for food and medicine purposes making it an ideal candidate for market-driven production (Himanshu et al., 2016).
Despite its food security and economic benefits, production of yams in the Southwest region of Ethiopia is mostly on a small scale and average crop yield is low about 4 t/ha (Tamiru et al., 2011) as compared to West African countries (10.5 t/ha) (Asiedu and Alieu, 2010; Dansi et al. 2013). Hence there is an urgent need to increase the production of yam in order to meet up the demand and genetic resource conservation. Furthermore, a large number of yam landraces are cultivated in the region, but no serious attempt has been made to improve them for higher productivity and acceptability (Hildebrand et al., 2002). Thus, there is a need to improve productivity of the crop through breeding. To enhance productivity, genetic restructuring of landraces is essential to develop high yielding landraces (Rakesh et al., 2013). In addition, much confusion exists among the cultivated varieties (landraces) of the species, perhaps due to the presence of hundreds of variants carrying numerous complex characteristics that overlap (Sesay et al., 2013). Yam breeders are interested to develop cultivars with high yield and other desirable agronomic characters through selection. Selection of important traits depends on the amount of
129 genetic variation present in the area (Okoli, 1988; Mazid et al., 2013). The assessment of genetic variability and association between agronomic traits in the existing landrace is the key component of any breeding program for broadening the gene pool of any crop (Appalaswamy and Reddy, 2004; Arshad et al., 2006). Moreover, estimates of heritability of a trait is crucial in determining the response to selection, it implies the extent of transmissibility of traits into next generations (Mazid et al., 2013). Besides, the genetic improvement of plants for quantitative traits requires reliable estimates of heritability in order to plan an efficient breeding program. In addition to heritability, estimate of genetic advance offers the best instrument of any crop including yam in any trait by selection of superior landraces (Larik and Rajput, 2000; Surek and Beser, 2003; Male et al., 2014).
Tuber yield and yield components are important selection criteria of promising yam genetic resources. Tuber yield is directly or indirectly affected by yield components requiring selection of relevant traits positively correlated with it. Therefore, knowledge on the interrelationship and degree of association of yield and yield components is useful to improve selection efficiency in yam breeding and conservation efforts. According to Kumar and Shukla (2002) information of association such as genotypic and phenotypic correlation between yield and its component traits is vital for yield improvement through selection programs. As correlation alone hardly explain the relationships among the characters, therefore partitioning of total correlation into direct and indirect effects by path analysis helps make selection more effective (Faisal et al., 2007; Biabani and Pakniyat, 2008). Path coefficients give the relative contribution of various yield determining traits, enabling breeders to decide between direct and indirect selection procedures (Paul et al., 2013). The direct and indirect effects of traits on economic yield can be determined through path analysis. Path analysis has been used in a number of crops to study the relationships between yield and yield components (Christopher, 2000). There is little information regarding the application of path analysis in the selection and conservation of yam landraces. This information could provide valuable insight when evaluating diverse landraces of yam for targeted breeding. Further, isolating the most influential yam traits would enhance the selection responses in yam improvement programs. Therefore, the objectives of this study were to determine the magnitude of variability, character association between tuber yield and related traits and to identify the most
130 influential character(s) involving 36 landrace collections of yam for effective selection and conservation.
5.2. MATERIALS AND METHODS
5.2.1. Description of the study area The experiment was conducted at Jimma Agricultural Research Center (JARC). The center is located at latitude 7o 40.00' N and longitude 36o 47’.00’ E with an altitude of 1753 meters above sea level (m.a.s.l.). The area receives mean annual rainfall of 124.6 mm with mean maximum and minimum temperatures of 26.2 0C and 12.0 0C, respectively. The soil of the study site is Eutric Nitosol (reddish brown) with pH of 5.3.
5.2.2. Experimental materials, design and management A total of 36 yam landraces were collected from Jimma, Sheka and Bench-maji zones of Southwest Ethiopia. The experiment was laid out in a 6x6 simple lattice design with two replications. Plants were field established using a 7m long rows using inter-row spacing of 1.5m and intra-rows spacing of 1m. Tubers of the same size which started sprouting were used as planting material. One month after planting, seedlings were earthed up followed by frequent weeding. All other agronomic practices were followed according to the recommendations and farmers practices of the areas. Each yam plant was tended using dried coffee stick of 3.5-4.5 meters long to provide support and induce good canopy and vine development. Five middle plants within a row were sampled and tagged for data collection and final harvest.
5.2.3. Data collection A total of 13 quantitative data were collected according to the descriptor of yam (Dioscorea spp.) developed by Bioversity International (IPGRI, 1999). Data were collected on individual plant and the whole plot basis from the middle of five plants and the average value was used for analysis. Data from individual plant and plot basis are; leaf length (cm), leaf width (cm), vine length (cm), petiole length (cm), days to maturity, number of internode vine-1, number of vine hill-1, internode length (cm), tuber length (cm), tuber diameter (cm), tuber fresh weight (t/ha), tuber dry weight (t/ha) and harvest index (%). The detail procedure of all quantitative data is presented in chapter 4 section 4.2.4.2.
131 5.2.4. Data analysis All quantitative data were subjected to analysis of variance (ANOVA) using the lattice procedure as suggested by Gomez and Gomez (1984) using SAS version 9.0 (SAS, 2000), Genres (2008) and META R version 5.0 (META R, 2015) statistical software packages. Means were separated using the Least Significant Difference (LSD) procedure at 5% and 1% level of significance. The analysis of variance (ANOVA) was made using model for simple lattice design as follow: Yijklm = µ + ti + j + + yl + + th Where, Yijklm = response of Y trait from the i landraces, jth replication, µ= Overall mean th th effects, ti= Effects of i level of treatments, β= Effects of j level of replication, χk= Effects of th th K level of blocks within replications (adjusted for treatments), yl = Effects of l level of intra th block error, πm= Effects of the m randomized complete block error and Σijklm= is a random error component.
Analysis of covariance was done for each pair of characters to obtain the sum of cross products to be used in covariance calculation. 2 g xy = MSCPaxy - MSCPexy )/r 2 Where: gxy= genotypic covariance between character x and y; MSCPaxy = Mean sum of cross product of landraces for variable x and y, MSCPexy = Mean sum of cross product of error for variable x and y.
Variability was estimated by simple measures, viz., range, standard error, phenotypic and genotypic variances and coefficient of variations. Based on the expected mean squares, phenotypic variations and coefficient of variations were calculated according to the method suggested by Burton (1953) and Burton and de Vane (1953). 2 2 2 p = g + e 2 2 2 Where, σ p = phenotypic variance, σ g = genotypic variance and σ e = environmental variance
2 Phenotypic Coefficient of Variation (PCV) = (√σ p / grand mean) x 100 2 Genotypic Coefficient of Variation (GCV) = (√σ g / grand mean) x 100
2 2 2 Heritability in broad sense (H B) = (σ g/σ p) x 100 (Allard, 1960). The expected genetic advance (GA) for each trait was computed using the formula adopted by Johnson et al. (1955) as: GA = (k) (√σ2p) (h2), and GA as % of the mean (GAM) = (GA/ ) x 100; where, k= selection 2 2 differential at 5% selection intensity (k= 2.06), p = phenotypic variance, H = heritability in broad sense and is grand mean of the population in which selections was employed.
132 The Genotypic (rg) and phenotypic (rp) correlation coefficients for tuber yield and its components were estimated by calculating the variance and covariance at phenotypic and genotypic levels using the formula suggested by Singh and Chaudhury (1985) by using Genres statistical software (Genres, 2008). 2 rp = σ pxy / σpx.σpy 2 rg = σ gxy /σgx.σgy 2 Where, rp= Phenotypic correlation coefficient, rg= Genotypic correlation coefficient, σ pxy= 2 Phenotypic covariance between traits x and y and σ gxy = Genotypic covariance between traits x and y.
The values of phenotypic correlation coefficient were tested for significance using tabulated value at a-2 degree of freedom, where a= number of landraces and the values of genotypic correlation coefficients were tested for significance employing ‘t’ statistics as follows: 2 2 t= rg /√ (1-r gxy) /2HxHy
Where, Hx and Hy are the heritability value for character x and y, respectively. The calculated t– value was compared with ‘t’ tabulated value for a-2 degrees of freedom at 5% and 1% levels of significance.
Path coefficient analysis was carried out to partition the total genotypic correlation coefficients into direct and indirect effects. Total tuber fresh yield was considered as dependent variable, while the rest of the variables were considered as independent variables. The independent characters that showed relatively high correlation with total tuber fresh yield at genotypic level were used for path analysis. It was computed according to the method suggested by Dewey and Lu (1959).
rij = pij + r ik p kj
Where, rij = mutual association between the independent character (i) and dependent characters (j) as measured by correlation coefficients, pij = components of direct effects of the independent characters (i) on the dependent characters (j), r ik p kj = summation of components of indirect effect of a given independent character (i) on the dependent characters (j) via all other independent characters (k).
The residual effect (h) was estimated using the formula shown below (Dewey and Lu, 1959).
2 2 h = √ 1-R Where, R = rij pij
133 5.3. RESULTS AND DISCUSSION
5.3.1. Estimates of variability
5.3.1.1. Phenotype and genotype variation The phenotype and genotype variances are given in Table 5.1. The value of phenotype and genotype variation cannot be used for comparing the magnitude of variance for different characters since the mean and units of measurement of the character may be different. Hence, the coefficient of variation expressed at phenotype and genotype levels have been used to compare the observed variability among the characters. In the present study, the Genotype coefficients of variation (GCV) ranged from 1.034 % for tuber length to 14.87% for tuber fresh weight, whereas phenotype coefficients of variation (PCV) ranged from 6.107% for vine length to 40.11% for tuber fresh weight (Table 5.1). This revealed that tuber length and vine length had the least GCV and PCV and tuber fresh weight had the highest GCV and PCV value. The least value of PCV and GCV on tuber length and vine length indicates the existance of limited scope for improvement of these traits through selection of yams due to high influence of environmental factor. This result is supported by the work of Sendek (2004) who reported bulb length and plant height showed the lowest PCV and GCV in shallot. High GCV values on tuber fresh weight indicated the existence of genetic variation for such character. Therefore, selection based on tuber fresh yield is effective. This view is also in agreement with the observation of Baye et al. (2005) and Kifle (2006) who reported selection of potato and taro based on tuber yield and number of corm per hill, respectively, were found to be for their improvement.
-1 Genetic variability of internode length, leaf width, number of vine hill , tuber diameter, leaf length, tuber length and tuber dry weight were relatively lower (Table 5.1) suggesting that there is a need to search for diverse landraces in order to ensure effective selection and hybridization. In this study, wide variations between GCV and PCV were observed in most of the characters, for example, harvest index, petiole length, tuber diameter and number of vine hill-1, indicating high pressure of the environment on these characters and less effect of genetic factors. Thus, selection on phenotype basis may not be effective for the genetic improvement of the crop. On contrary, the narrow gap between PCV and GCV for tuber dry weight, tuber length, internode
134 length and number of internode vine-1, suggests the influence of environment in phenotypic performance to be minimal.
Table 5.1. Estimates of components of variance, PCV, GCV, heritability and genetic advance for 13 quantitative characters of Dioscorea spp. grown at Jimma. 2 2 2 Traits Range Mean g p GCV PCV H Genetic GAM Min Max (%) advance (%) LL 7.34 14.32 10.55 0.150 2.233 3.661 14.143 6.72 0.206 1.953 LW 2.72 5.60 4.27 0.047 0.308 5.076 12.944 15.26 0.176 4.121 VL 224 303.00 254.61 19.16 241.759 1.719 6.107 7.93 2.530 0.994 PL 4.83 14.47 10.25 0.735 6.074 8.362 24.038 12.10 0.614 5.993 DM 98.98 161.2 138.44 3.143 149.957 1.280 8.845 2.10 0.529 0.382 NIPV 20.75 33.60 25.97 0.902 7.811 3.655 10.757 11.55 0.662 2.558 NVPH 3.20 6.20 4.27 0.053 0.419 5.377 15.175 12.65 0.168 3.935 IL 8.54 11.35 9.70 0.012 0.368 1.144 6.254 3.26 0.041 0.422 TL 34.16 45.39 38.82 0.161 5.930 1.034 6.273 2.72 0.135 0.348 TuDi 10.88 20.0 14.91 0.118 4.431 2.301 14.120 2.66 0.113 0.757 TFW 6.80 65.60 29.90 20.608 149.996 14.869 40.114 13.74 3.456 11.558 TDW 16.04 24.60 20.70 0.796 3.321 4.300 8.782 24.00 0.900 4.348 HI 28.98 91.21 68.40 3.143 149.961 2.590 17.892 2.10 0.529 0.773 LL=Leaf length (cm); LW= Leaf width (cm); PL= Petiole length (cm); VL= Vine length (cm); IL= Internode length (cm); NIPV= Number of internode vine-1; NVPH= Number of vine hill-1; DM= Days to maturity, TL=Tuber length (cm); TDi= Tuber diameter (cm); TFW=Tuber fresh weight (t/ha); 2 TDW=Tuber dry weight (t/ha) and HI= Harvest Index (%). Min= Minimum, Max= Maximum, g= 2 Genetic variance, p= Phenotypic variance, PCV= Phenotypic coefficient of variation, GCV= Genotypic coefficient of variation, H2= Broad sense heritability, GAM= genetic advance as percent of mean.
5.3.1.2. Estimates of heritability and expected genetic advance In the present study, the heritability estimates ranged from 2.1% for harvest index and days to maturity to 24.0% for tuber dry weight (Table 5.1). Maximum heritability was obtained from tuber dry weight, followed by leaf width. On the other hand, harvest index, days to maturity, tuber diameter, tuber length and internode length have relatively low heritability estimates (Table 5.1). Moderate heritability estimates were observed for tuber fresh weight, number of vine hill-1 and petiole length. Heritability indicates the ease with which a trait can be improved through
135 selection and could vary with materials studied and the environment. It also indicated the relative importance of genetic makeup in the expression of the characters. The higher value of heritability suggests that selection will be more effective and improvement can be expected for that trait in future breeding programs for similar condition.
In most cases, high heritability alone does not guarantee a large enough to make sufficient improvement through selection in advance generations unless accompanied by a substantial amount of genetic advance (Sendek, 2004; Hefny, 2013). It has been emphasized that without genetic advance, the heritability values would not be of practical importance in selection based on phenotypic appearance. In this study, the values of genetic advance for different characters of Dioscorea spp. landraces were different. These values are also expressed as percentage of the landrace mean for each character, so that comparisons could be made among various characters, which had different units of measurement. The result revealed that the progress that could be expected from selection of landraces ranged from 0.35% for tuber length to 11.56% for tuber fresh weight (Table 5.1).
Genetic advance along with high heritability is a key instrument for selection of the best individuals. In this study, high heritability along with high genetic advance as percent of the mean was obtained for tuber dry weight, tuber fresh weight, leaf width and petiole length. Besides, high GCV along with high heritability and high genetic advance will provide better information than single parameters alone (Sahel et al., 2004; Garedew, 2006). Hence, tuber fresh weight, petiole length, leaf width and number of vines hill-1 exhibited high genotype coefficients of variation, high heritability together with high genetic advance as percent of means. Therefore, selection based on these characters would be very useful for genetic improvement of yam.
High heritability with low genetic advance was recorded for tuber dry weight and leaf width indicating less influence of environment but prevalence of non additive gene action for which simple selection will be less effective. Thus, heterosis breeding would be recommended for these traits improvement. In line with this, Desalegn (2005) reported large value of heritability with less genetic advance of the characters such as bean width, bean thickness and fruit width of coffee. In quantitative traits, the poor estimates of heritability and genetic advance indicate that
136 inheritance of these traits is being influenced by inter allelic interaction rather than intra allelic interaction (Birenda et al., 2014).
5.3.2. Association between characters 5.3.2.1. Genotype correlation coefficients The analysis of correlation coefficients among tuber yield and its contributing characters are shown in Table 5.2. Tuber yield is the result of the expression and association of several plant growth components. Although, correlation coefficient is useful in quantifying the size and direction of trait association can be misleading if the high correlation between two traits is a consequence of the indirect effect of the traits (Dewey and Lu, 1959). Hence, association analysis was undertaken to determine the direction of selection and number of characters to be considered in improving tuber yield. In the present study, more traits were found having high correlation coefficients at genotypic level than at phenotypic level, indicating the inherent association between the traits studied. In agreement with the current study, higher genotype correlation coefficients than their respective phenotype correlation coefficients were reported by Sarkar et al. (2007), Anbanandan et al. (2009), Sabesan et al. (2009), Jayasudha and Sharma (2010) and Keya et al. (2015) on different crops.
Tuber fresh weight (t/ha) had significant positive correlation with tuber length (r =0.34*), vine length (r =0.39**), days to maturity (r =0.92**), internode length(r =0.34*), tuber diameter (r =0.45**) and harvest index (r =0.92**) at genotypic level (Table 5.2). This result is in agreement with Bahandari and Gubta (1991) who reported positive association in plant height and days to flowering, days to maturity, with seed yield in coriander. Garedew (2006) also reported positive association in plant height, stem girth, number of nodes, number of stem hill-1, number of branches, number of tubers hill-1 and tuber dry weight in Ethiopian potato. Similarly, Dagne (2007) reported positive and significant association of tuber length, leaf length and leaf width with tuber fresh yield in taro. Thus, selection of yams having these characters will enhance tuber yield improvement. Tuber dry weight was negatively and significantly correlated with petiole length (r =-0.36*), tuber diameter (r =-0.65**) and harvest index (r =-0.33*). The positive association of internode length with tuber length will make easy simultaneous improvement through selection for two traits.
137 Petiole length was negatively and significantly associated with number of internode vine-1 (r = - 0.48**) and number of vine hill-1 (r = -0.44**) and was positively associated with tuber length (r = 0.42**), leaf length (r = 0.58**), leaf width (r = 0.51**), internode length (r = 0.33*) and tuber diameter (r = 0.49**). Whereas, more number of internodes vine-1 may leads to decrease leaf length, leaf width and petiole length due to negative associations with these traits at genotypic level. Tuber length and diameter were positively and significantly associated with leaf length, leaf width, vine length, petiole length, internode length and with tuber fresh yield at genotypic level. The result also showed that longer and wider leaves would increase tuber yield significantly. Based on the associations between characters, landraces with longer and wider tuber, late maturity and higher harvest index will maximize fresh tuber yield and may need high concern towards tuber yield improvement.
Harvest index showed significant and positive association with days to maturity (r=0.99**), tuber diamete (r=0.40**) and tuber frsh weight (r=0.87**). Longer leaf produced lower number of internode vine-1. Leaf length and petiole length showed significant negative association with number of vine hill-1(r= -0.44) and (r= -0.46) and with number of internode vine-1 (r= -0.40) and (r= - 0.50), respectively. Increase in leaf length and petiole length may lead to reduced number of vine and internode due to negative and significant correlation of the traits. Moreover, vine number hill-1 showed significant and positive genotypic associations with vine length and number of internode vine-1. Ghafoor et al. (2003) showed that more number of vine produced longer vine and more number of internodes.
138 Table 5.2. Genotypic (above diagonal) and phenotype (below diagonal) correlation coefficient among 13 traits in 36 Dioscorea spp landraces grown at Jimma.
Traits TL LL LW VL PL DM NOIV NVPH IL TuDi TDW HI TFW TL 1.00 0.49** 0.34* 0.67** 0.42** 0.16 -0.08 0.07 0.99** 0.22 -0.05 0.16 0.34* LL 0.51** 1.00 0.78** 0.08 0.58** -0.14 -0.40** -0.44** 0.49** 0.42** -0.09 -0.15 0.08 LW 0.37* 0.76** 1.00 -0.05 0.51** -0.02 -0.43** -0.27 0.34* 0.56** -0.30 -0.02 0.19 VL 0.68** 0.11 -0.01 1.00 0.05 0.29 0.57** 0.53** 0.66** -0.02 0.22 0.29 0.39** PL 0.33* 0.6 0.56** -0.01 1.00 0.02 -0.50** -0.46** 0.42** 0.53** -0.36* 0.03 0.095 DM 0.17 -0.15 -0.01 0.29 0.08 1.00 0.21 0.27 0.16 0.41** -0.11 0.99** 0.92** NOIV -0.05 -0.39** -0.43** 0.55** -0.48** 0.18 1.00 0.59** -0.09 -0.29 0.28 0.21 0.19 NVPH 0.03 -0.41 -0.26 0.45** -0.44** 0.21 0.55** 1.00 0.07 -0.34* 0.04 0.27 0.17 IL 0.99** 0.5 0.37* 0.68** 0.33* 0.17 -0.06 0.03 1.00 0.23 -0.05 0.16 0.34* TuDi 0.22 0.39** 0.52 -0.03 0.49** 0.35* -0.26 -0.26 0.22 1.00 -0.65** 0.40** 0.45** TDW -0.05 -0.16 -0.39** 0.17 -0.31 -0.18 0.26 0.05 -0.05 -0.64** 1.00 -0.11 -0.28 HI 0.17 -0.15 -0.01 0.29 0.08 0.99** 0.18 0.21 0.17 0.35* -0.18 1.00 0.92** TFW 0.31* 0.05 0.16 0.36* 0.12 0.87 0.17 0.17 0.32* 0.42** -0.33* 0.87** 1.00 * Significant 0.05 probability level; **= highly significant at 0.01 probability level. TL=Tuber length (cm); LL=Leaf length (cm); LW= Leaf width (cm); VL= Vine length (cm); PL= Petiole length (cm); DM= Days to maturity, NIPV= Number of internode vine-1, NVPH= Number of vine per hill; IL= Internode length (cm); TuDi= Tuber diameter (cm); TDW=Tuber dry weight (t/ha) and HI= Harvest index (%) and TFW=Tuber fresh weight (t/ha);
139 5.3.2.2. Phenotype correlation coefficients As compared to genotypic correlation, the value of phenotype correlation coefficients between most of the characters was non significant with tuber fresh weight (Table 5.2). This may suggest that the genotypic association in most characters with tuber fresh weight is stronger than the environmental correlation which is similarly justified by Arshad et al. (2003). Besides, tuber dry weight also showed non-significant and positive correlation with leaf length, leaf width, number of internode vine-1, days to maturity, number of vine hill-1 and petiole length.
There was significant and negative association between leaf width and tuber diameter with tuber dry weight at phenotype level indicating that wider petiole and tuber reduced tuber dry weight, it might be due to yam tuber might have high moisture content. The result is in agreement with that of Dagne (2007) who also reported that leaf width and tuber diameter reduced tuber dry weight in taro. In this study, the nature of phenotype and genotype correlation coefficients either positive or negative was observed to be more or less similar in respect of the majority of the characters studied. It is of interest to note that the significant and positive correlation coefficients estimated at genotypic level was also found significant and positive at phenotypic level. Moreover, the significantly higher magnitudes of positive genotypic correlation than the corresponding phenotypic correlation in respect to some of the characters suggested that these characters were genetically controlled. Under complex situation the estimates of correlation alone does not provide the true contribution of the characters towards the yield, these genotypic correlation was partitioned into direct and indirect effects through path coefficient analysis. Besides, tuber yield is a complex character associated with a number of component characters that may be interrelated. Rakesh et al. (2013), indicated, interdependence of contributing factors often affecting their direct relationship with yield, and making correlation coefficients unreliable as selection indices. Thus, assessments of direct and indirect effects of different characters on yield are essential (Weber and Moorthy, 1952).
140 5.3.3. Path coefficient The result of path coefficient analysis showed that, tuber length had maximum positive direct effect on tuber fresh weight (p=48.292) followed by vine length (p=2.089), suggesting that simultaneous selection of the two traits may improve genetic gain of tuber yield in yam breeding. This finding is in agreement with Kifle (2006) who reported that tuber length and internode length are important character in making selection in taro. Further, Tsegaye et al. (2006) reported storage tuber length and dry matter contents are the best characters to select Ethiopian sweet potato landraces. Hence, selection on the bases of longer tuber and increased leaf length may maximize storage tuber yield in yam. The negative direct effect of internode length on tuber fresh weight may be explained by the fact that selection based on internode length might reduce tuber yield. Similar finding was reported by Monkola (2013) who indicated, the direct effect of internode length on tuber fresh weight of cassava was small and negative. Whereas these are in contradiction with the result of Dominic et al. (2014) who reported that number of verticals contributed more for tuber yield on taro and cassava, in that order. However, leaf width, days to maturity and harvest index had positive direct effects (Table 5.3).
Vine length also had positive indirect effect on tuber fresh weight through most of the traits except, leaf width and tuber diameter where the direct effect of vine length was found to be positive (Table 5.3). Vine yield components seem to have less competitive effect with tuber fresh weight at path coefficient analysis level and selection based on this trait is important. Leaf width had negative indirect effect through vine length, days to maturity, number of internode vine-1, number of vine hill-1, tuber dry weight and harvest index. It is interesting to note that days to maturity itself had positive direct effect on tuber fresh weight and positive indirect effect through all characters except leaf length, leaf width and tuber dry weight. This finding is similar with the result of (Monkola, 2013) who reported days to maturity being the novel character and had higher direct effect on fresh tuber yield on cassava. The low negative association of tuber dry weight with tuber fresh weight (t/ha), which is not as such important on the basis of correlation estimates, revealed positive direct and indirect supplier to tuber fresh weight via path analysis. Thus selecting landraces based on this character would contribute for a rapid yam tuber yield enhancement program.
141 Table 5.3. Genotypic direct (bold and underlined) and indirect effects of some characters on tuber fresh weight of Dioscorea spp.
Traits TL LL LW VL PL DM NoIV NVPH IL TuDi TDW HI rg
TL 48.292 -0.026 0.338 1.401 -0.268 0.131 0.045 -0.142 -49.21 -0.467 0.094 0.153 0.34* LL 23.830 -0.053 0.762 0.172 -0.366 -0.119 0.208 0.804 -24.303 -0.866 0.157 -0.139 0.08 LW 16.81 -0.042 0.972 -0.107 -0.326 -0.014 0.226 0.502 -17.172 -1.152 0.517 -0.017 0.19 VL 32.38 -0.004 -0.050 2.089 -0.003 0.238 -0.299 -0.970 -32.924 0.048 -0.388 0.278 0.39** PL 20.51 -0.031 0.503 0.011 -0.631 0.017 0.261 0.848 -20.953 -1.092 0.623 0.020 0.095 DM 7.935 0.008 -0.017 0.622 -0.013 0.799 -0.111 -0.501 -8.064 -0.833 0.195 0.935 0.92** NoIV -4.221 0.021 -0.425 1.211 0.319 0.171 -0.516 -1.085 4.399 0.601 -0.489 0.201 0.19 NVPH 3.787 0.023 -0.269 1.118 0.295 0.221 -0.309 -1.812 -3.760 0.698 -0.078 0.258 0.17 IL 48.291 -0.026 0.339 1.398 -0.268 0.131 0.046 -0.138 -49.212 -0.469 0.095 0.152 0.34* TuDi 11.096 -0.022 0.551 -0.049 -0.339 0.327 0.152 0.622 -11.346 -2.034 1.107 0.383 0.45** TDW -2.692 0.004 -0.296 0.479 0.232 -0.092 -0.149 -0.083 2.786 1.329 -1.694 -0.107 -0.28 HI 7.935 0.008 -0.017 0.622 -0.013 0.799 -0.111 -0.501 -8.094 -0.833 0.195 0.935 0.92** Residual effect= 0.012 TL=Tuber length (cm); LL=Leaf length (cm); LW= Leaf width (cm); VL= Vine length (cm); PL= Petiole length (cm); DM= Days to maturity, NIPV= Number of internode vine-1; NVPH= Number of vine hill-1; IL= Internode length (cm); TuDi= Tuber diameter (cm); TDW=Tuber dry weight (t/ha) and HI= Harvest index (%).
Days to maturity had comparatively high positive direct effect (0.799) on tuber fresh weight. Besides, it had positive and highly significant (p<0.01) association with tuber fresh weight (t/ha). This is in agreement with Norman et al. (2011), who reported in taro landraces with late maturing and wider leaves had high fresh tuber yield. It had negative indirect effects via leaf length, tuber dry weight and negligible indirect negative effect through leaf width. Therefore, it is important to consider landrace with late maturity in improving tuber yield in yam, as it was strong and positively associated with tuber fresh weight (t/ha) and its direct and indirect positive effect through yield contributing traits to tuber fresh yield (t/ha).
The direct effect of tuber length on tuber fresh weight was positive and high (48.292). Positive direct effect of tuber length on tuber fresh weight was also reported by Ntawuruhunga et al. (2001) in cassava. Besides, the indirect effect of tuber length on tuber fresh weight through leaf length, leaf width, vine length, petiole length, days to maturity, number of vine hill-1, tuber diameter, internode length and harvest index was high and positive. In contrast, the indirect influence through number of internode vine hill-1 and tuber dry weight was higher and negative.
142 Therefore, selection based on tuber length is important to maximize tuber yield. Internode length had a significant positive association with tuber fresh weight. This positive association did not contribute to fresh tuber yield directly but, indirectly through tuber length, leaf width, vine length, days to maturity, number of internode vine-1, tuber dry weight and harvest index. Besides, internode length and tuber diameter had contributed indirectly to fresh tuber yield. The value of number of internode vine hill-1 had small and negative direct (-0.516) effect on tuber fresh yield. Therefore, efforts required in breeding cultivars with a higher length of storage tuber can be achieved through selection of landraces. Landraces which produced the highest tuber fresh weight were 10/002, 56/76, 17/02, 39/87, 27/02, 116, 7/83, 08/02, 59/02, 45/03 and 6/02. The residual effect (h=0.012) is indicated that, the trait considered in this study are enough to adequately explain the variation in fresh tuber yield. This means, about 98.80% of the total variability in fresh tuber yield t/ha was contributed by 12 independent traits that were assessed in this study.
143 5.4. CONCLUSION
Based on the results of the present study, more traits were found to have high correlation coefficients at genotypic level than at phenotype level. This may suggest that the inherent association of landrace in most characters with tuber fresh weight is stronger than the environmental factor.
Tuber fresh weight, petiole length, leaf width and number of vines hill-1 exhibited high genotype coefficients of variation, high heritability together with high genetic advance as percent of means. Thus, selection of yams having these characters will enhance genetic improvement of Dioscorea spp.
The results of path coefficient analysis at genotype level revealed that tuber length had maximum positive direct effect on tuber fresh weight followed by vine length. High indirect effect was also exhibited via tuber length by most of the traits; hence, selection on the bases of tuber length and increased vine length may maximize storage tuber yield in yam.
The value of residual effect was low (h=0.012), indicating, the trait considered in this study are enough to adequately explain the variation in fresh tuber yield of yam.
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149 VI ______
INFLUENCE OF MATURITY STAGES OF YAM (Dioscorea spp.) LANDRACES ON YIELD AND QUALITY TRAITS ______
150 Influence of maturity stages of Yam (Dioscorea spp.) landraces on yield and quality traits
ABSTRACT
The study was conducted to appraise the effect of harvesting stages of yams on yield and quality traits. Two dominant landraces Woko and Welmeka from Southwest Ethiopia were harvested at six different stages at Jimma Agricultural Research Center using randomized complete block design with three replications. Data on 22 agronomic and 14 biochemical traits were collected and subjected to analysis of variance and mean analyses. The analysis of variance of agronomic traits revealed that, maturity stages had highly significant (p≤0.01) effect on most of the traits was considered in this study. At biochemical level, maturity stages had also highly significant effect (p≤0.01) on flour moisture content, dry matter, ash, organic carbon, fat, carbohydrate, total phosphorus, energy, tannin and saponin contents. From all traits, tip length, tuber diameter, tuber dry weight, total yield, flour moisture content, total phosphorus and saponin contents were showed significant effect on landrace and stages of maturity. Besides, there was significant interaction effect between maturity stages and landraces on tuber diameter, flour moisture, ash, and organic carbon contents. Mean square due to landrace was found to be highly significant variation (p≤0.01) for bulbils length, tip length, bulbils fresh weight, bulbils dry weight, tuber diameter, tuber dry weight, total yield and flour moisture content. The significant variation among yield and quality traits at genotypic level further indicated the positive chance for developing varieties with high yield and quality of yam. Delaying maturity had great contribution to increased bulbils length, number of internode vine-1, tuber fresh weight, tuber dry weight, total yield, flour moisture content, dry matter, organic matter, ash, crude fiber, total nitrogen, protein, fat, carbohydrate, total phosphorus, total energy and saponin contents in yams, while, harvest index, organic carbon and tannin contents decreased at the later harvests on bulbils and storage tuber of yams. Using the overall comparison, later maturing landraces, structurally and physiologically matures in all aspects, high possibility to draw nutrients from the soil and photosynthesize longer time than early maturing and had significant impacts on economic yield and quality traits of yam.
Keywords: Agronomic, biochemical, harvesting stages, welmeka, woko
151 6.1. INTRODUCTION
Yam (Dioscoria spp.) is a crop of major economic and cultural importance in tropical Africa, which accounts for 95% of the world production (FAO, 2005; Dansi et al., 2013). In sub-Saharan Africa, it is the third most important staple food crop after cassava (Manihot esculenta Crantz) and sweet potatoes [Ipomoea batatas (L.) Poir] and the major source of food energy providing up to 285 calories per person per day for 300 million people (Degras, 1983; Bensi et al., 2004; Abebe et al., 2013). Yam is mainly grown for their storage tuber and bulbils yield which are consumed as fresh after boiling or processed in different forms. Its contribution as an alternative to bread is very pronounced in rural and urban households (Sahel et al., 2004; Lebot, 2009). Besides, there is also an interest in using high quality yam flour as a substitute for wheat flour in food and non food industries. Apart from being staple food, yams are used as medicine to cure various ailments (Choudhary et al., 2008; Marcos et al., 2014). Dioscorea bulbifera is used by tribal ladies as contraceptive and had high contribution by reducing the growing of people currently we face (Schott et al., 2000). The tribal people also used Dioscorea pentaphylla as medicine for ailments related to digestive tract and respiration. Further, Dioscorea bulbifera has also steroidal sapogenins, used in the production of cortisone and synthetic steroid hormones (Narula et al., 2007). According to Sunarsih et al. (2007), tubers infuse from Dioscorea hispida decrease the blood glucose level.
The most important feature of yam are; good adaptability and produce high amount of yields in broad agro ecology (Mesut and Ahmet, 2002; Himanshu et al., 2016), annual cycle of food availability through different species, diverse maturity and flexibile harvesting periods without the use of large amounts of agricultural inputs (Onwueme and Charles, 1994). Although reliable statistical information on the area and production on yam in Ethiopia is missing (Tamiru, 2006), however, the crop has been in cultivation particularly in the South and Southwestern parts for different utilization purposes and product forms. In these regions, yam production faces various constraints, amongst which, low yield potential and long maturity period are the main factors affecting yam production. For example, the yield potential of yam in Ethiopia is about 4 t/ha, by far lower than the global average yields of 10.5t/ha (Tamiru, 2008; FAO, 2012). Improving the yield potential and developing early maturing materials are the best ways to increase yam production and may also allow a possible double cropping (two harvests) within a year. This in
152 turn could enable food yam to be available in the market throughout the year with lower price (Sartie et al., 2011). Tuber and bulbils yields of yams are complex traits and interact with different factors and are difficult to measure directly in the field. Further, it was reported that, the economic yield of yam had extensive variability within and between species, cultural practices and environmental conditions and as a result, also difficult to assess the quality (Diby et al., 2009). In line with this, different elucidations were reported by different authors on the effects of tuber maturity, for example, according to Ketiku and Oyenuga (1973), tuber maturity is a reduction of metabolic activity in an environment no longer favorable for growth; reduction in starch content, dry matter accumulation (Okoli, 1980), high total sugar content and an increase in the accumulation of citric and malic acids in the tuber (Osagie, 1981).
The physiological maturity of yam vary between species (Njoh et al., 2015), for example, Dioscorea bulbifera matures between 180-240 days after plant; Dioscorea cayenensis matures within 280-350 days after plant and Dioscorea rotundata matures within a range of 200-330 days after planting (Onwueme and Charles, 1994; FAO, 2008). Even though, in Ethiopia different yam species were grown as a sole and mixture of different crops, there has been no research so far done in relation to determining the time of maturity on different root and tuber crops including yam. Besides, the yield potential of the landraces and quality variation between time of harvest across agro-ecological zones have never been assessed, this lead, to a reduced quality of the tuber and bulbils yield extensively (Wireko-Manu et al., 2013). The current status of the yield and its variation between species in major growing areas are still unknown. Furthermore, lack of quantitative methods for determining maturity indices makes it difficult for a processor to establish appropriate stages of maturity of yam for processing and also to optimize storage conditions (Osagie, 1992; Wireko-Manu et al., 2013). According to farmers and as reported by Dumont et al. (2005), lack of appropriate time of maturity, agronomic practices, poor soil and climate changes are the main factors that reduce the growth and delay the maturity of yams as well. In addition, the age of harvested tubers, sources of origin and environmental conditions (poor management, rainfall amount and soil temperature) during planting and root development period affect the tuber yield significantly (Defloor et al., 1998; Santisopasri et al., 2001; Diby et al., 2009). These factors can influence the quality of harvested produce and ultimately the final products made from the flour. Further, the physico-chemical and functional properties of the
153 flour and starch from the cultivated yam species in relation to how they are affected by age of harvested products in Ethiopia is also poorly investigated. Hence, to enhance the yield and the nutritional composition of the existing species/ landraces, assessment of appropriate time of maturity at harvest is crucial to achieving good yield and quality of yam. Therefore, the present study was design, to determine the effect of different maturity stages of aerial yams for better yield and quality traits in Southwest Ethiopia.
6.2. MATERIALS AND METHODS
6.2.1. Description of the study area A field study was conducted during 2015 growing season at Jimma Agricultural Research Center (JARC). The center is located at latitude 7o 40.00' N and longitude 36o 47’.00’ E with an altitude of 1753 meters above sea level (m.a.s.l.). The area usually receives mean annual rainfall of 124.6 mm with mean maximum and minimum temperatures of 26.2 0C and 12.0 0C, respectively. The soil of the study site is Eutric Nitosol (reddish brown) with pH of 5.3. The detail climate data are presented in Appendix Table 6.1.
6.2.2. Experimental materials Yam landrace which are dominantly grown in the regions namely; Woko and Welmeka with six different harvesting stages (5 MAP, 6 MAP, 7 MAP, 8 MAP, 9 MAP and 10 MAP) used for the study. The landraces identified and selected for this study during the field survey of Southwest Ethiopia.
6.2.3. Experimental design and management The experiment was laid out in randomized complete block design with three replications as factorial arrangement. The gross plot size for each treatment was 6m x 4m. The spacing between rows and plants was 1.5m x 1.0m respectively. Bulbils of the same size which started sprouting were used as planting material. One month after planting, seedlings were earthed up followed by frequent weeding. All other agronomical practices were followed according to the recommendations and farmers practices of the areas. Each yam plant was tended using dried coffee stick of 3.5-4.5m long to provide support and induce good canopy and vine development. Six plants from the plot were sampled and tagged for data collection and final harvest.
154 6.2.4. Data collection Agronomic and bio chemical data were collected for this study.
6.2.4.1. Agronomic data Data were collected from six plants from each plot and the average values were used for data analysis. The characters that manifested for data collection were: leaf length (cm), leaf width (cm), length of leaf lobes (cm), petiole length (cm), distance between leaf lobs (cm),vine length (cm), number of bulbils plant-1, bulbils length (cm), bulbils diameter (cm), tuber length (cm), tuber diameter (cm), internode length (cm), number of internode vine-1, tip length (cm), vine fresh weight (t/ha), vine dry weight (t/ha), bulbils fresh weight (t/ha), bulbils dry weight (t/ha), tuber fresh weight (t/ha), tuber dry weight (t/ha), total yield (t/ha) and harvest index (%). The detail procedure of data collection for each trait is presented in chapter 4 section 4.2.4.2.
6.2.4.2. Biochemical data
The analysis was carried out with the flour from tuber and bulbils and the samples run in duplicates. The flour moisture content (%), dry matter (%), organic matter (%), ash (%), organic carbon (g), crude fibre (%), tannin (mg/100g), total nitrogen (%), total phosphorus (mg/100g), crude fat (%), crude protein (%), saponnin (mg/100g), carbohydrate (%) and total energy (kcal/100g DM) were determined. The detailed procedure of biochemical analysis for each trait is presented in chapter 7 section 7.2.2.
6.2.5. Data Analysis Two- way analysis of variance (ANOVA) was used as with months of sampling and landraces as factors for agronomic and bio chemical traits, to determine the significant differences (p<0.01) and (p<0.05) levels of probability. The basic model for analysis of agronomic traits at different stages of maturity is as follows:
Yijk = µ + Hi+ Lj + HLij + Єijk
Where: Yijk = is the dependent variable, µ is the overall mean; Hi,= the effect of harvesting interval, Lj = the landrace effect, HGij= the interaction effects between harvesting intervals and landrace and Єijk= the random error, independent and normally distributed.
155 The statistical model for analyzing the biochemical traits of yams at different stages of maturity is as follows:
Yijk = µ + Li+ Mj + LMij + Єijk
Where: Yijk = is the dependent variable, µ is the overall mean; Li,= the landrace effect, Mj = the effect of stages of maturity, GMij = the interaction effects between landrace and stage of maturity and Єijk= the random error, independent and normally distributed.
Differences between the mean values established with Least Significant Difference (LSD) and Duncan multiple range tests by using Statistical Analysis System (SAS) package (version 9.0 of SAS Institute Inc, 2000).
156 6.3. RESULTS AND DISCUSSION
6.3.1. Analysis of variance The analysis of variance revealed that maturity stages had significant (p<0.01) effects on tip length, vine fresh weight, vine dry weight, tuber length, tuber diameter, tuber fresh weight, tuber dry weight, total yield and harvest index (Table 6.1). The landrace and maturity stage interaction was not significant for all parameters except tuber diameter. The result also further revealed that the existence of variation between landrace and maturity stage of this trait. Maturity stages showed significantly different (p<0.05) for only in vine length. From all traits considered, tip length, tuber diameter, tuber dry weight and total yield were significantly different at main effect of landrace and maturity stages. The observed difference might be due to plants were generated from bulbils obtained from different portions of a vine (bottom, middle and top) and had different dormancy, performance and time of maturity. This result is in line with Sartie et al. (2011) who reported the significant variation between landrace and maturity stage of yam (D. alata) was supposed to be due to plants were developed from tuber setts obtained from the head, middle and tail portions of the tuber and their time of sprout and maturity are different. Mean square due to landrace found to have highly significant variation (p<0.01) for bulbils length, tip length, bulbils fresh weight, bulbils dry weight, tuber diameter, tuber dry weight and total yield (Table 6.1). The significant variability among yield and related traits at genotypic level further indicated the positive chance of developing high yielding varieties of yam.
The considered landraces differed with regard to yield and yield components in all investigated harvesting stages (Table 6.1). Consequently, the mean values of yield and yield components across different maturity stages were used. The mean values of yield and related traits harvested at different maturity stages are presented in Table 6.2. Landraces at 10 MAP had significantly highest vine fresh weight, vine dry weight, tuber fresh weight, tuber dry weight and total yield than landraces harvested at 7 MAP and 8 MAPs. On contrary, vine length, leaf length, leaf width, petiole length, distance between lobs, number of internode vine-1, internode length, number of vine hill-1, number of bulbils plant-1, bulbils length, bulbils diameter, bulbils fresh weight and bulbils dry weight were not significantly different among different stages of maturity (Table 6.2).
157 Table 6.1. Analysis of variance for growth and yield related traits of two Dioscorea spp. landraces grown at Jimma 2015.
Traits Mean square Rep MAP (A) Lan (B) A*B Error CV (DF=2) (DF=5) (DF=1) (DF=5) (DF=22) (%) Leaf length (cm) 7.79 0.19 0.65 1.81 1.89 8.11 Leaf width (cm) 2.23 0.38 0.73 1.49 1.74 8.48 Petiole length (cm) 19.73 0.80 0.05 0.99 1.03 8.91 Distance between lobs(cm) 7.04 0.04 0.09 0.04 0.11 8.99 Vine length (cm) 7705.6 1291.2* 898.2 572.2 514.3 7.38 Number of vine plant-1 0.37 0.15 0.04 0.15 0.17 21.64 Number of bulbils plant-1 41.45 74.85 37.37 49.57 102.3 15.79 Bulbils length (cm) 0.77 0.35 5.79** 0.87 0.88 9.39 Bulbils diameter (cm) 0.66 0.27 0.23 0.50 0.29 11.84 Internode length (cm) 8.98 0.81 2.36 0.64 1.05 8.00 Number of internode vine-1 138.7 40.29 70.84 44.34 121.3 19.48 Tip length (cm) 1.50 0.25** 0.70** 0.08 0.07 7.81 Vine fresh weight (t/ha) 1.52 31.48** 8.01 4.94 2.52 23.25 Vine dry weight (t/ha) 0.14 6.63** 0.62 1.32 0.96 42.38 Bulbils fresh weight (t/ha) 11.07 6.71 72.59** 3.58 7.40 27.82 Bulbils dry weight (t/ha) 0.002 0.001 0.02** 0.001 0.001 47.74 Tuber length (cm) 2.59 4.88** 1.44 0.91 0.48 7.24 Tuber diameter (cm) 0.15 2.40** 0.90** 1.22** 0.14 4.49 Tuber fresh weight (t/ha) 0.89 19.85** 1.33 0.85 0.78 11.64 Tuber dry weight (t/ha) 0.05 8.48** 1.04** 0.06 0.08 10.72 Total yield (t/ha) 6.47 31.51** 93.54** 5.35 6.74 14.91 Harvest index (%). 34.33 109.47** 9.21 39.91 27.84 7.21
Rep= replication, MAP= Months After Planting, Lan= Landraces and A*B= MAP* Lan, CV= Coefficient of variation
In agreement with the results of current study, there was no significant difference (p>0.05) among different maturity stages in yield and yield related traits was reported by different researchers (Hirka and Sharma, 1994); Tolera et al. (1998) for maize (Zea mays L.) and Sartie et
158 al. (2011) for yam (Dioscorea alata). They also reported on crop residue, total biomass and tuber yield to be affected by stages of maturity. On the contrary, leaf length showed an increasing trend, whereas most traits considered in this study increased significantly up to 6 MAP, and declined at 7 MAP and then increased significantly up to the final harvest (Table 6.2). The up and down (undulation) trend when increased stage of maturity was due to the fluctuation of environmental condition in the study area (Appendix 6.1). There was highly significant decrease (p≤0.01) on tip length, tuber length, tuber diameter and harvest index from 6 MAP to 10 MAPs. On contrary, significant increase in tuber fresh, tuber dry weight and total yield from the six to the final stages of maturity. Decrease in harvest index with advanced stage of maturity is consistent with previous finding of Sartie et al. (2011). In contrast, Hirka and Sharma, (1994) reported that harvest index was not affected by maturity stage in maize. According to Njoh et al. (2015), leaf length, leaf width and petiole length showed greater weight loss at the last harvest due to their rapid drying rate and susceptibility to wind damage in yam.
The mean performance of landraces (Woko and Welmeka) with important yield and yield related traits are presented in Appendix Table 6.2. The highest vine length of Woko was 315.9 cm at 10 MAP and 331.3 cm from Welmeka at 6 MAP (Appendix Table 6.2). This difference is likely to be due to the staking and inherent effects of the landraces. The highest mean total yield (20.90 t/ha) was obtained from Welmeka at 8 MAP and ranged from 16.10 to 20.90 t/ha. Moreover, landrace Woko produced 18.20 t/ha at 7 MAP and ranged from 11.11-18.20 t/ha. Number of bulbils per plant ranged from 55.06 to 63.11 and 50.06 to 61.17 from Woko and Welmeka respectively. Most of the higher yielding landraces tend to mature later (Table 6.2). It is a general belief among breeders that, later maturing landraces do yield higher than the early maturing ones, because, late maturing landraces have opportunity to draw nutrients and photosynthesize over a longer period.
159 Table 6.2. The mean value of growth and yield related traits harvested at different maturity stages of yam
Traits 5 MAP 6 MAP 7 MAP 8 MAP 9MAP 10MAP Mean SE CV (%) b a a a a a VL 278.8 318.6 315.3 306.0 308.2 315.6 307.1 3.88 7.38 a a a a a a LL 16.89 16.89 16.89 16.98 17.31 16.91 16.96 0.96 8.11 a a a a a a LW 15.67 15.38 15.35 15.58 16.00 15.36 15.56 0.94 8.48 a a a a a a PL 10.92 11.32 11.49 11.19 11.90 11.76 11.43 0.82 8.91 a a a a a a DBL 3.75 3.83 3.88 3.75 3.72 3.63 3.76 0.47 8.99 abc a bc c ab ab TiL 3.5 3.86 3.4 3.24 3.6 3.6 3.56 0.43 7.81 a a a a a a NIPV 58.27 56.89 53.27 54.88 60.5 55.36 56.53 2.70 19.48 a a a a a a IL 12.38 12.59 13.16 12.65 12.86 13.36 12.83 0.82 8.00 a a a a a a NVPH 1.72 1.75 1.91 1.91 1.97 2.16 1.9 0.52 21.64 a a a a a a NBPP 64.25 62.16 58.02 67.86 65.5 66.47 64.04 2.59 15.79 a a a a a a BL 9.52 10.07 10.00 10.20 10.13 9.97 9.98 0.78 9.34 a a a a a a BDi 4.61 4.63 4.83 4.63 4.29 4.29 4.55 0.60 11.84 a a a a a a BFW 7.92 10.66 10.32 9.10 10.24 10.41 9.78 1.34 27.82 a a a a a a BDW 0.06 0.1 0.09 0.08 0.09 0.10 0.09 0.17 47.74 c c b ab ab a VFW 3.92 4.07 7.46 8.05 8.00 9.46 6.83 1.02 23.25 b b a a a a VDW 1.04 1.06 2.35 2.70 3.30 3.41 2.31 0.80 42.38 a b b b c c TL 10.8 10.0 9.7 9.8 8.39 8.66 9.57 0.64 7.24 b c a b c c TDi 8.66 8.1 9.67 8.64 8.1 7.94 8.53 0.50 4.49 b b a a a a TFW 5.72 4.94 8.22 8.94 8.92 9.01 7.63 0.76 11.64 e e d c b a TDW 1.42 1.20 2.46 2.95 3.69 4.15 2.65 0.43 10.72 c bc ab ab a a TY 13.64 15.6 18.54 18.0 20.78 20.91 17.41 1.31 14.91 ab a b c b c HI 77.7 79.27 71.2 68.70 71.9 70.3 73.18 2.04 7.21 a,b,c * Means bearing different superscripts in the same row are significantly different (p<0.05). * Means with the same superscripts in the same row are not significantly different (p>0.05).
VL= Vine length (cm), LL= Leaf length (cm), LW= Leaf width (cm), PL= Petiole length (cm), DBL= Distance between lobs (cm), TiL= Tip length (cm), NIPV= Number of internode vine-1, IL= Internode length (cm), NVPH=number of vine hill-1, NBPP= number of bulbils plant-1, BL= bulbils length (cm), BDi= bulbils diameter (cm), BFW= Bulbils fresh weight (t/ha), BDW= Bulbils dry weight (t/ha), VFW= Vine fresh weight (t/ha), VDW= Vine dry weight (t/ha), TL= Tuber length (cm), TDi= Tuber diameter (cm), TFW= Tuber fresh weight (t/ha), TDW= Tuber dry weight (t/ha), TY= Total yield (t/ha) and HI= Harvest index (%); SE= Standared error, CV= Coefficient of variation
160 Vine fresh weight revealed 8.70 t/ha at 9 MAP from landrace Woko with range between 2.8-8.70 t/ha. The highest yielding variety, Welmeka produced 11.4 t/ha biomass yield at 10 MAP with ranges from 4.20-11.4 t/ha. Bulbils fresh weight produced 10.10 t/ha and 12.10 t/ha from 8 and 9 MAPs with a ranges from 6.20-10.10 to 9.60-12.1 t/ha for Woko and Welmeka, respectively. In most of the characters in this study showed small difference with advanced stages of maturity in both landraces. The highest mean tuber fresh weight 9.10 t/ha were harvested at 9 MAP and 10 MAP from landraces Woko and Welmeka, respectively. On the basis of tuber dry weight, both landraces could evidently said to be suitable for the production of acceptable yams. The stage of maturity had significant effect on tuber dry weight with those harvested at 10 MAP having higher levels compared to those of early harvesting. This difference could be explained by the fact that tubers harvested at 10 MAP well developed structurally and physiologically as opposed to those harvested at 5 MAP. This result is in agreement with Hagenimana (1996) and Abong et al. (2009) who described the dry matter content of sweet potato and Irish potato increased at 120 days after planting. At 10 MAP, the yams are considered physiologically mature in all aspects, the leaves become yellow, dry well and all bulbils fall down in the ground. Thus, it is always advisable to harvest aerial yams at this period (Njoh et al., 2015). Significantly, more harvest index was absorbed in tubers harvested at 5 MAP and 6 MAPs in both landraces, due to increasing total yield (bulbils and tuber fresh weight) and under developed cell structures. In this study, landrace Welmeka showed the highest mean performance in most of the characters that was considered than Woko.
6.3.2. The effects of maturity stages on the quality of yams
6.3.2.1. Analysis of variance
The analysis of variance revealed that maturity stages had highly significant effects (p≤0.01) on most biochemical traits obtained from storage tuber and bulbils (Table 6.3) indicating the presence of variability between harvesting tubers and bulbils from studied landraces. The results further indicated that there is possibility of developing of best landraces of yam with high biochemical compositions. On contrary, the stage of maturation had not significant e ect on total nitrogen and protein contents derived from storage tubers and aerial bulbils. Similarly,ff mean squares due to landraces showed highly significant difference (p≤0.01) for flour moisture contents, crude fiber and total phosphorus contents from storage tuber and bulbils of aerial yams.
161 Table 6.3. Analysis of variance of 14 biochemical traits from storage tuber and bulbils of aerial yam landraces grown at Jimma, 2015.
Traits Mean square Storage tuber Bulbils MAP Land A*B Error CV MAP Land A*B Error CV (A) (B) DF=5 DF= (%) (A) (B) DF=5 DF= (%) DF=5 DF=1 11 DF=5 DF=1 11 FMC 9.74** 14.26** 3.62** 0.05 2.11 20.13** 0.53** 1.81** 0.03 1.85 DM 6.12** 3.29** 3.93** 0.40 3.52 156.7** 0.34 6.48 3.34 6.93 OM 8.64** 3.79** 4.34** 0.41 4.96 230.9** 0.12 7.85 3.34 8.29 Ash 2.79** 0.11 0.09 0.04 4.02 7.56** 0.87** 0.58** 0.001 1.00 OC 0.86** 0.05 0.03 0.01 4.02 2.33** 0.27** 0.18** 0.001 1.00 CF 0.28** 0.72** 0.68** 0.04 12.62 0.22** 0.75** 0.39** 0.05 14.65 N 0.01 0.04 0.04 0.03 10.91 0.09 0.05 0.09 0.07 15.69 Pro 0.53 1.63 1.78 1.43 10.91 3.55 1.97 3.80 2.82 15.70 Fat 0.03** 0.002** 0.04** 0.01 3.97 0.01** 0.01** 0.04** 0.001 7.00 CHO 10.1** 0.31 11.4** 1.90 28.34 21.08** 0.34 2.68 3.01 10.42 P 0.05** 0.002** 0.006** 0.001 2.00 0.007** 0.03** 0.04** 0.001 1.00 Ene. 110.9** 8.46* 107.4** 1.54 1.80 209.5** 9.56 212.2** 9.15 2.68 Tan 0.10** 0.02** 0.05** 0.005 2.00 0.25** 0.04** 0.20** 0.001 2.12 Sap 21.40** 1.77 22.9** 3.66 30.09 31.35** 3.46** 7.53** 0.18 7.62 * Significant at 0.05 probability level; **= highly significant at 0.01 probability level. MAP=Months after plant, Land= Landraces A*B= MAP*Land FMC= Flour moisture content (%), DM= Dry matter (%), OM= Organic matter (g), Ash= Ash (%), OC= Organic carbon (g), CF= Crude fiber (%), N= Total nitrogen (%), Pro= protein (%), Fat= Fat (%), CHO= Carbohydrate (%), P= Total phosphorus (mg/100g), Ene= Total Energy (kcal/100g DM), Tan= Tannin (mg/100g) and Sap= Saponin (mg/100g); MAP= Months ater planting, and CV= Coefficient of variation
The significant variation among flour moisture contents at genotypic level further indicated the positive chance of developing high quality flour from storage tuber and aerial bulbils of yams. Conversely, the dry matter, organic matter, fat, energy and tannin contents showed highly significant difference only between storage tubers of yams, in line with this, there is highly significant differences observed between ash, organic carbon, tannin and saponin contents between bulbils from tested landraces. From all biochemical traits considered, flour moisture, crude fiber, fat, total phosphorus, energy, tannin and saponin contents showed significant effect
162 at landrace and maturity stages interaction level in both storage tuber and bulbils (p≤0.01) while the mean squares were not significant for the remaining two biochemical traits.
6.3.2.2. Biochemical composition The mean biochemical composition of bulbils and storage tuber of yams with six maturity stages are presented in Table 6.4 and Table 6.5. Stages of maturity significantly influenced the flour moisture content of aerial bulbils and storage tuber of yams. Likewise, the mean flour moisture and dry matter contents of aerial bulbils and storage tuber also significantly differed with increased harvesting stages. Similarly, the organic matter, ash, organic carbon, crude fiber, carbohydrate, total nitrogen, protein, fat, total phosphorous, total energy, tannin and saponin contents of aerial bulbils showed significant difference among maturity stages (Table 6.4). Conversely, the total nitrogen and protein contents obtained from storage tuber didn’t show significant differences, but the rest of biochemical traits showed significant differences between maturity stages (Table 6.5). In this regard, there is no sufficient information about the influence of the maturity stages on the biochemical composition of yams in the available literature. It is well established fact that the amount of biochemical composition increases with the advancement of maturity stages in different crops. For example, according to Hagenimana (1996) and Abong et al. (2009), the dry matter content of sweet potato and Irish potato increased from 90 to 120 days after planting. At final stage of maturity, the yams are considered physiologically mature in all aspects.
Comparison of the biochemical composition of landraces with different maturity stages are a key to get high quality of yam and to determine the critical time of harvesting. In the present study, the mean individual proximate, mineral and anti nutritional composition of bulbils and storage tuber of two landraces with different stages of maturity are presented in Appendix Table 6.3 and Appendix Table 6.4. The flour moisture content of bulbils and storage tuber from both landraces increased considerably from 6 MAP to 10 MAP (7.21–12.11% and 7.57–12.01%) for bulbils from Woko and Welmeka and (7.89–12.34% and 9.53–13.14%) for storage tuber from Woko and Welmeka respectively. The result of flour moisture content obtained from this study is in contrary with the work of Abera et al. (2003) who reported that the flour moisture content of yam tubers decreased 20% with increasing maturity. The high flour moisture content of yams may adversely affect their keeping quality. The dry and organic matter content of bulbils also
163 decreased in di erent proportions (29.11-18.88% and 34.02-18.26%) and (25.82-13.26% and
30.47-11.73%) ff from landraces Woko and Welmeka, respectively. This synchronism can be explained by the degradation of the organic compounds under the action of the enzymes. The opposite result was observed on the dry matter content of storage tuber in both landraces, while the organic matter content of Welmeka was stable with increasing maturity.
The organic carbon profile of both landraces at di erent maturity stages were quantitatively different. The ash content from bulbils and storage tuberff varied between landraces and stages of maturity. The ash content of bulbils ranged from 3.29 to 5.62% and 3.55 to 6.53% with a mean 4.31% and 4.52% for Woko and Welmeka, respectively. The highest ash content was recorded at the final stage of maturity in both landraces (5.62 and 6.53%). In line with this, a similar trend was observed on the ash content of storage tuber of aerial yam with different stages of maturity. The ash content of storage tuber ranged from 4.25% to 6.85% for Woko and 4.48 to 6.14% for Welmeka with a mean of 5.02 and 5.07, respectively. The observed result from this study was higher than the reported result of Udensi et al. (2010) on Dioscorea alata ranged from 2.15- 3.08%. Further, Princewill-Ogbonna and Ibeji (2015) reported on the ash content ranged from 3.77 to 3.97% on aerial yam. This variation was due to the chemical composition of the soil, cultural practices, time of planting and the amount of water available to the tuber plant (Bell and Favier, 1981; Osagie, 1992).
The crude fibre content varied between landraces and stages of maturity. The crude fibre content of bulbils ranged from 1.42% to 1.86% for Woko and 1.27 to 2.34% for Welmeka with a mean value of 1.46 and 1.78, respectively. Similarly, the crude fibre content of storage tuber ranged from 0.88% to 2.32% for Woko and 1.06 to 2.18% for Welmeka with a mean of 1.79 and 1.44 respectively. The crude fiber content obtained from in this study was relatively higher than the crude fibre content of Dioscorea bulbifera based on the avilable literature and ranged from 0.72% to 1.39 (Wanasundera and Ravindran, 1994; Abera et al., 2003). Such variation in the crude fiber content might be related to their genetic origin, geographical sources, and the level of soil fertility where the yams are grown (Ferguson et al., 1980; Megh et al., 2003). The protein content of bulbils ranged from 7.50-11.25% for Woko and 8.75 to 11.87% for Welmeka with a mean value of 10.52% and 10.99% respectively. Likewise, the protein content of storage tuber ranged from 10.0-12.50% for Woko and 10.0 to 11.25% for Welmeka with a mean of 11.25% and
164 10.73%, respectively. The result of protein contents obtained in this study was considerably higher than the reported value of wild yam species in Nepal (Megh et al., 2003), sweet potato 5.6% (Bradbury and Holloway, 1988) and cassava roots 1.7% (Gomez and Valdivieso, 1983). The higher protein content on bulbils and tuber of D. bulbifera indicated the nutritional superiority over the other wild yam species. In line with this, the result of this study was consistent with the report of Ferguson et al. (1980). Available data also showed that the yam peel has higher protein content than the tissue (Oyenuga, 1968). The protein content of yams also showed considerable variation among species and between cultivars, environmental factors, management, maturity, the length of storage time and processing method (Martin, 1979; Afiukwa et al., 2013). There are few reports on the protein content of D.bulbifera in the literature indicated that the protein content of D.bulbifera ranged from 1.1-2.31%. According to FAO (1972) reported a value of 1.40% and Coursey (1983) reported a range of 1.1-1.5%, Egbe and Treche (1984) reported 1.78% while Abera et al. (2003) found a value of 5.75% for protein in D. bulbifera.
The crude fat and phosphorus, concentrations were notably lower than those observed by Egesi et al. (2003). These results were consistent with the report of Abera (2011) on the biochemical composition of yams. Reports from available literature also showed that yam tubers contain generally low level of fats which do not exceed 2% on dry weight basis and 0.3% on wet weight (Abera et al., 2003). The distribution of fats content in storage tuber of yam is higher than bulbils (Oyenuga, 1968; Faboya, 1990). The crude fat content (bulbils and storage tuber) obtained from this study was similar with common cultivated yams species in West Africa (Osagie, 1992) and lower than the reported value of Abera et al. (2003) 1.5% and Lawal et al. (2012) 0.84% for Dioscorea rotundata. All the other results were below 1%. This may be due to the genetic di erences in landraces and geographical factors. Nevertheless, the tannin and saponin contents offf bulbils and storage tuber were found to be higher than the fat and the phosphorus contents. In both landraces, the late maturity had relatively similar crude fat, saponin and tannin contents. The contents of crude fats in yam tuber and bulbils were found to be relatively the same. This result was consistent with the report of Faboya (1990) who reported the crude fat content of D. bulbifera was 0.20%. Furthermore, Abera et al. (2003) also described the fat content of aerial yam to reach 0.43%. These reports deal only with the fat content of storage tuber and lack data
165 on the content of fat in the bulbils of D. bulbifera. The total carbohydrate as determined by difference consists of sugars, dextrins, starches, pectins, hemicelluloses, celluloses and lignin. In the present study, the carbohydrate content of aerial yam storage tuber ranged from 2.24-9.55% and 2.90 to 8.48% with a mean 4.75 and 4.98% for Woko and Welmeka, respectively. Further, the carbohydrate content of aerial bulbils ranged from 12.24-19.57% and 13.76 to 19.91% with a mean of 14.47 and 16.54% for Woko and Welmeka respectively.
Although, the storage tuber of aerial yam constitutes the major components of carbohydrate (Osagie, 1992; Udensi et al., 2008), in the present study the mean values were lower than bulbils in both landraces. The carbohydrate content of D.bulbifera has been reported to be 18.0% (FAO, 1972); According to Coursey (1983), the carbohydrate content ranged from 27-33%, while Princewill-Ogbonna and Ibeji (2015) reported a value ranging from 76.42 to 77.41% on three cultivars of aerial yams. The result obtained from the present study was lower than the reported value of 18.0% by FAO (1972) and from 27-33%, by Coursey (1983) while Abera (2011) and Afiukwa et al. (2013) reported a value of 33.11% and 83.03% in the fresh tissue of D. bulbifera and D. esculenta respectively. This variation may be associated with the environmental condition and stage of maturity of yam. The concentrations of nitrogen and phosphorus content on bulbils and storage tuber of aerial yams significantly increased from 5 MAP to 8 MAP, and then slightly reduced at the later stages of maturity (9 MAP and 10 MAP). During the early stage of the crop the total nitrogen and phosphorus had relatively higher (Appendix Table 6.3 and Appendix Table 6.4) and decreased the lower rate at the later stages of development, reaching the physiological maturity (Panneerselvam, 2007; Wireko-Manu et al., 2013). This reduction in nutrient concentration with advanced maturation was most likely due to the pattern of translocation of nutrients to the developing flower and seeds at the higher rate (Pascal and Crookstone, 1981; Lebot, 2009). The nutrient translocation to seeds does not continue after seeds reach physiological maturity, indicating that mineral movement to seeds was directly associated with to assimilate movement.
166 Table 6.4. Biochemical composition from bulbils with different maturity stages
MAP BFMC St.d DM St.d OM St.d Ash St.d OC St.d CF St.d N St.d b a ab d e c b 5 MAP 9.44 0.39 31.56 3.47 28.14 3.29 3.42 0.17 1.90 0.09 1.39 0.04 1.47 0.38 c bc b c c abc ab 6 MAP 7.39 0.25 27.99 1.15 24.45 1.10 3.53 0.11 1.96 0.06 1.59 0.46 1.77 0.17 d a a c d bc ab 7 MAP 6.99 0.02 32.50 1.00 29.06 1.16 3.43 0.15 1.91 0.08 1.54 0.18 1.75 0.07 b c c c bc ab a 8 MAP 9.91 0.44 22.46 8.51 18.56 9.38 3.89 0.87 2.16 0.48 1.91 0.44 1.95 0.07 a d d a a c ab 9 MAP 12.03 0.29 18.60 0.94 12.47 2.13 6.12 1.18 3.40 0.66 1.35 0.69 1.77 0.03 a d d b b a ab 10 MAP 12.06 0.06 18.57 0.43 12.49 1.07 6.07 0.64 3.37 0.35 1.93 0.57 1.60 0.28
MAP Pro St.d Fat St.d CHO St.d P St.d Ene St.d Tan St.d Sap St.d 5 MAP b c c c b d b 9.21 2.43 0.24 0.17 8.08 8.26 0.53 0.24 109.95 0.65 0.69 0.20 6.39 3.92 6 MAP ab c b a c d a 11.09 1.10 0.29 0.10 14.42 0.94 0.58 0.08 104.75 0.34 0.69 0.31 10.47 0.45 7 MAP ab c a cd d a b 10.93 0.44 0.29 0.10 19.21 0.42 0.52 0.06 85.80 30.30 1.15 0.57 5.78 1.74 8 MAP a a a bc a e c 12.18 0.44 0.52 0.10 19.74 0.23 0.54 0.27 123.35 0.96 0.47 0.09 4.44 0.84 9 MAP ab b b d bc c e 11.09 0.22 0.37 0.02 15.06 0.24 0.48 0.03 107.83 0.16 1.00 0.03 2.25 0.13 10 MAP ab c b b a b d 10.0 1.76 0.29 0.25 16.51 1.86 0.55 0.09 119.82 9.08 1.11 0.18 3.86 0.01 * Means bearing different superscripts in the same vertical row are significantly different (p<0.05). * Means with the same superscripts in the same column are not significantly different (p>0.05). FMC= Flour moisture content (%), DM= Dry matter (%), OM= Organic matter (g), Ash= Ash (%), OC= Organic carbon (g), CF= Crude fiber (%), N= Total nitrogen (%), Pro= protein (%), Fat= Fat (%), CHO= Carbohydrate (%), P= Total P (mg/100g), Ene= Total Energy (kcal/100g DM), Tan= Tannin (mg/100g) and Sap= Saponin (mg/100g)
167 Table 6.5. Biochemical composition of storage tuber with different maturity stages
MAP FMC St.d DM St.d OM St.d Ash St.d OC St.d CF St.d N St.d c b a d d c a 5 MAP 10.7 3.04 18.87 3.37 14.51 3.53 4.36 0.15 2.42 0.08 1.17 0.41 1.70 0.00 d a a c c ab a 6 MAP 8.7 1.15 19.95 0.09 15.15 0.25 4.79 0.34 2.66 0.19 1.75 0.69 1.75 0.21 c cd b d d ab a 7 MAP 10.4 2.3 16.97 0.09 12.59 0.09 4.37 0.04 2.43 0.02 1.65 0.83 1.80 0.14 a d bc cd d bc a 8 MAP 13.1 0.3 16.57 0.19 11.99 0.09 4.58 0.09 2.54 0.05 1.44 0.22 1.85 0.21 b c c a a a a 9 MAP 12.0 0.36 17.89 0.05 11.53 0.36 6.36 0.31 3.53 0.17 1.87 0.63 1.70 0.14 b bc bc b b a a 10 MAP 12.0 0.36 18.06 0.07 12.25 0.06 5.80 0.06 3.22 0.03 1.82 0.50 1.75 0.07
MAP Pro St.d Fat St.d CHO St.d P St.d Ene St.d Tan St.d Sap St.d a b c c c e a 5 MAP 10.62 0.00 0.39 0.27 3.83 1.32 0.49 0.07 62.46 4.05 0.54 0.13 9.12 0.24 a e c b a b ab 6 MAP 10.93 1.32 0.25 0.01 3.30 1.50 0.56 0.07 72.75 13.77 0.73 0.02 7.51 4.22 a d c a bc f bc 7 MAP 11.25 0.88 0.29 0.04 3.80 1.64 0.69 0.06 62.90 2.65 0.33 0.19 5.16 5.60 a a a e b d ab 8 MAP 11.56 1.32 0.54 0.09 7.00 2.09 0.37 0.03 64.32 0.07 0.67 0.18 6.78 2.47 a b bc d bc a c 9 MAP 10.62 0.88 0.40 0.18 4.53 1.08 0.41 0.07 63.68 7.04 0.77 0.18 2.41 0.12 a b a c a c ab 10 MAP 10.93 0.44 0.34 0.05 6.69 4.04 0.48 0.03 74.11 3.16 0.71 0.19 7.17 1.67 * Means bearing different superscripts in the same vertical row are significantly different (p<0.05). * Means with the same superscripts in the same column are not significantly different (p>0.05). FMC= Flour moisture content (%), DM= Dry matter (%), OM= Organic matter (g), Ash= Ash (%), OC= Organic carbon (g), CF= Crude fiber (%), N= Total N (%), Pro= protein (%), Fat= Fat (%), CHO= Carbohydrate (%), P= Total P (mg/100g), Ene= Total Energy (kcal/100g DM), Tan= Tannin (%) and Sap= Saponin (mg/100g)
168 Besides, during the rapid expansion phase the nutrient content had the most pronounced increase, but subsequent increases were progressively smaller. Such information emphasized the importance of maturity stage on the concentration and accumulation of mineral nutrients, suggesting that the seeds attained the minimum concentration of mineral after 8 MAP and the concentration remains relatively stable thereafter. This is common in most field crops for example, in soybean, the nutrient concentrations were high during lag phase (the initial phase of seed development) of seed growth and the concentration decreased during the period of linear growth rate of seed (Sale and Campbell, 1980; Afiukwa et al., 2013).
The energy values of storage tuber ranged from 65.33 to 82.49 and 59.59 to 63.02 kcal/100 g dry matter at 5 MAP and 10 MAP for Woko and Welmeka, respectively. The energy value increases at later harvesting. This result is similar to that of Njoh et al. (2015) who reported the energy value increase with later harvesting of yams. This might be due to the accumulation of carbohydrates (reserves) during growth of the tubers. Besides, the energy value obtained in the present study is comparable with the reported value of FAO (1990) and Udensi et al. (2008) on water yam. There are similar trends observed on the tannin contents from bulbils and storage tuber of aerial yam. The tannin contents increased from (0.84 to 1.25 mg/100g and 0.55 to 0.98 mg/100g) for bulbils from Woko and Welmeka, respectively. Similarly, the tannin contents from storage tuber of two landraces follow similar trends. The tannin contents obtained from this study is relatively higher than the reported value (0.186 to 0.227 mg/100 g) by Princewill- Ogbonna and Ibeji (2015) on three cultivars of aerial yams. The bitter principles of D bulbifera may be due to the presence of tannins in them (Okwu and Ndu, 2006). On contrary, the saponin content decreased with increased maturity in Woko and Welmeka of bulbils and storage tuber. This result was consistent with the work of Jautien et al. (2009) who reported the saponin content decrease with an increase in maturity stage.
169 6.4. CONCLUSION
Based on morphological traits, maturity stage had significant effects (p≤0.01) on tip length, vine fresh weight, vine dry weight, tuber length, tuber diameter, tuber fresh weight, tuber dry weight, total yield and harvest index. Although, the amount of variability in most traits increased up to 6 MAP, significantly highest total yield was recorded at 9 MAP and 10 MAPs. This difference could be explained by the fact that late harvested tuber and bulils well developed structurally and physiologically mature in all aspects and have good opportunity to draw nutrients and photosynthesize over a longer period than those harvested early. Thus, harvesting of yams at 9MAP to 10 MAP will be more economical, due to high total yield of yams. Correlation analysis revealed significant association among some of the characters suggesting that, some traits could be used to predict the other. Traits that showed significant and positive correlation could be improved simultaneously and had high contribution to maximize total yield and may need high efforts to enhance the yield improvement of yam.
In the present study, maturity stage had significant effect on flour moisture content, dry matter, ash, organic carbon, fat, carbohydrate, total phosphorus, total energy, tannin and saponin contents. The nutritional composition of bulbils and storage tuber is varied with the advancement of stages of maturity. In both Woko and Welmeka, the flour moisture, ash, organic carbon, crude fiber, total nitrogen, protein and tannin content of bulbils increased with increasing maturation, however, the dry matter, organic matter, fat, carbohydrate, total energy and the saponin content of storage tuber reduced when increasing the harvesting stages. Succinctly, the yield performance and chemical compositions of landraces were more genetic based than environmentally influenced. Consequently, through investigation, selection based on morphological (tip length, tuber diameter, tuber dry weight and total yield) and biochemical (flour moisture content, ash and carbohydrate) on yam tuber and bulbils are recommended for high yield and quality traits of yams from Southwest Ethiopia.
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175 VII
BIOCHEMICAL COMPOSITION OF YAM (Dioscorea spp.) LANDRACE COLLECTIONS OF SOUTHWEST ETHIOPIA ______
176 Biochemical Composition of Yam (Dioscorea spp.) Landrace Collections of Southwest Ethiopia
ABSTRACT
Yams make a significant contribution to food security and medicinal importance in developing countries. In Ethiopia, there is insufficient scientific study on proximate, mineral and anti nutritional factors of yams. In order to fill the knowledge gap, this study was conducted to assess the biochemical composition of yams collected from Southwest Ethiopia. Flour from storage tuber of 36 landraces of yam collected and the samples run in duplicates. Data on 14 biochemical traits were collected and subjected to various data analyses. Results of the analysis of variance indicated significant variation (p<0.01) among the landraces on organic matter, total nitrogen, protein, fat, carbohydrate, total phosphorus, total energy, tannin and saponin contents. The flour moisture contents ranged from 17.75 to 27.47% with a mean of 22.03%. The ranges of dry matter (15.80 to27.28%), organic matter (21.38 to 43.56%), ash (1.13 to 3.56%), organic carbon (0.63 to 1.98 g), crude fiber (0.41 to 2.05%), total nitrogen (1.00 to 1.32%), protein (6.25 to 8.28%), fat (0.09 to 0.65%), carbohydrate (12.71 to 33.94%), total phosphorus (23.7 to 53.0 mg/100g), total energy (92.66 to 173.30 kcal/100 g DM), tannin (19.80 to 181.0 mg/100 g) and saponin (2.31 to 13.94 mg/100 g) contents, respectively. The cluster and pair wise distance analysis of biochemical traits showed, the existence of eight divergent groups. The maximum inter cluster distance was found between clusters VII and VIII (23.02), followed by clusters V and VIII (22.92), clusters I and VIII (17.40 and clusters III and V (15.61), in order of magnitudes. Maximum genetic divergence between the clusters points to the fact that hybridization among the landraces included with them would produce potential and meaningful hybrids and desirable segregants.
Keywords: Cluster, diversity, food security, nutritional composition, storage tuber
177 7.1. INTRODUCTION
Yam (Dioscorea spp.) is the most important food crop since the time of immoral in the tropics and sub tropics (Asiedu et al., 1999; FAO, 2012). It is highly linked with the human existence, endurance, and the socio economic history (Kambaska et al., 2009). It is cultivated to a greater extent to combat the food security threats of the increasing population in the world (Asiedu and Sartie, 2010). It is the third most important tuber crop in West Africa, after cassava (Manihot esculenta Crantz) and sweet potato [Ipomoea batatas (L.) Poir] (IITA, 2004; Dansi et al., 2013). Moreover, during the off seasons, some people prefer using yams to solve their seasonal food shortage rather than cassava and sweet potato (Opara et al., 1999; Norman et al., 2012). Yams have been domesticated and cultivated by over 60 million of people in tropical and sub-tropical regions (Lebot, 2009; Sesay et al., 2013). In these regions, yams are well integrated into the social and cultural lifestyle of the people who cultivate and consume them and have significant contribution for food security, medicine and commercial value particularly in rural areas where they are freely available (Megh et al., 2003; Kambaska et al., 2009; Himanshu et al., 2016). Apart from providing basic food security and source of income, yam is a rich source of carbohydrate, vitamins and minerals, especially where it is consumed in large amounts (Tetchi et al., 2007; Baah et al., 2009; Marcos et al., 2014). The crop is estimated to provide more than 285 dietary calories per person per day for 300 million people in sub Saharan Africa (Adejumo et al., 2013).
In Ethiopia, yam is a highly valued crop, which provides food for household consumption and improves many livelihoods through the sale of harvested tubers. Wild yams are also consumed by some farming communities in South and Southwest Ethiopia to overcome hunger and make a significant contribution in the diets of the people. The tubers were found with a high amount of carbohydrates, crude fibers, and low level fats and protein, a good proportion of essential amino acids which make them a good dietary source and could be eaten as cooked vegetable, boiled yam, steamed, baked or fried in oil (Osman, 1990; Nashriyah et al., 2011). Conversely, the wider utilization of yam in Ethiopia is limited, due to information on the proximate and mineral composition of yam is meager. Further, yam by itself is not a balanced food and malnutrition occurs when it is consumed alone as staple food (Shanthakumari et al., 2008). Studies on the nutritional value of yam as a food is of considerable significance since it may help to identify
178 long forgotten food resources (Asiedu et al., 1997; Alozie et al., 2009). In this regard, few attempt was made to understand the proximate composition and anti nutritional factors of the underutilized tuber of yam to make edible as the safe food sources for mass consumption (Arinathan et al., 2009). Moreover, many di erent forms and landraces of the edible yam species are available in different areas with variableff in composition and nutritional values. In contrast to cultivated tubers, little is known about the proximate composition and reasons to expect that some of the species di er in composition from common agricultural varieties (Schoeninger et al.,
2000). Furthermore, severalff species of yams also have medicinal properties and the storage tuber contains some pharmacologically active substances including dioscorine, saponin and sapogenin (Jaleel et al., 2007).
In spite of their importance as a food security and medicinal source, to the best of our knowledge, there are no research efforts so far done with regard to the nutritional composition and medicinal value on Ethiopian yams and information on the biochemical composition of the yam is scarce. For example, during the surveying of the pesent study, a total of 38 farmers named landraces were identified on farm from Southwest Ethiopia, however, the biochemical composition of the landraces are still unknown. Furthermore, the culinary attributes of the existing landraces have never been assessed and the nutritional importance of yam at country level is still unknown; which hinders the wider utilization and researchers to access the biochemical composition of indigenous yam genetic resources in the country. Thus, exhaustive imagery of landraces based on biochemical composition and medicinal values in connection with farmers’ indigenous knowledge and use have tremendous impact to make the genetic enhancement and sustainable use of yam genetic resource in Ethiopia. Consequently, the present study was designed to evaluate the biochemical composition of yams collected from Southwest Ethiopia.
179 7.2. MATERIALS AND METHODS
7.2.1. Samples collection and preparation
A total of 36 yam landraces collected from major yam growing areas of Southwest Ethiopia. Names of the landraces and areas of collection are presented in Table 4.1. Yam tubers weighed, peeled, cut into small pieces and dried at 650C for 72 hours until constant weight was obtained (10%). The dried chips were then milled using an electric grinder (Holloway et al., 1989; Megh et al., 2003), to obtain fine powder yam flour. The flour was sieved through 1mm sieve, measured, packed into airtight plastic bag and stored in a refrigerator until used for analysis. The proximate and mineral analysis was conducted at Ethiopian Institute of Agricultural Research Food and Nutrition Laboratory and the protein, phosphorus, tannin and saponin contents were analyzed at Jimma University College of Agriculture and Veterinary Medicine (JUCAVM) Animal Sciences Nutrition Laboratory.
7.2.2. Biochemical analysis The analyses were carried out using the flour form the storage tuber of yam and the samples were run in duplicates and the mean value was used. The flour moisture, dry matter, organic matter, ash, organic carbon, crude fibre, tannin, nitrogen, phosphorus, crude fat, crude protein, saponin, carbohydrate and total energy contents were determined in accordance with the standard methods of the AOAC (2000). The flour moisture content was obtained by standard analytical method (AOAC, 1984). Duplicate flour samples (100 g) weighed in aluminum dishes and oven dried at 65oC for three days. The dried samples cooled in a desiccator’s room temperature and weighed. The flour moisture content was determined by loss of weight due to drying was converted to percent flour moisture content as follows: Moisture % = (weight of moisture evaporated/weight of flour sample) x 100. The flour dry matter content (DM) was calculated by taking a o representative duplicate sample of 100 g (W1) was oven dried at 65 C for 72 hours and weighed
(W2) and the value was expressed in percentage (Cozzolino and Labandera, 2002; Egesi et al., 2003). The percentage dry matter content was calculated as:
% DM = ( ) ( ) x100 or % DM = 100 − % moisture content
180 To determine the ash content, the instruction of AOAC (2000) was adhered. Crucibles were rinsed and dried in hot air oven (SM9053) maintained for 30 minutes at 105 oC. These were cooled in desiccators and weighed. Five gram of the sample was burnt on a heater inside a fume cupboard to get rid of smoke. The samples moved to preheated muffle furnace (SM9080) maintained at 550 oC until such a time when a light grey ash was noticed. The crucibles were cooled in desiccators and weighed. The ash content was calculated as:
%Ash = ( ) ( ) The organic matter content was determined by subtracting the percent100 ash from percent total dry matter and the value was expressed in percentage. The % organic matter content was calculated; Organic matter content (%) = % DM – % ash. Similarly, the amount of organic carbon is determined by dividing weight of ash by sample weight. That is, Organic carbon (g) = weight of ash/ sample weight. The crude fiber of the sample was determined according to AOAC (2000). Two gram of the sample was defeated with petroleum ether. The defeated sample was boiled in reflux for 30 minutes with 200 ml of a solution containing 1.25g of H2SO4 per 100 ml of solution. The solution was then filtered through linen on a fluted funnel. After that the sample was washed with hot water, using a two-food muslin cloth to trap the particles, the washed sample was transferred quantitatively back to the flask and boiled again in 200ml of 1.25g of carbonate free NaOH per 100 ml for 30 minutes and washed before it was transferred to a weighed Gooch crucible and dried in the oven at 105ºC for three hours. After cooling in a desiccators it was re-weighed. The percentage crude fiber was calculated as: %CF = ( ) ( ) The fat contents were determined using fat extractor with automated control unit100 (FOSS Soxtec 2055) according to AOAC (2000). The equipment has six extraction units with each unit carrying a thimble which accommodates the samples and aluminum cups for collection of the extracted fat. These units enable six samples to be analyzed within 75 minutes. Percentage of fat was considered as, the difference between weight of the pre weighed cups and after extraction. One gram of the sample was weighed into the thimble and its mouth plugged with defatted cotton wool, after which it was inserted into the extraction unit. Eighty ml of petroleum ether was dropped into each cup and maintained at 135oC. Each cup was aligned with its corresponding thimble.
181 The extraction and rinsing were done for 30 minutes each, after which the sample was aerated for 15 minutes and crude fat calculated as:
%Fat = ( ) Where: W1 = weight of sample,100 W2= weight of empty cup and W3= weight of cup with the extracted oil
The saponin content was determined by following spectrophotometric method of Bruner (1984). Two grams of the sample was put into a 250 ml beaker and 100 ml of ISO butyl alcohol added. A shaker was used to shake the mixture for 5 hours to ensure uniform mixing. The mixture was then filtered using the No.1 Whatman filter paper into a 100 ml beaker containing 20 ml of 40% saturated solution of magnesium carbonate (MgCO3). The obtained mixture was filtered again through No.1 filter paper to obtain a clean colorless solution and was taken to a 500 ml volumetric flask using a pipette made to mark with the distilled water. It was allowed to stand for 30 minutes for the need color to develop. The absorbance was read after the color development on the spectrophotometer at 350 nm. The saponin content was calculated as: (absorbance sample/concentration of sample) x absorbance of standard sample.
The analysis of crude protein content was conducted with an aid of micro Kjedhal system in accordance with AOAC (2000). A small quantity of the yam flour sample (1g) was introduced into the digestion tube (Kjeltec 2200 FOSS) and, a catalyst (2 tablets of 5g K2SO4 and 5 mg of
Se) and 12 ml of concentrated tetra oxosulphate VI acid (H2SO4) were added. The digestion was run for one hour at 42oC. Eighty (80 ml) and 40 ml of water and sodium hydroxide (NaOH) respectively were used in the distillation using 2200 FOSS distillation unit and the distillate was collected in 4% Boric acid. The percentage nitrogen (N) was calculated as:
% N = ( ) Where; a = ml of H2SO4 required0.014 100for titration of sample, b = ml of H2SO4 required for titration of blank, S = air-dry sample weight in mg, n = normality of H2SO4 (0.1 N), 0.014 = meq weight of nitrogen in g and mcf = moisture correction factor. Then the protein content of the sample was estimated by percent nitrogen multiplied protein coefficient (6.25).
The protein content calculated as ‘N’ x 6.25. (Bressani, 1994; Amoo, 1998; Adeyeye, 1995). The tannin content was determined by using a method Pearson (1976). One gram of each sample was weighed into a centrifuge tube with 2 ml of distilled water. It was centrifuged at 1500 rpm
182 for 10 minutes. The centrifuge samples were then poured out into a beaker and the supernatant
(extract) dispersed. One ml of NaCO3 and Folin Denis reagent was added in the beaker and allowed to settle. The readings were taken using a spectrophotometer. Tannin content was calculated as follows:
Tannin (mg/100g) = ( ) Where; An= absorbance test sample, As= absorbance of standard sample, C= concentration of standard solution, W= weight of sample, Vf= total filtrate volume and Va = volume of filtrate analyzed
The phosphorus content of each sample was determined by the dry ash extraction method following specific mineral element (AOAC, 1990). Exactly 5.0 g of the sample was burnt to ashes in a muffle furnace (SM9080) at 500ºC. After complete ashing, the ash was diluted with 1% Hydrochloric (HCl) acid, then filtered into a 100ml standard flask, and made up to the mark with deionized water. The solution was read with UV-visible spectrophotometer machine (model No: UV-1600, Shimadzu Corporation, Japan) for the determination of phosphorus in mg/100 g. The carbohydrate content of the sample was determined by estimation using arithmetic difference (Udosen, 1995). The energy value was calculated by application of the thermal coefficients of Atwater and Rosa (1899) with 4 calories for 1g of carbohydrates; 4 calories for 1g of proteins and 9 calories for 1g of crude fat. The available carbohydrate (CHO) and energy value were determined by using the formula as given below; CHO = [100 - (% moisture + % crude protein + % crude fat + crude fibre + % ash)]. Total energy (kcal) = [(% CHO× 4) + (% CP×4) + (% CF × 9)]. Where; CHO, CP and CF are; carbohydrate, crude protein and crude fat, respectively (Hassan et al., 2008; Elinge et al., 2012; Tawheed and Monika, 2014).
7.2.3. Data analysis The collected data were subjected to analysis of variance (ANOVA) using the complete randomized design (CRD) by using Statistical Analysis System (SAS) package (version 9.0 of SAS Institute Inc, 2000). The entire dataset was standardized by dividing each variable with its respective range, and was subjected to clustering based on Un-weighted Pair Group Method of Arithmetic mean (UPGMA) and pair wise generalized square distances (D2) between the clusters and principal component analysis (PCA) was used to group the landraces based on biochemical traits and to assess correlations between principal components and the parameters measured.
183 7.3. RESULTS AND DISCUSSION
7.3.1. Analysis of variance The result on the analysis of variance indicated, mean squares due to landraces were highly significant (p<0.01) for organic matter, total nitrogen, protein, fat, carbohydrate, total phosphorus, total energy, tannin and saponin contents indicating the existence of sufficient genetic variability of these traits within yam landraces from Southwest Ethiopia (Table 7.1). The variability among landraces also revealed wide chance of developing yam varieties possessing desirable biochemical traits. While, mean squares due to the flour moisture content, dry matter, ash, organic carbon and crude fiber contents showed non-significant difference.
Table 7.1. Analysis of variance of different biochemical traits of yams.
Biochemical trait Mean square Landrace Error CV R2 DF=35 DF=35 (%) Flour moisture content (%) 0.015 0.02 15.49 0. 48 Dry matter (%) 0.002 0.02 15.30 0.68 Organic matter (%) 0.07** 0.001 2.47 0.84 Ash (%) 0.01 0.005 9.00 0.68 Organic carbon (g) 0.004 0.005 9.13 0.43 Crude fiber (%) 0.045 0.015 16.2 0.75 Total N (%) 0.006** 0.002 3.13 0.75 Protein (%) 0.24** 0.07 3.60 0.75 Fat (%) 0.03** 0.005 7.72 0.98 Carbohydrate (%) 0.015** 0.003 4.44 0.80 Total P (mg/100g) 0.008** 0.001 0.65 0.99 Total Energy(kcal/100g DM) 0.006** 0.001 1.89 0.79 Tannin (mg/100g) 3244.92** 0.003 0.01 1.00 Saponin (mg/100g) 0.046** 0.007 10.62 0.85
**, Highly significant at 0.01% of probability level
184 7.3.2. Mean response to biochemical traits The descriptive value of the landraces based on biochemical characters was showed (Table 7.2). The mean values of different biochemical traits revealed remarkable differences among the landraces. The flour moisture and dry matter contents ranged from 17.75 to 27.47% and 15.80 to 27.28% with a mean of 22.03% and 21.76%, respectively. The mean dry matter content of 21.76% found in this study is comparable with the value of 23.1% and 19.9% reported by Megh et al. (2003) on Dioscorea triphylla and Dioscorea versicolor species, but different from the value reported by Abera (2011). On contrary, the result obtained from this study was lower than the flour and dry matter contents reported from the wild yam species collected from the South pacific region (Bradbury, 1988). The differences observed between the result of this study and the report of other researchers might be due to experimental methods of analysis and the inherent character of Dioscorea species. The range of organic matter and ash contents were 21.38 to 43.56% and 1.13 to 3.56% with a mean of 31.13 and 2.61%, respectively. The result obtained for ash content in this study is consistent with the result of Coursey (1983) and Abera (2011), but lower than the value (3.41%) reported by Princewill-Ogbonna and Ibeji (2015). The value of organic carbon and crude fiber varies from 0.63 to 1.98 g and 0.41 to 2.05 % with a mean and standard deviation of 1.45 0.35 and 1.28 0.39, respectively (Table 7.2). The crude fiber content obtained from this study was almost similar to the reported value of 1.5% by Wanasundera and Ravindran (1994), 1.5% Megh et al. (2003) and 1.13%, Udensi et al. (2010) on yam. The mean crude fiber content obtained from this study was lower than the value 1.68% reported by Abera (2011) and 1.98% by Princewill-Ogbonna and Ibeji (2015). This difference might be due to climate condition, the level of soil fertility where the yams are grown, varietal differences and the age of harvested tuber.
The range of total nitrogen content was 1.00 to 1.32% and a mean of 1.25%. The total nitrogen content in the studied yam tubers were higher than reported value of 0.48% by Abera (2011) and comparable with the reported value of 1.08%, Udensi et al. (2008). The crude protein content of yam tubers ranged from 6.25 to 8.28% with a mean of 7.82%. This value was consistent with the value of 8.31% reported by Udensi et al. (2008) on water yam and Tamiru (2006) on yams from South Ethiopia collections. The higher protein content indicated its higher total nitrogen in the storage tuber of yams.
185 The protein content was varying with different species of yams. For example, the mean and standard deviation of the crude protein content of Dioscorea bulbifera was 3.1 0.03 g/100 g,
Dioscorea deltoidea was 1.6 0.06 g/100 g, Dioscorea versicolor 1.7 0.02 g/100 g and
Dioscorea triphylla 2.3 0.05 g/100 g (Megh et al., 2003). Similarly, the crude protein content of yam was different in wet 1.68 to 3.00 g/100 g and dry 2.89 to 6.36 g/100 g processing methods respectively (Abera, 2011). The crude fat content ranged from 0.09 to 0.65 % with a mean of 0.32 %. This value is higher than reported value (0.20) for Cameroonian yam species (Egbe and Treche, 1995) and wild yams tubers from the central region of Nepal (Megh et al., 2003). Comparatively, this result was consistent with the report of FAO (1972) and Wanasundera and Ravindran (1994) on yams. On contrary, the mean fat content presented in this study by far lower than the reported value (2.24%) by Princewill-Ogbonna and Ibeji (2015) on three cultivars of Dioscorea bulbifera. The distribution of fat in different yams tuber showed the peel contained higher levels than tissue (Faboya et al., 1990; Abera, 2011). The carbohydrate content ranged from 12.71 to 33.95% and the energy values ranged from 92.66 to 173.30 kcal/100g with a mean value of 21.84% and 130.19 kcal/100g respectively. This result in agreement with those reported for yam (FAO, 1990), (Abera, 2011) and (Megh et al., 2003), but lower than the reported value of 82.50% and 359.81 kcal/100 g) of Udensi et al. (2008). The variation of carbohydrate content between yam species might be due to the genetic factor, maturity and management of yams.
The phosphorous content varied between 23.7 mg/100 g and 53.0 mg/100 g with a mean of 39.0 mg/100 g. This result was consistent with the report of Megh et al. (2003) on different yam species for example, 61.61 mg/100 g for Dioscorea bulbifera, 33.1mg/100 g for Dioscorea deltoidea, 40.8 mg/100 g for Dioscorea versicolor and 56.6 mg/100 g for Dioscorea triphylla. On contrary, the results of this study by far lower than the reported value ranged from 120-340 mg/100 g on Dioscorea alata by Udensi et al. (2008). The observed disparity between the results could be explained on the basis of the species difference and the environmental conditions upon which the tuber was grown.
186 Table 7.2. Mean, standard deviation and ranges of 14 biochemical traits of Dioscorea spp.
Biochemical traits Mean Sd Range
Flour moisture content (%) 22.03 2.40 17.75- 27.47
Dry matter (%) 21.76 3.16 15.80-27.28
Organic matter (%) 31.13 4.30 21.38 – 43.56 Ash (%) 2.61 0.63 1.13 – 3.56
Organic carbon (g) 1.45 0.35 0.63 -1.98
Crude fiber (%) 1.28 0.39 0.41 – 2.05 Total nitrogen (%) 1.25 0.06 1.00 – 1.32 Protein % 7.82 0.34 6.25 – 8.28 Fat (%) 0.32 0.14 0.09 – 0.65
Carbohydrate (%) 21.84 4.13 12.71 -33.94
Total phosphorus (mg/100g) 39.0 0.07 23.7 –53.0 Total Energy(kcal/100g DM) 130.19 16.84 92.66 – 173.30 Tannin (mg/100g) 64.67 40.28 19.80 – 181.00
Saponin (mg/100g) 5.91 3.72 2.31 - 13.94
The result of anti-nutritional factors such as tannin and saponin contents on yams from Southwest Ethiopia was presented in Table 7.2. The tannin content ranged from 19.80 to 181.0 mg/100 g with a mean value of 64.67 mg/100 g. This result was higher than the reported value for Dioscorea rotundata (20mg/100 g), which implies that less protein may be available in studied landraces from Southwest Ethiopia than in Dioscorea rotundata due to protein-tannin complex formation (Uka, 1985; Shajeela et al., 2011). However, it is important to note that heat treatment which is usually given to yams landraces before utilization will remove or reduce the level of tannin in the food system thereby making the protein available (Osagie, 1992). Comparatively, the result of this study was consistent with the work of Udensi et al. (2010) who reported the tannin content ranged from 46.5 to 180.25 mg/100 g on Dioscorea alata. Similarly, the saponin contents of yams ranged from 2.31-13.94mg/100 g with a mean of 5.91mg/100 g. This result is almost similar with the reported values of saponin (8.49-14.03 mg/100 g) of other yam species (Princewill-Ogbonna and Ibeji, 2015).
187 7.3.3. Principal component analysis The patterns of variation and the relative importance of each biochemical trait in explaining the observed variability was assessed through principal component analysis (PCA). The result of PCA grouped the variables into six components based on nine biochemical traits, among which the first three are significant (Eigen value > 1) and explained 73.9% of the total variability (Table 7.3). The first principal component (PC-1) accounted 35.10% of the total variation and was correlated positively with organic matter (0.545), total nitrogen (0.194), protein (0.194), carbohydrate (0.533) and total energy (0.540), while fat (-0.077), total phosphorus (-0.067), tannin (-0.204) and saponin (-0.012) contributed negatively. The second principal component (PC-2) accounted 24.5% of the total variability and mainly correlated with total nitrogen (0.597), protein (0.597), fat (0.324) and saponin (0.212) and negatively with the total phosphorus (- 0.237), total energy (-0.137), carbohydrate (-0.181) and organic matter (-0.133). The third principal component (PC-3) had 14.30% of the total variation. The total phosphorus content contributed (0.525), tannin (0.504) and fat (0.395), while PC-4 accounted 10.30% of the variation and correlated with saponin (0.567), fat (0.537) and tannin (0.459). PC-5 accounted 8.20% of the variation and negatively correlated with total phosphorus content (-0.779) and saponin content (-0.540). Finally, PC-6 had 7.10% of the total variation and mainly correlated with the tannin content (0.617) and negatively with the fat content (-0.665).
For proximate, anti-nutritive and mineral compositions, six principal components accounted for 99.5% of the total genetic variation where organic matter, total nitrogen, protein, fat, carbohydrate, total phosphorus, total energy, tannin and saponin contributed maximally to the PCs. This variation is attributable to environmental and genetic factors (Sultan, 2006; Sartie et al., 2011). Plotting the first and second principal components (Figure 7.1) from the matrix showed majority of biochemical traits clustering together at the origin of the plot. On contrary, the fat, tannin and saponin showed as an outlier far from the rest.
188 Table 7.3. Eigen values, proportion, cumulative variance and component scores of the first six principal components for quality traits in 36 landraces of yams.
Variable PC1 PC2 PC3 PC4 PC5 PC6 Eigen value 3.159 2.206 1.319 0.923 0.738 0.626 Proportion 35.10 24.50 14.30 10.30 8.20 7.10 Cumulative 35.10 59.60 73.90 84.20 92.40 99.50 Organic matter (%) 0.545 -0.133 0.057 0.120 0.028 -0.003 Total nitrogen (%) 0.194 0.597 0.157 -0.201 -0.017 0.200 Protein 0.194 0.597 0.157 -0.201 -0.017 0.200 Fat (%) -0.077 0.324 0.395 0.537 -0.042 -0.665 Carbohydrate (%) 0.533 -0.181 0.071 0.118 -0.011 0.042 Total P (mg/100g) -0.067 -0.237 0.525 -0.226 -0.779 0.072 Total Energy (kcal/100g DM) 0.540 -0.137 0.059 0.141 -0.001 0.055 Tannin (mg/100g) -0.204 -0.112 0.504 0.459 0.314 0.617 Saponin (mg/100g) -0.012 0.212 -0.503 0.567 -0.540 0.296
Figure.7.1. The Bi-plot diagram of PCA I and PCA II of 36 yam landraces based on nine biochemical traits
189 7.3.4. Cluster analysis Grouping of landraces based on their similarity is crucial. In the present study, the clustering approach was adopted to assembly 36 landraces into eight different groups by cut the dendrogram at 0.5 average distances between clusters (Table 7.4). The distribution of the landraces was evident from different clusters. Among the clusters, Cluster II was the largest, having 18 landraces and 50.0% of the overall genetic variation. Cluster V, VI, VII and VIII having one landraces of each and contributed 8.3% of the total variation. Cluster I,IV and III having the total of 14 landraces and contributed 16.67%,13.89% and 8.33% of the total variation, respectively (Table 7.4).
Table 7.4. Distribution of 36 Dioscorea spp. into eight clusters based on biochemical traits
Clusters Number of Serial number Name of landraces in each Percent of landraces in cluster contribution each cluster I 6 33,11,34,5,8, and 1 0004/07, 01/75, 7/84, 3/87, 16.67 46/83 and 59/02 II 18 19,18,7,25,36,35,3 76/02, 10/002, 54/02, 24/02, 50.0 1,4,22,10,28,29,9,3 06/2001, 7/85, 57/76, 75/02, 58/02, 116, 15/2000, 34/87, 0,32,24,12 and 2 08/02,21/02,0001/07,32/83, 06/83 and 68/01 III 3 17,16 and 3 37/87, 27/02 and 6/02 8.33
IV 5 14,21,27,26 and 13 07/03, 7/83, 60/87, 2/87 and 13.89 17/02 V 1 20 06/2000, 2.78 VI 1 15 45/03 2.78 VII 1 6 56/76 2.78 VIII 1 23 39/87 2.78
The cluster mean for various traits revealed that considerable differences were noticed between the cluster means of different biochemical characters (Table 7.5). Landraces from cluster V and VII produced the highest organic matter (43.56 and 33.63%); total carbohydrate content (33.95 and 23.27%) and energy (173.31 and 142.38 kcal/100 g DM) contents.
190 Landraces grouped in under clusters VIII and IV had highest fat (0.60and 0.45%) contents. Besides, landraces grouped under cluster III and VI had highest total phosphorus (0.40 and 0.52 mg/100 g) and landraces in cluster VII and II produced highest total energy (142.38 and 135.76), cluster VI and VII; highest tannin (170.00 and 181.00) content and saponin (8.83 and 6.03) contents in cluster I and II respectively. This implies that the landraces grouped under cluster VII and III (Figure 7.2) were found to be superior with regard to total biochemical traits than other clusters. For example, higher organic matter, total nitrogen, protein, fat, carbohydrate, total phosphorous, total energy, tannin and saponin contents. For emphasis, most of the landraces in cluster IV were obtained from Jimma zone, except 60/87 and 17/02 that were obtained from Sheka zone.
VI VII III VIII V
II
IV
I
Figure 7.2. Dendrogram showing hierarchical clustering patterns of 36 Dioscorea spp. landraces (UPGMA) based on nine biochemical traits
191 This could inform importantly that the chances of environmental influences were reduced drastically with genetic factor playing an active role. On the countrary, cluster I and VIII, consisted of seven landraces, and had 19.44% of the total variation and having the medium performance for the majority of biochemical characters (Table 7.5).
7.3.5. Distance between clusters The pair wise generalized square distances (D2) between the clusters (Table 7.6) showed that the distance between clusters were highly significant (p<0.01) suggesting diversity among landraces grouped into different clusters. The maximum inter cluster distance was found between clusters VII and VIII (23.02), followed by clusters V and VIII (22.92), clusters I and VIII (17.40),and clusters III and V (15.61), in order of magnitudes. Selection of parents from such clusters for breeding program would help to achieve novel recombinants in view of biochemical composition.
The clustering pattern suggested that landraces of the same origin were distributed into different groups, indicating that there was no parallelism between clustering pattern and geographic distribution of landraces. This might be due to difference in adoption, selection pressure and environmental conditions. For example, in the present study, crossing of landraces falling in the most distant clusters i.e., VII (56/76) and VIII (39/87) could result in maximum hybrid vigor and eventually may give rise to desirable recombinants. The minimum inter cluster distance was recorded between clusters II and IV (1.88), followed by clusters IV and V (2.25) and clusters I and II (3.34). Thus, the landraces belonging to the distant clusters could be used for breeding program to obtaining a wider range of variability.
192 Table 7.5. Cluster means of nine biochemical traits of Dioscorea spp. grown at Jimma
Cluster OM N Pro Fat CHO P Ene Tan Sap I 25.97 1.22 7.62 0.30 16.63 0.37 108.63 57.05 8.83 II 32.47 1.26 7.87 0.28 23.23 0.39 135.76 39.61 6.03 III 32.67 1.23 7.67 0.31 23.58 0.40 137.59 135.20 3.66 IV 32.43 1.28 8.00 0.45 22.86 0.37 135.43 74.78 3.88 V 43.56 1.22 7.59 0.15 33.95 0.45 173.31 45.20 3.37 VI 21.39 1.23 7.69 0.35 12.72 0.52 92.67 170.00 2.54 VII 33.63 1.25 7.81 0.29 23.27 0.35 142.38 181.00 10.87 VIII 21.83 1.30 8.13 0.60 13.25 0.38 93.00 97.00 4.12 Mean 30.49 1.25 7.80 0.34 21.19 0.40 127.35 99.98 5.41 S.div 7.28 0.03 0.19 0.13 6.92 0.06 27.55 55.86 2.96
OM= Organic matter (%), N= Total nitrogen (%), Pro= protein, Fat= Fat (%), CHO= Carbohydrate (%), P= Total phosphorous (mg/100 g), Ene=Total Energy (kcal/100 g DM), Tan= Tannin (mg/100 g) and Sap= Saponin (mg/100 g), S.div= standared deviation
Table 7.6. Pair wise generalized squared distances between eight clusters of Dioscorea spp. collected from Southwest Ethiopia
Cluster I II III IV V VI VII VIII I - 3.34 8.81 2.20 5.07 13.55 7.04 17.40* II - 7.45 1.88 5.24 5.59 8.89 14.08* III - 8.12 15.61* 8.11 6.78 12.13 IV - 2.25 6.82 6.35 16.60* V - 11.87 11.01 22.92** VI - 13.21 15.35* VII - 23.02** VIII - 2 *= Significant at 0.05 probability level (x 7 = 14.07) 2 **= Highly significant at 0.01 probability level (x 7 = 18.47)
193 7.4. CONCLUSION
The result of analysis of variance indicated significant variation (p<0.01) among the landraces for organic matter, total nitrogen, protein, fat, carbohydrate, total phosphorus, total energy, tannin and saponin contents indicating the existence of sufficient genetic variability of these traits within yams landraces collected from Southwest Ethiopia.
The principal component analysis grouped the variables into six components based on nine biochemical traits among which the first three are significant (Eigen value > 1) and explained 73.90 % of the total variability. For proximate, anti-nutritive and mineral compositions, six principal components accounted for 99.50% of the total genetic divergence, where organic matter, total nitrogen, protein, fat, carbohydrate, total phosphorus, total energy, tannin and saponin contributed maximally to the PCs. This variation is attributed to environmental and genetic factors.
The cluster and pair wise generalized squared clusters distances analysis of biochemical traits revealed, the existence of eight divergent groups. The maximum distances obtained between cluster VII and VIII. Thus, crossing between landraces grouped under these clusters may give desirable recombinants for high biochemical composition; due to widest inter cluster distance.
The results obtained from this study confirmed the existence of potential for selection of nutritionally superior landraces of yams from Southwest Ethiopia. The variability in the biochemical composition and functional properties of yams landraces are vital for plant breeders that may select landraces with high nutritional compositions of yams. From the results of the present investigation concluded that, different collections of yams vary greatly for their dry matter, protein, fat, ash and crude fiber content.
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199 VIII
______GENETIC DIVERSITY OF YAM (Dioscorea spp.) LANDRACE COLLECTIONS OF SOUTHWEST ETHIOPIA USING SSR MARKERS ______
200 Genetic Diversity of Yam (Dioscorea spp.) landrace Collections of Southwest Ethiopia using SSR markers
ABSTRACT
The study was conducted to quantify the genetic diversity of yam from Southwest Ethiopia using SSRs molecular markers. To this end, thirty-six diverse yam landraces were genotyped using 10 selected simple sequence repeat markers. The number of alleles detected per marker ranged from 1 to 5, with a mean of 3.0 per locus and a total of 22 putative alleles being amplified. Allele sizes ranged from 155-495 base pair. Number of effective alleles ranged from 1.00 to 3.57. The most variable locus was found to be YM02, YM09, YM12 and YM17 with the total of 12 alleles, while, the least variability was observed from locus YM13 and YM18 with two alleles. The expected heterozygosity ranged from 0.116 to 0.802 with a mean of 0.567. The mean polymorphic information content was 0.30. Euclidean similarity values ranged from 0.0 to 5.3, with a mean of 3.10. Analysis of molecular variance revealed that 79%, 17% and 4%, of the variation in yam landraces was attributable to within individual, among individual and among population, respectively. The study established the existence of considerable genetic diversity among Ethiopian yam landraces. Distinctive landraces such as 32/83, 54/02, 24/02, 56/76 and 76/02 from cluster I and 2/87, 07/03, 57/76, 06/2001 and 6/02 from cluster II were selected based on their highest dissimilarity index. These could be useful in source of genes of novelty into yam breeding. The patterns of genetic diversity and the relationships observed in this study should provide insights for genetic resource conservation and utilization of yam in the Southwest Ethiopia.
Keywords: Genetic diversity, heterozygosity, PIC values, simple sequence repeat markers
201 8.1. INTRODUCTION
Root and tuber crops occupy a prominent position as food crops, next to cereals and grain legumes, and they also form the subsidiary staple over 20% of world population (Dansi et al., 2000). Among the tropical tuber crops, Guinea yam (Dioscorea cayenensis - rotundata complex) is one of the most important species, especially in the yam belt of West Africa (Asiedu et al., 1997; Mignouna et al., 2003; Loko et al., 2015) and 60 millions of people depends on it (Mignouna et al., 2002; Asiedu and Sartie, 2010; Adejumo et al., 2013). Dioscorea species are widely adapted in Ethiopia, as cultivated and wild relatives and believed to be the wild progenitor of the major species cultivated in Africa (Terauchi et al., 1992); an isolated center of yam diversity in East Africa (Norman et al., 1995) and the source of materials for genetic improvement of the crop.
Currently, yam is becoming an imperative crop and used as livelihood security in the densely populated areas of South, Southwest, and Western parts of Ethiopia (Norman, 1995; Hildebrand et al., 2002). In these regions, yam is an important crop for producing enough food in a sustainable manner to meet the needs of an increasing population currently the greatest challenges we face. Further, it also provides in the form of “insurance” against social disruption, prolonged droughts and/or other periods of stress and unrest. Thus, yams are an important means by which food production could be increased without the use of large amounts of agricultural inputs (Onwueme and Charles, 1994).
Despite the importance of this crop, the diversity of yams in Ethiopia is poorly understood and leads severe yield and diversity losses. In the present study 38 farmers named yam landraces with varying degree of abundance and distribution identified in Southwest Ethiopia. However, the extent of genetic diversity in identified species, their relationships and the use of advanced breeding on this crop is hardly investigated at molecular level mainly due to; very little is known about yam breeding and genetics (Tamiru et al., 2015). Further, the uses of standard descriptor hardly allow accurate classification of the majority of the landraces from Ethiopia into any of the known cultivated Dioscorea species. Traditional methods using morphological data for estimation of genetic diversity and relationships among landraces were largely unsuccessful due to the strong influence of environmental factors (Vijayanand et al., 2013). Additionally, the
202 inherent characters of the crop including the polyploidy, dioecy, non synchronous flowering of parental landraces and long time of maturity reticent the genetic improvement of yam (Asiedu et al., 1998; Tamiru et al., 2011). Furthermore, the available yam genetic resource is poorly characterized, limiting utility of the existing diversity in genetic improvement program in the country.
Assessing the diversity of the existing landraces by molecular markers is crucial as it is more reliable, consistent, not influenced by environmental conditions and the best means to examine the genetic diversity of cultivated and wild species of yam. Previous study on genetic diversity of yam by using AFLP markers from Southern Ethiopia (Tamiru et al., 2007) confirmed the presence of significant genetic variation in yam collections from Wolayta and Gamo-gofa zones. However, the report did not fully cover the landraces collected from various regions of Ethiopia and not assessed by SSR molecular markers. Thus, comprehensive assessment of genetic diversity present in Ethiopian yam landrace is required using a relatively greater number of samples, representing the diverse yam growing regions with a suitable and sufficient number of SSR markers for conservation.
Microsatellites markers (simple sequence repeats) have been increasingly used as the marker of choice, because of their locus specificity, extensive genome coverage, high degree of polymorphism, co-dominant; inheritance and easy automated scoring of landraces (Anas and Yoshida, 2004; Zalapa et al., 2012; Alina et al., 2014). Microsatellites are widely used to characterize the genetic variability within and between population and at present they allow the standard method to estimate genetic diversity (Emmanuel et. al., 2015). SSRs are thus an abundant resource in the genome and have high level of allelic diversity; consequently, they are frequently used as genetic markers in plant breeding studies (Powell et al., 1996; Vijayanand et al., 2013). Taking the advantages of SSR markers, the present study was designed to quantify the genetic diversity present among 36 yam landraces collected from Southwest Ethiopia using SSR molecular markers.
203 8.2. MATERIALS AND METHODS
8.2.1. Plant materials and study sites A total of 36 yam landraces were collected from seven districts of Jimma, Sheka and Bench-maji zones of Southwest Ethiopia. The list of 36 yam landraces and their area of collections was presented in Table 8.1.
8.2.2. DNA extraction Five plants from each landrace were selected and tagged individually before sampling for DNA extraction. Genomic DNA was collected from healthy leaves of three month old plants using the Whatman Flinders Technology Associates (FTATM) cards. The sampling protocol was done according to the modified protocols of FTA card technology (Mbogori et al., 2006). FTA classic card (Whatman Inc., Clifton, NJ) is a Whatman paper that has been impregnated with a patented chemical formulation that lyses cells, then captures and immobilizes nucleic acids in the paper matrix. In addition, they contain compounds for denaturing, chelating and trapping free radicals which prevent damage of the nucleic acids (http://www.whatman.com). Prior to DNA extraction, labeled the name of each landrace at the back of FTA card and a back of pen was used to press the leaf sample extract onto the FTA paper until the FTA card was soaked. Adequate quantities of sap have been collected onto FTA cards by placing the plant samples directly onto the FTA paper and applying moderate pressure with a pen (Lange et al., 1998; Lin et al., 2000). Ethanol (70%) was used before and after each sample to prevent cross contamination. The FTA card was then hanged on the drying line using a paper clip for air drying for 1 to 3 hours and later stored in an air tight plastic container. Genotyping was conducted at Incotec Biotechnology laboratory, South Africa.
8.2.3. FTA processing for PCR Processing of Flinders Technology Associates (FTATM) for PCR was performed as described by the manufacturer with little modification. Briefly, 2 FTA discs measuring 1.2 mm each were punched from the FTA disc using 1.2 mm Harris Micro punch (Whatman, Inc. US), and placed in a 96 well or 384 well PCR plate containing 50µl and 35µl of FTA wash solution, respectively.
204 The PCR plate was incubated at room temperature for 15 minites with shaking, and the solution removed with pipettor. FTA purification reagent (50 µl) was used twice, followed by one rinse with 100 µl of double distilled water for 5 min, and once with 50 µl absolute ethanol for 5 min. Checked the use of double distilled water instead of TE buffer (10 mM Tris-HCL, 0.1 mM EDTA, pH 8). The FTA discs were dried in an oven for 15 minites at 56 oC and ready for PCR amplification. Washing was carried out directly in the PCR tubes/plates to minimize the tedious step of transferring discs from wash tubes/plate to new PCR tubes/plate. From extracted genomic DNA, 5µl was used as template.
8.2.4. PCR amplification and product analysis
Samples on FTA cards from 36 yams were analyzed at the Incotec Proteios laboratory in South Africa (Incotec, SAPty. Ltd. South Africa). All samples were used in bulked amplification, using DNA from 36 individual leaf samples. Samples were bulked per landrace to rule out variation within entry. A single punch of each card per submission was taken and homogenized in the Finnzymes dilution buffer (Kit). Two uL of each of bulked sample was used in the polymerase chain reaction (PCR). Ten SSR markers were used in this study (Table 8.2). The primer sequences used for PCR amplification were selected from the literature (Tamiru et al., 2015) based on product size and genomic coverage. The microsatellite loci used was chosen based on the size of the repetitions and their location, to obtain a representative sampling of the whole genome (Table 8.2). PCR products were fluorescently labeled and separated by capillary electrophoresis on an ABI 3130 automatic sequencer (Applied Biosystems, Johannesburg, South Africa).
205 Table 8.1. List of 36 yam landraces and their areas of collection
Region Zone Districts Name of Latitude Longitude Altitude landrace (m.a.s.l) 68/01 07030’63N 036053’45E 1784 Dedo 46/83 07031’28N 036053’59E 1771 Oromia 116 07031’28N 036053’63E 1683 06/83 07031’32N 036053’64E 1692 07/03 07031’50N 036053’60E 1733 34/87 07031’37N 036053’44E 1911 Jimma 0 0 75/02 07 40’43N 036 48’76E 1734 Kersa 08/02 07040’46N 036048’79E 1740 76/02 07040’64N 036048’84E 1728 0004/07 07040’55N 036048’75E 1741 59/02 07040’37N 036049’10E 1718 3/87 07040’58N 036048’75E 1731 Manna 56/76 07041’89N 036048’06E 1837 45/03 07041’86N 036048’08E 1810 37/87 07041’87N 036048’13E 1940 27/02 07035’06N 036041’91E 1877 06/2000 07035’43N 036041’86E 1850 Seka chekorsa 7/83 07035’06N 036041’91E 1898 39/87 07035’42N 036042’94E 1885 21/02 07036’48N 036045’09E 1785 32/83 07026’74N 036024’01E 1372 Shebe sombo 24/02 07026’75N 036024’07E 1379 2/87 07026’76N 036024’12E 1365 0001/07 07026’74N 036024’12E 1367 Sheko 6/02 06059’66N 035034’11E 1728 54/02 07002’03N 035032’77E 1892 10/002 07002’91N 035029’76E 1668 Bench maji 15/2000 07004’13N 035037’74E 1320 57/76 07002’88N 035029’74E 1654 7/84 07002’88N 035029’74E 1661 SNNPRS 06/2001 06059’69N 035034’09E 1387 Yeki 01/75 07011’30N 035026’22E 1171 17/02 07011’27N 035026’26E 1176 Sheka 58/02 07011’22N 035026’25E 1192 60/87 07011’72N 035026’48E 1199 7/85 07014’30N 035026’17E 1173 SNNPRS=Southern Nation, Nationalities and People Regional State
206 Table 8.2. Selected SSR primers for yam genetic diversity study
Locu Repeat Primers (5´to 3´) Forward Primers (3´to 5´) Reverse Tm 0C GC (%) Prod s motif uct F R F R Size
YM02 (AAG)6 TAGATTTCGCTTTTCCACTAGC CCTAATCATCATCATCGTCATC 58 57 41 41 263 YM03 (GAT)6 TCACTCAAACAATGAGCGTAG GATGGCTGCTGCATGACTG 60 60 58 58 202 YM05 (AAG)8 AGGATTATCACTGAAAGGGCT CCTTCCAATTACTCTCCAAGA 57 56 43 43 140 YM09 (CTT)12 AGGAACATTCCCACTCAGTTA ATTGGGCAAGTGTGGTGTG 59 59 43 53 193 YM12 AAC)8 TGAGCATTCTTGTTTTGCCG CTTTCAGGGCGTGCATGG 58 60 45 61 215 YM13 (CTT)8 CCAATCACATCACGTCTAGTC GACAATAGAAACTTCGAGACC 57 57 45 45 328 YM15 (CTT)7 CCATCTCCTCCCTTATCTACAC GGGATTGAAGTTCCAGAGACT 57 57 50 45 485 YM17 (AC)8 TCCCTCAATTAAAGCATAGCC AGCCACCAAACATCTTGCTC 59 60 43 50 181 YM18 (GT)19 GACATTGGGGATCTCTTATCA TAGCAGCAGTAACGTTAAGGA 57 57 41 41 266 YM21 (GAT)5 AATGATGCATCTGAGGATAGT GATGCTATTACGACAACCTTG 57 57 41 41 340 Sources: Tamiru et al. (2015)
8.2.5. Data analysis Data were captured using the Genscan® software (Applied Biosystems) and the resulting fragments were analyzed and the alleles scored using the GeneMapper® version 4.1 software (Applied Biosystems). PCR was done for all of the 10 primers (Table 8.2). The molecular size of the DNA fragments was estimated using 50bp DNA ladder (Fermentas). The binary data matrix was prepared for each primer separately and merged as combined data for overall analysis. For each primer, number of different alleles, allele size, effective number of alleles, Shannon’s information index, expected gene diversity, observed hetrozygosity, expected heterozygosity, number of putative alleles, percent polymorphism, and Fixation index of phenotypic diversity for the binary data were calculated using GenAlEx software version 6.5 (Peakall and Smouse, 2012). GenAlEx was also used for computing analysis of molecular variance (AMOVA) and principal coordinate analysis (PCoA) via distance matrix. The fixation index (Fst) to measure genetic differentiation among the populations. A dissimilarity matrix was generated using Darwin version 5.0 software (Perrier and Jacquemoud Collet, 2006). The data matrices of the genetic distances were used to create the dendrogram using the un-weighted pair group method with arithmetic mean (UPGMA) algorithm. The assay efficiency index referred to as the polymorphic information content (PIC) was calculated using the genetic analysis package Power-Marker,
207 version 3.2 (Liu and Muse, 2005). PIC refers to the value of a marker for detecting polymorphism within a population, depending on the number of detectable alleles and the distribution of their frequency; thus, it provides an estimate of the discriminating power of the marker. The PIC value of an allele locus can be calculated the formula described by (Bhat, 2002) as:
Where, pi and pj are frequencies of corresponding alleles
The heterozygosity of a locus is defined as the probability that an individual is heterozygous for the locus in the population (Liu, 1998; Sa´ndor et al., 2012) and can be calculated as:
Where, H= heterozygosity pi = the frequency for the ith allele
Effective number of alleles (n) was calculated using the formula:
( ) ∑ Where, pi= = frequency= of the ith allele in a locus h = 1 – ∑pi2 = heterozygosity in a locus
The Euclidian distances were calculated between bulked samples, using the program GGT 2.0.
208 8.3. RESULTS AND DISCUSSION
8.3.1. Polymorphism and allelic diversity of SSR markers
In the present study, 36 yam landraces were analyzed by using ten SSR molecular markers. Among these, three landraces namely, 59/02, 68/01 and 0001/07 were excluded from final analysis due to poor amplification. The analysis of the genetic parameters was summarized in Table 8.3.
Ten microsatellites primers generated a total of 22 putative alleles (different fragment sizes) with an average of 2.2 bands per primer. The number of alleles investigated ranged from 1 to 5, with a mean value of 3.0 per locus and the total number of alleles detected was 30. The lowest and the highest numbers of alleles were detected from the markers YM18 and YM09, respectively. The size of polymorphic bands ranges from 155 to 495bp. The size of polymorphic bands obtained from this study is greater than the study reported by Abebe et al. (2013) on yam. The most variable locus was found to be YM02, YM03, YM09, YM12, YM15, YM17 and YM21 with 26 alleles, while the least variability was shown by the YM13 and YM18 with two alleles. Most of the markers generated 1–5 alleles; three markers generated 3 alleles each. This result was fairly similar to 2.8 alleles per locus reported by Silva and Gustavo (2006). In yam genetic diversity studies a greater number of alleles (97) were reported by Obidiegwu et al. (2009) and 9.0 by Marcos et al. (2012). Greater number of alleles generated by SSR markers suggests allelic richness and a good indicator of genetic diversity for successive selection and conservation of yam.
The heterozygosity value ranged from 0.116 to 0.802 with an average of 0.567. Marker YM13 had the lowest and YM17 is the highest range of heterozygosity. The average heterozygosity observed in this study was quite similar to the previously report of Obidiegwu et al. (2009) at 0.67 in Côte d’Ivoire, who used 13 SSR markers and compared 89 water yam (Dioscorea alata L.) landraces from west African countries. The higher level of allelic diversity of SSR loci found in this study was probably associated with the wide range of genetic diversity represented in the landrace of yams from Southwest Ethiopia and which will enhance selection efficiency. Increased levels of heterozygosity indicate significantly greater proportion of genetic diversity,
209 which will enhance selection response in breeding programs. The high level of heterozygosity observed among landraces signified the fact that landraces used in this study were collected from a wide range of geographic areas with different levels of selection pressure. Besides, He et al. (1995) reported a high level of polymorphisms in sweet potato which was fixed through vegetative reproduction and maintained through a high level of gene flow due to self- incompatibility. Therefore, the SSR markers used in this study confirmed the existence of genetic variability in yams landrace.
Increased allelic number in the present study is probably attributed to significant genetic variation among the sampled yam gene pool. The yam scientists feel that wild yam landraces are an excellent genetic resource to improve genetic diversity and to introduce useful genes into cultivated yams (Mignouna et al., 2005). Therefore, it is important to understand the genetic diversity among them. Wild yams landraces originated from different geographical regions could have important genetic differences (Lee et al., 2008; Hernando et al., 2011). In many crops, farmers maintain a large number of landraces on farm to cope with the diverse environmental conditions, resulting in a continuous exchange of genes through pollen flow (Manzelli et al., 2007; Barnaud et al., 2008). In addition, farmers exchange planting materials through gifts and via markets to renew old genetic stocks or to acquire new varieties. Consequently, there may be a continuous exchange of genes among landraces (Mekbib, 2007; Alina et al., 2014). The SSR markers revealed marked genetic diversity among the yam landraces.
The number of effective alleles per locus ranged from 1.00-3.57 with a mean value of 1.71. The markers YM13 and YM09 had the lowest and the highest numbers of effective alleles per locus, respectively. The highest number of effective alleles per locus obtained from in this study was quite similar to the previously report by Abebe et al. (2013) on yam. The result of the observed value of gene diversity within landraces ranged from 0.00 to 0.71 with a mean 0.34 (Table 8.3). The marker YM13 and YM02 had the lowest and the highest diversity values, respectively. The average gene diversity ranged from 0.00 to 0.73 with a mean of 0.31. The markers YM18 and YM09 had the lowest and the highest average genetic diversities among ten markers. The high level of genetic diversity described the out crossing and self incompatibility in yams. Besides, vegetative propagation could have attributed to maintaining the levels of genetic diversity (Ngailo et al., 2016). The polymorphic information content (PIC) values reflecting the genetic
210 diversity of ten SSR markers used ranged from 0.00 for the marker YM13 and YM18 to 0.72 for YM09 with a mean of 0.30, implies the medium discriminating ability of the SSR markers used for this study; hence the markers can suitably be used in genetic diversity and relationship analysis. The PIC values calculated in the present study were in agreement with the report of Serge et al. (2007). In yam characterization study, Silva et al. (2014) also reported a greater mean PIC value (0.62) than the present estimates. Obidiegwu et al. (2009) found PIC values ranging from 0.30 to 0.82 among Guinea yam landraces evaluated using 13 SSR markers. High PIC values implied their informative potential to detect differences among the yam landraces. Most of the markers in this study had PIC values <0.5, suggesting a medium discriminating ability for classifying the landraces.
Table 8.3. Genetic diversity of 33 yam landraces based on 10 microsatellites markers
Locus N As (bp) K Na Ne Ho He FIS PIC YM02 3.0 237-242 0.471 3.0 2.22 0.71 0.56 -0.29 0.55 YM03 4.0 214-235 0.735 2.0 1.13 0.03 0.12 0.74 0.12 YM05 2.0 155-158 0.735 2.0 1.10 0.10 0.09 -0.05 0.09 YM09 5.0 201-225 0.645 3.0 3.57 0.68 0.73 0.06 0.72 YM12 4.0 221-232 0.657 3.0 2.34 0.70 0.58 -0.22 0.57 YM13 1.0 0-319 0.116 1.0 1.00 0.00 0.00 0.00 0.00 YM15 3.0 491-495 0.793 2.0 1.19 0.03 0.16 0.78 0.16 YM17 4.0 192-211 0.802 3.0 1.41 0.30 0.29 -0.05 0.29 YM18 1.0 0-256 0.280 1.0 1.00 0.00 0.00 0.00 0.00 YM21 3.0 368-373 0.403 2.0 2.14 0.89 0.54 -0.67 0.53 Total 30.0 2079-2746 5.637 22 17.1 3.44 3.07 0.40 3.03 Mean 3.0 207.9-274.6 0.567 2.2 1.71 0.34 0.31 0.04 0.30 SE 0.42 3.47 0.15 0.88 0.27 0.11 0.09 0.16 0.08 Where, N= Total number of alleles per locus, As= Allele size, K= Expected heterozygosity, Na= Number of putative alleles, Ne= Number of effective alleles per locus, Ho=Observed gene diversity within landraces, He=Average gene diversity within landraces, FIS=Inbreeding coefficient, PIC= Polymorphic information content and SE= Standard error.
The inbreeding coefficient (FIS), value measures the reduction of heterozygosity in a population. The results revealed that, five of the 10 loci (YM02, YM05, YM12, YM17 and YM21) showed excess of the heterozygotes (negative FIS value) and the remaining five loci showed deficit in heterozygotes (YM03, YM09, YM13, YM15 and YM18).
211 The analysis of genetic diversity within and among the 33 yam landraces classified by areas of collection was presented in Table 8.4. The result of Shannon’s information index ranged from 0.35 to 0.65 with a mean of 0.45. The value of Shannon’s information index from this study was slightly lower as compared to those reported by Obidiegwu et al. (2009) and Abebe et al. (2013) with a mean Shannon’s information index value of 0.94 and 0.65 respectively. Similarly, the result of this study was also by far lower than the finding of Ngailo et al. (2016) who reported the genetic diversity from 0.08 to 1.69 with a mean 1.22. The landrace sampled this study are genetically diverse. The high level of genetic diversity might have been contributed more by cross and vegetative propagated crops (Yada et al., 2010).
Table 8.4. Genetic diversity within and among the 33 yam landraces classified by areas of collection
District N Na Ne I Ho He FIS %P PIC Dedo 5.0 2.30 1.83 0.60 0.34 0.41 0.13 70.0% 0.33 Kersa 4.0 1.60 1.53 0.35 0.40 0.26 -0.80 40.0% 0.23 Manna 4.0 1.70 1.51 0.38 0.30 0.28 -0.29 50.0% 0.24 Seka chekorsa 5.0 1.90 1.48 0.39 0.34 0.26 -0.37 60.0% 0.24 Shebe sombo 3.0 2.10 1.69 0.47 0.33 0.30 -0.23 60.0% 0.27 Sheko 7.0 2.20 1.90 0.65 0.37 0.55 0.08 80.0% 0.41 Yeki 5.0 1.70 1.45 0.35 0.33 0.25 -0.45 50.0% 0.22 Mean 4.71 1.93 1.63 0.45 0.34 0.33 -0.22 58.6% 0.22 SE 0.15 0.11 0.08 0.05 0.04 0.04 0.06 5.1% 0.02 Where, N= Number of individual within each population, Na= Total number of alleles per locus, Ne= Number of effective alleles per locus, I= Shannon’s information index, Ho= Observed gene diversity within landraces, He=Average gene diversity within landraces, FIS= Inbreeding coefficient, % P= Percentage of polymorphic loci and PIC= Polymorphic information content
The higher percent of polymorphism in the present study could suggest the extent of genetic diversity among the different yams in Southwest Ethiopia. The diversity could mainly be attributed to diverse agro-climatic conditions in the country. Landraces from different regions were sometimes closely related and landraces from the same region had different genetic background. Even so, the intra-regional diversity could be as a valuable source as inter-regional diversity for crop improvement (Benson et al., 2013). The mean inbreeding coefficient (FIS)
212 value was found to be 0.04 0.16 implying that landraces collected from different districts were slightly heterozygous. There were low levels of inbreeding within the landraces, with the mean
FIS value being -0.22 0.06.
8.3.2. Analysis of molecular variance and partitioning of genetic diversity The Analysis of Molecular Variance (AMOVA) was conducted to examine the differences among the populations, among individual and within individual. The result revealed that there was high significant difference (p < 0.001) within and among individual. Genetic differentiation within individual accounted for 79.0 % and among individual (17%) of the total variation while the variance among the populations was 4.0% and showed non significant variation (Table 8.5). Similar to this study, Gichuki et al. (2003) reported a significantly high contribution among landraces with in districts variation to the total variation. According to Veasey et al. (2007) the higher variability observed could provide some insights to the evolutionary dynamics of yams. The result of AMOVA suggests that a small collection within a given district will capture the genetic diversity existed in Southwest Ethiopia. The result of the present study further revealed a great intra-population genetic diversity and no significant difference was detected among the populations. Comparable results have been reported in recent studies in Guinea yams (Abebe et al., 2013). The result of variation detected (79.0%), found to be greater than the previous report of Zhigang et al. (2014) who obtained 23.83% variation within population and 76.17% variation among population. Marie et al. (2015) also found similar results in AMOVA of yam landraces from different yam species collected from Cameroon, and results 64.0% of the variance was done due to differences within population and 20.0% was due to differences among populations.
The high within individual variation in this study could mainly be due to the large number of landraces included and due to evolutionary dynamics and out-crossing nature of yam. Although yam is mainly propagated by storage tuber, some authors have reported yams are cross pollinated plant and reproduced by botanical seed (Okereke, 1977; Akoroda, 1983). According to Obidiegwu et al., (2009), yams are dioecious plants and spontaneous hybridization may have contributed to the ancestry of some landrace, though the selection of somatic mutants might have been the main source of variability used by farmers in their plant improvement practices. This genetic variation offers high potential for genetic improvement, because it implies high amount of genetic variance upon which selection could be made for breeding. The lower variance among
213 populations of this study could be explained by exchange of yam landrace among nearby districts through farmers and traders that may enhance gene flow across regions of Southwest Ethiopia.
Table 8.5. Analysis of Molecular Variance (AMOVA) among 33 yam landraces collected from seven districts using 10 SSR markers
Source df SS MS Est. Var. Per. Var F-Statistics Among Populations 6 18.239 3.040ns 0.082 4% 0.060 Among individual 26 59.125 2.274** 0.342 17% 0.008 Within individual 33 52.500 1.591** 1.591 79% 0.001 Total 65 129.864 2.015 100%
Table 8.6. Pair-wise estimates of gene flow (Nm) (above diagonal) and genetic differentiation (FST) (lower diagonal)
Gene Flow (Nm) Dedo Kersa Manna Seka Sheko Shebe Yeki chekorsa sombo Dedo - 2.852 3.852 3.652 4.282 2.479 2.999 Kersa 0.081 - 3.078 6.368 7.942 1.766 5.883 Manna 0.061 0.075 - 7.242 6.990 1.572 4.979 Sekachekorsa 0.064 0.038 0.033 - 13.452 2.053 13.762 Sheko 0.055 0.031 0.035 0.018 - 2.485 9.812 Shebe sombo 0.092 0.124 0.137 0.109 0.091 - 1.994 Yeki 0.077 0.041 0.048 0.018 0.025 0.111 -
Genetic differentiation (FST)
** Significant at 0.01 level of probability, ns= non significant at 0.05 level of probability DF= Degree of freedom, SS= Sum of squares, MS= Mean sum of squares, Est. var. = Estimated variance, Per. Var. = Percentage variation, Nm= Gene flow = 0.25 (1-FST)/FST
According to standard guidelines for the interpretation of genetic differentiation (Wright, 1978), the range 0 to 0.005 indicates little, 0.05 to 0.15 moderate, 0.15 to 0.25 great, and above 0.25 very large genetic differentiations. According to Slatkin (1989) and Morjan and Rieseberg (2004), gene flow <1 is considered to be low, while Nm = 1 is considered to be moderate and Nm > 1 is considered to be high. Moderate or relatively low levels of gene flow can significantly
214 alleviate the loss of genetic diversity by preventing the effect of genetic drift (Aguilar et al., 2008). The result of this study, lower gene flow is shown between closer districts and vice versa, this found to be high exchange of materials among nearby districts by farmers and traders and that may enhance gene flow across in the region.
8.3.3. Principal coordinate analysis (PCoA) The result of PCoA axis indicated that the first two coordinates accounted 53.70% of the total variation. Axis I recorded the highest (38.90%) of variation. This was followed by axis II (14.80%) in order of magnitude. However, the plots of PCoAI versus PCoAII demonstrated a wide dispersion of landraces along the four quadrants (Figure 8.1). Quadrant I had different landraces which are mainly collected from Dedo, Sheko and Shebe sombo districts of Jimma and Bench maji zones. Quadrant II had comprising different collections from Sheko, Kersa and Dedo districts. Most landraces form all districts were distinct and closer to the origin that separating quadrants showing that these landraces are genetically closer than the others. The rest of the landraces were grouped in quadrant IV. Landraces collected from Sheko district are distinct and distributed in the whole quadrants and showing these landraces are genetically diverse. Similar to this study, Emmanuel et al. (2015) reported the distribution of water yam landraces in different quadrants.
Figure 8.1. The Bi plot analysis of 33 yam landraces using 10 SSR markers Besides, some landraces in this study hardly grouping in quadrants where the SSR primer failed to distinguish the landraces from their areas of collection. Generally, the PCoA result indicated that most of the landraces did not group together with other landraces from the same district. One
215 possible reason for this is the exchange of yam landraces among farmers across districts. This movement of landraces could have provided an opportunity for cross border gene flow.
8.3.4. Genetic dissimilarity and distance analysis among landraces In order to estimate the genetic distances among landraces, the dissimilarity matrix was computed through Euclidean method. The Euclidean dissimilarity matrix estimates as measures of genetic distances ranged from 0 to 5.3 with a mean value of 3.10 (Table 8.7). This result indicated wide range diversity among the yam landraces collected from Southwest Ethiopia. Among the 33 studied landraces, the lowest dissimilarity value (0.0) was scored between 116 and 01/75, 75/02 and 27/02, 7/83 and 004/07, whereas, 01/75 and 116 showed the highest dissimilarity (5.3) indicating yam landraces existed in Southwest Ethiopia had higher polymorphic and rich genetic diversity. Landraces, 3/87, 56/76, 116, 01/75 and 06/83 were distantly related landraces and were sourced from Jimma and Sheka zones of Southwest Ethiopia. Among the landraces, 76/02, 10/002, 34/87, 32/83, 21/02, 15/2000 and 54/02 were the most dissimilar among the tested landraces. These landraces were collected from Kersa, Seka chekorsa and Sheko districts of Jimma and Bench maji zones, respectively. The landraces that showed low genetic dissimilarity would results from narrow genetic diversity and low allelic richness when compared to the tested landraces. In line with this, Yanqing et al. (2008) reported genetic dissimilarity estimates ranged from 0.33 to 0.96 with a mean value of 0.62 among 28 yam cultivars studied. The genetic dissimilarity estimates provide in selection for parents and development of a segregating population in order to maintain genetic diversity in crop breeding program (Beyene et al., 2016). The estimates of genetic distance are valuable when assigning landraces into heterotic groups in hybrid development from crosses between different groups (Xiao et al., 1996; Emmanuel et al., 2015). Muthamia et al. (2013) estimated a wide genetic diversity when analyzing 187 Kenyan yam landraces using 12 SSR markers. The SSR markers also revealed wide genetic diversity among yam collections in Kenya. The higher proportion of polymorphism in the present study could suggest the extent of genetic diversity among the different yam landraces of Ethiopia.
216 Table 8.7. A similarity matrix among 33 yam landraces using Euclidian measure
Gebotype 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 6/02 75/02 1.8 3/87 2.7 2.1 56/76 3.4 4.1 3.4 54/02 4.5 4.8 5.1 5.0 46/83 4.2 4.2 3.9 4.0 3.8 08/02 2.7 2.0 2.5 3.8 4.8 4.2 116 3.7 3.8 3.0 1.5 5.3 3.9 3.5 01/75 3.7 3.8 3.0 1.5 5.3 3.9 3.5 0.0 06/83 2.7 2.1 0.0 3.4 5.1 3.9 2.5 3.0 3.0 17/02 1.8 0.0 2.1 4.1 4.8 4.2 2.0 3.8 3.8 2.1 07/03 1.6 0.5 2.1 3.8 4.8 4.2 2.1 3.5 3.5 2.1 0.5 45/03 2.9 2.2 2.9 3.2 4.5 4.1 2.2 2.9 2.9 2.9 2.2 2.3 27/02 1.8 0.0 2.1 4.1 4.8 4.2 2.0 3.8 3.8 2.1 0.0 0.5 2.2 37/87 2.7 2.0 2.5 3.8 4.8 4.2 0.0 3.5 3.5 2.5 2.0 2.1 2.2 2.0 10/002 4.3 4.8 4.2 3.0 3.7 3.2 4.6 3.6 3.6 4.2 4.8 4.7 4.2 4.8 4.6 76/02 2.5 2.5 2.9 3.3 5.0 4.2 1.5 2.9 2.9 2.9 2.5 2.2 2.7 2.5 1.5 4.5 06/2000 1.8 2.0 2.9 3.5 5.2 4.2 2.8 3.2 3.2 2.9 2.0 1.5 3.0 2.0 2.8 4.8 2.1 7/83 1.8 0.0 2.1 4.1 4.8 4.2 2.0 3.8 3.8 2.1 0.0 0.5 2.2 0.0 2.0 4.8 2.5 2.0 58/02 2.5 2.1 2.5 3.6 4.8 4.2 0.5 3.2 3.2 2.5 2.1 2.0 2.3 2.1 0.5 4.5 1.0 2.5 2.1 39/87 2.9 2.2 2.9 3.2 4.5 4.1 2.2 2.9 2.9 2.9 2.2 2.3 0.0 2.2 2.2 4.2 2.7 3.0 2.2 2.3 32/83 3.5 4.2 4.6 3.6 2.9 3.2 4.2 4.1 4.1 4.6 4.2 4.1 3.8 4.2 4.2 3.3 3.9 4.0 4.2 4.1 3.8 24/02 4.3 5.0 5.1 4.3 2.7 2.9 5.0 4.7 4.7 5.1 5.0 4.8 4.9 5.0 5.0 3.6 4.6 4.6 5.0 4.8 4.9 2.9 2/87 2.1 1.0 1.1 3.7 4.9 4.0 2.2 3.4 3.4 1.1 1.0 1.1 2.4 1.0 2.2 4.5 2.7 2.2 1.0 2.3 2.4 4.3 5.0 60/87 1.8 2.0 2.9 3.5 5.2 4.2 2.8 3.2 3.2 2.9 2.0 1.5 3.0 2.0 2.8 4.8 2.1 0.0 2.0 2.5 3.0 4.0 4.6 2.2 15/2000 3.7 4.0 3.5 4.2 3.8 4.4 4.3 4.6 4.6 3.5 4.0 4.1 4.5 4.0 4.3 3.7 4.5 4.5 4.0 4.3 4.5 4.8 4.0 3.6 4.5 34/87 2.1 3.0 3.6 3.9 4.0 4.1 3.6 4.4 4.4 3.6 3.0 2.9 3.7 3.0 3.6 3.8 3.5 3.0 3.0 3.5 3.7 3.7 4.0 3.2 3.0 3.0 21/02 4.2 4.3 3.6 2.5 4.9 3.6 4.0 2.0 2.0 3.6 4.3 4.1 3.5 4.3 4.0 4.1 3.5 3.8 4.3 3.8 3.5 3.6 4.5 3.9 3.8 5.0 4.8 57/76 1.9 0.5 2.1 3.8 4.7 4.2 2.1 3.5 3.5 2.1 0.5 0.7 1.8 0.5 2.1 4.6 2.5 2.1 0.5 2.1 1.8 4.1 4.9 1.1 2.1 4.1 3.0 4.1 7/84 2.7 2.0 2.5 3.8 4.8 4.2 0.0 3.5 3.5 2.5 2.0 2.1 2.2 2.0 0.0 4.6 1.5 2.8 2.0 0.5 2.2 4.2 5.0 2.2 2.8 4.3 3.6 4.0 2.1 06/2001 1.6 1.5 2.5 3.6 5.0 4.2 2.5 3.2 3.2 2.5 1.5 1.0 2.7 1.5 2.5 4.7 2.0 0.5 1.5 2.2 2.7 3.9 4.6 1.8 0.5 4.3 2.9 3.8 1.6 2.5 7/85 2.7 2.1 2.5 3.6 4.7 4.2 0.5 3.2 3.2 2.5 2.1 2.1 1.8 2.1 0.5 4.4 1.6 2.9 2.1 0.7 1.8 4.1 4.9 2.3 2.9 4.3 3.6 3.8 2.0 0.5 2.5 0004/07 1.8 0.0 2.1 4.1 4.8 4.2 2.0 3.8 3.8 2.1 0.0 0.5 2.2 0.0 2.0 4.8 2.5 2.0 0.0 2.1 2.2 4.2 5.0 1.0 2.0 4.0 3.0 4.3 0.5 2.0 1.5 2.1
217 8.3.5. Cluster analysis The clustering of 33 landraces produced an agglomerative coefficient of 0.44 indicating a moderately good clustering structure. The Euclidean dissimilarity matrix was performed to grouping landraces using the UPGMA algorithm. The resulting dendrogram revealed two major distinct clusters of yam landraces (Figure 8.2). Cluster I consisted 7(21.21%) of the total landraces that formed subgroups IA and IB with Euclidean distance of 0.51. The landraces were collected from Jimma (two each from Dedo and Shebe sombo) and Bench maji (three Sheko) zones from Southwest Ethiopia. Landraces in subgroup IA (32/83, 54/02 and 24/02) were distantly related. In subgroup IB landraces 01/75 and 116 were closely related, whereas, 56/76 and 76/02 were distinct from the rest of landraces.
Cluster II was composed of two subgroups (IB and IIB), which consisted of 26 (78.79%) of the total yam landraces. Among the total landraces, 18(54.54%), 4(12.12%) and 4(12.12%) where collected from Jimma, Sheka and Bench maji zones respectively. The subgroup IB consisted of 4 (12.12%) landraces and subgroup IIB, 22 (66.67%) of landraces with 0.31 Euclidean distance. In the latter subgroup, the landraces 2/87, 07/03, 57/76, 06/2001 and 6/02 were distantly related to the other landraces. In cluster II subgroup IB landraces, 7/84, 37/87, 08/02, 39/87, 45/03, 06/83, 3/87, 7/83, 27/02, 0004/07, 75/02, 27/02, 60/87 and 06/2000 were closely related. Among these, more than 85% of the landraces were collected from different districts of Jimma zone. This may be suggest that landraces collected from similar zone/districts belong to the same boundary and have possibilities to share genetic materials between farmers and have similar genetic makeup. In addition, in most landraces situated geographically far apart were grouped together in the same cluster. These results are in agreement with earlier studies which showed that geographical separation did not generally result in greater genetic distance (Kumar et al., 2012; Wei et al., 2008; Zhang et al., 2012). Ganesamurthy et al. (2010) who also concluded that geographic location could not be used as diversity during selection of crops. This could be a consequence of exchange of genetic materials among the neighboring farmers as well as traders in the region. Besides, farmers selections and management practice of the landraces may enhance the genetic purity every year, and the storage tuber is used in the following planting season increased the genetic similarity of landraces.
218 In present study, landraces from the same area of collection were grouped in different clusters, although most of them were grouped according to the area of collection. This may suggest wide genetic variation among the landrace. According to Barnaud et al. (2008) farmers’ practices and historical factors affect patterns of genetic diversity. Despite the gene flow and farmers’ management practices are key to preserve genetic diversity of landraces with related agro morphological traits and agro ecologies. This result in line with Manzelli et al. (2007) who reported farmers selected and preserved landraces on the basis of the phenotypic and agronomic traits. Several studies were conducted based on geographical location, either at country, regional, or agro climatic level (Marcos et al., 2012; Dansi et al., 2013; Loko et al., 2015). The results of the present study coincide with the report by Yanqing et al. (2008) who also found the clustering of landraces studied did not agree based on the diverse origins of the yam landraces. Plant breeders use different breeding methods in selecting the best parents for making crosses and for assigning landraces to a particular genetic group.
Estimates of genetic divergence at molecular markers have been reported as very useful tools in selecting the best parent combination for new assigning landraces into genetic groups (Paterson et al., 2009; Abate, 2015). Landraces with distant genetic relationship across clusters and sub clusters should be selected as parent for next crosses for genetic recombination and to improve genetic advancement in breeding programs (Sakhti et al., 2009). Understanding the extent of genetic diversity among landraces are useful in planning crosses, selection of landraces to specific heterotic groups and for precise identification of plant material for conservation (Perumal et al., 2007).
The SSR markers used in this study had moderate PIC values that provided information to uniquely identify most of the landraces. About 8(80.0%) SSR markers were polymorphic, confirming that each marker would be effective and valuable for genetic analysis. The degree of precision of molecular markers in estimating genetic relatedness between landraces is strongly dependent on the type and number of markers that are used and their genome coverage (Wei et al., 2008). The use of SSRs for the analysis of genetic diversity in yam could reduce the limitations in identifying polymorphisms and result in more complete genomic coverage (Perumal et al., 2007). High level of gene flow among landraces and high level of variation, farmers’ indigenous management system has played a significant role in maintaining genetic
219 diversity (Dansi et al., 1999). Mekbib (2007) pointed out, the role of farmers in the management and preservation of genetic diversity over time. The high genetic variability among landraces provides enough genetic plasticity to adapt to the diverse environmental conditions in the tropical areas, and allow reducing crop failure by reducing vulnerability to environmental stresses.
II IIB
I
IB
I IIA I I IA
I Figure 8.2. Dendrogram revealing genetic relationships among 33 yam landraces from South west Ethiopia based on ten SSR analyses of Euclidian similarity coefficients with UPGMA clustering.
220 8.4. CONCLUSIONS
In this study, the genetic diversity present in 33 yam landraces was collected from seven districts of Southwest Ethiopia using 10 microsatellite (SSR) molecular markers. The result revealed, SSRs markers detected an average of 3.0 alleles per locus, Gene diversity value 0.710 was also observed. This genetic variation offers high potential for genetic improvement through selection. Polymorphism was observed in eight microsatellite loci, confirming that each marker would be effective and valuable for genetic analysis. Polymorphic information content ranged from 0.00 (locus Ym13 and YM18) to 0.720 (locus YM09) within an average value 0.30. The mean value 0.567 for heterozygosity in this crop is expected due to the fact that yams mainly are propagated by storage tuber and improve by farmers’ selection and management practices. Average gene diversity was highest (0.550) landraces collected from Sheko district of Bench maji zone. This may suggest that landraces collected from similar zone/districts belong to the same boundary and have possibilities to share genetic materials between farmers over the years to improve the local landraces and/or completely replace them and have similar genetic makeup. The relatively low diversity observed in Yeki (0.25) and Kersa (0.26) suggests the dominance of narrower range of cultivars suited to the utilization pattern of yam in the districts over the years.
The SSR markers revealed wide genetic divergence among the yam landraces studied. The analyses formed two major distinct clusters without allocating landraces based on their areas of collection. Further sub grouping of landraces showed close genetic relationship of landraces 7/84, 37/87, 08/02, 39/87, 45/03, 06/83, 3/87, 7/83, 27/02, 0004/07, 75/02, 27/02, 60/87 and 06/2000 with the widely cultivated landraces in different districts of Jimma zone. The non distinction between districts landraces of yams as shown in the clustering pattern strengthens the inference that landraces must have been widely distributed. The distantly related yam landraces, such as 46/83, 32/83, 54/02, 24/02, 2/87, 07/03, 57/76, 06/2001, 34/87 and 6/02, can be useful in source of genes of novelty into yam breeding programs. Information generated in this study would be valuable for breeding and conservation strategies of yams.
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227 9. SUMMARY, CONCLUSION AND RECOMMENDATIONS
9.1. SUMMARY AND CONCLUSION Yam is one of the food security and economically important crop grown mainly in major growing areas of Southern, Southwestern and Western Ethiopia and has considerable importance for rural livelihood security. Nevertheless, there is insufficient information on yam in Southwestern part of the country. To this end the present study was designed to assess on farm diversity, management, and distribution and analyze the diversity based on morphological, biochemical and molecular markers and to identify appropriate harvesting stages of yams for yield and quality. Toward this effort, seven research activities were assessed and their results have been presented.
Diversity, distribution and management of yams were investigated through on farm level survey that covered 240 households in seven districts of Jimma (Manna, Shebe sombo, Seka chekorsa, Dedo and Kersa), Sheka (Yeki) and Bench-maji (Sheko) Zones of Southwest Ethiopia. From the result of the present study, a total of 38 farmers named landraces were identified on farm. The number of landraces maintained on individual farmers’ varied from one to six with a mean of 2.78. Most (60.52%) of the landraces had narrow abundance and uneven distribution and only a few (39.47%) grew dominantly (anchiro, gaeno-boye and badaye). From all districts, Manna and Dedo exhibited high richness, and have less diversity due to comparatively lower number of unique landraces. The lowest number of landraces and least diversity were observed in Kersa and Seka chekorsa districts. The number of farmers growing yam landraces decrease considerably in all surveyed areas, mainly due to, low attention given to the value of the crop (95%), drought at early stage of the crop (93%), porcupine attack (90%), need more management (82%), shortage of farm land (74%), replaced by high value crop (72%), shortage of stake (49%) and labour (23%) leads to genetic erosion. The rate of genetic erosion at district level varied from 28.80% in Yeki to 57.93% in Kersa with an average rate of 44.48%.
The morphological characterization of 36 yam landraces was assessed based on 32 qualitative and 19 quantitative traits revealed six distinct groups from each, respectively. The result of Shannon-Weaver diversity index (H`) indicated that high level of diversity existing among Dioscorea spp. landraces based on the frequency distribution of phenotypic diversity that was
228 considered. The analysis of variance for quantitative characters showed significant difference among the landraces on 13 characters. In this study, more traits were found to have high GCV value than PCV. This may suggested that the inherent association of landrace in most characters with tuber fresh weight is stronger than the environmental factor. Besides, tuber fresh weight, petiole length, leaf width and number of vines hill-1 exhibited high genotype coefficients of variation, high heritability together with high genetic advance as percent of means. Thus, selection of yams having these characters will enhance genetic improvement of Dioscorea spp.
Cluster and pair wise distance analysis based on various quantitative characters was carried out simultaneously to facilitate the selection of potential landraces for future improvement efforts. Six different clusters became evident. Cluster I and II were the largest, having 30 landraces of indicating the overall genetic similarity among them, cluster III and IV accommodated four landraces and the remaining clusters accommodated two landrace. The landraces that were grouped under cluster III and IV showed ranked first by having the highest mean performance for most of characters considered in this study. The pair wise cluster distance revealed that, the widest inter cluster distance was found between cluster III and V (380.09) followed by cluster IV and VI (261.5), suggesting the divergence existing in the material, while the minimum distance was between cluster I and IV (D2 =23.49) followed by cluster I and III (29.81). In the present study, it appeared that there was no reasonable degree of correspondence between cluster groups formed by qualitative and quantitative traits.
Stage of maturity had significant effects (p<0.01) with most of the characters considered in this study. Although, the amount of variability that was considered in most traits increased up to 6MAP, significantly highest total yield was recorded at 9 MAP and 10 MAPs. At 10MAP, the yams are physiologically mature in all aspects and have good opportunity to draw nutrients and photosynthesize over a longer period. Similarly, maturity stage had also significant effect in most of biochemical traits. The nutritional composition of bulbils and storage tuber is varying with advanced stages of maturity. In both landraces (Woko and Welmeka), the flour moisture, ash, organic carbon, crude fiber, total nitrogen, protein and tannin content from bulbils increased with increasing maturation, however, the dry matter, organic matter, the fat carbohydrate, total energy and the saponin content of storage tuber reduced when increasing the harvesting stages. On contrary, the flour moisture, dry matter, ash, organic carbon, crude fiber, crude fat and tannin
229 content from storage tuber increased with advancing maturation. Succinctly, the yield and bio chemical compositions of landraces were more genetic based than environmentally influenced.
The analysis of variance of biochemical traits indicated significant variation (p<0.01) among the landraces for organic matter, total nitrogen, protein, fat, carbohydrate, total phosphorus, total energy, tannin and saponin contents indicating the existence of sufficient of genetic variability of these traits within yams landraces collected from Southwest Ethiopia. The cluster and distance analysis of biochemical traits revealed the existence of eight divergent groups. The cluster mean for various traits revealed that landraces grouped under cluster VII and III had the highest mean performance for most of biochemical traits considered in this study. The maximum inter cluster distance was found between clusters VII and VIII (23.02), followed by clusters V and VIII (22.92), clusters I and VIII (17.40),and clusters III and V (15.61), in order of magnitudes. Thus, crossing between landraces grouped under cluster VII and VIII may give desirable recombinants for high biochemical composition; due to widest inter cluster distance. The results obtained from this study also confirmed the potential exists for selecting nutritionally superior landraces of yams from Southwest Ethiopia. Further the generated data in this study is useful as a ready reference for selection of yams with high nutritional compositions, to maintain the nutritional security and development of conservation strategies on yams in Southwest Ethiopia.
The results of molecular analysis revealed that, the number of alleles detected per marker ranged from 1 to 5, with a mean value of 30 per locus and a total number of 30 putative alleles being amplified. Allele sizes ranged from 155-495 base pair (bp). Number of effective alleles ranged from 1.00 to 3.57. The most variable locus was found to be YM02, YM03, YM09, YM12, YM15, YM17 and YM21 with 26 alleles, while, the least variability was observed from locus YM13 and YM18 with two alleles. The expected heterozygosity values ranged from 0.116 to 0.802 with a mean of 0.567. The mean polymorphic information content (PIC) was 0.30. Euclidean similarity values ranged from 0.0 to 5.3, with a mean value of 3.10. The result of Analysis of Molecular Variance revealed, there was high significant difference (p<0.001) within and among individual. Genetic differentiation within individual accounted for 79.0% and among individual (17%) of the total variation while the variance among the populations was only 4% and showed non significant variation. The result of cluster analysis based on SSR markers revealed two major distinct clusters without allocating landraces based on the areas of collection.
230 Landraces 7/84, 37/87, 08/02, 39/87, 45/03, 06/83, 3/87, 7/83, 27/02, 004/07, 75/02, 27/02, 60/87 and 06/2000 showed close genetic relationship. This may suggest that landraces collected from similar zone/districts belong to the same boundary and have possibilities to share genetic materials between farmers over the years to improve the local landraces and/or completely replace them and have similar genetic makeup.
9.2. RECOMMENDATIONS
Farmers are a source of information of on-farm diversity, distribution and management of yam, thus, farmers’ indigenous knowledge of yam and local landraces must be collected, analyzed and properly documented for research and development program in the country.
The genetic diversity of yam varied among in tested districts; thus, promote exchange of knowledge and local landraces between farmers and districts/ kebeles are valuable for conservation and sustainable utilization of yam in Southwest Ethiopia.
Yam is an important for food security and major economic importance crop as compared to other root and tuber crops in most tested districts. Thus, it needs a great effort for research and development program to maximize the use of the existing diversity to meet the need for food and livelihood security through broadening the knowledge base of the crop. Besides, in this study, the major causes of genetic erosion were identified, thus, the analyses of each cause, in association with socio-economic factor of the household are vital for conservation.
The analysis of Dioscorea spp. landraces based on morphological, bio-chemical and molecular levels had enormous contribution for breeding and for genetic resource conservation of yams in Southwest Ethiopia. The present effort carried out in Southwest Ethiopian collection; however, yams are adapted diverse agro-ecological areas of the country. Thus, additional collection out of this region and evaluate the landraces in to more seasons and location are vital for breeding and conservation.
Maturity assessment on yams is crucial to get higher yield and quality tuber and bulbils. Based on the results of the present study, harvesting of yams at 9MAP to 10MAP produced high yield and quality of tuber.
231 10. APPENDIX
232 Appendix Table 3.1. Questionnaire
Instruction
This questionnaire was used to farmers, key informant and group discussion during survey work on yam in selected districts of Jimma, Sheka and Bench-maji zones of Southwest Ethiopia. It is important to gather their awareness, knowledge and perception about yam production, diversity, distribution, management and classification system.
The consent was obtained from the concerned district agricultural offices and community leaders before implementing the actual study. Tested farmers’ were selected with full consent of farmers’ and key informants of the yam grower in Southwest Ethiopia. Before each sampling, the study objective was clearly explained to the selected farmers’ and key informants, that the aim of the study is neither to evaluate the performance of the individual nor to blame anyone for weakness, but to gather information that might lead to eventual improvement in the situation. Each farmer’s and key informants are assured the information provided confidential and used only for the purpose of research.
1. General information of the study area Region------• Zone: ------• District/district: ------• Kebele/Village:------Code------ Date------• Farmers name------ What position does the respondent hold? ------ Agricultural institution (Governmental & non-governmental organizations involved in agricultural development such as research extension services) Human population density ------ Estimated average family size: ------ Occupation,------ Number of female headed households------ Number of male headed households------ Agricultural productivity------ Land use patterns Arable Land ______ha/ local measurements Forest land ______ha------ Grazing Land ______ha------ Cultivated land including fallow ___ha------ Other type of land ______ha------ Availability of infrastructure (Road, Water, supply, School, Flour mill, etc.)
233 Major ethnic groups(s)------ Major religion(s)------ Accessibility of the district with other districts Number and sex of agricultural extension beneficiaries Physical environment Mean annual rainfall------mm Mean maximum temperature------oC Mean minimum temperature------oC Topography (predominant) Agro-ecological zone (sub-zones) Altitude range ______to ______masl Vegetation cover------ Soil types------ What are the available agricultural service?------ Types of agricultural extension services made available to male and female farmers Land holding by------
2. Socio-economic characteristics 2.1. Sex and age of the respondent 1.Male ------2. Female------3. Age------2.2. Major occupation------2.3. Educational level of the respondent 1). Illiterate 2). Read and write 3).1st –4th 4). 5th –8th 5). 9th-12th 2.4. Religion------5. Marital status ------2.5. Economic status of the family (low, medium or high income) ------2.6.Total land size (ha)------2.7. Land covered by yam production------(ha) 2.8. Family size------Male Female Total a) Ages under 14 years ------b) Ages between 15 to 30 years ------c) Ages between 31 to 60 years ------d) Ages above 60 years ------e) Total number ------2.9. Crop ownership, sale and consumption by the household Crop type Number of Purpose species Consumed Sold Medicinal Other
3. Production system/Husbandry practices - 3.1. Crop ownership and division of labor Crop type Owner Responsible member of the family
Men Women Children Other specify
234 3.2. State the member or members provide control the farm land? (based on sex and age group)
Age group Men Women Children Other specify Under 14 years Age between 15 and 30 years Age between 31 and 60 years Age above 61 years
3.3. Major land ownership and labor related constraints, its causes and solutions? 4. Landraces grown
4.1. Do you usually grow yam______? yes------No------4.2. If yes, how many yam landraces do you have------4.3. How long you grow yam? ------4.4. Where do you get yam species? a) market b) neighbor c) exchange d) backyard 4.5. List the number of yam landraces in your Kebele that you know?
Landrac Tuber Tuber Planting harvesting Storage Use of land es color shape races
4.6. List the number of landraces that you have?
Landrac Tuber Tuber Time of Time of Twin Sex of Use of es color shape Planting harvesting direction landraces land races
4.7. Which one is the best landraces?------4.8. What amount of land did you allocate for yam production in 2012-2014 (in timad)?------4.9. For what purpose you grow yam? a) Food security b) Medicinal value c) For social value d) For cash e) Other specify 4.10. State the major potential threat/ Production constraints to yam production and productivity in order of economic importance 1st ------2nd ------3rd ------4th ------5th ------6th ------4.11. Do you usually hire labor for yam production? 1)Yes------2)No------4.12. If yes, how many casual laborers do you hire?------4.13. Have you ever received credit for buying seed? 1) Yes------2)No------4.14. If yes, where do you get the credit from?------4.15. Do you purchase yam seed? Yes (1) No (2) 4.16. If yes, how often do you purchase seed of yam?
235 4.17. Distance (km) travelled to buy yam seed? 4.18. Major yam landrace conservation constraints its causes and solutions?
5. Seed (tuber) production
5.1. Is there any special production activity you follow for yam seed production? Yes – No--- 5.2. If yes, list the down the major activities? 5.3. If no, why don’t you practice a different activity for seed production? 5.4. Have you had contractual seed production with other institute / farmers? a)Yes b) No 5.5. Have you used stake for yam production? a)Yes b) No 5.6. If Yes, when? 5.7. If yes, what type staking method you used?______5.8. What type of staking materials you used?______5.9. Major yam seed production constraints its causes and solutions?
5.2. Harvesting 5.2.1. What are the activities you follow for yam production before harvest? 5.2.2. How do you know physiological maturity time? 5.2.3. How many times you harvest your yam a) once b) twice c) three times 5.2.4. What main activities you follow for yam production after harvest? 5.2.5. Major yam harvesting constraints its causes and solutions?
6. Landrace diversity and Management
6.1. Which yam land race do you usually grow? 6.2. Do you tell me the reason why you select this cultivar? 6.3. How do farmers rate on farm plant genetic resources? 1. High, 2. Medium 3. Low 6.4. Do you tell me the trend of yam genetic resources before 10 years a) increase b) decrease c) no change? 6.5. If increase, how do think the reason?______6.6. If decrease, how do think the reason?______6.7. Do you tell me the mechanism that improve yam genetic resources in your area?______6.8. Major yam management constraints its causes and solutions?
7. Farmers breeding (landrace selection and maintenance)
7.1. Do farmers practice varietal/ landraces selection? Yes (1), No (2) 7.2. If yes, what are farmer’s variety selection criteria? 7.3. Do farmers exercise specific selection methods? Yes (1) No (2) 7.4. If yes, what is the selection method you used? 7.5. Stage of selection?______7.6. Who select yam______7.7. Major yam selection constraints its causes and solutions?
236 8. Yam classification 8.1. Are you classify your yam landraces Yes (1) No (2) 8.2. If yes, what type of criteria you used to classify your yam landraces? 8.3. If yes, at what growing stage you classify yam landraces? 8.4. Who classify yam?______8.5. Major yam classification constraints its causes and solutions?
9. Yam Utilization
9.1. What type of utilization you grow yam?______9.2. Major yam utilization constraints its causes and solutions?______
Checklist for collected data from key informants
The objective of this questionnaire is to collect information on yam production system, distribution, management, diversity and classification system of yam in study area.
1. Do you provide extension service on yam production in the district? 1. Yes__ 2.No____ 1.1. If yes what are the services? 1.2. If no what are the reason?______2. Where do the farmers in the district get yam seed as planting material?______3. Which yam landrace are preferred by farmers to grow?______4. What type of selection criteria are used by farmers?______5. What the reason for selecting the yam landrace?______6. What do you think are farmers’ reason for adopting best landraces?______7. Is there any dis-adoption of best landraces? 1. Yes 2. No 8. Which crops are being replaced by best landraces? ______9. If yes what are the reasons?______10. Which seed source do you think the farmers prefer most? Why?______11. Do you think farmers have access to best seed? 1. Yes, 2. No 12. If no what are the reasons for lack of access to best seed?______13. Do you provide improved yam production and management trainings to growers? 14. Is yes, what are the contents of the training?______15. If no, what are the reasons?______16. Do you know what contractual seed production is? 1.yes 2.no 17. Are you involved in one way or another (for example, in farmers selection, monitoring, seed purchase etc.,) contractual seed production of improved yam landraces?______18. What are the challenges and opportunities of contractual yam seed production (farmers based seed production or community based seed production? Challenges______Opportunities______19. What are the services that you provide to yam growers in general?______
237 20. Are there any problems that you feel important in production and distribution of improved yam landraces?______
Checklist for collected data from group discussion
1. How many yam landraces that are grow in your district? A. at present B. at the previous year 2. What are the major reasons for replacing the yam landraces? 3. What are the characteristics of good yam landrace seed? 4. What are the challenges and opportunities of yam production in your district? 5. Do you think there are rich and poor farmers in your area? 1. Yes 2. No a). if yes, what are the wealth indicators? b). how do you think these wealth indicators? 6. What are the main activities done during yam seed production a. before harvest b. after harvest 7. How do you know the physiological maturity? And when do you harvest? 8. Is there any type of disease and pest on yam in your area? A) yes b) No 9. If yes, what type of measures you used for yam protection from pests? 10. What do you think is the future trend of yam in this district? 11. What are the challenges and opportunities of yam production in your district? 12. Are there any problems you feel important in improvement, production, management and distribution of yam landraces? 13. Would you please give your general comments on yam seed production and improvement?
238 Appendix Table 4.1. Mean performance value of 19 quantitative characters of 36 Dioscorea spp. landrace evaluated at Jimma, in 2015
Acc LL LW PL Llo DBL VL IL TiL NoI NV DM NT TuL TuDi VFW VDW TFW TDM HI% PH PH 1.0 12.68 5.13 7.13 3.33 3.18 262.33 9.81 2.83 26.83 4.28 147.75 4.08 39.22 15.84 11.56 2.71 40.40 23.36 77.75 2.0 11.91 4.67 14.00 1.96 3.30 265.42 10.46 2.78 24.55 3.90 142.29 4.33 41.83 15.76 11.50 2.97 30.00 21.48 72.29 3.0 14.16 5.00 12.88 1.90 3.03 267.00 10.34 2.22 26.30 3.50 140.04 4.08 41.35 16.74 15.00 3.24 35.10 21.62 70.04 4.0 11.07 4.54 8.77 1.53 2.81 280.85 10.29 2.22 29.00 4.80 145.53 4.17 41.17 14.88 11.11 3.17 33.80 21.36 75.53 5.0 10.07 4.16 7.98 1.60 3.96 251.85 9.51 2.64 27.90 3.80 125.88 4.33 38.03 13.12 13.44 2.82 16.80 23.57 55.88 6.0 11.82 4.44 11.53 2.50 3.77 267.25 10.26 2.83 25.40 4.20 157.57 4.50 41.04 16.40 8.00 3.01 53.80 20.96 87.57 7.0 8.65 3.72 9.58 2.34 3.00 267.00 9.44 2.23 30.00 4.50 143.44 4.50 37.77 12.40 11.00 2.83 30.40 22.45 73.44 8.0 9.83 3.84 13.28 2.09 4.08 257.25 9.34 2.88 26.80 3.80 142.68 4.17 37.37 13.92 10.72 2.94 28.55 20.81 72.68 9.00 11.81 4.66 10.17 2.49 2.81 256.50 10.45 2.05 23.10 3.90 144.84 4.33 41.81 18.00 13.94 2.91 41.40 20.74 74.84 10.0 9.05 4.66 11.15 1.57 3.27 250.10 10.08 2.51 21.40 3.90 139.72 4.33 40.33 16.08 12.89 2.82 29.60 19.81 69.72 11.0 10.09 3.50 10.50 2.71 4.29 253.75 9.66 3.20 22.70 3.88 146.11 4.33 38.66 13.38 10.61 2.72 33.80 21.97 76.11 12.0 10.20 4.08 9.40 1.63 2.86 252.25 9.66 2.13 25.60 4.30 145.25 4.50 38.66 13.76 14.56 2.94 44.00 21.80 75.25 13.0 10.16 4.31 9.77 1.37 3.32 240.50 9.12 2.44 27.10 3.40 149.39 4.17 36.49 17.20 12.89 2.87 49.20 18.14 79.39 14.0 9.29 3.00 9.80 1.43 3.09 263.50 9.95 2.23 28.60 4.20 136.59 4.08 39.79 15.04 12.00 3.21 24.00 22.51 66.59 15.0 11.28 4.86 11.73 1.72 3.53 260.75 10.03 2.48 26.80 4.60 142.79 4.67 40.13 17.76 14.27 2.58 37.40 18.47 72.79 16.0 10.93 4.49 12.38 1.53 3.44 271.50 10.96 2.68 25.10 4.30 142.31 4.75 43.83 15.92 19.83 2.87 47.40 18.86 72.31 17.0 8.75 3.58 5.82 1.63 3.03 296.75 10.10 2.14 33.00 6.10 141.29 3.92 40.42 12.48 12.39 2.97 30.60 22.50 71.29 18.0 10.28 4.43 9.60 1.66 3.40 263.85 9.68 2.48 28.30 5.00 152.13 4.58 38.72 15.60 13.67 2.95 63.00 17.83 82.13 19.0 10.17 4.06 8.23 1.43 3.71 247.00 9.28 2.42 26.03 4.40 144.25 3.92 37.13 12.16 11.56 3.13 33.40 23.33 74.25 20.0 9.67 4.08 11.83 1.72 3.68 240.38 9.52 2.55 23.78 4.10 142.93 3.92 38.08 13.92 9.94 3.36 26.80 19.70 72.93 21.0 10.78 4.72 11.57 1.57 3.64 254.25 9.53 2.30 26.93 4.60 151.14 4.17 38.13 16.48 10.56 3.04 45.60 19.10 81.14 22.0 9.71 4.05 10.23 1.18 2.95 241.75 8.89 2.05 24.35 3.93 143.71 4.42 35.56 19.52 10.44 3.22 29.20 16.93 73.71 23.0 12.34 4.73 13.50 2.01 3.17 258.50 9.97 2.70 23.33 4.10 113.78 4.58 39.89 16.08 15.50 2.77 11.40 20.76 43.78 24.0 9.62 4.05 8.10 1.34 2.57 242.25 9.02 1.99 27.00 3.70 125.77 4.42 36.08 13.68 12.78 3.24 16.20 23.17 55.77 25.0 8.28 3.48 7.48 2.33 4.16 259.25 9.40 3.14 31.80 4.70 130.80 4.50 37.61 12.72 12.89 3.00 19.40 21.22 60.80 26.0 11.20 4.42 12.92 2.88 3.63 239.00 9.25 2.65 22.10 4.00 126.75 4.33 37.01 14.48 11.29 2.82 14.80 20.53 56.75
239 Appendix. Table 4.1. Continued Acc LL LW PL Llo DBL VL IL TiL NIPV NV DM NT TuL TuDi VFW VDW TFW TDM HI% PH PH 27.0 10.65 4.58 11.12 2.95 3.53 254.25 9.93 2.53 25.43 5.10 145.06 4.58 39.71 14.64 9.26 7.05 27.80 21.30 75.06 28.0 11.86 4.22 8.17 2.46 3.07 243.25 9.51 2.37 24.50 4.33 114.93 4.75 38.06 12.10 14.56 2.97 11.80 20.86 44.93 29.0 10.72 4.16 8.17 3.73 3.73 235.00 9.01 2.73 22.50 4.20 98.98 4.83 36.06 12.64 16.67 2.76 6.80 20.55 28.98 30.0 9.76 4.30 9.92 2.08 3.82 237.50 8.92 3.38 26.40 4.50 139.13 4.50 35.69 16.72 12.83 3.15 28.40 20.61 69.13 31.0 9.88 4.42 10.45 2.13 3.48 246.35 9.22 2.45 26.33 5.10 141.72 4.25 36.90 12.72 3.27 32.00 32.00 19.17 71.72 32.0 10.20 4.23 11.12 2.52 4.03 239.75 9.17 2.96 25.20 3.90 142.30 4.75 36.69 9.11 3.41 23.40 23.40 22.06 72.30 33.0 10.04 4.33 10.78 2.33 3.75 243.05 9.20 2.70 25.76 4.50 142.01 4.50 36.79 10.92 3.34 27.70 27.70 20.62 72.01 34.0 8.77 3.61 8.92 1.22 3.83 243.75 9.30 2.47 26.38 4.92 133.89 4.42 37.19 13.78 3.26 23.67 23.67 21.71 63.89 35.0 12.39 4.63 12.60 1.35 3.71 252.25 9.75 2.97 26.00 3.80 115.42 4.17 39.02 12.39 3.26 9.60 9.60 19.91 45.42 36.0 8.53 3.50 6.15 2.97 3.36 241.25 9.47 2.56 24.90 4.50 130.39 4.17 37.90 10.61 3.22 14.40 14.40 19.84 60.39 St.div 1.48 0.55 2.45 0.81 0.67 15.86 0.60 0.55 2.77 0.64 12.34 0.35 2.41 2.09 3.87 1.01 13.03 1.80 12.34 Mean 10.46 4.24 10.19 2.03 3.44 254.09 9.65 2.55 26.03 4.30 138.02 4.36 38.61 14.88 12.42 3.13 29.77 20.82 68.02 Min 8.28 3.00 5.82 1.18 2.57 235.00 8.89 1.99 21.40 3.40 98.98 3.92 35.56 11.47 8.00 2.58 6.80 16.93 28.98 Max 14.16 5.13 14.00 3.73 4.29 296.75 10.96 3.38 33.00 6.10 157.57 4.83 43.83 19.52 19.83 7.05 63.00 23.57 87.57
LL= Leaf length (cm); LW= Leaf width (cm); LLo = Length of leaf lobe (cm); PL= Petiole length (cm); DBL= Distance between lobs (cm); VL= Vine length (cm); NTPH= Number of tubers hill-1; TL= Tuber length (cm); TDi= Tuber diameter (cm); IL= Internode length (cm); NIPV= Number of internode vine-1; NVPH= Number of vine hill-1; TiL= Tip length (cm); DM= Days to maturity; VFW= Vine fresh weight (t/ha); VDW= Vine dry weight (t/ha);TFW= Tuber fresh weight (t/ha); TDW= Tuber dry weight (t/ha) and HI= Harvest Index (%) and St.div-= Standard deviation; Min= Minimum and Max= Maximum
240 Appendix Table 6.1. Summary of climate data on the research site during the growth period
Month Total rain fall (mm) Mean temperature (oC) Mean Relative Mean Soil Mean Sunshine Minimum Maximum humidity (%) temperature (hours) (0-30cm) (oC) Mean 2015 Mean 2015 Mean 2015 Mean 2015 Mean 2015 Mean 2015 (1968-2014) (1968-2014) (1968-2014) (1968-2014) (1968-2014) (1968-2014) Jan 44.9 56.2 12.1 10.4 27.6 25.61 58 69.4 21.8 23.9 7.4 7.8 Feb 41.8 61.6 12.8 12.5 28.4 29.00 57 53.0 22.5 24.1 7.0 7.4 Mar 98.9 97.8 13.6 12.5 28.2 25.61 59 61.4 23.2 24.1 6.5 8.2 Apr 136.7 96.5 14.7 11.3 27.6 25.97 63 59.3 23.4 23.8 6.4 8.0 May 191.3 192.4 14.8 11.9 26.4 27.00 68 67.0 23.1 23.8 6.5 6.7 Jun 218.1 185.9 14.5 10.6 24.7 26.90 74 66.0 22.3 23.4 5.1 5.4 Jul 229.5 205.6 14.5 12.0 23.2 23.90 79 62.7 21.2 20.8 3.4 4.9 Aug 235.3 210.4 14.3 13.6 23.5 24.60 79 68.0 21.3 23.1 3.8 3.9 Sep 210.6 250.2 14.2 15.6 24.7 24.80 75 76.0 22.1 23.8 5.1 5.6 Oct 122.7 63.3 11.9 12.8 25.9 27.00 69 65.0 22.6 23.8 7.2 7.2 Nov 63.8 22.1 10.4 11.8 26.5 28.50 67 58.0 22.3 23.9 8.0 6.4 Dec 58.4 53.2 8.7 8.9 27.1 28.80 61 53.0 21.8 23.6 7.9 6.7 Total 1652.0 1495 165.5 143.9 313.8 317.7 809.0 758.8 267.60 282.1 74.30 78.20 Mean 137.7 124.6 13.0 12.0 26.2 26.5 67.4 63.2 22.30 23.5 6.20 6.50 Source: JARC metrology department, 2015.
241 Appendix Table 6.2. The mean values of landraces and respective quantitative traits
Traits Woko Welmeka 5 6 7 8 9 10 5 6 7 8 9 10 MAP MAP MAP MAP MAP MAP MAP MAP MAP MAP MAP MAP Vine length (cm) 273.0 305.9 321.5 286.3 310.0 315.9 284.64 331.3 309.1 325.8 306.3 315.33 Leaf length (cm) 16.76 17.14 17.14 16.28 18.28 17.03 17.03 16.47 16.64 17.69 16.36 16.81 Leaf width (cm) 15.21 15.39 15.06 14.89 16.75 15.19 16.12 15.39 15.64 16.28 15.25 15.53 Petiole length (cm) 10.56 12.06 11.42 10.88 12.03 11.58 11.28 10.58 11.58 11.50 11.78 11.94 Distance between lobs(cm) 3.72 3.78 3.94 3.56 3.67 3.61 3.78 3.89 3.83 3.94 3.78 3.67 Number of vine plant-1 1.89 1.94 1.78 1.94 2.11 2.00 1.56 1.56 2.06 1.89 1.83 2.33 Number of bulbils plant-1 55.39 58.11 56.22 59.72 63.11 55.06 61.17 55.67 50.33 50.06 57.89 55.67 Bulbils length (cm) 8.97 9.76 9.89 10.20 9.81 8.90 10.10 10.40 10.10 10.20 10.50 11.00 Bulbils diameter (cm) 3.42 3.69 3.47 3.18 3.29 3.47 3.66 4.03 3.46 3.3 3.98 3.78 Internode length (cm) 12.11 12.33 13.52 12.25 12.56 12.72 12.67 12.86 12.82 13.06 13.17 14.00 Number of internode vine-1 55.40 58.10 56.20 59.72 63.11 55.05 50.16 55.70 50.33 50.05 57.90 55.70 Tip length (cm) 3.42 4.85 4.88 4.42 4.49 3.47 3.30 4.42 4.79 4.84 4.09 4.78 Vine fresh weight (t/ha) 2.80 3.96 7.10 8.00 8.70 7.50 4.90 4.18 7.85 8.10 7.3 11.40 Vine dry weight (t/ha) 0.76 1.02 2.22 2.74 3.84 2.50 1.32 1.11 2.50 2.66 2.77 4.32 Bulbils fresh weight (t/ha) 6.20 6.60 9.50 10.10 8.40 9.20 9.60 11.8 10.5 11.60 12.10 11.60 Bulbils dry weight (t/ha) 0.04 0.08 0.09 0.04 0.06 0.08 0.09 0.13 0.10 0.13 0.13 0.11 Tuber length (cm) 10.11 10.39 9.56 9.83 7.89 8.44 11.56 9.61 9.91 9.78 8.89 8.89 Tuber diameter (cm) 7.72 8.56 9.5 8.56 8.06 7.83 9.61 7.61 9.84 8.72 8.28 8.05 Tuber fresh weight (t/ha) 4.90 5.00 8.11 8.60 9.10 8.9 6.60 4.80 8.30 9.30 8.70 9.10 Tuber dry weight (t/ha) 1.19 1.14 2.43 2.8 3.49 3.83 1.65 1.27 2.51 3.11 3.89 4.48 Total yield(t/ha) 11.11 14.5 18.2 15.20 17.6 18.10 16.10 16.70 18.80 20.90 20.80 20.70 Harvest index (%) 79.40 78.60 72.00 65.4 67.00 70.60 76.50 80.00 70.60 72.10 73.90 64.50 Mean 26.59 28.77 29.72 27.94 29.70 30.44 27.95 29.98 28.71 30.28 30.66 29.72
242 Appendix Table 6.3. The mean value of 14 biochemical traits of two Dioscorea spp. landraces collected from bulbils of aerial yam
Woko MAP FMC DM OM Ash OC CF N Pro Fat CHO P Ene Tan Sap 5 MAP 9.16 29.11 25.82 3.29 1.83 1.42 1.20 7.50 0.57 15.19 0.71 113.40 0.84 9.17 6 MAP 7.21 28.85 25.24 3.61 2.01 1.92 1.65 10.31 0.37 15.09 0.52 104.99 0.92 10.15 7 MAP 7.02 31.79 28.25 3.55 1.97 1.42 1.70 10.62 0.22 19.51 0.57 122.67 0.75 4.55 8 MAP 13.08 16.44 11.93 4.52 2.51 1.60 2.00 12.50 0.60 12.24 0.35 64.38 0.54 5.03 9 MAP 11.83 19.27 13.99 5.28 2.93 1.86 1.75 10.94 0.35 15.23 0.46 107.72 1.03 2.34 10MAP 12.11 18.88 13.26 5.62 3.12 1.52 1.80 11.25 0.12 19.57 0.62 109.49 1.25 3.86 Mean 10.07 24.06 19.75 4.31 2.39 1.46 1.68 10.52 0.37 14.47 0.54 103.77 0.89 5.85 SD 2.63 6.57 7.42 0.98 0.54 0.35 0.27 1.66 0.19 6.36 0.13 20.26 0.24 3.10 Welmeka 5 MAP 9.72 34.02 30.47 3.55 1.97 1.36 1.75 10.94 0.32 17.83 0.48 126.25 0.55 3.62 6 MAP 7.57 27.12 23.67 3.45 1.92 1.27 1.90 11.87 0.22 13.76 0.46 104.51 0.47 6.79 7 MAP 6.98 33.22 29.89 3.33 1.85 1.68 1.80 11.25 0.37 18.92 0.48 107.24 1.56 7.02 8 MAP 6.75 28.48 25.19 3.28 1.82 2.23 1.90 11.87 0.45 13.92 0.74 124.04 0.41 3.84 9 MAP 12.25 17.93 10.96 6.96 3.87 1.84 1.80 11.25 0.39 14.89 0.50 107.95 0.99 2.16 10MAP 12.01 18.26 11.73 6.53 3.63 2.34 1.40 8.75 0.48 19.91 0.48 110.41 0.98 3.87 Mean 9.21 26.50 21.99 4.52 2.51 1.78 1.76 10.99 0.37 16.54 0.53 113.40 0.83 5.22 Sd 2.49 7.03 8.65 1.74 0.96 0.44 0.19 1.16 0.09 2.68 0.13 9.32 0.44 3.16 FMC= Flour moisture content (%), DM= Dry matter (%), OM= Organic matter (%), Ash= Ash (%), OC= Organic carbon (g), CF= Crude fiber (%), N= Total N (%), Pro= protein(%), Fat= Fat (%), CHO= Carbohydrate (%), P= Total P (mg/100 g), Ene= Total Energy ( kcal/100 g DM), Tan= Tannin (mg/100 g) and Sap= Saponin (mg/100 g) and Sd= Standard deviation
243 Appendix Table 6.4. The mean value of 14 biochemical traits of two Dioscorea spp. landraces collected from storage tuber of aerial yam Woko MAP FMC DM OM Ash OC CF N Pro Fat CHO P Ene Tan Sap 5MAP 8.55 12.26 17.01 4.25 2.36 0.88 1.70 10.62 0.20 4.77 0.55 65.33 0.64 8.95 6MAP 7.89 12.89 15.33 4.55 2.53 1.60 1.90 11.87 0.25 5.52 0.56 71.87 0.72 6.50 7MAP 8.82 16.04 12.66 4.37 2.43 1.46 1.90 11.87 0.33 2.64 0.64 61.03 0.47 4.21 8MAP 10.08 17.44 11.92 4.51 2.51 2.32 1.74 12.50 0.60 2.24 0.35 64.38 0.54 5.03 9MAP 11.82 17.85 11.27 5.58 3.66 2.24 1.70 10.62 0.54 3.77 0.47 58.70 0.64 2.50 10MAP 12.34 18.06 12.21 6.85 3.25 2.24 1.60 10.00 0.39 9.55 0.51 82.49 0.58 8.36 Mean 10.42 18.42 13.40 5.02 2.79 1.79 1.80 11.25 0.38 4.75 0.51 67.30 0.60 6.09 SD 2.24 1.82 2.26 0.96 0.53 0.58 0.15 0.97 0.16 2.66 0.10 8.69 0.09 3.76
Welmeka 5MAP 9.82 16.49 12.01 4.48 2.49 1.06 1.70 10.62 0.25 2.90 0.44 59.59 0.45 9.29 6MAP 9.53 16.71 14.97 5.04 2.80 1.26 1.60 10.00 0.27 3.83 0.57 76.35 0.75 4.52 7MAP 12.08 16.90 12.53 4.38 2.43 1.28 1.70 10.62 0.27 4.97 0.74 64.78 0.20 9.13 8MAP 12.86 17.93 12.06 4.65 2.59 1.46 1.70 10.62 0.38 4.37 0.40 64.27 0.81 8.53 9MAP 12.34 18.05 11.79 5.76 3.41 1.42 1.80 11.25 0.28 5.31 0.36 68.67 0.90 2.33 10MAP 13.14 20.02 12.30 6.14 3.20 2.18 1.80 11.25 0.48 8.48 0.46 63.02 0.85 5.99 Mean 11.63 17.68 12.61 5.07 2.82 1.44 1.717 10.73 0.36 4.98 0.49 66.11 0.66 6.63 Sdiv. 1.56 1.31 1.18 0.72 0.40 0.39 0.08 0.47 0.13 1.92 0.14 5.81 0.28 2.84 FMC= Flour moisture content (%), DM= Dry matter (%), OM= Organic matter (%), Ash= Ash (%), OC= Organic carbon (g), CF= Crude fiber (%), N= Total N (%), Pro= protein(%), Fat= Fat (%), CHO= Carbohydrate (%), P= Total P (mg/100 g), Ene= Total Energy ( kcal/100 g DM), Tan= Tannin (mg/100 g) and Sap= Saponin (mg/100 g) and Sdiv= Standard deviation
244 Appendix Table 7.1. Mean performance value of 14 bio-chemical traits of 36 Dioscorea spp. landraces evaluated at Jimma, in 2015 landrac FMC DM% OM Ash OC CF N Pro Fat CHO P Ene Tan Sap 1 20.989 20.592 25.807 2.598 1.443 1.751 1.275 7.969 0.324 14.778 23.700 106.747 38.20 11.543 2 22.955 22.744 31.234 2.234 1.241 1.534 1.230 7.688 0.324 21.756 35.650 131.582 48.70 2.318 3 27.474 26.801 31.385 2.447 1.359 1.047 1.150 7.188 0.117 23.300 33.350 131.368 134.0 2.782 4 25.506 26.493 35.743 1.162 0.645 0.562 1.300 8.125 0.422 25.862 28.475 141.002 31.30 10.280 5 19.827 20.214 26.653 2.294 1.274 1.594 1.250 7.813 0.277 17.206 31.100 114.420 56.10 11.960 6 24.901 25.035 33.630 2.752 1.529 2.005 1.250 7.813 0.292 23.270 34.525 142.377 181.0 10.868 7 22.473 27.281 33.480 2.215 1.231 1.347 1.240 7.750 0.419 24.281 38.150 140.249 22.80 12.237 8 18.424 17.390 26.524 2.037 1.131 1.409 1.300 8.125 0.150 15.978 43.175 109.095 38.00 3.779 9 24.384 23.428 29.609 2.728 1.516 2.053 1.235 7.719 0.095 20.117 38.550 129.821 39.80 6.915 10 20.917 16.788 30.094 3.250 1.805 0.947 1.230 7.688 0.374 21.752 36.800 126.279 44.90 3.521 11 24.018 24.544 25.193 1.636 0.909 1.252 1.000 6.250 0.237 16.975 41.700 104.167 75.00 2.858 12 22.356 21.962 32.808 1.760 0.978 1.000 1.250 7.813 0.274 23.164 43.775 132.905 43.90 2.524 13 21.949 18.538 31.858 2.573 1.429 1.334 1.285 8.031 0.523 22.435 33.200 133.870 74.90 4.613 14 21.816 19.166 32.300 2.439 1.355 0.979 1.265 7.906 0.349 23.104 37.225 132.852 88.60 3.451 15 21.743 21.036 21.387 2.638 1.466 1.227 1.230 7.688 0.349 12.719 52.125 92.668 170.00 2.540 16 21.183 21.177 33.455 1.136 0.631 1.573 1.230 7.688 0.473 24.194 45.825 141.687 132.20 3.044 17 18.072 18.537 33.166 2.449 1.361 1.588 1.300 8.125 0.337 23.233 39.675 139.725 139.40 5.169 18 20.996 21.851 32.705 2.809 1.561 1.017 1.325 8.281 0.324 22.904 32.125 133.892 19.80 2.944 19 23.660 22.522 33.939 2.621 1.456 0.903 1.290 8.063 0.274 24.781 30.050 139.498 21.80 4.319 20 22.913 22.891 43.565 1.469 0.816 0.793 1.215 7.594 0.147 33.948 44.500 173.305 45.20 3.374 21 24.108 25.237 34.138 2.044 1.136 1.641 1.250 7.813 0.412 24.503 42.950 144.036 66.10 4.248 22 22.431 17.724 29.104 3.356 1.865 1.083 1.275 7.969 0.125 19.967 39.675 121.492 44.20 2.429 23 18.404 15.805 21.834 3.483 1.935 0.835 1.300 8.125 0.599 13.247 37.600 93.002 97.00 4.121 24 20.008 21.989 30.305 2.925 1.625 1.732 1.220 7.625 0.200 20.940 43.625 129.845 55.50 2.816 25 18.484 18.464 35.722 3.089 1.716 1.572 1.265 7.906 0.182 25.518 33.15 147.842 52.20 3.750 26 21.246 20.513 32.997 2.608 1.449 1.353 1.280 8.000 0.599 23.070 32.27 136.458 77.30 3.380
245 Appendix. Table 7.1. Continued landrace FMC DM% OM Ash OC CF N Pro Fat CHO P Ene Tan Sap 27 25.461 26.032 30.849 2.908 1.616 1.345 1.320 8.250 0.367 21.212 39.55 129.952 67.00 3.694 28 18.663 18.832 30.039 3.127 1.737 1.133 1.230 7.688 0.327 21.485 34.15 126.889 39.80 7.024 29 24.788 26.471 30.517 2.799 1.555 1.303 1.265 7.906 0.352 21.695 46.07 130.136 34.80 7.236 30 21.242 23.150 31.923 2.827 1.570 1.188 1.285 8.028 0.232 22.933 42.85 134.540 47.90 13.940 31 24.224 24.054 34.844 2.604 1.446 1.898 1.230 7.688 0.200 25.212 33.70 148.682 33.90 11.972 32 17.755 16.974 32.880 3.561 1.978 0.415 1.255 7.844 0.274 24.740 52.90 134.070 52.40 2.343 33 22.331 22.388 27.681 3.186 1.770 1.019 1.265 7.906 0.649 18.646 46.95 115.381 75.00 11.032 34 23.878 24.170 23.960 3.314 1.841 0.703 1.230 7.688 0.200 16.222 36.50 101.963 60.00 11.801 35 19.846 19.002 34.743 3.357 1.865 1.373 1.300 8.125 0.249 25.565 36.45 147.121 38.10 3.456 36 23.494 23.632 34.716 3.502 1.945 1.713 1.235 7.719 0.329 25.407 49.60 147.923 41.20 8.463 Mean 22.025 21.762 31.133 2.609 1.450 1.284 1.252 7.822 0.316 21.837 38.65 130.190 64.66 5.910 Min 17.755 15.805 21.387 1.136 0.631 0.415 1.000 6.250 0.095 12.719 23.70 92.668 19.80 2.318 Max 27.474 27.281 43.565 3.561 1.978 2.053 1.325 8.281 0.649 33.948 52.90 173.305 181.00 13.940 Stdiv 2.400 3.167 4.305 0.633 0.352 0.394 0.056 0.347 0.136 4.139 6.656 16.843 40.28 3.728 FMC= Flour moisture content (%), DM= Dry matter (%), OM= Organic matter (%), Ash= Ash (%), OC= Organic carbon (g), CF= Crude fiber (%), N= Total N (%), Pro= Protein(%), Fat= Fat (%), CHO= Carbohydrate (%), P= Total P (mg/100 g), Ene= Total Energy (kcal/100 g DM), Tan= Tannin (mg/100 g) and Sap= Saponin (mg/100 g), Stdiv-= Standard deviation; Min= Minimum and Max= Maximum
246