1 BREEDING FOR DROUGHT TOLERANCE IN COWPEA [Vigna unguiculata (L.) Walp.] USING MARKER ASSISTED BACKCROSSING By BATIENO TEYIOUE BENOIT JOSEPH (10325391) THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF DOCTOR OF PHILOSOPHY DEGREE IN PLANT BREEDING WEST AFRICA CENTRE FOR CROP IMPROVEMENT SCHOOL OF AGRICULTURE COLLEGE OF BASIC AND APPLIED SCIENCES UNIVERSITY OF GHANA LEGON December, 2014 University of Ghana http://ugspace.ug.edu.gh i DECLARATION I hereby declare that except for references to works of other researchers, which have been duly cited, this work is my original research and that neither part nor whole has been presented elsewhere for the award of a degree. .................................................. Teyioue Benoit Joseph BATIENO (Student) .................................................. Professor Eric Y. DANQUAH (Supervisor) .................................................. Professor Kwadwo OFORI (Supervisor) Doctor Martin YEBOAH (Supervisor) .................................................. Doctor Issa DRABO (Supervisor) University of Ghana http://ugspace.ug.edu.gh ii ABSTRACT The potential of cowpea to address food security in Burkina Faso in particular is well established. However, there is limited information on drought tolerance and diversity in the germplasm in Burkina Faso and farmers’ perceptions on the effects of drought and their varietal preferences are not known. The present study was, therefore, conducted to: (1) identify farmers’ perceptions on the impact of drought on cowpea production and identify their preferences regarding cultivars and traits, (2) identify drought tolerant varieties in cowpea germplasm, (3) determine single nucleotide polymorphisms (SNPs) based genetic diversity in the cowpea germplasm, and (4) implement marker-assisted backcrossing to transfer yield and stay-green QTLs into Moussa local, a farmer preferred landrace. A participatory rural appraisal (PRA) was conducted to identify farmers’ perceptions on the impact of drought on cowpea production. This study established that farmers have a deep knowledge about cowpea production constraints. Limited access to seed of improved variety was ranked as the most important constraint in all the areas where the study was conducted. Drought was classified among the four most important constraints to cowpea production in the three districts where the PRA was conducted. The preferred grain traits for all regions were white colour, large seeds with a rough texture for food and market purposes, except for the northern region where brown grain colour was preferred for food. The identification of drought-tolerant varieties in cowpea germplasm through field screening of fifty genotypes and the use of selection indices revealed wide genotypic variability among the tested germplasm. Biplot displays indicated that the genotypes could be grouped into four categories according to their drought tolerance and yielding ability as indicated below: high yielding-drought tolerant (group A), high yielding-drought susceptible (group B), low yielding-drought tolerant (group C), and low yielding-drought susceptible (group D). Genotypes like Djouroum local, KVx404-8-1, IT98K-1111-1, Gorom local, CB27, IT93K-693-2, Mouride, and KVx61-1 were clustered in group A, that is they were high yielding and drought tolerant. The stress tolerance index was the best criterion University of Ghana http://ugspace.ug.edu.gh iii for assessing genotypes for variability to drought tolerance because it enabled the identification of high yielding and drought tolerant genotypes. Genetic diversity was assessed using 181 SNP markers on 50 cowpea lines. The phylogenetic pattern of this germplam revealed seven clusters. The lines were almost grouped based on their geographical origin, and the breeding background. Thus, materials which originated from Burkina Faso were clustered in the same group while those from IITA/Nigeria were also almost all clustered in the same group. The genetic distance was low (≤0.29) suggesting a narrow genetic base in the cowpea germplasm used in this study. SNPs were efficient in the study of the diversity and a core collection of 20 lines was generated for further use in the breeding program. Marker-assisted backcrossing (MABC) was used to transfer QTLs for yield under drought and stay- green into Moussa local, a farmer preferred landrace. Two backcrosses assisted by SNP markers in foreground and background selections were sufficient to select for QTLs presence and to recover the background of Moussa local, the recurrent parent. The BC3F1s were selfed and six BC3F2s were evaluated for preliminary yield under drought stress and non-stress conditions. Out of the six, three MABC selected lines were promising and yielded better than the check and the parents. From these recombinant lines, several high yielding lines are likely to be developed for release in the near future. Most of them could be used in intercropping which will make great impact on cowpea production in Burkina Faso. In general, potential parents for genetic improvement for yield and drought tolerance were identified. However, further studies for assessing yield stability of cowpea genotypes are necessary and could be achieved by including more seasons and sites to get a better understanding of the genotype × environment interaction and yield stability of cowpea in Burkina Faso for all the materials identified including the MABC lines. Key words: Burkina Faso, farmers, drought tolerance, cowpea, genotypes, genetic distance, SNPs, marker-assisted backcrossing, Participatory Rural Appraisal. University of Ghana http://ugspace.ug.edu.gh iv DEDICATION I dedicate this thesis to: Alice, my lovely wife I am grateful for all love, care, and advices you have been providing to Yiyé Yanis Ebenezer and Yissainè Jorès, our children. My mother Epio Elisabeth, my father Babou Thomas, my sisters Reine and Rosalie, my brothers André, Christian, and Alain for their prayers and support throughout this Doctor of philosophy (PhD) thesis work. Thank you all University of Ghana http://ugspace.ug.edu.gh v ACKNOWLEDGEMENTS I thank God, the Almighty for great things He has done in my life I am sincerely grateful to: The Generation Challenge Programme (GCP) for the scholarship award to attend the West Africa Centre for Crop Improvement (WACCI), University of Ghana, The Alliance for Green Revolution in Africa (AGRA) who created the opportunity for young African scientist to undertake PhD studies through WACCI, WACCI and the University of Ghana for the opportunity given to me through the admission and the training received in this prestigious Centre. Specifically, to Professor E.Y. Danquah for the special attention to multiform requests and to WACCI staff and associate faculties to the hard work to satisfy our needs. I am also very grateful to my research supervisors, Professors E. Y. Danquah, K. Ofori., M. Yeboah from the WACCI and Dr Issa Drabo my In-country supervisor for the guidance of this study, for the opportunity to work with them and for what I learnt from their experiences. To Professor P. Tongoona and Dr Beatrice from WACCI for their guidance in the write-up, Professors T. Close and P. Roberts, Dr J. D. Ehlers, and Dr B-L Huynh from the University of California Riverside (USA) for their assistance in the molecular aspect of this research work,. I give special thanks to Dr T. J. Ouédraogo, and Dr J. B. Tignegré for their support, advices and guidance to achieve this work. They were also like supervisors to me for this work. I am grateful to cowpea multidisciplinary research team: Dr C. Dabiré, Dr D. Ilboudo, Dr J. Néya, and Dr E. Z. Zida. I am also thankful to Dr O. Traore, Dr Koussao Some, and Dr Edgar Traore for their contributions to the achievement of this PhD research. I thank my home institutions, the “Institut de l’Environnement et de Recherches Agricoles’’ (INERA) and the “Centre de Recherches Environnementales, Agricoles et de Formation de Kamboinse (CREAF-K)”. I also thank the team of technicians for the work we achieved together: H. Zongo, H. Sidibé, S. F. Zida, L. Poda, B. Tapsoba, B. Kama, L. Ouédraogo, Z. Zabré, and René Nanema, All my friends and relatives thank you for having been particularly supportive of me and my family during my frequent and long absence from home. I thank all the farmer organizations for their involvement and interests during the PRA sessions. University of Ghana http://ugspace.ug.edu.gh vi TABLE OF CONTENTS DECLARATION ............................................................................................................................. i ABSTRACT .................................................................................................................................... ii DEDICATION ............................................................................................................................... iv ACKNOWLEDGEMENTS ............................................................................................................ v TABLE OF CONTENTS ............................................................................................................... vi LIST OF TABLES ......................................................................................................................... xi LIST OF FIGURES ..................................................................................................................... xiii LIST OF ABBREVIATIONS ...................................................................................................... xiv CHAPTER ONE ............................................................................................................................. 1 1.0. GENERAL INTRODUCTION ............................................................................................ 1 CHAPTER TWO ............................................................................................................................ 6 2.0. LITERATURE REVIEW .................................................................................................... 6 2.1. Introduction .......................................................................................................................... 6 2.1.1. Cowpea: Origin and domestication .............................................................................. 6 2.1.2. Botany of cowpea ......................................................................................................... 8 2.1.3. Cultivation and utilization of cowpea in Africa ........................................................... 9 2.1.4. Marketing and economics of cowpea in West Africa................................................. 11 2.1.5. Constraints to cowpea production .............................................................................. 12 University of Ghana http://ugspace.ug.edu.gh vii 2.2. Challenges in breeding for drought tolerance .................................................................... 14 2.2.1. Mechanisms of drought tolerance in cowpea ............................................................. 15 2.2.2. Physiological responses to drought ............................................................................ 16 2.2.2.1. Plant water use efficiency ................................................................................... 16 2.2.2.2. Osmotic adjustment ............................................................................................. 17 2.2.3. Breeding for drought tolerance in cowpea ................................................................. 17 2.2.4. Genetics of drought tolerance in cowpea ................................................................... 18 2.3. Application of molecular markers in cowpea .................................................................... 19 2.4. Overview of DNA markers, QTLs mapping, and marker-assisted selection .................... 21 CHAPTER THREE ...................................................................................................................... 25 3.0. FARMERS’ PERCEPTION OF DROUGHT IMPACT ON COWPEA ........................... 25 3.1. Introduction ........................................................................................................................ 25 3.2. Materials and Methods ....................................................................................................... 28 3.2.1. Study sites ................................................................................................................... 28 3.2.2. Focus group discussion ............................................................................................... 29 3.2.3. Pair-wise ranking ........................................................................................................ 29 3.2.4. Participatory variety selection .................................................................................... 30 3.3. Results ................................................................................................................................ 30 3.3.1. Constraints to cowpea production .............................................................................. 30 3.3.2. Drought symptoms and impact on cowpea production .............................................. 32 University of Ghana http://ugspace.ug.edu.gh viii 3.3.3. Cowpea participatory variety selection ...................................................................... 33 3.4. Discussion .......................................................................................................................... 37 3.5. Conclusion ......................................................................................................................... 41 CHAPTER FOUR ......................................................................................................................... 43 4.0. FIELD ASSESSMENT OF COWPEA GENOTYPES FOR DROUGHT TOLERANCE 43 4.1. Introduction ........................................................................................................................ 43 4.2. Materials and Methods ....................................................................................................... 44 4.2.1. Experimental materials, design and field layout ........................................................ 44 4.2.2. Data collection ............................................................................................................ 47 4.2.3. Data analysis ............................................................................................................... 48 4.3. Results ................................................................................................................................ 49 4.3.1. Performance of genotypes yield and yield components ............................................. 49 4.3.2. Grouping of cowpea genotypes using quantitative stress indices .............................. 55 4.4. Discussion .......................................................................................................................... 61 4.5. Conclusion ......................................................................................................................... 65 CHAPTER FIVE .......................................................................................................................... 67 5.0. SNP-BASED GENETIC DIVERSITY IN A SET OF COWPEA GERMPLASM .......... 67 5.1. Introduction ........................................................................................................................ 67 5.2. Materials and Methods ....................................................................................................... 69 5.2.1. Cowpea genotypes ...................................................................................................... 69 University of Ghana http://ugspace.ug.edu.gh ix 5.2.2. SNP genotyping .......................................................................................................... 69 5.2.3. Analysis of genetic diversity ...................................................................................... 69 5.3. Results ................................................................................................................................ 70 5.3.1. Descriptive statistics ................................................................................................... 70 5.3.2. Core collection of cowpea germplasm ....................................................................... 73 5.3.3. Phylogenetic relationships between cowpea lines ...................................................... 74 5.4. Discussion .......................................................................................................................... 77 5.5. Conclusion ......................................................................................................................... 79 CHAPTER SIX ............................................................................................................................. 81 6.0. QTL INTROGRESSION FOR DROUGHT TOLERANCE IN COWPEA ...................... 81 6.1. Introduction ........................................................................................................................ 81 6.2. Materials and methods ....................................................................................................... 83 6.2.1. Leaf sampling procedure ............................................................................................ 83 6.2.2. DNA extraction........................................................................................................... 84 6.2.3. Selection of markers ................................................................................................... 84 6.2.4. Plant materials and QTL introgression procedures .................................................... 85 6.3. Results ................................................................................................................................ 87 6.3.1. QTLs introgression ..................................................................................................... 87 6.3.2. Morphological characterization of the MABC selected lines .................................... 91 6.3.3. Grain yield performance of the MABC selected lines ............................................... 93 University of Ghana http://ugspace.ug.edu.gh x 6.4. Discussion .......................................................................................................................... 94 6.5. Conclusion ......................................................................................................................... 97 CHAPTER SEVEN ...................................................................................................................... 98 7.0. GENERAL DISCUSSION AND CONCLUSION ............................................................ 98 7.1. Introduction ........................................................................................................................ 98 7.2. Main research findings and breeding implications ............................................................ 98 7.3. General conclusion and way forward .............................................................................. 102 References ................................................................................................................................... 104 Appendices .................................................................................................................................. 126 University of Ghana http://ugspace.ug.edu.gh xi LIST OF TABLES Table 3.1: General information on the PRA research sites. .......................................................... 28 Table 3.2: Cowpea production constraints ranked by farmers at three districts. .......................... 31 Table 4.1: Cowpea genetic materials screened for tolerance to drought in field experiments in 2012 ....................................................................................................................................................... 46 Table 4.2: Analysis of variance for yield and yield components of 49 cowpea genotypes under stressed and non-stressed conditions ............................................................................................ 51 Table 4.3: Grain yield performance of 49 cowpea genotypes under water-stressed and well-watered conditions at Saria research station ............................................................................................... 52 Table 4.4: Pod yield performance of 49 cowpea genotypes under water-stressed and well-watered conditions at Saria research station ............................................................................................... 53 Table 4.5: Hundred seed weight of 49 cowpea genotypes under water-stressed and well-watered conditions at Saria research station ............................................................................................... 54 Table 4.6: Correlations among grain yield, pod yield, and 100-seed weight of 49 genotypes grown under stressed conditions .............................................................................................................. 55 Table 4.7: Stress tolerance indices of the 49 cowpea genotypes, in 2012 .................................... 57 ....................................................................................................................................................... 57 Table 4.8: Correlation among stress index scores and yield under stressed (Ys), and non-stressed (Yw) of 49 cowpea genotypes ...................................................................................................... 58 Table 5.1: Summary statistics of genetic variation using 170 SNP markers among 47 cowpea lines ....................................................................................................................................................... 71 Table 5.2: Core collection of cowpea germplasm ........................................................................ 74 University of Ghana http://ugspace.ug.edu.gh xii Table 6.1: Position of trait-linked markers on cowpea consensus genetic map ........................... 86 Table 6.2: Percentage of Moussa background and genotypes of BC2F1 plants carrying donor alleles (for yield, stay green, and nematode resistance) from the cross Moussa local /IT93K-503- 1//Moussa local. Alleles A and B are designated for Moussa local and IT93K-503-1, respectively. ....................................................................................................................................................... 89 Table 6.3: Percentage of Moussa background and genotypes of BC2F1 plants carrying donor alleles (for yield and Striga resistance) from the cross Moussa local /IT97K-499-35//Moussa local. Alleles A and B are designated for Moussa local and IT97K-499-35, respectively. ................................ 90 Table 6.4: Morphological characteristics of MABC selected lines and their recurrent parent ..... 91 University of Ghana http://ugspace.ug.edu.gh xiii LIST OF FIGURES Figure 2.1: Worldwide distribution and gene pool structure of cowpea landraces. Relative proportions of blue and red colors for each symbol represent the likelihood of an accession assigned to gene pools 1 and 2, respectively. (Source: Huynh et al. 2013). ................................... 8 Figure 2.2: A cowpea flower showing female (stigma) and male (anthers) sex............................. 9 Figure 2.3: Different types of products made of cowpea or enriched with cowpea. .................... 11 Figure 3.1: Cowpea variety acceptability (%) based drought and on farmers’ preferred characteristics at Pobe-Mengao (Djibo), 2012. ............................................................................. 34 Figure 3.2: Cowpea variety acceptability (%) based drought and on farmers’ preferred characteristics at Pissila (Kaya), in 2012. ..................................................................................... 35 Figure 3.3: Cowpea variety acceptability (%) based drought and on farmers’ preferred characteristics at Donsin (Ziniare) in 2012. .................................................................................. 37 Figure 4.1: Biplot display of mean productivity (MP), geometric mean productivity (GMP), tolerance index (TOL), stress susceptibility index (SSI), stress tolerance index (STI), and yield of 49 cowpea genotypes under stressed (Ys) and non-stressed (Yw) conditions. ............................ 60 Figure 5.1: UPGMA dendrogram of 47 cowpea genotypes constructed using 170 SNP markers 76 Figure 6.1: The interface of the SNP_selector tool ....................................................................... 85 Figure 6.2: Plant growth habit of selected MABC lines (A); selected lines dry pod curvature and colour compared to Moussa local (B) ........................................................................................... 92 Figure 6.4: Yield performance of selected BC3F2 families, their parents and a local check under well water and water limited conditions. Values are the yield mean (kg.ha-1) of two replications. ....................................................................................................................................................... 93 University of Ghana http://ugspace.ug.edu.gh xiv LIST OF ABBREVIATIONS ABA: Abscisic Acid AFLP: Amplified Fragment Length Polymorphism AGRA: Alliance for Green Revolution in Africa cDNA: complementary Deoxyribonucleic Acid cM: Centimorgan CRSP: Collaborative Research Support Program DLS: Delayed leaf senescence DM: Dry matter DNA: DeoxyriboNucleic Acid EST: Expressed Sequence Tag EUW: Efficient Use of Water FAO: Food and Agriculture Organization of the United Nations FFF: Farmer Field Fora FFS: Farmer Field School FGD: Focus Group Discussion GBS: Genotyping By Sequencing GCP: Generation Challenge Programme GGT: Graphical GenoType GMP: Geometric Mean Productivity He: Expected heterozygosity HTTP: High-Throughput IITA: International Institute of Tropical Agriculture University of Ghana http://ugspace.ug.edu.gh xv Indel: Insertion deletion INERA: Institut de l’Environnement et de Recherches Agricoles KASP/KASPAR: Kompetitive Allele Specific PCR MAB: Marker-Assisted Breeding MABC: Marker-Assisted Backcrossing MAF: Major Allele Frequency MARS: Marker-Assisted Recurrent Selection MAS: Marker-Assisted Selection MB: Molecular Breeding MP: Mean Productivity mRNA: messenger Ribonucleic Acid NaClO: Sodium hypochlorite OA: Osmotic Adjustment PCA: Principal Component Analysis PCR: Polymerase Chain Reaction PIC: Polymorphic Information Content PRA: Participatory Rural Appraisal PVS: Participatory Variety Selection QTL: Quantitative Trait Loci RAPD: Random Amplified Polymorphism DNA REML: Residual Maximum Likelihood RFLP: Restricted Fragment Length Polymorphism SAMPL: Sequence Amplified Microsatellite Polymorphism Locus University of Ghana http://ugspace.ug.edu.gh xvi SCARs: Sequence Characterized Amplified Regions SI: Stress Intensity SNP: Single Nucleotide Polymorphism SSI: Stress Susceptibility Index SSR: Simple Sequence Repeat STI: Stress tolerance index TE: Transpiration Efficiency TOL: Tolerance index UCR: University of California Riverside UK: United Kingdom UPGMA: Unweighted Pair-wise Group Method USA: United State of America WUE: Water Use Efficiency Ys: Yield under stressed conditions Yw or Yp: Yield under non-stressed conditions Ῡ²w: Mean yield under non-stressed conditions University of Ghana http://ugspace.ug.edu.gh 1 CHAPTER ONE 1.0. GENERAL INTRODUCTION Cowpea (Vigna unguiculata (L.) Walp) is one of the most important grain legumes grown in the semi-arid regions of Africa. Out of the world’s total production area of 14 million hectares, West Africa accounts for about 9 million ha (Singh et al., 2003). Cowpea is mostly grown in the semi- arid region of West Africa (Ehlers and Hall, 1997) because of its large adaptation to climatic conditions. It is grown in over 9.5 million ha with a production of 2.9 million tons (Omo-Ikerodah et al., 2005). In the USA, under optimum conditions, the average yield of cowpea is 7000 kgha-1 whereas in Africa, average yield of the resource poor farmer is 300 kgha-1 (Ehlers and Hall, 1997; Tignegre, 2010). Burkina Faso is ranked amongst the top three cowpea producers in West Africa with a production of 580,000 tons in 2013 after Nigeria (2.50 million tons) and Niger (1.30 million tons) (Faostat, 2014). Cowpea production has increased from 276,349 tons in 2004 to 580,000 tons in 2013 with rapid decline in some years as a result of drought occurrence. Similarly, area under cultivation increased from 588,000 ha (2004) to 1,200,500 ha (2013) indicating that increase in production was associated with increase in area under cultivation. However, cowpea has contributed about 20 billion CFA (32,542,604 USD) to Burkina Faso’s net domestic product for the last ten years (Statistika, 2002). It has recently been recognized by the government of Burkina Faso as a strategic crop that would contribute to achieving food security and alleviating poverty, due to its market potential. University of Ghana http://ugspace.ug.edu.gh 2 Cowpea production is suitable for subsistence farming systems in which low inputs are involved due to its ability to thrive on relatively poor soil (Pasquet, 1999; Pronaf, 2003). It has high level of adaptation due to its inherent ability to withstand drought, tolerate shade, and fix atmospheric nitrogen (Singh, 1997). It is the first crop harvested during the cropping season before staple cereals crops (largely pearl millet and sorghum) and, therefore, referred to as a ‘‘hungry-season crop’’(Agbicodo et al., 2009). It is an important cash-crop and source of nutrients (protein 23- 25%) to the rural communities in tropical Africa (Ehlers and Hall, 1997; Ouedraogo, 2001). After harvesting the pods, the fodder is also harvested and used as feed for livestock. In terms of utilization, the diversity of diets based on cowpea, and the short cooking time renders cowpea popular for rural people and low income workers in towns (Tignegre, 2010). Leaves, fresh peas and fresh pods are also consumed (Ehlers and Hall, 1997). However, production is affected by several biotic and abiotic factors that lead to severe yield reduction at the smallholder farmer level (Ehlers and Hall, 1997). Drought is one of the most important constraints threatening the food security of the world (Barters and Nelson, 1994). Cowpea production in Burkina Faso is hampered by recurrent drought. The rainfall patterns have been irregular and below normal throughout the semi-arid zones of West Africa including Burkina Faso. In the Sudan and Sahelian semi-arid regions, the frequency and intensity of drought have increased over the last 30 years (Hall et al., 2003) due to climatic changes and human activities (Wittig et al., 2007). Estimates on yield reduction due to terminal drought range from 21-30% between stressed and non-stressed conditions (Chiulele, 2010). However, yield losses in plant production depend on geographical region and length of cropping season (Sabaghpour et al., 2006). Drought spells in farmers’ field has results in reduction of yields of University of Ghana http://ugspace.ug.edu.gh 3 available genotypes. Most of these genotypes are susceptible to drought. Drought can strike at anytime, anywhere. Plants are most prone to damage due to limited water during flowering and pod setting stages (Bahar and Yildirim, 2010). Recently drought episodes (1984, 1991, 2004, and 2011) resulted in important crop yield losses and famine in Burkina Faso. The most recent drought was in 2011. In that year, a deficit of about 154462 tons was recorded in crop production (http://fr.allafrica.com/stories/) in Burkina Faso. Therefore, it is desirable to improve these adapted genotypes for tolerance to drought in order to obtain high and stable yields. Until recently, the strategy for improving cowpea varieties in Burkina Faso for drought depended on the use of drought avoidance mechanism through the use of early maturing varieties. These varieties are meant to attain physiological maturity before drought occurs. However, the erratic rainfalls at the beginning and towards the end of the rainy season affect the early maturing varieties resulting in a substantial reduction in grain yield and total biomass production (Agbicodo et al., 2009). Therefore, the use genetic tolerance would be the best alternative for reducing the effects of drought in cowpea. Despite the genetic variability existing within wild Vigna species, improved varieties, and landraces in Burkina Faso (Tignegre, unpublished data), no in-depth investigation has been done to determine their tolerance to drought. Studies on genetic variability and diversity in drought tolerance need to be conducted to assist in the identification of suitable parents to improved cowpea for drought tolerance. Drought tolerance in cowpea is governed by multiple genes whose effects are often masked by or interact with the environment (Timko and Singh, 2008). As such, breeding for drought using conventional methodologies is not easy to achieve. Molecular markers can be used to identify University of Ghana http://ugspace.ug.edu.gh 4 regions of the genome that harbor the genes that contribute to drought tolerance (Timko and Singh, 2008). If the most important genes can be tagged with molecular markers; they could be reliably introgressed into highly desirable cultivars that are susceptible to drought therefore, improving their tolerance to drought. The availability of high throughput genotyping platforms provides new opportunities for improvement of complex traits like drought tolerance through marker-assisted breeding (MAB). Cowpea remained among “orphan crops” with limited genomic resources for long but significant genomic resources have recently been developed and made available to the public. These new development include high quality consensus genetic map (Muchero et al., 2009a; Lucas et al., 2011), high-throughput genotyping systems based on the Illumina GoldenGate and KBiosciences KASPAR systems, fingerprints of more than 600 potential parent lines, and a physical map (Http://Phymap.Ucdavis.Edu/Cowpea/, 2013). With the map density now available in cowpea (average marker density of 0.6 cM and no gap > 4 cM), in many cases it will be possible to identify flanking markers. By using markers that flank the target drought tolerance QTLs, linkage drag can be minimized. These advances in plant molecular genetics have provided plant breeders with powerful tools to identify and select Mendelian components underlying both simple and complex agronomic traits (Ribaut and Hoisington, 1998). SNP markers tightly linked to drought tolerance QTLs have been identified in cowpea (Muchero et al., 2010). These markers will be useful in the introgression of drought tolerant genes into desirable cowpea genotypes using MAB. The overall research goal of the study was to identify promising cowpea genotypes that are drought tolerant and high yielding that would contribute to food security. University of Ghana http://ugspace.ug.edu.gh 5 The specific objectives were to:  Determine farmer perceptions on the impact of drought in cowpea  Identify sources of tolerance to drought stress in the cowpea germplasm  Determine the SNP-based genetic diversity of a set of cowpea germplasm  Introgress drought tolerant (stay green and yield) QTLs into Moussa local, a farmer preferred cowpea genotypes using marker-assisted backcrossing. University of Ghana http://ugspace.ug.edu.gh 6 CHAPTER TWO 2.0. LITERATURE REVIEW 2.1. Introduction This review focuses on tolerance of cowpea (Vigna unguiculata (L.) Walp.) to drought in the semi- arid areas of Africa. The importance of cowpea, drought as a constraint to cowpea production, conventional breeding approaches and the potentials of molecular breeding tools in developing drought tolerant cowpea varieties are also discussed. 2.1.1. Cowpea: Origin and domestication The origin of the cultivated cowpea has been a matter of speculation and discussion for many years. Cowpea has been mentioned since the antiquity by Dioscoride, has been described by Linne from a cultivated species from Antilles as Dolichos unguiculata, then, Vigna sinensis and later became Vigna unguiculata (Faris, 1963; Pasquet and Baudoin, 1997). Early observations in cowpea revealed that cowpeas in Asia are different from cowpeas grown in Africa, suggesting that both Asia and Africa could be independent centers of origin for the crop (Timko and Singh, 2008). Based on cytological and morphological studies, it has been reported that Nigeria is the center of domestication of cowpea in West Africa (Faris, 1963). Some other studies confirmed that statement and indicated that V. unguiculata is from West Africa where some wild relatives are found at the edge of the forest (Pernes, 1984). Controversially, some studies using DNA markers, especially the amplified fragment length polymorphism (AFLP) profiles, led to propose domestication in North eastern Africa (Coulibaly et al., 2002). University of Ghana http://ugspace.ug.edu.gh 7 V. unguiculata has 22 chromosomes (2n= 2x= 22). The genus Vigna is pantropical and highly variable (Timko and Singh, 2008). This genus contains, in addition to cowpea, other members like mungbean (V. radiata), and the bambara groundnut (V. subterranea). The genus was initially divided into several subgenera based upon morphological characteristics, extent of genetic hybridization/reproductive isolation, and geographic distribution of species (Marechal et al., 1978). In contrast to many other important world crops, relatively little is understood about the domestication history, worldwide dispersal and distribution of genetic variation of cowpea (Huynh et al., 2013). The location of cowpea domestication in Africa is still uncertain. Different centers of origin and diversity have been proposed (Ba et al., 2004). Evidence were provided based on molecular markers that early domestication occurred in northeastern Africa (Coulibaly et al., 2002). For Steele (1976), cowpea in these regions could have been domesticated together with sorghum (Sorghum bicolor) and pearl millet (Pennisetum typhoides) in the third millennium Before Christ. Some speculations support that cowpea may have followed the same route out of Africa as sorghum, moving first from eastern Africa to the Arabian Peninsula and then onto the Asian subcontinent (Faris, 1965; Pant et al., 1982) and to East Asia. For Tosti and Negri (2002), cowpea may have also moved to Europe from the Middle East because cowpea was known in southern Europe during Roman times. Therefore, it is plausible that cowpea first moved from western Africa to the New World with African people during the slave-trading period (Huynh et al., 2013), but little or no documentation exists to support the extent of this movement. Huynh et al. (2013) used SNP makers to examine the gene pool structure of African wild annual cowpea V. unguiculata subsp. dekindtiana from both East and West Africa and to determine their relatedness University of Ghana http://ugspace.ug.edu.gh 8 to African wild cowpeas and non-African domesticated cowpeas. These authors found out the genetic materials into two gene pools. The two gene pools were distributed in two distinct geographical zones separated by the dense and vast rainforests of the Congo River basin (Figure 2.1). This region is too wet and not Figure 2.1: Worldwide distribution and gene pool structure of cowpea landraces. Relative proportions of blue and red colors for each symbol represent the likelihood of an accession assigned to gene pools 1 and 2, respectively. (Source: Huynh et al. 2013). 2.1.2. Botany of cowpea Cowpea is a self-pollinated crop with a little rate of outcrossing attributed to insect activities (Rachie and Roberts, 1974). The floral structure of cowpea is characterized by a symmetric flower with a style and a short beak (stigma) (Marechal et al., 1978) (Figure 2.2). One flower contains ten stamens and each stamen carries one anther sac which contains pollen grains for pollination and fertilization. Flower opening occurs after pollination and fertilization, which reduces chances for out-crossings due to foreign pollen (Marechal et al., 1978). The plant has large flower buds that facilitate the emasculation during the process of crosses. This makes crosses easier in cowpea as compared to other grain legume crops. University of Ghana http://ugspace.ug.edu.gh 9 Figure 2.2: A cowpea flower showing female (stigma) and male (anthers) sex The cowpea plant is a herbaceous, warm-season annual requiring temperature of at least 18°C throughout all stages of its development and having an optimal growing temperature of about 28°C (Craufurd et al., 1997). 2.1.3. Cultivation and utilization of cowpea in Africa Cowpea is of major importance to the livelihoods of millions of relatively poor people in less developed countries of the tropics (Quin, 1997). From the fresh leaves to the dry grain, the whole plant is utilized either for human consumption or animal feed. The production provides an important source of income to most rural families and is, therefore, considered as a cash crop for University of Ghana http://ugspace.ug.edu.gh 10 rural people (Quin, 1997; Ouedraogo, 2001). The fresh young leaves and immature pods are used as vegetables, while various meal dishes are prepared from the dry grain. All the plant parts that are used for food are nutritious, providing protein, vitamins, and minerals (Quin, 1997; Timko and Singh, 2008). Cowpea grain contains, on average 23-25% protein, and 50-67% starch (Bressani, 1985; Carnovale et al., 1990). The digestibility of protein is higher in cowpea than other legumes (Carnovale et al., 1990; Marconi et al., 1992). For these authors the protein digestibility ranges from 75-84% in wild relatives, with available protein ranging from 17 - 22%. After harvesting the pods, the aboveground parts of the plant are harvested and the haulms are used as a nutritious fodder for the livestock. Cowpea consumption and processing in Burkina Faso has really evolved. A survey reported that cowpea was mentioned in 84% of household as a staple food (Konkobo et al., 2002). In the same study, the authors reported that cowpea meals were known in a local tongue as “yamleog ribo” which means food of desire but have now evolved to the level of common food because all the households questioned in the survey eat cowpea in one or another way. Seventy eight percent (78%) of the household prepares cowpea mixed with rice or couscous (maize semolina) (Konkobo et al., 2002). A lot of processors are using cowpea to enrich children’s food and to make cakes, couscous etc. (Figure 2.3). University of Ghana http://ugspace.ug.edu.gh 11 Figure 2.3: Different types of products made of cowpea or enriched with cowpea. Going beyond its importance for food and feed, cowpea is important in farming systems in semiarid lands. That is because of its ability to fix nitrogen from the atmosphere and to withstand hard climatic conditions such as heat and drought (Hall et al., 2002). One hectare of cowpea can bring to the soil about 40-80 kg of nitrogen (Quin, 1997). 2.1.4. Marketing and economics of cowpea in West Africa Cowpea is the most economically important indigenous African legume crop (Langyintuo et al., 2003). Examination of data from the FAO, national statistics, and the Bean/Cowpea Collaborative Research Support Program (CRSP), allowed these authors consolidate information on cowpea production, marketing and trade in West and Central Africa. The study showed that in the 1990s, University of Ghana http://ugspace.ug.edu.gh 12 West and Central Africa produced about 70% of the world’s cowpea in 80% of the area allocated to this crop in the world. During the same time period and specifically in 1998, Burkina Faso imported about 8000 tons of cowpea from Niger and exported a total of 5500 tons to Togo, Cote d’Ivoire, Ghana and Benin (Langyintuo et al., 2003). At that period, the estimated cowpea production in Burkina Faso was higher than the demand with a surplus of about 146 000 tons (Langyintuo et al., 2003) making the country a net exporter of cowpea. The available markets for the exports are Ghana and Cote d’Ivoire (Statistika, 2002; Langyintuo et al., 2003). 2.1.5. Constraints to cowpea production The most important causes of yield loss in cowpea are (i) insect pests, (ii) virus and fungal pests that attack the foliage and stems, including Cowpea Aphid-Borne Mosaic Virus, charcoal rot or ashy stem blight due to Macrophomina phaseolina, and pod and seedling diseases, (iii) drought and heat, (iv) the parasitic weed Striga gesnerioides, (v) low inherent yield potential of landraces, (vi) the unavailability of seed of improved varieties, and (vii) the inaccessibility to inputs such as pesticides (Singh and Tarawali, 1997; Tignegre, 2010). These constraints are classified as biotic and abiotic constraints for cowpea production (Singh and Jackai, 1985). The biotic constraints are:  the pre-flowering insects (aphids)  the post-flowering insects (thrips, maruca, )  the storage insects (bruchids)  The cryptogrammic, bacterial, and viral diseases  Striga gesneroides and Alectra vogeolii. University of Ghana http://ugspace.ug.edu.gh 13 The main abiotic constraints are:  drought  heat  low soil fertility. Drought is one of the most important constraints threatening the food security of the world (Barters and Nelson, 1994). Linsley et al. (1959) have defined drought as a sustained period of time without significant rainfall. Katz and Glantz (1977) differentiate between meteorological and agricultural drought. For these authors, a meteorological drought could be defined as a time period when the amount of precipitation is less than some designated percentage of the long term mean in one hand and in the other hand, an agricultural drought could be defined in terms of seasonal vegetation development. Agricultural drought occurs when there is not enough moisture available at the right time for the growth and development of crops through to harvest of the economic part of the plant (Magloire, 2005). Some genotypes escape drought (drought avoidance) while some others are drought tolerant (Mitra, 2001). Plant resistance to drought stress can be improved through drought avoidance or drought tolerance. Drought avoidance is the ability of a plant to escape periods of drought, particularly during the most sensitive periods of its development due to earliness. Drought avoidance mechanisms tend to conserve water by promoting water use efficiency (WUE) (Blum, 2005, 2009; Amudha and Balasubramani, 2011). Although some genotypes can escape drought as a result of it being early this, however, is not the best because drought is not predictable. If such genotypes are subjected to drought they could be susceptible, therefore, it is important for genotypes should also University of Ghana http://ugspace.ug.edu.gh 14 incorporate drought tolerance genes. The combination of drought avoidance and tolerance would be an effective strategy to improve cowpea for tolerance to drought. Drought tolerance as defined by Magloire (2005) is the ability of the plant to endure or withstand a dry period by maintaining a favorable internal water balance under drought conditions. 2.2. Challenges in breeding for drought tolerance Success in breeding for drought tolerance in cowpea has not been as pronounced as for many other traits partly due to the lack of simple, cheap, and reliable screening methods to select drought tolerant plants and progenies from the segregating populations (Singh et al., 1997). Drought tolerance is physiologically and genetically a complex trait whose expression depends on the magnitude and timing of stress in relation to plant-growth stage (Blum, 1996). It is by far the most important environmental stress in agriculture and many efforts have been made to improve crop productivity under water-limiting conditions (Cattivelli et al., 2008). Drought studies typically distinguish between early-, mid- and late-season drought stress, all of which present unique challenges to plant growth and productivity. Late-season or post-flowering drought stress occurs during the grain filling stage causing depressed photosynthesis capacity leading to poor source development and diminished ability to allocate carbon toward the developing grain (Blum, 1996). One of the challenges in breeding for drought tolerance is the screening methodology. Different workers used different methods to evaluate genetic differences in drought tolerance (Bidinger et al., 1982). These methods ranged from wooden boxes (Mai-Kodomi et al., 1999a; Muchero et al., 2008), pots and hydroponic (Ogbonnaya et al., 2003) to simple field screenings (Singh et al., 1999; Chiulele, 2010; Ishiyaku and Aliyu, 2013). However, progress in breeding cultivars for dry University of Ghana http://ugspace.ug.edu.gh 15 environments has been slow, and selection for yield has been mainly achieved by testing advanced lines over several locations and years (Hall et al., 1997; Cattivelli et al., 2008). This is because of the need to assess the yield of large numbers of lines across several locations and years, and the substantial variation from the effects of environment, error and genotype x environment interactions. Therefore, breeding for drought tolerance is a challenging task because of the complexity of drought responses, environmental factors, and their interactions. 2.2.1. Mechanisms of drought tolerance in cowpea All plant species respond to water stress by reducing transpiration by closing of its stomata and the reduction of leaf area (Amigues et al., 2006). When plants are subjected to drought stress, a number of physiological biochemical and morphological responses have been observed and the magnitude of the response varies among species and between varieties within a crop species (Kramer, 1980). To avoid dehydration, cowpea ensures the maintenance of water absorption and the ramification of its roots system. Sarr et al. (2001) showed that water deficit in the soil is responsible for root elongation and ramification of the secondary roots in the deep humid parts of the soil allowing the optimization of water absorption. An important strategy of drought resistance in cowpea seems to rely on its capacity to regulate the expression of enzymes responsible for the degradation of membrane lipids (El Maarouf et al., 1999). Roy-Macauley (1999) reported four genes controlling these mechanisms as two phospholipases C and D, one ascorbate peroxydase, and one other protein. Mai-Kodomi et al. (1999a) observed two types of drought tolerance response in cowpea screened in boxes. Type 1 tolerant line stopped growth and conserved moisture in all the plant tissues, stayed alive for over two weeks, and died gradually. The Type 2 line University of Ghana http://ugspace.ug.edu.gh 16 continued slow growth of the trifoliate leaves and showed early senescence on unifoliates which dropped off with continued water stress but the growing tip remained turgid. 2.2.2. Physiological responses to drought Plants respond by a number of physiological responses at the molecular, cellular, and whole-plant levels when subjected to water stress. Plant water use efficiency and osmotic adjustment are two major physiology mechanisms used by plants to respond to drought stress. 2.2.2.1.Plant water use efficiency Water use efficiency (WUE) is a key factor determining plant productivity under limited water supply (Blum, 2009; Amudha and Balasubramani, 2011). This parameter is often used to imply that rainfed plant production can be increased per unit water used, resulting in ‘more crop per drop’. Agronomically, WUE is defined as the ratio between total dry matter (DM) produced and water used (Jones, 1993). However, it is defined physiologically as the ratio between the rate of carbon fixed and the rate of water transpired. Water use efficiency (WUE), measured as the biomass produced per unit transpiration, describes the relationship between water use and crop production (Blum, 2009; Amudha and Balasubramani, 2011). In water-stress conditions, it would be important to produce a high amount of biomass, which contributes to crop yield, using a low or limited amount of water (Amudha and Balasubramani, 2011). There are some concerns about WUE. Blum (2009), argues that selection for high WUE in breeding for water-limited conditions will most likely lead, under most conditions, to reduced yield University of Ghana http://ugspace.ug.edu.gh 17 and reduced drought resistance. For this author, as long as the biochemistry of photosynthesis cannot be improved genetically, greater genotypic transpiration efficiency (TE) and WUE are driven mainly by plant traits that reduce transpiration and crop water-use. On the other hand, efficient use of water (EUW) implies maximal soil moisture capture for transpiration which also involves reduced non-stomatal transpiration and minimal water loss by soil evaporation (Blum, 2009). Therefore, EUW is a major target for yield improvement in water-limited environments and it is not just a coincidence that EUW is an inverse acronym of WUE because very often high WUE is achieved at the expense of reduced EUW. 2.2.2.2. Osmotic adjustment Morgan (1984) defined the Osmotic adjustment (OA) as the net increase in intercellular solutes in response to water stress. The OA allows turgor maintenance at lower water potential. It has been considered as one of the crucial processes in plant adaptation to drought, because it sustains tissue metabolic activity and enables re-growth upon rewetting but varies greatly among genotypes. Plant productivity under arid conditions has been associated with OA in a number of species such as sorghum (Tangpremsri et al., 1995). 2.2.3. Breeding for drought tolerance in cowpea Plants response to drought stress could be assessed at the morphological, biochemical and physiological level (Hamidou et al., 2007). Traits such as leaf area index, chlorophyll stability index, relative water content, diffusion pressure deficit (Yadava and Patil, 1984; Singh et al., 1999), osmotic adjustment (Flower and Ludlow, 1987), carbon isotope discrimination, water use University of Ghana http://ugspace.ug.edu.gh 18 efficiency, and root/shoot ratio (Hall et al., 1990; Hall et al., 1992; Ismail and Hall, 1992, 1993; Matsui and Singh, 2003) have been used as indices for drought tolerance. There are several screening methods available for selecting drought tolerant genotypes in cowpea. The most common are wooden boxes, pots, hydroponic and field screenings (Singh et al., 1999; Ogbonnaya et al., 2003). Agbicodo et al. (2009) reported that the wooden box is effective for screening cowpea at the seedling stage for drought tolerance when large number of lines are used. The authors reported that traits such as delayed leaf senescence (DLS), free proline levels, abscisic acid (ABA), stomatal conductance and chlorophyll florescence could be used as indices for drought tolerance. Significant progress has been made at the International Institute of Tropical Agriculture (IITA) in the development of drought tolerant cowpea genotypes. Crosses made from Suvita 2 (Gorom local) in Kamboinse and Pobe-Mengao (Iita-Safgrad, 1984, 1985, 1986, 1987) showed transgressive segregation in the F2 population but variety was released from these crosses. Singh and Matsui (2002) identified IT89KD-374-57, IT88DM-867-11, IT98D-1399, IT98K-131-1, IT97K-568-19, IT98K-452-1, and IT98K-241-2 as best drought-tolerant lines. Other drought tolerant genotypes identified include Gorom local and TN88-63 by Hamidou et al. (2007), and IT93K-503-1 and INIA-41 (Chiulele, 2010). 2.2.4. Genetics of drought tolerance in cowpea Mating design is a term usually applied to schemes used in plant breeding programs to design crosses for specific purpose (Acquaah, 2007). In cowpea breeding the most commonly used mating designs are the diallel (Chiulele, 2010), North Carolina design II (Orawu, 2007; Alidu et al., 2013), biparental mating design and generation mean analysis. (Mai-Kodomi et al., 1999b) studied the University of Ghana http://ugspace.ug.edu.gh 19 inheritance of drought tolerance in cowpea lines (TVu11986) with Type 1 drought tolerance, Dan Ila with Type 2 drought tolerance, and TVu7778 (susceptible) using a diallel. Drought screening was carried out using the wooden box method. The results revealed that both Type 1 (Rds1) and Type 2 (Rds2) reactions are controlled by a single dominant gene that is independent. Tests of allelism indicated that Type 1 is dominant over Type 2 and the F2 population between the two types segregated 3 Type 1 : 1 Type 2 indicating that the genes Rds1 and Rds2 are either closely linked or are allelic at the same locus. Chiulele (2010) found that both additive and non-additive gene action were important for yield and yield components under drought. However, additive gene action was more important than non-additive gene action for days to flowering, number of pods per plant, number of seeds per pod and hundred seed weight. Chozin et al. (2006) reported delayed leaf senescence, stem diameter and leaf temperature were controlled by additive effects. These studies showed that variability exist in cowpea as far as drought is concerned. 2.3. Application of molecular markers in cowpea Recent efforts have focused on the genetic dissection of drought tolerance through identification of markers defining quantitative trait loci (QTLs) with effects on specific traits related to drought tolerance (Agbicodo et al., 2009). Advances in molecular genetics have led to the identification of multiple genes or genetic markers associated with genes that affect traits of interest in crop plants, including genes for single-gene traits and QTLs or genomic regions that affect quantitative traits. According to Dekkers (2004), this has provided opportunities to enhance response to selection, especially for traits that are difficult to improve by conventional selection due to their low heritability or traits for which measurement of phenotype is difficult and expensive. Biochemical University of Ghana http://ugspace.ug.edu.gh 20 markers have been used to identify markers that are linked to the gene for resistance to Aphis craccivora in cowpea (Githiri et al., 1996). Vaillancourt and Weeden (1992) used them to demonstrate the lack of isozyme similarity between Vigna unguiculata and other species of subgenus Vigna. Isozyme analysis has also been used by Pamella and Gepts (1992) to study the genetic relationships within Vigna unguiculata. Pasquet (1999) used allozyme variation in a study of the genetic relationships among subspecies of V. unguiculata. Genetic maps have also been constructed for cowpea (Fatokun et al., 1993; Fatokun et al., 1997; Menendez et al., 1997; Ouedraogo, 2001; Ouedraogo et al., 2002) using DNA markers combined with morphological markers. Ouedraogo et al. (2012a) have also identified AFLP markers linked to some Striga resistant genes and converted them into SCARs for easy use in marker assisted selection. DNA markers have also been used for germplasm characterization (Coulibaly et al., 2002) and assessing the genetic diversity in cowpea (Ba et al., 2004). By comparing AFLPs, RAPD, and SAMPL (Sequence Amplified Microsatellite Polymorphic Locus) markers, Tosti and Negri (2002) have been able to study the diversity amongst farmer varieties of cowpea. Li et al. (2001) have used SSRs markers to study the genetic similarities and relationship between improved cowpea varieties and cultivars. SSRs used by Li et al. (2001) have also been used by (Batieno, 2008) to screen cowpea RILs to identify linkage between the markers and aphid resistance genes in cowpea. A modern consensus map has been developed (Muchero et al., 2009a) and updated in April 2011 (Lucas et al., 2011). The new consensus map contains 1,107 EST- derived SNP markers (856 bins) on 11 linkage groups (680cM). University of Ghana http://ugspace.ug.edu.gh 21 Molecular markers have been extensively used in the study of drought tolerance. cDNAs homologous to alcohol dehydrogenase, dehydrin, NADPH-dependent aldehyde reductase, 12- oxo-phytodienoic acid reductase, 9-cis-epoxycarotenoid dioxygenase, and lipoxygenase were isolated (Luchi et al., 1996a; Luchi et al., 1996b) from a drought tolerant cowpea genotype. In the same way, Badiane et al. (2004) screened cowpea varieties by inducing water deficit and running RAPD analyses. QTLs have been mapped by Muchero et al. (2009b) for drought stress-induced premature senescence and maturity in cowpea. A restriction site polymorphism-based has also been used to investigate the co-location of candidate genes with QTLs for drought stress-induced premature senescence previously identified in cowpea (Muchero et al., 2010). All these works reported here demonstrate the fast evolution of molecular methods used in cowpea. It is, therefore, possible to conclude that cowpea is no longer an orphan crop in the domain of molecular breeding. 2.4. Overview of DNA markers, QTLs mapping, and marker-assisted selection The goal of plant breeding is to assemble desirable combinations of genes in new varieties. In the commonly used pedigree breeding method, selecting desirable plants begins in early generations for traits of high heritability. However, for traits of low heritability, selection is often postponed until the lines become more homozygous in later generations (F5 or F6) (Collard and Mackill, 2008). Selection of superior plants involves visual assessment for agronomic traits or resistance to stresses, as well as laboratory tests for quality or other traits. Based on the extent and complexity of selection required in breeding programs and the number and size of populations to be handled, one can easily appreciate the usefulness of new tools that may assist breeders in plant selection. University of Ghana http://ugspace.ug.edu.gh 22 DNA marker technology, derived from research in molecular genetics and genomics, offers great promise for plant breeding. Reliability, quantity and quality of DNA, technical procedure for marker assay, level of polymorphism and cost are important factors of consideration in marker- assisted breeding (Collard and Mackill, 2008). Simple sequence repeats (SSRs) or microsatellites were the most commonly used markers (Gupta et al., 1999; Gupta and Varshney, 2000) because they are highly reproducible, co-dominant in inheritance, relatively simple and cheap to use and generally highly polymorphic. But, they require polyacrylamide gel electrophoresis and generally give information only about a single locus per assay, although multiplexing of several markers is possible (Collard and Mackill, 2008). In recent years, a novel class of markers named single nucleotide polymorphism (SNPs) has emerged as an important tool in plant genomics and it is increasingly used as molecular markers for various applications in several laboratories (Jehan and Lakhanpaul, 2006). SNPs are markers that have a single base pair position in the genomic DNA in which different alleles exist in normal individuals in some populations. SNP markers derived from specific DNA sequences quantitative trait loci (QTLs) are cheaper and more useful for marker-assisted selection (MAS) and have become the marker of choice when high-throughput genotyping assays have been developed. The last two decades witnessed the widespread use of molecular markers to study complex quantitative traits in different crop species (Bernardo, 2008). Thus, the aggressive use of marker-based selection in a breeding program will eventually lead to large amounts of marker and phenotypic data. MAS and marker-assisted backcrossing (MABC) have been the first successful wide-scale implementations of marker technology in breeding programs (Delannay, 2009). These University of Ghana http://ugspace.ug.edu.gh 23 methodologies work by using markers linked to specific traits to quickly and effectively select plants carrying those traits in segregating populations. The main benefit of MAS is to provide quick and accurate genotyping of progenies for traits that are typically hard or laborious to phenotype accurately. MABC aims at introgressing a mapped trait from a donor parent to a recurrent parent via multiple backcrossing steps in order to convert the recurrent parent with the new trait. The use of markers in such case allows a clean transfer of the trait with a minimum amount of donor parent genetic material being carried along (foreground selection), and facilitates the quick recovery of the recurrent parent background in a reduced number of backcrossing generations (background selection). Unlike the conventional backcross breeding, the MAB method can be viewed as a four-step selection process to quickly recover the recurrent parent genotype (Frisch et al., 1999). This includes (1) selecting individuals carrying the targeted alleles, (2) selecting individuals homozygous for the recurrent parent genotype at loci flanking the target locus, (3) selecting individuals homozygous for recurrent parent genotype at remaining loci on the same chromosome comprising the targeted allele, and (4) selecting individuals that are homozygous for the recurrent parent genotype at most loci (across the whole genome) among those that remain. A MABC project consists of two main parts being performed in tandem (Delannay, 2009): (i) Selection of plants carrying the target trait from the donor parent through the successive backcrossing generations (foreground selection) (ii) Among those plants carrying the target trait, identification at each generation of individuals carrying the largest representation of the genome of the recurrent parent (background selection). University of Ghana http://ugspace.ug.edu.gh 24 Plant breeders are using molecular markers extensively to increase the efficiency of their backcross breeding programs (Fatmi, 1999). The use of molecular markers reduces the number of backcross generations and the time required to recover a very high level of similarity to the recurrent parent, and insures that no large, unwanted segments of donor parent genome remain intact. MABC has proven especially effective for incorporating genes into commercially desirable lines and varieties. Zhao et al. (2012) reported that qHSR1, a major quantitative trait locus for resistance to head smut in maize was successfully integrated into ten high-yielding inbred lines that were susceptible to head smut. Each of the ten high-yielding lines was crossed with a donor parent Ji1037 that contains qHSR1 and is completely resistant to head smut, followed by five generations of backcrossing to the respective recurrent parents. A marker linked at 0.7 cM to the Yd2 gene for resistance to barley yellow dwarf virus was successfully used to select for resistance in a marker- assisted backcrossing methods in barley (Jefferies et al., 2003). In maize, MABC was also successfully employed to improve complex traits such as grain yield. By using MABC, six chromosomal segments each in two elite lines (Tx303 and Oh43), were transferred into two inbred lines (B73 and Mo17) with three generations of backcrossing followed by two generations of selfing (Stuber et al., 1999). Then, the improved lines with better performance were selected based on initial evaluations of testcross hybrids. The single-cross hybrids between improved B73 x improved Mo17 yielded better than the check hybrids by 12-15% (Stuber et al., 1999). Semagn et al. (2006) provided a detailed review on the progress and prospects of MABC in crop breeding. Based on the successful use of MABC in maize and in some other crops not reported here, it is, possible to use similar method in cowpea to introgress drought-tolerant genes into farmer preferred cowpea genotypes in Burkina Faso that are susceptible to drought. University of Ghana http://ugspace.ug.edu.gh 25 CHAPTER THREE 3.0. FARMERS’ PERCEPTION OF DROUGHT IMPACT ON COWPEA 3.1. Introduction In the past, plant breeders have not engaged farmers in the development of new varieties. Consequently, a lot of work was done and the process of adoption of many new varieties failed. These failures were imputable to the fact that those farmers’ and consumers’ preferences were not taken into account in the process of varietal development. In Burkina Faso, the current extension system is based on the classical training and visit approach (Tignegre, 2010). In this system, the agricultural extension agents are linked to the farmers and the researchers for the release of new varieties. The failure to meet farmers’ and consumers’ needs by researchers might be the reason for which food security is still a major problem in developing countries. It is a waste of time and effort when farmers reject a variety at the end of the selection process, because their own criteria have not been taken into account (Chiulele, 2010; Tignegre, 2010). Farmers’ needs and preferences must be taken into account in the whole process of variety development. A better understanding of such issues could help researchers to define their own role in the research process, acknowledge the strengths and weaknesses of their own and farmers’ approaches, overcome communication gaps, and find novel solutions for problems that typically occur in the process of participatory technology development (Efisue, 2006; Hoffmann et al., 2006; Chiulele, 2010). To cope with these needs and preferences, the information must be collected with farmers using bottom to top approaches. Farmer participatory evaluation can produce valuable feedback for breeders and University of Ghana http://ugspace.ug.edu.gh 26 agronomists for the perception of research results and open new research areas (Kitch et al., 1997; Saidou et al., 2011). Recently, in Burkina Faso, some participatory rural appraisal (PRA) studies were conducted to identify cowpea production constraints (Pronaf, 2003). The objectives of these studies were to evaluate the social and economic impacts of cowpea technologies and this showed that the income generated by cowpea at Donsin had increased from 0.0% (1990) to 14.1% (2001). Later on, another PRA was conducted to study cowpea production system, the importance and production constraints of cowpea, farmers’ perception of Striga gesnerioides (Willd)Vatke and farmers’ preferred traits (Tignegre, 2010). The results showed that drought was always mentioned as an important constraint to cowpea production. As such, drought was ranked third at Donsin. A new participatory method has been implemented with success. This approach is known as farmer field schools (FFS) or fora (FFF) (Braun and Duveskog, 2008). The FFS technology has been used as a means for technology transfer in farmers’ fields (Nathaniels, 2005). The crop is of major importance to the livelihoods of millions of relatively poor people in less developed countries of the tropics (Bressani, 1985; Carnovale et al., 1990; Marconi et al., 1992; Quin, 1997). The production has really doubled between 2001 (300 000 T) and 2010 (600 000 T) with same trends for areas covered during the same period (Countrystat, 2012). In terms of cowpea exports, Burkina Faso is a net exporter, which generates income to cowpea farmers (Statistika, 2002; Langyintuo et al., 2003). Ghana and Cote d’Ivoire are the major markets for cowpea exports (Statistika, 2002; Langyintuo, 2004). University of Ghana http://ugspace.ug.edu.gh 27 Regarding drought management in Burkina Faso, some local techniques have been improved by researchers. Some of these techniques are the zai, the half-moon-shaped, and the rock-fenced techniques. All these techniques are used to allow a good infiltration of water from rains into the soil and reduce soil erosion. In addition, the government of Burkina Faso has implemented a cloud seeding program to induce artificial rains and to ensure that sufficient water is retained in dams for off-season cropping. In view of the importance of the farmers’ needs and preferences, farmers’ perceptions of drought and drought tolerant varieties have to be captured and included in variety development. In addition to drought tolerance, sometimes, farmers have certain preferences that breeders may ignore. In the most important cowpea growing regions of Burkina Faso, farmers are aware of variety’s traits and have peculiar preferences based on these traits. They adopt production practices that preserve these traits. Therefore, a participatory rural appraisal sets as a way for learning farmers’ knowledge in order to include them in the breeding objectives. Therefore, in order to capture farmers’ preferences and incorporate their views into a cowpea breeding program, it was necessary to conduct a participatory rural appraisal. This is expected to increase the adoption of the developed cowpea varieties. The objective of this study was to determine farmers’ perceptions about the effects of drought in cowpea production, to identify and ranked the main constraints to cowpea production, and to identify farmer preferred varieties based on traits like yield, tolerance to drought and income generation. The identification of farmers’ preferred traits were not part of this study. They are already known from the studies conducted by Pronaf (2003) and Tignegre (2010). University of Ghana http://ugspace.ug.edu.gh 28 3.2. Materials and Methods 3.2.1. Study sites Information on the participatory rural appraisal (PRA) research sites are presented in Table 3.1.The study was made in the most important cowpea production areas of Burkina Faso. The PRA was conducted in the villages of Donsin in the district of Ziniare, Pobe-Mengao in the district of Djibo, and Pissila in the district of Kaya. These three villages are distributed across two agro-ecological zones (Sahel and Sudan savanna) of the country where drought problem is recurrent. Table 3.1: General information on the PRA research sites. Villages Pobé Pissila Donsin Region Sahel Centre-Nord Plateau Central Province Soum Sanematenga Oubritenga District Djibo Kaya Ziniare Latitude N 13⁰54.190' N 13⁰09.829' N 12⁰35.426' Longitude W 001⁰45.691' W 000⁰49.549' W001⁰25.048 Altitude (m) 334 321 297 Rainfall (mm) 400-600 500-700 600-900 University of Ghana http://ugspace.ug.edu.gh 29 3.2.2. Focus group discussion Focus group discussions, pair-wise ranking, and participatory variety selection were used as PRA methodologies in this study for data collection. For the focus group discussions (FGDs), a random sample of one to five villages represented here by the name of the major village was used for the FGDs in each district. In each village, the FGDs were conducted with cowpea farmers (between 20 and 25 per group). When the number of participants exceeded 25 in a district, it was split into two groups. Where it was possible, about five to seven women were included in the groups to get the perceptions from both men and women. The need to have both women and men contacted was to allow the researcher to obtain perceptions on the characteristics of the cowpea from production to storage and marketing. A total of 87 cowpea farmers were involved in this PRA focus group discussion (25, 22 and 40 farmers from Pobe-Mengao, Pissila, and Donsin, respectively). Two extension specialists from the targeted zones were included to make a link with developers. The group of participants was guided by a moderator (or group facilitator), who introduced the topics for discussion and helped the group to participate in a lively and natural discussion amongst themselves. 3.2.3. Pair-wise ranking The pair wise ranking technique was used to rank cowpea production constraints. In this study, the different constraints encountered by farmers in cowpea production in each village were first listed. This list was made by farmers themselves and then, each constraint was compared to the others. University of Ghana http://ugspace.ug.edu.gh 30 3.2.4. Participatory variety selection Participatory variety selection (PVS) sessions were held with the same groups as in the FGDS. Therefore, 25, 22 and 40 farmers were involved in the PVS in the village of Pobe-Mengao, Pissila, and Donsin, respectively. Two koligrammes of cowpea seed samples per variety were presented to farmers for the purpose of selection. Matured fruits of neem plant (Azadirachta indica) or stones were used to help farmers for quantifying varieties and traits they preferred. The ranking of cowpea genotypes for desirable selection criteria were done by giving hundred fruit of neem plant (Azadirachta indica) or stones to one farmer (Figure 3.3). The preferred variety or trait was given the highest number of fruits or stones, while the rejected variety or trait was given zero or few number of fruits or stones. The rank (percentage) was obtained by counting the number of fruits with regards to the variety or trait. High number of allocated fruits meant that farmers approved the option. Data on different traits were also recorded individually for farmers’ preference (yield “productivity”, storage ability, tolerance to drought, susceptibility to Macrophomina, and marketability of cowpea grain) and the reasons they were preferred. Percentages for favorable cases for all traits were calculated for each site. 3.3. Results 3.3.1. Constraints to cowpea production Constraints to cowpea production were identified and ranked by farmers at the different sites. The unavailability of cowpea improved variety seed was the first problem mentioned in cowpea production at all sites (Table 3.2). At Pobe-Mengao, the unavailability of improved variety seeds was immediately followed by drought due to the infrequent rains and short rainy season. In this University of Ghana http://ugspace.ug.edu.gh 31 area, farmers namely also described four drought components occurring during the cropping season, planting period, flowering period, pod setting period, and the unpredictable drought or intermittent drought. At Donsin and Pissila, the limited access to improved variety seeds was rather followed by soil fertility issues but drought was always mentioned as a principal constraint for cowpea production in all the sites. Farmers at Donsin indicated that cowpea storage was no longer an issue due to the use of the triple bagging technology provided by INERA and the University of Purdue. When asked about the kind of breeding materials farmers are waiting at all sites they unanimously pointed to materials that are tolerant to terminal drought. For them, they can manage the other intermittent drought components by multiple plantings. Table 3.2: Cowpea production constraints ranked by farmers at three districts. RANKING DONSIN POBE-MENGAO PISSILA 1 LIMITED ACCESS TO IMPROVED VARIETY SEEDS LIMITED ACCESS TO IMPROVED VARIETY SEEDS LIMITED ACCESS TO IMPROVED VARIETY SEEDS 2 SOIL FERTILITY DROUGHT SOIL FERTILITY 3 FERTILEZER/INSECTICID E SOIL FERTILITY DROUGHT 4 DROUGHT FERTILEZER/INSECTICID E EQUIPMENTS 5 EQUIPMENTS STRIGA STRIGA 6 MACROPHOMINA EQUIPMENTS INSECTS 7 MARKET CABMV FERTILEZER/INSECTICIDE 8 STRIGA MARKET (for extra production) MARKET (for extra production) 9 INSECTS INSECTS CABMV 10 PYTHIUM PYTHIUM 11 CABMV - 12 FIELD (new airport under construction) - - University of Ghana http://ugspace.ug.edu.gh 32 3.3.2. Drought symptoms and impact on cowpea production Perceptions on the symptoms induced by drought on cowpea plants, the impact of drought on cowpea production, and the consequences on cowpea farmers are shown in Table 3.3. The symptoms as well as the consequences and the impact were brainstormed by the farmers during the focus group discussions (FGDs) at the three site. Table 3.3 shows the summary of the three studied sites (Pobe-Mengao, Donsin, and Pissila). The main symptoms identified flower abortion, poor pod and grain filling, plant death, and early senescence that affected the production by reducing grain and fodder yield, disturbed the planning of the cropping seasons and cause genetic erosion of landraces. The consequences of drought manifest in reduced income levels, poverty and famine which limits farmers’ capacity to take care of their families. Table 3.3: Perceptions of farmers on impacts of drought on cowpea production in three districtsa. Symptoms in cowpea field Impacts on cowpea production Consequences on cowpea farmers Poor pod filling Grain yield reduction Famine Poor grain filling Fodder yield reduction Total loss of crop and landraces Poverty Indebtedness Loss of income Reduction in branching Difficulties in planning cropping season Children schooling compromised Rural exodus Reduction in grain weight Loss of arable land Begging Green seeds Leaf yellowing wilting of plants Plant death Dropping of leaves (senescence) and flowers Shortening of plant cycle Breaking of grains during threshing a: Donsin, Pobe-Mengao, and Pissila University of Ghana http://ugspace.ug.edu.gh 33 3.3.3. Cowpea participatory variety selection Four to five criteria were used in the cowpea participatory variety selection. These criteria are the productivity of the variety, the marketability, the ability in drought tolerance and/or susceptibility to Macrophomina, and the storage ability. Grains of cowpea varieties that were already grown by farmers and known by them and new varieties that are about to be released were presented to farmers for the purpose of variety selection at all three sites. The PRA results at this site are shown in Figure 3.1. In terms of drought tolerance, variety KVx61- 1 was ranked first at Pobe-Mengao. The most drought-susceptible variety was variety KVx414- 22-2 at Pobe-Mengao. For marketability, the same variety KVx61-1 ranked well at Pobe-Mengao. It was ranked first by women’s group with a score of 56% and second by men’s group with a score of 28%. In the men’s group, variety KVx396-4-5-2D was ranked first with a score of 54% and third in the women’s group (12%). The marketability of variety KVx745-11P, a dual purpose variety was the lowest among the ranked varieties with a score of 4% in men’s group and it was not even ranked by women’s group. In terms of productivity, variety KVx61-1 was ranked first by the men’s group with a score of 50% and was the only productive variety selected ranked by the women’s group and scored 100%. It was followed in the men’s group by varieties KVx396-4-5-2D (30%) and KVx414-22-2 (14%). For storage, variety Gorom local (42%) was ranked first in the women’s group followed by varieties KVx61-1 (30%), KVx745-11P (18%), and KVx396-4-5-2D (10%). The three remaining varieties were not classified by women farmers. The men’s group ranked KVx396-4-5-2D (36%) University of Ghana http://ugspace.ug.edu.gh 34 as first variety followed by varieties KVx396-4-4 (24%), Gorom local (16%), KVx61-1 (12%), and KVx414-22-2 (12%). Figure 3.1: Cowpea variety acceptability (%) based drought and on farmers’ preferred characteristics at Pobe-Mengao (Djibo), 2012. The PRA results at this site are shown in Figure 3.2. In terms of drought tolerance, the women’s group ranked variety KVx396-4-5-2D (47%) first at Pissila followed by varieties IT98K-205-8 (24%), KVx61-1 (17%) and KVx414-22-2 (11%). In the men’s group, variety KVx61-1 (30%) got the highest score followed by varieties KVx414-22-2 (28%), IT98K-205-8 (22%) and KVx396-4- 5-2D (20%). Three varieties have not been chosen by the two groups. For marketability, varieties KVx61-1 (29%) and KVx414-22-2 (28%) almost shared the lead in the women’s group, while in the men’s group, variety KVx414-22-2 (42%) was ranked first and variety IT98K-205-8 (29%) was second. 0 20 40 60 80 100 120 women men women men women men women men Drought (%) Market (%) Production (%) Storage (%) p er ce n ta g e (% ) KVx61-1 Gorom KVx396-4-4 KVx396-4-5-2D Melakh KVx414-22-2 KVx745-11P University of Ghana http://ugspace.ug.edu.gh 35 In terms of productivity, KVx61-1 (31%) was ranked first by the women’s group and was also ranked first by the men’s group with a score of 41%. It was followed in the women’s group by varieties IT98K-205-8 (28%), KVx396-4-5-2D (21%), and KVx414-22-2 (20%). In the men’s group variety KVx61-1 was followed by KVx396-4-5-2D (27%), IT98K-205-8 (23%) and KVx414-22-2 (8%). For storage ability, variety KVx396-4-5-2D was ranked first in both men’s and women’s group with, respectively, a score of 38% and 29%. The second variety was IT98K-205-8 (23%) in the men’s group and variety KVx414-22-2 (28%) in the women’s group. Variety KVx61-1 was third in both men’s and women’s group with respectively, percentages of 21% and 17%. Figure 3.2: Cowpea variety acceptability (%) based drought and on farmers’ preferred characteristics at Pissila (Kaya), in 2012. 0 5 10 15 20 25 30 35 40 45 50 women men women men women men women men Drought Market Production Storage p er ce n ta g e (% ) KVx61-1 KVx396-4-5-2D KVx414-22-2 KVx745-11P IT98K-205-8 KVx396-4-4 Melakh University of Ghana http://ugspace.ug.edu.gh 36 The PRA results at this site are shown in Figure 3.3. In terms of drought tolerance, variety KVx61- 1was ranked first by both men’s group (59%) and women’s group (49%) at Donsin followed by variety Moussa local in both cases with a score of 41% for men and 38% for women. The rest of the varieties were not scored by the two groups. For marketability, variety KVx780-1 a new variety which is yet to be released and intentionally proposed to Donsin farmers was ranked first in women’s group with the percentage of (56%) and the third place in men’s group (15%) after another new variety KVx442-3-25 (16%). The landrace Moussa local got the highest score in the men’s group with 45% and ranked the second place in women’s group with 38%. In terms of productivity, variety KVx61-1 was ranked first by both women’s and men’s groups with score of 45% and 39% respectively. This variety was followed in the men’s group by Moussa local only (27%). Variety KVx61-1 was the only variety ranked for high productivity by the women’s group. For storage ability, the landrace Moussa local was ranked first in both men’s and women’s groups, with scores of 44% and 41% respectively. The second and last classified variety for storage ability was KVx61-1 in both groups with a score of 36% for women and 33% for men. At Donsin the presence of Macrophomina was notified, and then, farmers were asked to rank this constraint based on the level of susceptibility of the different varieties to this fungal disease. Therefore, KVx61-1 was ranked in both groups as highly susceptible to Macrophomina followed by Moussa local in the men’s group. University of Ghana http://ugspace.ug.edu.gh 37 Figure 3.3: Cowpea variety acceptability (%) based drought and on farmers’ preferred characteristics at Donsin (Ziniare) in 2012. 3.4. Discussion In this study, focus group discussions (FGDs) and pair-wise ranking methods were used to engage farmers regarding their opinion on cowpea constraints mostly associated with drought and cowpea varieties. In general farmers demonstrated that they have a deep knowledge about cowpea production constraints. Fertilizers, agro-chemicals were rarely used, especially for cowpea production. Farmers are aware of cowpea capacity to fix nitrogen and to enrich the soil for the next crop. Farmers were still practicing mixed-cropping because they perceived that it reduced high insect pressure on cowpea 0 10 20 30 40 50 60 70 Women Men Women Men Women Men Women Men Women Men Drought Market Production Storage MacrophominaS p er ce n ta g e (% ) KVx780-1 KVx442-3-25 KVx61-1 KVx771-10 Moussa local KVx775-33-2 KVx745-11P University of Ghana http://ugspace.ug.edu.gh 38 than when cowpea was grown in sole cropping system. These perceptions about the inter-cropping were also reported (Tignegre, 2010). None of the farmer-proposed control methods was effective in controlling drought in the field. However, a huge number of them are using soil and water control techniques to protect their soil from erosion and allow water to infiltrate the soil. These techniques keep the soil wet for long period and prevent plants from early dehydration. The techniques that are regularly used are: (1) the half-moon-shaped which is a kind of a hole dug to allow water to infiltrate the soil and keep the soil wet for plants to grow, (2) the zaï which is made of simple holes dug and filled with organic manure before the rains. It has almost the same role as the half-moon, and (3) the last technique is the rock-fenced techniques. Several cowpea production constraints were highlighted by the farmers. There were poor access to agricultural inputs (fertilizers, pesticides). There was a general perception that rainfall had been declining over time and soils had been degraded over the past few years. As a result of that drought was mentioned between the first four constraints. KVx61-1 was the best variety for farmers in terms tolerance to drought and yield. Some of the constraints were linked to the lack of training for the extension system, the lack of infrastructure (equipment, access to a range of improved variety seeds); the other constraints were biotic stress, such as insects, diseases and Striga. These observations were also reported by other authors (Tignegre, 2010). Diseases such as seedling dumping due to Pitium and viral diseases were cited by the farmers as a constraint. This was mainly due to the fact that farmers did not know or differentiate disease symptoms from other constraints on the cop. University of Ghana http://ugspace.ug.edu.gh 39 In this PRA study, the impact of drought in cowpea production was more or less general for all crops for farmers. For them, cowpea was better than other crops like maize, because cowpea can overcome some short water deficit and produce. The effects of drought in crop production were also notified in a study regarding climate change in Burkina Faso (Pana-Bf, 2006). This study showed that, because of recurrent drought, the area covered by crops like yam, maize, and cotton are being reduced year after year. The rural exodus reported by farmers during the PRA is confirmed (Pana-Bf, 2006), which showed that an important number of migrants were moving from the northern part of the country to the western part for those who want lands for agriculture up to big cities like Ouagadougou and Bobo-Dioulasso and even to the neighboring countries (Ghana, Cote d’Ivoire, etc.). Productivity is a weighting factor for farmers, though yield is not the sole criterion upon which farmers always choose a variety. For instance, the choice of KVx61-1 by farmers at Donsin, Pobe- Mengao, and Pissila showed their preferences for more than one characteristic in the same variety (source of income, drought and Striga resistance). The choice of varieties KVx414-22-2 and the landrace Moussa local as income generating varieties was due to the quality of their grain: large- sized grain or whiteness of the testa. It was also chosen by farmers for the same characteristics (Tignegre, 2010). This is confirmed by the choice of the newly pre-released large-sized KVx780- 1 proposed only for selection at Donsin, where the variety was part of the on-farm trials. This variation in the variety choices by farmers appeared to be influenced by the market demand. This observation confirmed the conclusion drawn by other authors (Coulibaly and Lowenberg-Deboer, 2002; Zannou et al., 2004). In general, landraces were the most income-generating genotypes University of Ghana http://ugspace.ug.edu.gh 40 because of their good agronomic and culinary characteristics (grain quality and taste) (Tignegre, 2010). These findings demonstrated that farmers’ choices are mostly driven by market characteristics of the cowpea seeds. Most of their landraces have farmers’ preferred traits. Unfortunately, their yields are very low in general and they are susceptible to multiple constraints. Therefore, there was needs to identify or improve cultivars including farmers’ preferences in order to suite their demand (Yadaw et al., 2006). The survey made by Pronaf (2003) has shown that cowpea has increasingly become a source of income for farmers in Burkina Faso. In addition to providing food, cowpea can generate as much income (for people of Sahel and North Savanna zones) as cotton for people in more humid areas (Ouedraogo et al., 1996). Farmers considered that the importance of cowpea was due to its roles as staple food, its adaptations (climate and local utilizations) and role as a source of income. The market demand can control farmers’ preferences for grain characteristics (Coulibaly and Lowenberg-Deboer, 2002; Zannou et al., 2004; Orawu, 2007). For example, in Sahel and Oudalan provinces, though farmers preferred the brown-coloured cowpea grain as their grown landraces, they also grow white-coloured grain of cowpea mostly for the market (Tignegre, 2010). Based on such characteristics, the breeding objectives in Burkina Faso could be selecting varieties with large-sized and white-coloured grain with local adaptation for food consumption and market demand at all sites. These findings confirmed those of Tignegre (2010) who reported the different constraints of cowpea production in Burkina Faso. There is a potential to increase cowpea production if farmers have access to more agricultural inputs, including improved, Striga-resistant, and drought tolerant varieties, with the preferred grain University of Ghana http://ugspace.ug.edu.gh 41 characteristics. The achievement of such potential would require that other abiotic constraints (soil degradation, rain declining over time), improved production systems (rotations, intercropping with cowpea) and market network be addressed. At the end of this PRA, farmers had the feeling that they had been involved and were excited because their views would be taken into account in cowpea variety selection process in order to meet their needs. They were hopeful that some urgent queries would be considered for the sake of their welfare: training, access to inputs (seeds, fertilizers, pesticides) and a better organized cowpea market network. They made a request to be involved in future research actions that would enable them to have access to seeds of improved varieties and need to be trained as cowpea producers. 3.5. Conclusion Grain yield reduction, fodder yield reduction, total loss of crop and landraces, difficulties in planning cropping season, loss of arable land were most of the time during this study the main impact of drought on cowpea production in the areas were the study was conducted. The consequences of this, is the reduction of income levels, poverty, famine, indebtedness, difficulties to send children to school, rural exodus, and increase in number of people begging. Farmers demonstrated that they have a deep knowledge about cowpea production constraints and difficulties to access improved variety seeds was the first constraint in all the areas where the study has been conducted. Other important constraints include drought tolerance, lack of equipment, soil fertility, and low access to fertilizer and pesticicdes. University of Ghana http://ugspace.ug.edu.gh 42 The preferred grain traits for all regions identified in the past studies were white, large seeded, with a rough texture for food and market purposes, except for the northern region where brown grain was preferred for food. Therefore, the development of new drought-tolerant cultivars for Burkina Faso will need a simultaneous selection for genotypes with resistance to the major abiotic and biotic constraints for farmers as well as for market preferred grain traits. These grain characteristics should be included in cowpea breeding programs to ease the adoption of improved varieties by Burkina Faso farmers. The participatory variety selection (PVS) during this participatory rural appraisal (PRA) paved the way for need-based selection by the farmers, and thereby could help promote quicker adoption of high yielding, large-seeded, and white coat colour varieties in the farming community. University of Ghana http://ugspace.ug.edu.gh 43 CHAPTER FOUR 4.0. FIELD ASSESSMENT OF COWPEA GENOTYPES FOR DROUGHT TOLERANCE 4.1. Introduction Despite the inherent capacity of cowpea to withstand drought, the erratic pattern of rainfalls exposes the crop to drought at the onset and at the end of the rainy season (Singh and Matsui, 2002). This can lead to yield losses ranging from 30% (Chiulele, 2010) to total crop failure. To assess the tolerance to drought stress, a number of criteria have been used in crop plants. The stability analysis criterion for identifying environmentally sensitive and insensitive genotypes when they are evaluated under series of environments has been used (Finlay and Wilkinson, 1963). By using this approach, drought tolerance is assessed by the intercept of genotype yield regressed on environmental index. Other selection criteria also known as stress tolerance indices were proposed for selection of genotypes based on their performance in stressed and non-stressed conditions (Fisher and Maurer, 1978; Rosielle and Hamblin, 1981). Tolerance index (TOL), relative yield of genotypes under stressed (Ys) and non-stressed (Yp or Yw) conditions, stress susceptibility index (SSI), stress intensity (SI), and mean productivity (MP) were defined and used in selecting genotypes for drought tolerance (Fisher and Maurer, 1978; Rosielle and Hamblin, 1981). In addition, geometric mean productivity GMP) and stress tolerance index (STI) were proposed by Fernandez (1992). These stress tolerance indices were reported to be the most suitable for screening genotypes for drought tolerance because they enable the identification of high yielding and drought tolerant genotypes (Fernandez, 1992). A limited number of authors have used University of Ghana http://ugspace.ug.edu.gh 44 these quantitative indices for stress tolerance to assess drought tolerant genotypes in cowpea (Chiulele, 2010; Ishiyaku and Aliyu, 2013). Very few researchers have worked on drought stress in cowpea in Burkina Faso. These works were mainly on pots and proline accumulation screenings, different planting dates screening at drought prone areas. These works revealed sources of drought tolerant genotype in the germplasm KVx61- 1 and Gorom local (Hamidou et al., 2007) and KVx525 (Sawadogo, 2009). These studies mainly reported on biochemical and physiological traits. Until now, no in depth study was reported on the performance of Burkina Faso cowpea germplasm under drought imposed conditions in the field. Therefore, this study was conducted to assess cowpea genotypes for drought tolerance using some quantitative indices of stress tolerance such as stress intensity, mean productivity, tolerance index, stress susceptibility index, geometric mean productivity and stress tolerance index to classify the genotypes into different yielding and drought tolerance group. 4.2. Materials and Methods 4.2.1. Experimental materials, design and field layout During the 2012 off-season from February to April, 50 cowpea genotypes (Table 4.1) were screened under run-off irrigation in the field conditions at Saria research station. Before planting, the land was ploughed and fertilized with organic manure (2.5t.ha-1) to remove nutrient deficiency as a limiting factor. Two days before planting, the field was watered to field capacity. After sowing, the plants were maintained at near field water capacity until the emergence of flower buds (50% flower buds initiation). The plants were then, subjected to two soil moisture regimes (water- University of Ghana http://ugspace.ug.edu.gh 45 stressed and well-watered or control). The experimental unit consisted of 2 row plots of 2 m long and about 10 plants per row. The spacing between rows was 0.8 m and the spacing between hills on the same row was 0.20 m with two plants per hill. The experimental design was an alpha lattice design with three replications, ten blocks per replication, and five genotypes per blocks. The field was regularly sprayed with insecticides (deltamethrin) at the dose 2 ml of insecticide per liter of water to avoid the effects on the insects’ attacks on the plants. University of Ghana http://ugspace.ug.edu.gh 46 Table 4.1: Cowpea genetic materials screened for tolerance to drought in field experiments in 2012 N0 Genotypes Origin Seed colour N0 Genotypes Origin Seed colour 1 KVx404-8-1 Burkina Faso White 26 Apagbaala Ghana White 2 Kaya local Burkina Faso White 27 IT96D-610 IITA/Nigeria White 3 KVX525 Burkina Faso White 28 IT95K-1479 IITA/Nigeria White 4 F8/SR Burkina Faso White 29 IT00K-901-6 IITA/Nigeria White 5 KVX421-2J Burkina Faso Brown 30 IT84S-2246 IITA/Nigeria White 6 Djouroum local Burkina Faso White 31 IT99K-499- 39 IITA/Nigeria White 7 KVx780-3 Burkina Faso White 32 IT98K-205-8 IITA/Nigeria White 8 KVx780-6 Burkina Faso White 33 IT98K-317-2 IITA/Nigeria White 9 KVX396-4-5- 2D Burkina Faso White 34 IT95M-190 IITA/Nigeria White 10 KVX771-10 Burkina Faso White 35 IT99K-573- 2-1 IITA/Nigeria White 11 KN1 Burkina Faso Brown 36 IT93K-693-2 IITA/Nigeria Brown 12 KVx780-1 Burkina Faso White 37 IT98K-1111- 1 IITA/Nigeria White 13 Pobe local Burkina Faso White 38 IT93K-503-1 IITA/Nigeria White 14 KVX61-1 Burkina Faso White 39 IT84S-2049 IITA/Nigeria White 15 Moussa Local Burkina Faso White 40 IT97K-207- 15 IITA/Nigeria White 16 KVX414-22- 2 Burkina Faso White 41 TN88-63 Niger White 17 Donsin local Burkina Faso White 42 Bambey-21 Senegal White 18 KVx780-4 Burkina Faso White 43 Mouride Senegal White 19 BulkF7/SR Burkina Faso White 44 Melakh Senegal White 20 KVX775-33- 2 Burkina Faso White 45 58-57 Senegal White 21 Komsare Burkina Faso Cream 46 UC-524B UCR-USA White 22 KVX30-309- 6G Burkina Faso White 47 UCR-P-24 UCR-USA White 23 KVX745-11P Burkina Faso White 48 CB46 UCR-USA White 24 KVX442-3- 25 Burkina Faso White 49 CB27 UCR-USA White 25 Gorom Local Burkina Faso Brown 50 Iron Clay UCR-USA White University of Ghana http://ugspace.ug.edu.gh 47 4.2.2. Data collection Data collected included grain yield, and yield components (pod yield, hundred seed weight, days to 50% flowering, and days to 95% maturity), total biomass, and fodder yield were recorded. Quantitative indices of stress tolerance were calculated using yield data. These stress tolerance indices were: (i) Mean productivity (MP) (ii) Tolerance index (TOL) (iii)Stress susceptibility index (SSI) (iv) Geometric mean productivity (GMP) (v) Stress tolerance index (STI) (vi) Stress intensity (SI) The selection indices of stress tolerance for the mean productivity (MP), the tolerance index (TOL), the stress susceptibility index (SSI), the stress intensity (SI), the geometric mean productivity (GMP), and the stress tolerance index (STI) were calculated based on yield data in the two contrasting environments using the following formulae: Mean productivity (MP) MP = (Ys+Yw)/2 Tolerance index (TOL) TOL = Yw-Ys Stress susceptibility index (SSI) SSI = [1-(Ys/Yw)]/[1-(Ῡs/Ῡw)] Stress intensity (SI) SI = 1-(Ῡs/Ῡw) University of Ghana http://ugspace.ug.edu.gh 48 Geometric mean productivity (GMP) GM P= √((Ys) x Yw) Stress tolerance index (STI) ST I= (Ys x Yw)/Ῡ²w Where Ys and Yw (known as Yp (Fernandez, 1992)) are the yields of each genotype under drought-stressed and non-stressed conditions. Ῡs and Ῡw are respectively the mean yields of all genotypes under drought-stressed and non-stressed conditions. The stress intensity (SI) score was classified into mild, moderate and severe. Stress intensity was mild when yield reduction was between 0 and 25%, moderate when yield reduction was situated between 25 and 50% and severe when yield reduction was between 50 and 100% (Chiulele, 2010). 4.2.3. Data analysis Data on grain yield and yield components were analyzed using GenStat 14.0 computer software. The mixed models residual maximum likelihood (REML) was used for computing variance component. The REML analysis of collected data was done for the following environments: (1) irrigated throughout the experiment (well-watered) and (2) irrigated with imposed drought stress from during flower bud initiation period (water-stressed) and grain, pod yield and hundred seed weight reduction was calculated for the two contrasting environments. University of Ghana http://ugspace.ug.edu.gh 49 Correlation analyses were made using yield and yield components data and calculated quantitative indices for stress tolerance. Principal component biplot analysis (PCA) was done using data on yield and the quantitative indices for stress tolerance to graphically display genetic relationships. 4.3. Results 4.3.1. Performance of genotypes yield and yield components In the analysis, genotype x water regime interaction was not significant for all the parameters except for fodder yield (Table 4.2). Difference among genotypes and between water regime were highly significant (p<0.01) for all parameters studied (grain yield, hundred seed weight, total biomass, pod yield, fodder yield, and days to 95% Maturity) except days to 50% Flowering. In general, the genotypes flowered and matured earlier under non-stressed conditions than under stressed conditions. The mean for days to 50% flowering was almost the same for the two water regimes (44 days), but the genotypes under stressed matured (62 days) earlier that those under normal conditions (63 days). The results of the analysis of variance per water regime (Table 4.3, 4.4, and 4.5) showed significant differences in grain yield and yield components between genotypes under non-stressed conditions while under stressed conditions only 100-seed weight showed significant difference (Table 4.3). The mean grain yields of genotypes under stressed and non-stressed conditions are respectively 542.59 kg.ha-1 and 871.53 kg.ha-1. The mean of genotypes ranged from 277.55 kg.ha-1 to 1543.00 kgha-1under non-stressed conditions and from 194.77 kg.ha-1 to 729.19 kgha-1 under stressed conditions. Under non-stressed conditions, genotypes KVX396-4-5-2D, KVX775-33-2, TN88-63, IT96D-610, Melakh, KVX61-1, BulkF7/SR, Mouride, KVX30-309-6G, KVx404-8-1, IT95K- University of Ghana http://ugspace.ug.edu.gh 50 1479, IT98K-317-2, Apagbaala, KVx780-4, Iron Clay, IT93K-693-2, KVX442-3-25, Gorom Local, KVx780-1, and KN1 were high yielding and produced more than 900 kgha-1while genotypes KVX414-22-2, Moussa Local, KVX525, IT98K-205-8, IT99K-499-39, KVX745-11P, and Bambey-21 were low yielding and produced less than 600 kg.ha-1. Under stressed conditions, genotypes KVX745-11P, IT93K-693-2, KVx404-8-1, KVX61-1, KVX775-33-2, IT98K-1111-1, CB27, KVX30-309-6G, IT96D-610, TN88-63, Djouroum local, Mouride, Gorom Local, KVx780- 3, BulkF7/SR, KVX396-4-5-2D, and KVX525 were moderate yielding and produced more than 600 kg.ha-1 while genotypes KVx780-4, UC-524B, Moussa Local, Kaya local, IT99K-573-2-1, IT98K-317-2, IT99K-499-39, F8/SR, and Bambey-21 were low yielding and produced less than 400 kg.ha-1 (Table 4.3). Drought stress reduced grain yield but the genetic materials reacted differently to the stress. The grain yield of IT95M-190, TN88-63, KVx780-1, BulkF7/SR, KVX775-33-2, Kaya local, IT93K- 503-1, IT95K-1479, Apagbaala, Iron Clay, F8/SR, UC-524B, IT99K-573-2-1, KVX396-4-5-2D, KVx780-4, Melakh, and IT98K-317-2 was reduced by more than 45% while that of KVX745- 11P, KVX525, KVX414-22-2, Pobe local, and Djouroum local was not affected. Larger grain yield reductions were mostly recorded in high yielding genotypes under well-watered conditions under stress conditions. In general, genotypes with high performance under normal conditions yielded poorly under stress conditions. Low grain yielding genotypes were not severely affected by the yield reduction (Table 4.5). University of Ghana http://ugspace.ug.edu.gh 51 Table 4.2: Analysis of variance for yield and yield components of 49 cowpea genotypes under stressed and non-stressed conditions Wald statistics Source DF Grain Yield 100-seed weight Total biomass Pod Yield Fod Yield 50%Flowering 95% Maturity Genotype 48 2.53** 7.83** 2.16** 2.08** 2.70** 6.72** 2.50** Water Regime 1 125.96** 6.39** 150.24** 147.38** 68.90** 1.80ns 14.18** Genotype x Water Regime 48 0.89ns 1.23ns 1.30ns 1.11ns 1.45* 0.50ns 1.15ns **: significant at p<0.01 University of Ghana http://ugspace.ug.edu.gh 52 Table 4.3: Grain yield performance of 49 cowpea genotypes under water-stressed and well-watered conditions at Saria research station ***: significant at p<0.001 and ns: not significant Yielding ability Genotypes Non-stressed Stressed Reduction (%) HIGH KVX396-4-5-2D 1543.00 620.04 59.82 KVX775-33-2 1338.92 702.52 47.53 TN88-63 1228.30 676.88 44.89 IT96D-610 1217.98 679.25 44.23 MELAKH 1184.97 459.08 61.26 KVX61-1 1169.64 716.87 38.71 BulkF7/SR 1161.49 623.11 46.35 MOURIDE 1144.11 671.28 41.33 KVX30-309-6G 1100.49 681.72 38.05 KVx404-8-1 1091.28 722.22 33.82 IT95K-1479 1074.62 517.22 51.87 IT98K-317-2 1034.75 318.82 69.19 APAGBAALA 1019.90 481.69 52.77 KVx780-4 1014.55 394.45 61.12 IRON CLAY 974.26 439.65 54.87 IT93K-693-2 959.32 728.36 24.08 KVX442-3-25 958.05 599.56 37.42 GOROM LOCAL 931.15 648.77 30.33 KVx780-1 907.94 496.86 45.28 KN1 904.36 522.70 42.20 MODERATE UC-524B 897.89 386.61 56.94 IT98K-1111-1 892.71 694.80 22.17 CB27 878.53 684.08 22.13 58-57 874.68 579.78 33.72 KVX771-10 863.69 502.17 41.86 IT84S-2246 858.95 598.94 30.27 ITOOK-901-6 841.02 597.76 28.92 IT95M-190 837.19 462.54 44.75 KVx780-3 836.67 645.11 22.90 IT93K-503-1 832.09 401.38 51.76 KVx780-6 810.88 501.65 38.14 Donsin local 788.86 521.94 33.84 Djouroum local 785.43 672.37 14.39 IT84S-2049 784.25 553.79 29.39 IT99K-573-2-1 771.71 328.74 57.40 IT97K-207-15 740.85 573.45 22.59 CB46 695.92 506.29 27.25 Kaya local 636.66 329.07 48.31 UCR-P-24 635.60 489.21 23.03 F8/SR 634.77 275.50 56.60 KVX421-2J 613.60 489.37 20.25 Pobe local 610.47 589.16 3.49 LOW KVX414-22-2 589.87 582.18 1.30 MOUSSA LOCAL 584.37 374.41 35.93 KVX525 578.47 612.11 -5.81 IT98K-205-8 568.13 454.97 19.92 IT99K-499-39 557.67 309.51 44.50 KVX745-11P 472.17 729.19 -54.44 Bambey-21 277.55 194.77 29.83 MEAN 871.53 542.59 CV(%) 29.34 32.34 Significance *** ns Grain Yield University of Ghana http://ugspace.ug.edu.gh 53 Table 4.4: Pod yield performance of 49 cowpea genotypes under water-stressed and well-watered conditions at Saria research station ***: significant at p<0.001 and ns: not significant Yielding ability Genotypes Non-stressed Stressed Reduction (%) HIGH KVX396-4-5-2D 1999.67 770.70 -75.13 KVX775-33-2 1832.97 958.82 28.42 TN88-63 1408.37 811.58 16.00 IT96D-610 1513.04 834.09 -3.82 MELAKH 1567.04 645.58 14.28 KVX61-1 1561.37 645.66 11.78 BulkF7/SR 1301.84 765.79 22.57 MOURIDE 1596.78 680.65 23.52 KVX30-309-6G 1455.64 856.86 15.35 KVx404-8-1 1396.37 703.79 27.15 IT95K-1479 1375.57 679.51 16.18 IT98K-317-2 1341.91 464.67 29.19 APAGBAALA 1652.41 604.54 27.93 KVx780-4 1332.28 577.20 34.48 IRON CLAY 1374.30 602.40 22.31 IT93K-693-2 1260.13 908.21 19.75 KVX442-3-25 1251.09 619.47 -55.49 GOROM LOCAL 1158.14 770.60 10.25 KVx780-1 1228.45 621.60 33.46 KN1 1435.04 826.29 37.65 MODERATE UC-524B 1171.15 479.41 49.60 IT98K-1111-1 1111.61 940.96 37.36 CB27 1180.75 903.05 41.05 58-57 1214.22 757.10 50.49 KVX771-10 1146.60 621.99 41.14 IT84S-2246 1094.39 982.22 38.95 ITOOK-901-6 946.06 735.03 58.65 IT95M-190 1484.57 662.06 57.37 KVx780-3 1192.63 999.64 45.75 IT93K-503-1 1155.70 569.40 42.42 KVx780-6 1138.28 694.94 44.87 Donsin local 1104.97 692.15 46.02 Djouroum local 988.56 847.35 55.40 IT84S-2049 1051.43 843.74 42.37 IT99K-573-2-1 1169.89 419.24 49.40 IT97K-207-15 1073.28 781.84 41.18 CB46 924.29 605.55 47.69 Kaya local 872.05 467.92 46.34 UCR-P-24 760.57 538.55 50.73 F8/SR 961.14 379.12 50.60 KVX421-2J 767.74 594.45 63.41 Pobe local 768.09 797.47 56.17 LOW KVX414-22-2 899.96 755.93 60.56 MOUSSA LOCAL 896.49 528.44 59.07 KVX525 696.56 498.58 64.16 IT98K-205-8 806.60 711.61 61.46 IT99K-499-39 796.12 429.74 56.68 KVX745-11P 597.60 1046.59 58.80 Bambey-21 381.04 592.46 65.37 MEAN 1172.21 698.98 CV(%) 28.84 32.06 Significance *** ns Pod Yield University of Ghana http://ugspace.ug.edu.gh 54 Table 4.5: Hundred seed weight of 49 cowpea genotypes under water-stressed and well-watered conditions at Saria research station ***: significant at p<0.001 and ns: not significant University of Ghana http://ugspace.ug.edu.gh 55 Correlation analysis indicated that grain yield was only significantly correlated with pod yield per plot in both stressed and non-stressed conditions (r=0.78, p<0.01; r=0.94, p<0.01), respectively (Table 4.6). Table 4.6: Correlations among grain yield, pod yield, and 100-seed weight of 49 genotypes grown under stressed conditions 100 Seed Grain Yield Pod Yield 100 Seed Grain Yield -0.103 -0.112 Pod Yield -0.078 0.785** -0.096 0.942** **: significant at p < 0.01 4.3.2. Grouping of 49 cowpea genotypes using quantitative stress indices Forty nine (49) Genotypes that combined lower tolerance index and stress susceptibility index and higher mean productivity and stress tolerance index were drought tolerant (Table 4.7). Examples of these genotypes are KVx404-8-1, Gorom local, IT93K-693-2, CB27, IT98K-1111-1, and Djouroum local. In addition, genotypes KVx404-8-1 were high yielding and IT98K-1111-1, Gorom local, and Djouroum local were moderate yielding. In contrast, genotypes that combined higher tolerance index and stress susceptibility index and lower mean productivity and stress University of Ghana http://ugspace.ug.edu.gh 56 tolerance index were drought susceptible. Examples of these genotypes are IT99K-573-2-1, Kaya local, Moussa Local, F8/SR, IT99K-499-39, and Bambey-21 (Table 4.7). The stress intensity applied to this experiment was considered as moderate. The intensity of drought measured by the stress intensity (SI) was 0.38 (38%) and was then, between the intervals of 25% to 50%. University of Ghana http://ugspace.ug.edu.gh 57 Table 4.7: Stress tolerance indices of the 49 cowpea genotypes, in 2012 University of Ghana http://ugspace.ug.edu.gh 58 Correlation analysis between quantitative indices of stress tolerance and stressed and non-stressed mean yield are mentioned in Table 4.8. The results showed that the STI were positively and strongly correlated with MP, TOL, Ys, Yw, and GMP. The correlation between STI and GMP was almost equal to one. GMP was strongly correlated MP, Ys and Yw. Table 4.8: Correlation among stress index scores and yield under stressed (Ys), and non-stressed (Yw) of 49 cowpea genotypes GMPc SSId STIe MPb TOLa Ysf Ywg GMP SSI 0.007 STI 0.986** 0.009 MP 0.989** 0.075 0.978** TOL 0.460 0.439 0.484 0.574** Ys 0.828** -0.265 0.795** 0.749** -0.113 Yw 0.871** 0.247 0.875** 0.930** 0.835** 0.453 a: Tolerance index; b: Mean productivity; c: Geometric mean productivity; d : stress susceptibility index; e : Stress tolerance index; f : yield under stressed condition; g : yield under non-stressed condition **: significant at P< 0.01 The total variation explained by the first two axes was 91.49% (Figure 4.1). The PC1 explained 67.31% of the total variation of the data matrix and had high correlation among non-stressed yield (Yw), stress tolerance index (STI), geometric mean productivity (GMP) and mean productivity (MP). This dimension can be named as the yield potential-mean productivity component, which separates the high yielding from the low yielding genotypes. Because the angles and the directions between the attribute vectors indicate the strength and the direction of the correlation between two attributes, the biplot displayed indicates that there was significant and positive correlation between stress tolerance index and geometric mean productivity, stress tolerance index and mean University of Ghana http://ugspace.ug.edu.gh 59 productivity, and stressed yield, and stress tolerance index and yield potential. The PC2 explained 24.17% of the total variation and had positive correlation with stressed yield (Ys), tolerance index (TOL), and stress susceptibility index (SSI). Thus, this dimension can be called stress tolerance dimension and it separates stress tolerant from stress susceptible genotypes. In relation to the two components of the biplot, the genotypes fell into distinct clusters that corresponded to their yield potentials and stress-tolerance. The stress tolerant attributes STI, GMP, MP and Yw were correlated with Djouroum local, KVx404-8-1, IT98K-1111-1, Gorom, Local, CB27, IT93K-693- 2, Mouride, and KVx61-1 which represent the group of higher yielding and stress tolerant genotypes. The stress tolerant attributes SSI and TOL were correlated with high yielding and stress susceptible genotypes such as KVx396-4-5-2D, Melakh, Apagbala, KVx775-33-2, BulkF7/SR, and IT95K-1479. The genotypes were distributed over the biplot space according to their yielding ability and adaptation to stressed or non-stressed environments. University of Ghana http://ugspace.ug.edu.gh 60 Figure 4.1: Biplot display of mean productivity (MP), geometric mean productivity (GMP), tolerance index (TOL), stress susceptibility index (SSI), stress tolerance index (STI), and yield of 49 cowpea genotypes under stressed (Ys) and non-stressed (Yw) conditions. Group C Group A Group B Group D University of Ghana http://ugspace.ug.edu.gh 61 4.4. Discussion Genetic variability is essential for the establishment of a breeding program in any crop. In this study, the genetic variation for yield, yield components, days to 50% flowering, and days to 95% maturity was detected under water-stressed and well-watered conditions indicating that improvement can be achieved using such germplasm. The strong and positive correlation between grain yield and pod yield suggested that yield improvement could be achieved by selecting genotypes based on the pod yield. These findings are in agreement with those of (Chiulele, 2010) who indicated that there were strong and positive correlations between grain yield and number of pods per plant and concluded that improvement could be achieved by using such germplasm. High yielding genotypes produced three times more than low yielding genotypes in normal conditions but under stressed conditions some low yielding remained stable across environment than some high yielding genotypes that yielded low. Because of that, (Richards, 2006) suggested that selection for yield is more efficient under stressed conditions than under non-stressed conditions. The stress intensity applied to this study was around 38%. This kind of stress is considered as moderate stress intensity. The genotypes tested reacted differently to water stress indicating the existence of genetic variability for drought tolerance amongst the tested germplasm. For example, the yield of genotypes like KVx396-4-5-2D, Iron Clay, IT98K-317-2, UC-524B, F8/SR, IT99K-573-2-1, and IT93K-503-1 was severely reduced by the imposed stress while that of KVx745-11P, KVx525, KVx414-22-2, Pobe local, KVx421-2J, Djouroum local, CB27, UCR-P-24, IT97K-207-15, IT93K-693-2, IT98K-1111-1, and KVx780-3 was less affected. The reduction in grain yield is in general linked with reduction in pod yield. This is in agreement with the findings of Turk et al. University of Ghana http://ugspace.ug.edu.gh 62 (1980) who reported that the reduction in grain yield of cowpea was a result of reduction in number of pods and seed weight due to detrimental effects of drought on pod set and grain filling. Likewise, Chiulele (2010) found that the reduction in yield was a result of reduction in number of pods per plant. As reported by Chiulele (2010), the difference in response of cowpea genotypes to drought is not surprising since the tested germplasm consisted of genotypes adapted to different growing conditions including the dry areas with high temperature of the Sahel and semi-arid Africa, and hot areas of California in the USA. Bahar and Yildirim (2010) reported that plants are most prone to damage due to limited water during flowering and pod setting stages. However, in this study hundred seed weight was less affected and most of the genotypes showed high hundred seed weight under stressed conditions and lower hundred seed weight under non-stressed conditions. Romanus et al. (2008) reported that additive gene action was more important than non-additive gene action for yield, number of seeds per pod, pod length, hundred seed weight and days to flowering. This implies that hundred seed weight passed from parents to offspring and the trait is less affected by environment. These results suggested that genetic improvement of cowpea yield using hundred seed weight as indirect selection criteria would be possible and it could be predicted based on performance of the parents. Nevertheless, results of phenotypic correlations between yield and yield components indicated that only the pods weight would be useful for improving yield since the correlation between yield and pods weight was high and positive. The correlation between stressed and non-stressed yield was 0.45 for the tested genotypes. This correlation is not too large. These results suggested that selecting genotypes based on yield potential would improve yield only under non-stressed environments. These results are in agreement with that of Rosielle and Hamblin (1981) who reported that for most of the yield trials, University of Ghana http://ugspace.ug.edu.gh 63 if the correlation between stressed and non-stressed yield is smaller it indicates that selection for yield potential would only increase yield under non-stressed environments while the selected genotypes would perform poorly under stressed conditions. The result is also consistent with the findings of Chiulele (2010) who reported that, it was better when looking for yield improvement for late maturing genotypes in cowpea to select under non-stressed conditions. Ishiyaku and Aliyu (2013), also reported that seed yield increases by 3.9 kg.ha-1 with every mm increase in rainfall. The correlation among quantitative indices of drought tolerance and stressed and non-stressed yield indicated that stress tolerance index (STI) was correlated with stressed (Ys) and non-stressed (Yw) yield, mean productivity (MP) and geometric mean productivity (GMP) suggesting that selection based on this index would improve both stressed and non-stressed yield. In addition, stress tolerance index (STI) enabled the identification of high yielding and stress tolerant genotypes, suggesting that this index was the best for selecting genotypes for drought tolerance. This statement is consistent with that of Fernandez (1992) showing that yield would be improved under both stressed and non-stressed environments when using stress tolerance index for selection. For Khayatnezhad and Gholamin (2010), the calculated gain from indirect selection from moisture stress environment would improve yield in moisture stress environment better than selection from non-stress environment. These author’s conclusions about the positive correlations among Yw, STI, MP, and GMP are in concordance with the results of this study that showed that significant and positive correlation for Yp and MP, GMP and STI showed that these indices were more effective in identifying high yielding cultivars under different moisture conditions. In wheat selection, Akçura et al. (2011) found that SSI was a useful indicator when the stress is severe while MP, GMP, TOL and STI were useful indicators when the stress is less severe. Looking at the level of the stress intensity (SI= 38%) applied in this study which is moderate, the conclusions of Akçura University of Ghana http://ugspace.ug.edu.gh 64 et al. (2011) can explain the fact that MP, GMP, TOL and STI were useful indicators for the grouping of the germplasm studied. The principal component analysis showed that the PC1 explained most of the variation observed in yield. The PC1 was correlated with non-stressed yield (Yw) and mean productivity (MP) while PC2 was correlated with stress tolerance suggesting that PC1 was a yield potential dimension while the PC2 stress tolerance dimension. Plotting the genotypes over the PC1 and PC2 with quantitative indices of stress tolerance and stressed and non-stressed yield, genotypes were distributed over the coordinate space indicating different drought adaptation and yielding ability. Different clusters of genotypes were identified as described by Fernandez (1992). High yielding and drought tolerant genotypes (yield not significantly reduced by drought) (group A), high yielding and drought susceptible genotypes (reduced by drought) (Group B), low yielding and drought tolerant genotypes (group C) and low yielding and drought susceptible genotypes (group D). Genotypes like Djouroum local, KVx404-8-1, IT98K-1111-1, Gorom, Local, CB27, IT93K-693-2, Mouride, and KVx61-1 were clustered in group A. Genotypes like KVx396-4-5-2D, Melakh, Apagbala, KVx775-33-2, BulkF7/SR, and IT95K-1479 were found in group B. the varieties KVx745-11P, KVx525, KVx414-22-2, Pobe local, KVx421-2J, and IT98K-205-8 were clustered in group C while Bambey 21, Moussa Local, F8/SR, Kaya local, and IT99K-499-39 were in group D. The biplot displays showed a clear indication of a genetic variability for yield under drought conditions for the screened cowpea genotypes suggesting that improvement for yield under drought conditions could be achieved. University of Ghana http://ugspace.ug.edu.gh 65 From the literature reviewed, some genotypes identified as drought-tolerant across countries seem to confirm their status in this study. This is the case of Gorom Local also known as Suvita2 (Muleba et al., 1997; Hamidou et al., 2007; Belko et al., 2012), KVx61-1 (Hamidou et al., 2007; Belko et al., 2012), IT98K-1111-1 identified as type 2 drought-tolerant (Singh and Matsui, 2002). Other authors confirmed their susceptibility to drought. This is the case of Bambey 21 (Hamidou et al., 2007; Sawadogo, 2009), Moussa Local (Sawadogo, 2009). Some genotypes identified as drought-tolerant in other parts of the world and in Burkina Faso were drought susceptible in this study. This is the case of UC-524B, IT99K-499-39, Apagbala (Sawadogo, 2009; Chiulele, 2010). The used of stress tolerance index in the identification of drought tolerant material is efficient. From the indices used the stress tolerance index (STI) is the best. It helps in cutting the population into groups. 4.5. Conclusion The objective of this study was to identify cowpea genotypes tolerant to drought based on quantitative stress indices and yields under stressed and non-stressed conditions. Based on the results obtained the following conclusion could be drawn: (i) Genotypic variability for drought tolerance existed amongst the tested genotypes (ii) From the biplot displays of yields and quantitative indices for stress tolerance, four clusters of genotypes were identified based on yielding ability and drought tolerance; high yielding and drought tolerant genotypes were in group A, high yielding and drought susceptible genotypes in group B, low yielding and drought tolerant genotypes were in group C and low yielding and drought susceptible genotypes in group D. University of Ghana http://ugspace.ug.edu.gh 66 (iii) Genotypes in group A were the best by combining high yield and tolerance to drought. (iv) Amongst the quantitative indices of drought tolerance, stress tolerance index (STI) was the best because it enabled the identification of group A genotypes. (v) The pod yield was strongly and positively correlated with yield. In general, drought tolerant genotypes did show high reduction in pod yield between normal and stress imposed conditions in the high yielding genotypes than the low yielding genotypes. (vi) Some genotypes already identified as drought tolerant confirmed their tolerance status. Examples of such varieties are Gorom Local, KVx61-1, and IT98K-1111-1. (vii) New drought tolerant genotypes were identified. Examples of these genotypes are Djouroum local, Pobe local, KVx404-8-1, KVx745-11P, KVx525, KVx414-22-2, and KVx421-2J. Of these, KVx745-11P, KVx525, KVx414-22-2, and KVx421-2J are low yielding genotypes but could be used in breeding program to improve high yielding drought-susceptible genotypes. University of Ghana http://ugspace.ug.edu.gh 67 CHAPTER FIVE 5.0. SNP-BASED GENETIC DIVERSITY ASSESSMENT IN A SET OF COWPEA GERMPLASM 5.1. Introduction Despite considerable phenotypic diversity that exists in cultivated cowpea germplasm, there is limited genetic variability in cowpea breeding programs (Pasquet, 1999, 2000). Breeding programs, which must focus most of their efforts on rapid delivery of varieties with a specific range of production and quality traits, tend to cross and re-cross cultivars containing these production and quality traits, and many of them are related to some degree. This leads to reduced genetic variability among cultivars that are released and among advanced breeding lines in the program, both of which are likely to be used as parents in new breeding cycles (Fang et al., 2007). The lack diversity is a special concern because cowpea appears to have lower inherent genetic diversity than other cultivated crops as a result of a hypothesized single domestication event (Pasquet, 1999, 2000). Markers based on single nucleotide polymorphisms (SNPs) have rapidly gained the center stage of molecular genetics during the recent years due to their abundance in the genomes and their amenability for high-throughput detection formats and platforms (Mammadov et al., 2012). Of these available platforms, there is the LGC genomics’ Kompetitive Allele Specific PCR (KASP) combined with the SNP line platforms in United Kingdom. SNP markers are increasingly being used for a large number of genetic studies including genetic diversities. Such studies have been University of Ghana http://ugspace.ug.edu.gh 68 reported in pea (Deulvot et al., 2010), cowpea (Huynh et al., 2013; Egbadzor et al., 2014), and cassava (Thompson, 2013). SNPs provide the simplest form of molecular markers as a single nucleotide base is the smallest unit of inheritance, and therefore, they can provide a large number of markers to be used in diversities or in marker assisted breeding. SNPs are co-dominant markers and they are most often linked to genes, and thus, they are the most attractive genetic markers in genetic studies (Jiang, 2013). The use of these markers could therefore help group germplasm for the use of breeding. SNP markers also help in decision making when knowing the variability within the germplasm. Available breeding materials should be well known and described in any breeding programme for any crop for better exploitation of the potential variability. To do that, morphological, biochemical, and molecular characterization can be used. An important cowpea genetic materials exists in Burkina Faso cowpea breeding programme but, no in deep investigation has been made to establish the variability using molecular markers. Therefore, the objective of this study was to assess the genetic diversity in the set of cowpea germplasm using SNP markers. University of Ghana http://ugspace.ug.edu.gh 69 5.2. Materials and Methods 5.2.1. Cowpea genotypes The same 50 cowpea genotypes used in field screening of cowpea for drought tolerance (chapter four) were used for genetic diversity study using SNP markers. The origin and seed coat color of the material have been described in (Table 4.1). 5.2.2. SNP genotyping Leaf samples of two weeks old plants were collected in a 96 wells plate and sent to LGC genomics in the United Kingdom for DNA extraction and SNP genotyping. The KASP technology as described by Thompson (2013) was used for the genotyping at LGC genomics. The DNA was extracted using LGC genomics internal protocol described in Appendix 4. One hundred and eighty one (181) SNP markers selected from the Generation Challenge Programme (GCP) platform were used (Appendix 1). After excluding the SNPs that were not informative enough (more than 10% missing data) a total of 170 markers and 47 cowpea lines were used for further analysis. 5.2.3. Analysis of genetic diversity Pair-wise genetic distances between genotypes were measured with the software GGT 2.0 (Van Berloo, 2008) based on the allele-sharing method (Bowcock et al., 1994). The simple matching algorithm considers both presence and absence of markers in calculating degrees of similarity. Phylogenetic relationships dendrogram were generated based on the genetic-distance matrix using the unweighted pair group method (UPGMA) with the software MEGA 6.0 (Tamura et al., 2013). University of Ghana http://ugspace.ug.edu.gh 70 Descriptive statistics like polymorphism information content (PIC) value, major allele frequency (MAF), and expected heterozygosity (He) were calculated for all the SNPs using PowerMarker 3.25 software (Lui and Muse, 2005). A core collection of genotypes was generated from GGT2.0 software based on the maximum diversity sum. 5.3. Results 5.3.1. Descriptive statistics The summary statistics for major allele frequencies (MAF), expected heterozygosity (He), and polymorphic information content (PIC) is presented in Table 5.1. A low expected heterozygosity (0.08) was observed with the SNP marker (1_0992) that has the high major allele frequency (0.96). The mean of the expected heterozygosity was 0.41 and that of the major allele frequency was 0.68. The allele frequencies of all the SNP markers were greater than their corresponding expected heterozygosity values. The allele frequencies of all the markers were below 0.95 except 1_0992 (0.96), indicating the polymorphic nature of the SNP markers used. The PIC values ranged from 0.08 (1_0992) to 0.38 with an average of 0.32. Out of the 181 SNPs used 4 did not amplify. 177 SNPs were successful and out of that, 170 were useful representing 96.04% of the total. 103 SNPs were the most informative markers with a PIC value greater than the mean which represents 60.59% of the useful SNPs. Out of the 103 SNPs seven have a PIC of 0.38, 40 a PIC of 0.37, 26 a PIC of 0.36, 13 a PIC of 0.35, nine a PIC 0.34, and eight a PIC of 0.33. The seven most informative markers are 1_0126, 1_0351, 1_0362, 1_0594, 1_1130, 1_1367, and 1_1393. University of Ghana http://ugspace.ug.edu.gh 71 Table 5.1: Summary statistics of genetic variation using 170 SNP markers among 47 cowpea lines Marker MAF Avail He PIC Marker MAF Avail He PIC 1_0126 0.50 0.94 0.50 0.38 1_1021 0.57 0.94 0.49 0.37 1_0351 0.50 0.98 0.50 0.38 1_1371 0.57 1.00 0.49 0.37 1_0362 0.50 0.98 0.50 0.38 1_0136 0.58 0.96 0.49 0.37 1_0594 0.50 0.94 0.50 0.38 1_0923 0.58 0.96 0.49 0.37 1_1130 0.50 0.94 0.50 0.38 1_0993 0.58 0.96 0.49 0.37 1_1367 0.50 0.98 0.50 0.38 1_1038 0.58 0.96 0.49 0.37 1_1393 0.50 0.94 0.50 0.38 1_0259 0.59 0.98 0.48 0.37 1_0531 0.51 1.00 0.50 0.37 1_1117 0.59 0.98 0.48 0.37 1_0605 0.51 1.00 0.50 0.37 1_1189 0.59 0.98 0.48 0.37 1_0123 0.51 0.96 0.50 0.37 1_0987 0.59 0.94 0.48 0.37 1_0771 0.51 0.96 0.50 0.37 1_0127 0.60 1.00 0.48 0.37 1_1467 0.51 0.96 0.50 0.37 1_0388 0.60 1.00 0.48 0.37 1_0183 0.52 0.98 0.50 0.37 1_0449 0.60 1.00 0.48 0.37 1_1007 0.52 0.98 0.50 0.37 1_0401 0.60 0.96 0.48 0.36 1_0001 0.52 0.94 0.50 0.37 1_0752 0.60 0.96 0.48 0.36 1_0982 0.52 0.94 0.50 0.37 1_0806 0.60 0.96 0.48 0.36 1_1141 0.52 0.94 0.50 0.37 1_1135 0.60 0.96 0.48 0.36 1_0905 0.53 1.00 0.50 0.37 1_0052 0.61 0.98 0.48 0.36 1_0604 0.53 0.96 0.50 0.37 1_0377 0.61 0.98 0.48 0.36 1_0425 0.54 0.98 0.50 0.37 1_0397 0.61 0.98 0.48 0.36 1_0565 0.54 0.98 0.50 0.37 1_0657 0.61 0.98 0.48 0.36 1_1072 0.54 0.98 0.50 0.37 1_0670 0.61 0.98 0.48 0.36 1_0081 0.55 0.94 0.50 0.37 1_0437 0.61 0.94 0.47 0.36 1_0146 0.55 0.94 0.50 0.37 1_1360 0.61 0.94 0.47 0.36 1_0153 0.55 0.94 0.50 0.37 1_0025 0.62 1.00 0.47 0.36 1_0056 0.55 1.00 0.49 0.37 1_0945 0.62 1.00 0.47 0.36 1_1103 0.56 0.96 0.49 0.37 1_1512 0.62 1.00 0.47 0.36 1_0058 0.57 0.98 0.49 0.37 1_0917 0.62 0.96 0.47 0.36 1_0062 0.57 0.98 0.49 0.37 1_0567 0.63 0.98 0.47 0.36 1_0525 0.57 0.98 0.49 0.37 1_0652 0.63 0.98 0.47 0.36 1_0690 0.57 0.98 0.49 0.37 1_0706 0.63 0.98 0.47 0.36 MAF: major allele frequency; Avail: allele availability; He: Expected Heterozogosity; PIC: polymorphic information content University of Ghana http://ugspace.ug.edu.gh 72 Table 5.1: Summary statistics of genetic variation using 170 SNP markers among 47 cowpea lines (continued) Marker MAF Avail He PIC Marker MAF Avail He PIC 1_1214 0.57 0.98 0.49 0.37 1_0937 0.63 0.98 0.47 0.36 1_1246 0.57 0.98 0.49 0.37 1_0977 0.63 0.98 0.47 0.36 1_1431 0.57 0.98 0.49 0.37 1_1096 0.63 0.98 0.47 0.36 1_1129 0.63 0.98 0.47 0.36 1_0022 0.70 0.91 0.42 0.33 1_1370 0.63 0.98 0.47 0.36 1_0746 0.70 0.91 0.42 0.33 1_0256 0.64 0.94 0.46 0.36 1_0807 0.70 1.00 0.42 0.33 1_0319 0.64 0.94 0.46 0.36 1_0647 0.71 0.96 0.41 0.33 1_1151 0.64 0.94 0.46 0.36 1_0709 0.71 0.96 0.41 0.33 1_0699 0.64 0.96 0.46 0.35 1_0392 0.72 0.98 0.41 0.32 1_0290 0.65 0.98 0.45 0.35 1_0755 0.72 0.98 0.41 0.32 1_0823 0.65 0.98 0.45 0.35 1_0853 0.72 0.98 0.41 0.32 1_0246 0.66 0.94 0.45 0.35 1_0242 0.72 1.00 0.40 0.32 1_0317 0.66 0.94 0.45 0.35 1_0957 0.72 1.00 0.40 0.32 1_0757 0.66 0.94 0.45 0.35 1_0142 0.73 0.96 0.39 0.31 1_0482 0.66 1.00 0.45 0.35 1_0775 0.73 0.96 0.39 0.31 1_0730 0.66 1.00 0.45 0.35 1_0983 0.73 0.96 0.39 0.31 1_1271 0.66 1.00 0.45 0.35 1_0107 0.74 0.98 0.39 0.31 1_0033 0.67 0.96 0.44 0.35 1_0330 0.74 0.98 0.39 0.31 1_0065 0.67 0.96 0.44 0.35 1_0529 0.74 0.98 0.39 0.31 1_0306 0.67 0.96 0.44 0.35 1_0679 0.74 0.98 0.39 0.31 1_0649 0.67 0.96 0.44 0.35 1_1281 0.74 0.98 0.39 0.31 1_0438 0.67 0.98 0.44 0.34 1_0060 0.74 1.00 0.38 0.31 1_0473 0.67 0.98 0.44 0.34 1_0238 0.76 0.96 0.37 0.30 1_0834 0.67 0.98 0.44 0.34 1_0451 0.76 0.96 0.37 0.30 1_1037 0.67 0.98 0.44 0.34 1_0583 0.76 0.96 0.37 0.30 1_1042 0.67 0.98 0.44 0.34 1_0053 0.76 0.98 0.36 0.30 1_1062 0.67 0.98 0.44 0.34 1_0323 0.76 0.98 0.36 0.30 1_1520 0.68 1.00 0.43 0.34 1_0740 0.76 0.98 0.36 0.30 1_0322 0.68 0.94 0.43 0.34 1_0876 0.76 0.98 0.36 0.30 1_0911 0.69 0.96 0.43 0.34 1_1087 0.76 0.98 0.36 0.30 1_0111 0.70 0.98 0.42 0.33 1_1170 0.76 0.98 0.36 0.30 1_0157 0.70 0.98 0.42 0.33 1_0128 0.77 1.00 0.36 0.29 1_0370 0.70 0.98 0.42 0.33 1_0663 0.77 1.00 0.36 0.29 MAF: major allele frequency; Avail: allele availability; He: Expected Heterozogosity; PIC: polymorphic information content. University of Ghana http://ugspace.ug.edu.gh 73 Table 5.1: Summary statistics of genetic variation using 170 SNP markers among 47 cowpea lines (continued) Marker MAF Avail He PIC Marker MAF Avail He PIC 1_0082 0.77 0.91 0.36 0.29 1_0866 0.83 0.98 0.29 0.25 1_0105 0.77 0.94 0.35 0.29 1_1092 0.83 0.98 0.29 0.25 1_1333 0.77 0.94 0.35 0.29 1_0074 0.83 1.00 0.28 0.24 1_0171 0.78 0.96 0.35 0.29 1_0262 0.83 1.00 0.28 0.24 1_1073 0.78 0.96 0.35 0.29 1_1039 0.84 0.91 0.27 0.24 1_1157 0.78 0.96 0.35 0.29 1_0067 0.85 0.98 0.26 0.22 1_0139 0.78 0.98 0.34 0.28 1_0703 0.85 0.98 0.26 0.22 1_0510 0.78 0.98 0.34 0.28 1_0878 0.85 0.98 0.26 0.22 1_0718 0.78 0.98 0.34 0.28 1_0432 0.87 0.96 0.23 0.20 1_0889 0.78 0.98 0.34 0.28 1_0420 0.87 0.98 0.23 0.20 1_1255 0.78 0.98 0.34 0.28 1_0588 0.87 0.98 0.23 0.20 1_0514 0.79 1.00 0.33 0.28 1_0754 0.87 1.00 0.22 0.20 1_1517 0.80 0.96 0.32 0.27 1_1492 0.87 1.00 0.22 0.20 1_0773 0.80 0.98 0.31 0.27 1_0732 0.88 0.91 0.21 0.18 1_0801 0.80 0.98 0.31 0.27 1_0678 0.89 0.98 0.19 0.17 1_1121 0.80 0.98 0.31 0.27 1_1249 0.91 0.96 0.16 0.15 1_0280 0.81 1.00 0.31 0.26 1_0421 0.91 0.98 0.16 0.15 1_0691 0.81 0.91 0.30 0.26 1_0539 0.91 0.98 0.16 0.15 1_0014 0.83 0.98 0.29 0.25 1_1217 0.93 0.98 0.12 0.11 1_0436 0.83 0.98 0.29 0.25 1_0992 0.96 0.98 0.08 0.08 1_0519 0.83 0.98 0.29 0.25 1_0625 0.83 0.98 0.29 0.25 Mean 0.68 0.97 0.41 0.32 MAF: major allele frequency; Avail: allele availability; He: Expected Heterozogosity; PIC: polymorphic information content 5.3.2. Core collection of cowpea germplasm Twenty cowpea genotypes forming a core collection is presented in Table 5.2. This collection comprises mainly improved varieties from Burkina Faso (15) followed by improved varieties from International Institute of Tropical Agriculture (IITA) in Nigeria (IITA/Nigeria) (three), one line from Niger and one from Senegal. University of Ghana http://ugspace.ug.edu.gh 74 Table 5.2: Core collection of cowpea germplasm Genotypes Origin MOURIDE Senegal KVX525 Burkina Faso KVX396-4-5-2D Burkina Faso KVX780-3 Burkina Faso KVX780-4 Burkina Faso IRON CLAY IITA/Nigeria KVX30-309-6G Burkina Faso KVX61-1 Burkina Faso TN88-63 Niger KVX404-8-1 Burkina Faso KVX780-6 Burkina Faso IT98K-317-2 IITA/Nigeria F8_SR Burkina Faso BULKF7_SR Burkina Faso KVX771-10 Burkina Faso KVX775-33-2 Burkina Faso KVX421-2J Burkina Faso KOMSARE Burkina Faso IT99K-499-39 IITA/Nigeria KVX414-22-2 Burkina Faso 5.3.3. Phylogenetic relationships between cowpea lines The cowpea lines were grouped into seven clusters based on genetic distance based on the allele sharing similarity. The cluster analysis showed that lines generally grouped together according to their geographical origin and traditional genetic background (Figure 5.1). Cluster VII and IV, can be considered as outliers as they contained only one line (Mouride, IT86D-610). Cluster I consisted of 16 genotypes, cluster II had six lines, Cluster III had 14 lines, Cluster V contains seven lines, and cluster VI has two lines. United State and Burkina Faso landraces respectively fell into clusters II (US) and V (BF2Loc) while the improved varieties were all in cluster III (BF1). The genetic University of Ghana http://ugspace.ug.edu.gh 75 material from IITA fell into two main clusters I (IITA1) and VI (IITA3) with slight mixture of some improved varieties from Burkina, Senegal, and Ghana. University of Ghana http://ugspace.ug.edu.gh 76 Figure 5.1: UPGMA dendrogram of 47 cowpea genotypes constructed using 170 SNP markers University of Ghana http://ugspace.ug.edu.gh 77 5.4. Discussion In the present study one hundred and seventy (170) SNP markers were used to genotype forty seven (47) cowpea lines. The results showed a good level of polymorphism but a moderate level of diversity based on the average polymorphic information content values (0.32). Almost all of the 47 lines shared a very narrow genetic distance (≤0.29), which is consistent with the results reported by Li et al. (2001). Moreover, the markers enabled the grouping of lines based on their similarity. Likewise, the SNP markers were able to associate more or less the cluster to the geographical origin of the line. Breeding programs generally work within restricted pools of genetic variation (Huynh et al., 2013) and might be the cause this narrow genetic diversity observe in this study. A number of authors have come to the conclusion that cowpea lacks significant variability (Pasquet, 1999, 2000; Fang et al., 2007). Narrow genetic base has been also observed within different lines from breeding programs (Li et al., 2001). The materials from IITA collection have been widely used by different breeding programs in different countries. This can explain the relatedness between some cowpea improved varieties from Burkina Faso (KVx745-11P, KN1, KVx780-6, and KVx61-1). Looking at also the pedigree of Melakh (IS86-292xIT83S-742-13) (Diouf and Hilu, 2005), it becomes easy to confirm that this line fell into the IITA varieties cluster because of its relatedness with line from the IITA breeding program. Huynh et al. (2013) provided some useful assumptions that tend to explain the reduction of the genetic distance between cowpea wild types, landraces, and improved germplasm within African germplasm accessions and among African and Non-African germplasm accessions. These authors concluded that the small genetic differentiation observed between the African and non-African collections indicated that the entire genetic diversity in the African germplasm might already have spread over cowpea-growing regions in the world as a whole although not completely within any single region. Nevertheless, University of Ghana http://ugspace.ug.edu.gh 78 the clustering of these forty seven lines into seven distinct groups gives important insights that can improve the efficiency of germplasm used in cowpea for breeding purposes. Except the material from Senegal (Bambey in cluster II, Mouride in cluster VII, 58-57 in cluster III, and Melakh in cluster I), from Niger (TN88-63 in cluster III), and from Ghana (Apaagbala in cluster I) that were not grouped according to their geographical origin, the rest were clustered in a country basis. That could be helpful for new ways of genetic improvement of cowpea by exchanging material from different countries to broaden the genetic base of the crop. In contrast with these findings, a numbers of genetic diversity studies conducted on cowpea have reported absence of correlation between geographical origin of the accessions and their clustering pattern (Asare et al., 2010; Egbadzor et al., 2014). This was also observed in a genetic diversity study in maize using SSR markers (Oppong, 2013). In this study, the genotypes were clustering following a regional basis of maize cultivation in Ghana. The differences shown between Burkina Faso landraces and the improved varieties may also be useful as a little diversity still exists among the local germplasm for new variety development. SNP markers have demonstrated their capacity in assessing genetic diversity in cowpea (Huynh et al., 2013; Egbadzor et al., 2014). Varshney et al. (2007) reported on the robustness of SNP markers. As compared to SSR markers, SNPs are more robust as they are able to detect slight changes in the genome and discriminate genotypes. This assumption is confirmed by the findings from a genetic diversity study on sweet cherry (Prunus avium, L.) (Fernandez I Marti et al., 2012). In this study SNP markers were able to discriminate mutants from their original parents than SSR markers. In addition, SNP markers confirmed parentage and also determined relationships of the accessions in a manner consistent with their pedigree relationships. The latter statement confirmed our findings. Lines like Melakh from Senegal, KVx745-11P from University of Ghana http://ugspace.ug.edu.gh 79 Burkina Faso grouped with the IITA accessions because of the large contribution in their genome of materials from IITA. Extension of gene pool is important for crop improvement (Varshney et al., 2007). As such a core collection of 20 lines was proposed from this study based on the maximum diversity between them. Several genetic diversity studies have been conducted in cowpea (Pamella and Gepts, 1992; Vaillancourt and Weeden, 1992; Fotso et al., 1994; Coulibaly et al., 2002; Ba et al., 2004). Despite of the presence of little diversity within the collection used for this study and the core collection, the separation of the broader germplasm of cowpea landraces into gene pools as done by Huynh et al. (2013) could be useful for expanding the genetic diversity within breeding materials and could lead to development of more efficient strategies and genetic gain within future breeding programs. 5.5. Conclusion The present study was undertaken to determine the genetic variability in a set of germplasm used by INERA cowpea breeding program in Burkina Faso using SNP markers. The germplasm used has some moderate variability with narrow genetic base. These results were comparable to previous studies that have also reported the narrow genetic base of cowpea. The phylogenetic patterns and clustering of relatively similar individuals into groups providing important information on the germplasm used for cowpea improvement. The materials grouped based on the geographic origin and the genotypic background. Materials from United University of Ghana http://ugspace.ug.edu.gh 80 State/University of California Riverside clustered together. Likewise, materials from IITA/Nigeria, Burkina Faso clustered in country base. SNP markers were able to group the genotypes in a way that they could be used to link the genotype clusters and their pedigree. A panel of 20 genotypes representing the maximum variability of the germplasm used in the study was generated based on the maximum diversity sum. This panel constituted a core collection could be together with the information on the clustering of great importance for further plant breeding to develop superior varieties of cowpea. University of Ghana http://ugspace.ug.edu.gh 81 CHAPTER SIX 6.0. QTL INTROGRESSION FOR DROUGHT TOLERANCE IN COWPEA 6.1. Introduction Backcrossing is a traditional breeding method commonly employed to transfer alleles at one or more loci from a donor to an elite variety (Allard, 1960). This method can take a lot of time before recovering the background of the elite cultivar. According to Semagn et al.(2006), the recovery of 99.2% of the elite cultivar could take at least six generations when there is no deviation. It is time and labor consuming. The molecular breeding technology allows transferring target genomic regions resulting in extensive genetic mapping experiments aiming at the development of molecular markers for marker assisted backcrossing (MABC) and marker-assisted selection (MAS) (Semagn et al., 2006). This technology may be defined in a broad-sense as the use of genetic manipulation performed on DNA at molecular levels to improve characters of interest in plants and animals, including genetic engineering or gene manipulation, molecular marker-assisted selection, genomic selection (Jiang, 2013). During the past two decades, the technology development has opened a way for gene transfer using molecular markers. As a result of that many studies were carried out in order to exploit the technology in crop improvement. The use of MAS for introgression of major quantitatively inherited trait loci for stress tolerance is increasingly being applied in crop improvement. MAS has been used in selection for drought tolerance in maize (Ribaut and Ragot, 2006), pearl millet University of Ghana http://ugspace.ug.edu.gh 82 (Howarth and Yadav, 2002), and rice (Courtois and Lafitte, 2003). Howard and Yadav (2002) have also reported a successful introgression of disease resistance using MAS. SNP markers derived from specific DNA sequences quantitative trait loci (QTLs) are cheaper and more useful for marker-assisted selection (MAS) and have become the marker of choice when high-throughput genotyping assays have been developed. It is an approach that has been developed to avoid problems resulting from conventional plant breeding by changing the selection criteria from selection of phenotypes towards selection of genes that control traits of interest, either directly or indirectly. However, the technology is sometimes not accessible. Until recently, cowpea was an orphan in the molecular domain. Recent development in the area moved cowpea from orphan to one the most important in terms of markers availability and uses. In terms of availability there is a physical map (Http://Phymap.Ucdavis.Edu/Cowpea/, 2013) with a density in average of 0.6 cM and no gap > 4 cM marker density. In many cases it will be possible to identify flanking markers that to be used in drought tolerance QTLs introgression with minimum linkage drag. These advances in plant molecular genetics have provided plant breeders with powerful tools to identify and select Mendelian components underlying both simple and complex agronomic traits (Ribaut and Hoisington, 1998). In terms of uses, the SNP markers available have extensively been used in diversity studies (Huynh et al., 2013) and to identify drought tolerant QTLs (Muchero et al., 2008 and Muchero et al., 2009b). This study was therefore undertaken to make use of the MABC method to introgress drought-tolerant (yield under drought and stay green) QTLs from two IITA cowpea varieties into Moussa local, a farmer-preferred variety using SNP markers. University of Ghana http://ugspace.ug.edu.gh 83 6.2. Materials and methods 6.2.1. Leaf sampling procedure The kit for leaf sampling was provided by LGC Genomics Ltd. (Hoddesdon, UK), including: 1. 1× 96‐well tube storage rack (the tubes can be removed from the rack if needed) with lid. 2. 1× perforated, gas permeable, heat‐sealable film seal. 3. 1× 50g desiccant pack in a small sealed bag that should be kept in the sealed bag until use .as they will rapidly absorb moisture when exposed to the air. 4. 1× heavy‐duty, sealable bag into which to return the samples. 5. Harris Uni‐Core leaf cutting tool for cutting 6mm leaf discs. 6. Harris self‐healing cutting mat for use with Harris Uni‐Core cutting tool. The sampling kit is designed to facilitate both the cutting of leaf discs and their transport and concomitant desiccation, for eventual DNA isolation. For each sample, four leaf discs of 6mm diameter were cut using the Harris Uni‐Core leaf cutting tool supported by the Harris self‐healing cutting mat and placed into a well of a storage plate. The plate was sealed with a perforated (gas‐ permeable) heat seal by applying a medium hot household iron to the top of the seal for about 2 seconds. The sealed plate was then, placed in a heavy‐duty, sealed bag in the presence of a desiccant to dehydrate and hence preserve the leaf tissue during transit at ambient temperature. Decontamination of the leaf cutting tool should be carried out between sampling different plants to eliminate the possibility of cross‐contamination. This is achieved using 70% ethanol or 2% NaClO (sodium hypochlorite). University of Ghana http://ugspace.ug.edu.gh 84 For this study, leave from two weeks old plants sown in pot under greenhouse were collected using the kit and sent to LGC Genomics. The samples were collected from the fully expand trifoliate leaves of the two weeks old plants. The samples were then put in a 96 well plate and map of the plate was made to help identified each sample. 6.2.2. DNA extraction The leave samples were sent to LGC Genomics for DNA extraction and SNP genotyping using an internal protocol described in Appendix 4. The SNP genotyping followed the KASP technology system as described by Thompson (2013). 6.2.3. Selection of markers A set of 184 SNP markers spanning every 2-cM internals was selected for this study using the BreedIt© SNP Selector tool (Http://Breedit.Org/, 2014) developed by the cowpea team at University of California Riverside (UCR). The SNP_Selector provides an interface to generate customized lists of SNPs based on cM distance between markers genome-wide, and between markers at known trait positions. The installer generates a folder C:\BreedIt\SNP_Selector\ and place three Excel spreadsheets inside this folder: (1) the master SNP spreadsheet including 1536 cowpea SNPs from the Illumina Golden Gate assay and the genotypes of some cowpea parental lines, (2) the SNPs that work well with the KASP platform (LGC Genomics), and (3) the markers linked to traits of interest. University of Ghana http://ugspace.ug.edu.gh 85 All SNP markers were developed from EST of drought-stressed tissues, so there is a chance the markers are associated with drought tolerance candidate genes (Muchero et al., 2009b). The interface of the SNP_selector is shown in Figure 6.1. Figure 6.1: The interface of the SNP_selector tool 6.2.4. Plant materials and QTL introgression procedures Two drought-tolerant lines from IITA (IT93K-503-1 and IT97K-499-35) that were found to be drought tolerant in Burkina Faso (Sawadogo, 2009) and in which drought-tolerant QTL have been discovered and mapped (Muchero et al., 2008; Muchero et al., 2009a; Muchero et al., 2009b; Muchero et al., 2010; Muchero et al., 2011) were used as donors of positive drought QTLs, striga and nematode resistance genes. Moussa local that is a locally farmer preferred variety from University of Ghana http://ugspace.ug.edu.gh 86 Burkina Faso was used as a recurrent parent. The donor alleles for yield, and nematode resistance were selected based on results from the UCR/INERA on-going and collaborative projects. The donor alleles for Striga were selected based on synteny with the Striga locus reported in Ouedraogo et al. (2002). Table 6.1 shows the position of trait-linked markers on cowpea consensus genetic map related to the QTLs introgressed in this study. Table 6.1: Position of trait-linked markers on cowpea consensus genetic map Trait Marker LG cM Donor Nematode 1_1170 3 28.568 IT93K-503-1 Yield, Stay green 1_0678 4 25.390 IT93K-503-1 Yield, Stay green 1_0128 4 27.408 IT93K-503-1 Yield, Stay green 1_0157 4 30.339 IT93K-503-1 Yield, Stay green 1_0992 4 33.146 IT93K-503-1 Yield 1_0022 8 7.935 IT97K-499-35 Yield 1_1370 8 9.173 IT97K-499-35 Yield 1_0567 8 19.501 IT97K-499-35 Striga 1_0583 10 50.534 IT97K-499-35 For the MABC scheme, IT93K-503-1 and IT97K-499-35 were crossed to Moussa local to obtain F1 progenies. The F1s were backcrossed to Moussa local to obtain 95 BC1F1 seeds for each recurrent-donor combination. The BC1F1 seeds were planted in boxes in the greenhouse. Two weeks after planting, leaf samples were collected from each plant and sent to LCG Genomics for genotyping with the 184 SNPs. This allowed the selection of BC1F1 individual plants that were heterozygous for SNPs associated with drought tolerance, Striga and/or nematode resistance (foreground selection SNPs) and carried as many recurrent-parent alleles as possible at other SNP loci (background selection SNPs). The selected BC1F1 individual plants were backcrossed with Moussa local to obtain 95 BC2F1 individuals for another round of genotyping. In the BC2F1 University of Ghana http://ugspace.ug.edu.gh 87 generation, the individual plants that were heterozygous for foreground SNPs and carried as many recurrent-parent alleles as possible at background SNPs were identified. In the next cycle, each of the selected BC2F1 individual plants was backcrossed to Moussa local to create BC3F1 lines. Four BC3F1 individuals from the cross Moussa local/IT97K-499-35 and three BC3F1 individuals from the cross Moussa local/IT93K-503-1 were selfed to obtain about forty BC3F2 seeds per line. Seed from BC3F2 were used for morphological characterization of the families and yield performance estimation. Ten entries (six MABC lines and four checks) were planted using a randomized complete block design with two replications and two water regimes (water-stressed WS, and well-watered WW). Two rows of 4 m with row spacing of 0.2 m between plants and 0.8 m between rows constituted the plot size per line. The checks comprised the three lines involved in the introgression process (IT97K-499-35, IT93K-503-1, and Moussa local) and one drought tolerant variety (Gorom local). The trial was conducted during the 2014 off-season from April to June under a drip-water irrigation system at the Kamboinse research station. Watering was withheld in the stressed block at 50% flower bud initiation till harvest. 6.3. Results 6.3.1. QTLs introgression Genotyping of the BC1F1 identified the plant named M503_BC1F1_31 carrying the donor IT93K- 503-1 alleles for yield and stay green under drought, Striga, and nematode resistance, and about 67% of variety Moussa local alleles at background markers. In the other cross, the plant named M499_BC1F1_04 carried the donor IT97K-499-35 alleles for yield under drought and Striga resistance, and about 70% of Moussa local alleles at background markers. In addition, some other University of Ghana http://ugspace.ug.edu.gh 88 BC1F1 plants (M499_BC1F1_49, M499_BC1F1_48, M499_BC1F1_44, M503_BC1F1_54 and M503_BC1F1_92) carrying donor alleles but less of Moussa local background than M503_BC1F1_31 and M499_BC1F1_04 were also selected for backcrossing to Moussa local. In total, 190 individuals were obtained from the BC2 backcrosses. The genotyping of these BC2F1 plants identified 10 individuals carrying different combinations of donor IT93K-503-1 alleles and 80–97% of Moussa local background (Table 6.2). Three selected plants with highest Moussa local background (M503_BC2F1_54P15, M503_BC2F1_54P8, and M503_BC1F2_92P27) were backcrossed to Moussa local to generate the M503_BC3F1 families. Likewise, 21 plants families were selected in the BC2F1 population; they carried different combinations of donor alleles for yield and Striga resistance and 69 – 93% of Moussa local background (Table 6.3). Five selected plants with highest Moussa local background (M499_BC2F1_48P90, M499_BC2F1_44P19, M499_BC2F1_48P93, M499_BC2F1_48P85, and M499_BC2F1_4P67) backcrossed to Moussa local generated the BC3F1 families. Of the total of six families derived from two donors were retained and selfed (five M499_BC3F2s and one M503_BC3F2s) enabled seed increase seed for further studies. University of Ghana http://ugspace.ug.edu.gh 89 Table 6.2: Percentage of Moussa background and genotypes of BC2F1 plants carrying donor alleles (for yield, stay green, and nematode resistance) from the cross Moussa local /IT93K-503-1//Moussa local. Alleles A and B are designated for Moussa local and IT93K-503- 1, respectively. Plant Nematode Yield, stay-green Moussa background (%) Note 1_1170 1_0678 1_0128 1_0157 1_0992 M503_BC2F1_54P15 AA AB AB AB AB 92 BC3 parent M503_BC2F1_54P8 AA -- AB AB AB 89 BC3 parent M503_BC2F1_54P14 AA AB AB AB AB 88 M503_BC2F1_54P11 AA AB AB AB AB 87 M503_BC2F1_82P19 AA AB AB -- AB 86 M503_BC1F2_92P27 AB AA AA AA AA 86 BC3 parent M503_BC1F2_83P34 AB AA AA AA -- 86 M503_BC2F1_54P16 AA AB AB AB AB 81 M503_BC1F2_92P24 AB AA AA AA AA 80 M503_BC1F2_77P55 AB AA AA AA AA 80 University of Ghana http://ugspace.ug.edu.gh 90 Table 6.3: Percentage of Moussa background and genotypes of BC2F1 plants carrying donor alleles (for yield and Striga resistance) from the cross Moussa local /IT97K-499-35//Moussa local. Alleles A and B are designated for Moussa local and IT97K-499-35, respectively. Plant Yield Striga Moussa background (%) Note 1_0022 1_1370 1_0567 1_0583 M499_BC2F1_4P67 AA AA AA AB 93 BC3 parent M499_BC2F1_49P28 AB AB AB AA 93 M499_BC2F1_48P90 AB AB AB AA 93 BC3 parent M499_BC2F1_44P17 AB AB AB AA 92 M499_BC2F1_49P32 AB AB AB AA 92 M499_BC2F1_44P19 -- AB AB AA 90 BC3 parent M499_BC2F1_4P72 AA AA AA AB 89 M499_BC2F1_49P31 AB AB AB AB 88 M499_BC2F1_49P25 AA AA AA AB 88 M499_BC2F1_48P85 AB AB AB AA 88 BC3 parent M499_BC2F1_48P93 AB AB AB AA 84 BC3 parent M499_BC2F1_10P39 AB -- AB AB 83 M499_BC2F1_49P26 AB AB AB -- 83 M499_BC2F1_31P47 AB AB AB AA 83 M499_BC1F2_67P86 AB AB AB AB 79 M499_BC2F1_49P23 AB AB AB AB 79 M499_BC2F1_31P51 AB AB AB AB 77 M499_BC2F1_70P38 AB AB AB AA 76 M499_BC2F1_66P77 AB AB AB AA 72 M499_BC2F1_66P75 AB AB AB AA 69 M499_BC2F1_29P63 AB AB AB AB 69 University of Ghana http://ugspace.ug.edu.gh 91 6.3.2. Morphological characterization of the MABC selected lines Seed from BC3F2 were not enough to undertake a multi-location trial, so they were used for morphological characterization of the families and a single site yield trial. The morphological characteristics of the selected families and the recurrent parent are shown in Table 6.4. The plant type or growth habit of the lines and their dry pods form and colour in comparison with Moussa local are shown in Figure 6.2. Table 6.4: Morphological characteristics of MABC selected lines and their recurrent parent Genotype Flower colour Green Pod colour Dry pod colour Plant growth habit Striga presence Moussa local (RP) White Purple Purple Spreading 1 M499_BC3F3_44P19 White Purple Purple Spreading 0 M499_BC3F3_4P67 White Purple Purple Spreading 0 M499_BC3F3_48P90 White Purple Purple Spreading 0 M499_BC3F3_48P85 White Purple Purple Spreading 0 M503_BC3F3_92P27 White Purple Purple Semi-erect 0 M499_BC3F3_48P93 White Purple Purple Semi-erect 1 RP: recurrent Parent; 1: Presence of Striga; 0: absence of Striga University of Ghana http://ugspace.ug.edu.gh 92 Figure 6.2: Plant growth habit of selected MABC lines (A); selected lines dry pod curvature and colour compared to Moussa local (B) A University of Ghana http://ugspace.ug.edu.gh 93 6.3.3. Grain yield performance of the MABC selected lines The grain yields of the ten lines per water regime are represented in Figure 6.4. The grain yields ranged between 287.16 and 1187.70 kg.ha-1 in the water-stressed environment, while in the well- watered environment the yields were higher, ranging from 272.29 to 1771.00 kg.ha-1. Three BC3F2 families (M499_BC3F3_48P85, M499_BC3F3_4P67, and M499_BC3F3_48P90) yielded better than all parents and the local check. All BC3F2 families appeared to perform better than the recurrent parent Moussa Local under water limited condition. Figure 6.4: Yield performance of selected BC3F2 families, their parents and a local check under well water and water limited conditions. Values are the yield mean (kg.ha-1) of two replications. University of Ghana http://ugspace.ug.edu.gh 94 6.4. Discussion This study involving MABC methodology using SNP markers seems to be the first report in cowpea breeding, particularly in the selection for drought tolerance. The methodology has allowed a quick recovery of the recurrent-parent (Moussa Local) background (up to 97%) with only two backcross cycles (BC2) by using ninety-five individuals for each set of backcross in BC1. The quick recovery of the recurrent parent background allows early selection and helps to reduce the population to be carried to next generation and therefore reduce time and work load. The levels of recovery of the recurrent parent background confirmed the findings reported by Jiang (2013) that revealed a percentage of recovery of 98% at BC3 with a number of 100 individuals selected at BC2. In the present study, several donor loci (yield under drought, stay-green, Striga resistance, and nematodes resistance) were introgressed at the same time. This decreases the chance to identify a line carrying all donor alleles and high Moussa background and, therefore, limits the number of offspring to be selected in the subsequent generations. Sebolt et al. (2000) also reported that the rate of success decreases when large numbers of QTLs are targeted for introgression; by using MABC for two QTLs for seed protein content in soybean introgression, they eventually found that only one QTL was confirmed in BC3F4:5. Compared to MABC, conventional backcross breeding, however, needs a much larger backcross population, as such 500 plants or more must be produced to ensure that there are sufficient plants for background selection after the foreground and recombinant selection have been performed. During the process, unless breeders screen the material to identify those that are carrying the gene of interest, they may end up doing blind crossing to the recurrent parent. In such condition conventional backcrossing is time consuming. By using flanking markers and better distributed markers across the recurrent parent genome, effective introgression can be done to avoid linkage drag and the use a large number of individuals. University of Ghana http://ugspace.ug.edu.gh 95 In Burkina Faso, most of cowpea landraces have a prostrate growth habit and are susceptible to Striga and are often grown with cereal crops like sorghum and millet. The prostrate growth habit allows soil conservation and soil humidity maintaining by the cover of the vines. These morphological characteristics are in accordance with the expectations (Figure 6.2). Moussa local, a farmer preferred landrace has some purple pods which remain purple even for dry pods. The six lines bore the purple green and dry pod colour character confirming that they recovered this character from the recurrent parent. The prostrate (spreading) growth habit of Moussa local is also found in the selected MABC lines. These results, therefore, confirmed the molecular results that showed a high level of recovery of the recurrent parent which is the same Moussa local. The same trend was observed for Striga resistance. Only one line (M499_BC3F3_48P93) that had no Striga donor allele had Striga emergence in the well-watered environment. The Striga-resistant checks did not emerge Striga confirming their resistance while Moussa local had emerged Striga confirming its susceptibility to Striga. This result also confirms that the lines selected based on the presence of the Striga gene through the MABC introgression are effective in controlling Striga. Yield is by far the first criteria for varietal selection by African farmers (Tignegre, 2010; Some, 2012; Traore, 2013). It was a promising achievement from the preliminary yield performance trial where three lines yielded better than the parents and the drought-tolerant check (Gorom local, Figure 6.5). In addition, the general performance of these lines reached the potential yield of the released variety in Burkina Faso which is around 1.5 tha-1(Ouedraogo et al., 2012b). The low yields of certain lines could be attributed to the fact that there was drought spells due to water shortages during the growing period at Kamboinse. However, other authors have reported in maize, three University of Ghana http://ugspace.ug.edu.gh 96 QTLs for two traits (earliness and yield) were introgressed between maize elite lines with MABC but the results were function of numbers of other genes controlling the traits (Bouchez et al., 2002). In the domain of molecular breeding, a lot of conventional breeding methods have been associated with markers to design a large number a marker-aided selection methods. Some examples are marker-assisted selection (MAS), marker-assisted recurrent selection (MARS), marker-assisted backcrossing (MABC), and the new method which involves genotyping by sequencing (GBS). Among the molecular breeding methods, MABC has been the most widely and successfully used in plant breeding up to date. Marker-assisted backcrossing (MABC) is an effective method for developing improved versions of widely cultivated varieties, also referred to as Mega varieties (Neeraja et al., 2007). It has been applied to different types of traits (e.g. disease/pest resistance, drought tolerance and quality) in many species, e.g. rice, wheat, maize, barley, pear millet, soybean, tomato, etc. (Collard et al., 2005; Dwivedi et al., 2007; Xu, 2010). In maize, for example, Bacillus thuringiensis is bacterium that produces insecticidal toxins, which can kill corn borer larvae when they ingest the toxins in corn cells was used (Ragot et al., 1995). The integration of the Bt transgene into various corn genetic backgrounds has been achieved by using MABC. Genotyping by sequencing (GBS) was also used by Asante (2012) to introgress effectively rice fragrance from jasmine rice into a local Ghanaian variety Digang. University of Ghana http://ugspace.ug.edu.gh 97 6.5. Conclusion In this study, the use of the technology has allowed rapid recovery of the background of the farmer preferred landrace. In two backcrosses the recurrent parent background recovered at 97% in some lines. The recovery captured all the characteristics of Moussa local, the farmer preferred that was used as recurrent parent. Pod colour and curvature, plant growth habit, flower colour were successfully transferred to the new lines together with QTLs for yield and stay-green under drought stress. This demonstrates that MABC can be used in introgressing important traits in cowpea. The morphological observation demonstrated that these lines could be good candidates for intercropping in farmers’ field. Until now, no Striga resistant variety combining farmers’ preferences has been proposed by INERA for intercropping. Since Moussa local was used in cowpea intercrops, these lines are promising. From this study three lines out of the six are more promising based on the good yield and their probable resistance to Striga. These three promising lines M499_BC3F3_48P85, M499_BC3F3_4P67, and M499_BC3F3_48P90 need to be advanced for multi-location trials to measure their performance and ability to withstand drought and Striga attacks. The lines could be phenotyped and genotyped to confirm lastly the presence of the introgressed donor alleles affecting yield under drought, stay-green, and Striga. The next decade will possibly see the marker assisted breeding technology spreading to benefit cowpea farmers in Africa. It is now practical to use marker-assisted methods for step-wise maintenance and enhancement in cowpea breeding program to more efficiently target step-wise improvement that best meets farmers’ needs. University of Ghana http://ugspace.ug.edu.gh 98 CHAPTER SEVEN 7.0. GENERAL DISCUSSION AND CONCLUSION 7.1. Introduction Drought, manifested in the form of high variability in amount and distribution of rainfall over seasons and agro-ecologies, is a major constraint threatening cowpea production in Burkina Faso. This study is a step towards the development of farmers’ preferred and drought tolerant cowpea cultivars in the country. This chapter aims at providing an overview of the research findings, the breeding implications of such findings, and the way forward. The objectives of this research were to:  Determine farmer perceptions on the impact of drought on cowpea  Identify sources of tolerance to drought stress in the cowpea germplasm  Assess the SNP-based genetic diversity of a set of cowpea germplasm  Introgress drought tolerant QTLs into Moussa local, a farmer preferred cowpea landrace using marker-assisted backcrossing. 7.2. Main research findings and breeding implications Research investigations were conducted in Burkina Faso from 2012 to 2014 to achieve these objectives. The overall research was supported by a literature review that reveals the following: - Cowpea (Vigna unguiculata (L.) Walp.) is an important staple in semi-arid areas of West Africa including Burkina Faso, where large quantities of cowpea is being produced. The University of Ghana http://ugspace.ug.edu.gh 99 production at farmers’ level is 300 kg ha-1 which is below the potential of 7000 kg ha-1 achieved in USA (Ehlers and Hall, 1997). - Drought due to erratic pattern of rainfall is one of the main constraints to cowpea production. Drought is a quantitative trait and as such breeding for drought tolerance is more complex than breeding for other simply inherited traits (Krishnamurthy et al., 1996). - Molecular markers are increasingly being discovered and used in cowpea for many kinds of studies (gene mapping, genetic diversity, molecular breeding, fingerprinting etc.). QTLs have been mapped for drought-tolerance, stay-green, nematode resistance, Striga resistance in cowpea (Ouedraogo, 2001; Muchero et al., 2009b). The breeding priorities for cowpea drought-prone areas of Burkina Faso were investigated using participatory research methods. Grain yield reduction, fodder yield reduction, total loss of crop and landraces, difficulties in planning cropping season, loss of arable land were most of the time during this study the main impact of drought on cowpea production in the areas where the study was conducted. - Farmers demonstrated that they have a deep knowledge about cowpea production constraints and difficulties to access improved variety seeds was the first constraint in all the areas where the study has been conducted. Other important constraints include: drought tolerance, lack of equipments, soil fertility, limited access to fertilizer and pesticicdes. - The preferred grain traits for all regions were white, large seeded, with a rough texture for food and market purposes, except for the northern region where brown grain was preferred for food. University of Ghana http://ugspace.ug.edu.gh 100 Therefore, the development of new drought-tolerant cultivars for Burkina Faso will need a simultaneous selection for genotypes with resistance to the major abiotic and biotic constraints for farmers as well as for market preferred grain traits. These grain characteristics should be included in cowpea breeding programs to ease the adoption of improved varieties by Burkina Faso farmers. The participatory variety selection (PVS) during this participatory rural appraisal (PRA) paved the way for need-based selection by the farmers, and thereby could help promote quicker adoption of useful varieties in the farming community. In this way, improved varieties seeds could be availed to framers. That could help fill the gap of the unavailability of improved variety seeds at farmers’ level. Field screening was conducted in 2012 to identify cowpea genotypes tolerant to drought based on quantitative stress indices and yields under stressed and non-stressed condition. - Genotypic variability for drought exists among the tested genotypes. - Different cowpea varieties were found to be highly or moderately drought tolerant based on the biplot analysis. - Gorom local, KVx61-1, and IT98K-1111-1 confirmed their tolerance to drought. - Djouroum local, Pobe local, KVx404-8-1, KVx745-11P, KVx525, KVx414-22-2, and KVx421-2J were new drought tolerant genotypes identified in this study. - Of these, KVx745-11P, KVx525, KVx414-22-2, and KVx421-2J are low yielding genotypes. University of Ghana http://ugspace.ug.edu.gh 101 These genotypes low or high yielding could be used in breeding program to improve high yielding drought susceptible genotypes. The existence of genetic variability for drought tolerance implied that genetic improvement of cowpea for drought tolerance could be conducted using this germplasm. Diversity or genetic variation is a pre-requisite for new variety development. Genetic variation can be found in traditional varieties, landraces, commercial cultivars, and other plant materials developed through breeding. A study was conducted to determine the genetic diversity in a set of germplasm used in INERA cowpea breeding program exploiting SNP markers technology. This study showed that: - The germplasm used showed some moderate diversity - Genotypes grouped into clusters based on their genetic background or their geographic origin - The SNP markers were able to group the genotypes in a way that they could be used to link the genotypes and their original parents A core collection of 20 genotypes was generated from the germplasm based on the maximum diversity between accessions. These findings implied that measures should be taken for introduction of new cowpea accessions from other breeding programs to broaden the genetic base of cowpea in Burkina Faso. Marker-assisted backcrossing (MABC) methodology was used to transfer drought tolerance QTL and Striga resistance into a farmer preferred landrace. The use of the technology in this study has allowed rapid recovery of the background of the farmer preferred landrace. Some promising lines University of Ghana http://ugspace.ug.edu.gh 102 were selected from BC1F1and BC2F1 populations using SNP markers for background and foreground selection. - Three lines out of the six are more promising based on the good yield and their probable resistance to Striga. These three promising lines M499_BC3F3_48P85, M499_BC3F3_4P67, and M499_BC3F3_48P90 need to be advanced for multi-location trials to measure their performance and ability to withstand drought and Striga attacks. - The morphological observation demonstrated that these lines could be good candidates for intercropping in farmer field. Until now, no Striga resistant variety combining farmers’ preferences has been proposed by INERA for intercropping. Since Moussa local was used in intercropping these lines are promising. It will also be practical to use marker-assisted methods for step-wise maintenance and enhancement cowpea breeding program in Burkina Faso to more efficiently target step-wise improvement that best meets farmers’ needs. 7.3. General conclusion and way forward In this study, cowpea production constraints, farmers’ preferences, and perceptions on drought impact on cowpea cultivars and traits in three major districts of cowpea production were identified and ranked. Genotypes with high variability for grain yield and drought tolerance were identified. A core collection of diverse cowpea lines based on the maximum diversity using SNP markers was built representing the variability within the germplasm used for in this study. Six promising cowpea lines were selected using MABC. Three of them showed good preliminary yield that need to be confirmed in additional trials. University of Ghana http://ugspace.ug.edu.gh 103 Efforts should be made to address the major production constraints through breeding to increase cowpea production in Burkina Faso. During the breeding process farmers’ preferences should be considered and farmers themselves should be involved to ensure varietal acceptance and adoption. Market aspects need to be considered to develop suitable varieties that meet the needs of farmers. A crossing program among diverse parents for the traits (drought and yield) and selection may generate a pool of individuals for the improvement of the crop. 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University of Ghana http://ugspace.ug.edu.gh 126 Appendices Appendix 1: SNP ID and sequences used for MABC selection SNPID SNPNum AlleleY AlleleX Sequence 1_0105 12650001 G A AAGTATGGCCAGACTTC[G/A]AATCTTGAGATCC 1_0709 12650016 G A AAGCCTGTCCGCAA[G/A]TTGTCTCTAGTCCAC 1_0917 12650017 G A ATAGCAAAGAAATG[G/A]TAAAAAGAAAGAAGG 1_0866 12650029 C A AACGCAAACTGTCGC[A/C]GGTTATATTTTCCT 1_1217 12650034 G A AAGCAGAGCCTGGA[G/A]TCGGACTCCGCCGGA 1_0594 12650038 C A ATTCTGTGCTGCCAC[A/C]TTAAGCAGGCTGTC 1_1370 12650039 G A TTCAATGCATTTCAC[A/G]TCTTCTGGCGGAAT 1_0706 12650043 G A TTTGTTGATGATTGT[A/G]TTCAAAGTGACATA 1_0754 12650049 G A GGACAGCACAAGTCT[A/G]ACTTCAGAAAAGCT 1_1413 12650048 C A ACTCCTCCTATGGC[C/A]GCAAAGGTCAAACCA 1_0256 12650056 G A GGCTCTTGGTAAGC[G/A]TATGCATAACGTTGT 1_0649 9030007 G A GTGAAAGTTGAAAAA[A/G]GTGAAACTGTCAAG 1_0262 12650063 T A AATCCCCGCCGCGTT[A/T]GCTCCACAGGGTCA 1_1103 12650070 G C AGCTTGCAGGATCAA[C/G]CCACCCTCCAGATT 1_1249 12650073 G A AAGTCATTGACGAT[G/A]TGAGGAATTTCATCG 1_0755 12650079 G C TGCTGCGGGGCATGT[C/G]AGAGAAGAATGTGA 1_0992 12650082 C G AGGGCAGAGATAAT[C/G]AATGAGGTAAAAAAT 1_0775 12650084 C G AGAAGAGTTCGAAA[C/G]AGATAAAATTATTTA 1_0392 12650095 G A CTGTTTCTTTGAGC[G/A]TCAAGTTGGGGTGGT 1_0370 12650098 G A TCGATGGACGATCC[G/A]GGAAGATTGGGCAGT 1_0126 12650104 G A ATTCGCATTTGGCG[G/A]GACTGAGGACCATCA 1_0757 12650116 C A TTATGAAGCTCTTGG[A/C]CTCACTTCCAAGCA 1_0081 12650121 G A AGAGCAAATATTTA[G/A]AACAAAATATCCCTC 1_0401 12650131 C A ATGCAAACTGAGAG[C/A]ATGCAAATACAAAAG 1_0432 12650136 C A CTTCGATTAAGTGCA[A/C]ACTCCTACTCTACC 1_0022 12650139 C A CCTCGTCTTCAAGTC[A/C]GGCATGGCCAAGTC 1_0307 12650150 G A ACACGTTTGTACATA[A/G]GAGTGTGTAAAGTT 1_0053 12650157 G A TTGCAGCAAGTACTC[A/G]TTTGACATGAGCTA 1_1360 12650159 T A TGGGTATGTAACTAA[A/T]GCCCTTAACCTTCA 1_0982 12650162 G A AAATTATTTTTGGTG[A/G]GCCTGAGGTTACAA 1_0993 12650174 G A TTGGGAAACACAAA[G/A]ATGTCACCTTTGTTA 1_0652 12650181 G A ACCTTAATTGGGGAC[A/G]TTGATCCAGTTCAA 1_0183 12650189 G A TCCGGAGAAACAGC[G/A]ACAGTGTTCACATAC 1_0052 12650197 C A TAGTTCTGGTGTGG[C/A]YTTGCAGGTACAGAA 1_1039 12650199 T A GATGAAACAGACTTA[A/T]GGGCTTATGATGTA 1_0033 12650200 G A CAAAAARATGTCCA[G/A]GCTAAAAAACAAAAG 1_0678 12650222 G A TGCTTCTTTTGATG[G/A]AAAATTTAGTTGTAC University of Ghana http://ugspace.ug.edu.gh 127 Appendix 1: SNP ID and sequences used for MABC selection. Continued 1_0983 12650225 G A CAGAGTTCCTCCTC[G/A]ACGTCCCCGAACCTT 1_0670 12650228 G A AGCTCAACCATTCA[G/A]GCCTCAAAATTCAAA 1_0142 12650229 A T TTTGCAGTTCCACA[A/T]CCTATAGACAGCAAC 1_0139 12650234 C A GGCTACCATGAATC[C/A]GGAAAATTGATCGTG 1_0547 12650239 G A CATAAAACACTGTCG[A/G]AAACAAAAAAATGT 1_0703 12650262 G A AAGCATTCTATTGG[G/A]AAGTTCTCCAGGTTA 1_0082 12650269 G A TCTAAGGAAAGATGG[A/G]AAGAAGCCCAGTGC 1_0290 12650276 G A TCAAAAGGTAGTGGT[A/G]GTGCGGTGCGAAGA 1_0987 12650281 G A CAGAGGAACTGTGT[G/A]GTGGAAGTCCATCTG 1_1517 12650284 G A CTACTGATTGGATA[G/A]CAGGCCCAATATTGG 1_0565 12650286 C G CTAAAGCACCARTA[C/G]ACACTGCCAACAACA 1_1151 12650294 G A AGTGTATCTGTTAC[G/A]TGGGCAAAATAAAAG 1_0153 12650304 G A TATTATAAGAATGTG[A/G]GAATATGCAATGGC 1_1042 12650308 G A GATAGATGAGTCATC[A/G]CCTGCTAAATACCG 1_0732 12650314 G A TGAACTCCGTGGCC[G/A]AACGTGTAAACCTCC 1_0519 12650322 G C TCTCATCCATGCTTT[C/G]TGCTCCTTTGGATC 1_0679 12650323 G A GCTCCAACAATTTC[G/A]GTGGGTTCCTCTGCA 1_0127 12650329 G A AACCCAGAGAAAAC[G/A]AACTTACAAGACCTA 1_0823 12650331 C A TCCCACCTCGAAAA[C/A]GACGTTTGGGTTGGA 1_0322 12650336 C A ATCAAATGTTACGGT[A/C]AATTTGGAAGGACA 1_1189 12650339 G A CAGTCTCACTGCCA[G/A]CAACTACATCACGGG 1_0280 12650342 G A ATGACGCGATCTGC[G/A]ACCTCGGACTTGTCG 1_0567 12650348 C G GTCGCCGGTTCGGA[C/G]TGCGAGTCGGACAGC 1_0539 12650356 G A ACACAAAAATATTG[G/A]CATYAATCTCAAGTG 1_0242 12650357 C A ACAGGGGATTCACC[C/A]TGCGAACCCGTTGCA 1_0598 12650360 G A GTAGGGAAGAAARAG[A/G]GAGAGATAAAATAC 1_0171 12650366 G A AACTGTGAAAGATGG[A/G]AAACTATACATCTG 1_1072 9030019 G A CCTAGACAACCAGCA[A/G]AGTATGTTCAGATT 1_1021 12650373 G C ATGTCTAACCCTCCT[C/G]GGTCGTAGATTTCA 1_0136 12650380 G A CTCGCTGAATACCA[G/A]AGGGGGCTGGTGCTT 1_0377 12650390 G A GGGTCATCTCGACCC[A/G]GGGGCCATTAGTTT 1_1467 12650393 G A CAACATATGCAGTG[G/A]TAAATCCCTGAGGTT 1_0317 12650396 G A CAACAACATTTACAA[A/G]CGCAAGTATGAGGA 1_0531 12650417 C G CAGTGCCTATCCTC[C/G]GCAAGCTCAACAATA 1_0067 12650411 G A TGAATGGCGCAGAG[G/A]TTAGTGTCTTCAAAG 1_1333 12650420 C A ATTTTTTTTTTACTT[A/C]CAAAAAAAAATGTT 1_0436 12650421 C G CGCAGAAGAGATTT[C/G]GAAGCCAACCCATCT 1_0111 12650431 G A TTGGCTTCTTGCCAG[A/G]ATGGTGTTGCAAAT 1_0420 12650436 G A AGCTGAAGGWCTTGA[A/G]AATGGTCCCTCAGC University of Ghana http://ugspace.ug.edu.gh 128 Appendix 1: SNP ID and sequences used for MABC selection. Continued 1_1214 12650443 C G AAGGCAAGCCAGAC[C/G]GCGGTGTTGCACTTG 1_0748 12650447 G A TCATTTTCATTCTGG[A/G]ACATGGGAAGATCG 1_0801 12650461 G A GGCCCTGAAAGTAGG[A/G]TTGTCCAGTCTGTT 1_1135 9030013 G A CCTCGCTTTAATCGT[A/G]CGCCACTGGGTTGA 1_1170 12650475 G A CAATGCGGCGACTA[G/A]CGTGAACACAACGGT 1_1431 12650476 G A TTCGAGCTCCAATA[G/A]ATTAGGTTGTTGCAA 1_0351 9030014 C A TTGCCTTAGTCTCAT[A/C]TCTCTGTTTTACGT 1_0752 12650483 G C GTTTCATGTGTATTT[C/G]ATGATTGCTATTGC 1_0937 12650516 G C GCCATACGACGTCGT[C/G]GCTGCGCTGCTCTG 1_1371 12650518 G A TCTGAACATATCTT[G/A]GCTTTCATTTCTTTA 1_0806 12650520 G A ATGCAGGAGTTACAT[A/G]TTAGAGGATGAGAA 1_1073 12650521 G A AGAGGAAAAGAAGGT[A/G]GAAGAGAAGAAGGA 1_0306 9030025 G A GCCACAGGAACCGGC[A/G]CCTGCTCCTTCAAC 1_0691 12650551 G A AACTCTTGAATTGGT[A/G]GCTATTGATGAGCC 1_1520 12650555 C G GAAACGACCCGATC[C/G]GTGATAACATCAATC 1_0157 12650562 G A GAAACCCTAGGTAAG[A/G]AAAAATGCCGGCTG 1_0807 12650566 C G CTAATCTGCGCTAC[C/G]GCAGAATTTAAAATC 1_1246 12650568 T A TCCGTCCGCTTCCTC[A/T]CCCGTCGGCGTTTC 1_0084 12650577 C A CGTTTTTCGTGATCG[A/C]ATGCCACGTTTGCA 1_0583 12650579 G A CTAGATCCCAAGACC[A/G]CCATAGATATCAAG 1_0794 12650583 G A TAGTCAATTTTAAC[G/A]GATCTTCAAAACTTG 1_1281 12650587 G A TGGTTTTGGCTCAAC[A/G]GAGTCTAAACAGGA 1_1157 12650589 G A ATTGAACAAGTGAA[G/A]AGAAAAATAGAAGGA 1_0060 12650602 C A TTATTTGTTGGTGGT[A/C]CCATTCATTCTGAT 1_0025 12650606 G A AATTTTCTTCCTTTC[A/G]GTTTCGTTAGCCAG 1_0123 12650616 G A AAAGGGAATTGGTAA[A/G]AGTGGAAAGCCTCT 1_0473 12650618 G A GCTCACGGATCTGGA[A/G]GAGGTTGAGGAGGT 1_0771 12650624 C A AACAGAAAATAATG[C/A]AACAGAGGAGGATCC 1_0388 12650635 T A GGCTACTTCCCACTT[A/T]CGCTTCACTTTAGT 1_0525 12650642 G A TGATGCTTTGATACA[A/G]AAAGTAAATGCTGA 1_0690 12650651 G A GGGCACCAGAGTCAG[A/G]GCACAAACCATGAA 1_1271 12650657 G A AATTACAAAATTCT[G/A]CGCATTACATCATCT 1_0330 12650662 A T TGGAGGCCAGGGTT[A/T]GCACTGCTGAAGATA 1_0438 12650667 G A CGTGAGTACCTCATC[A/G]CCAATTTTTAGCAG 1_1393 12650668 G A AAGAAAAAGAATGAA[A/G]TTAAAGAAGATTTT University of Ghana http://ugspace.ug.edu.gh 129 Appendix 1: SNP ID and sequences used for MABC selection. Continued 1_0065 12650669 C A GTGGCAGTGGCATCA[A/C]CTACAATCCTAGGA 1_1087 12650674 G A GTTCATGTTCCATA[G/A]CTAACTTTTCTTCAG 1_0625 12650675 G A CAAGTATCATATGTA[A/G]AAGACTGCAGACAT 1_1007 12650677 G A GATATATATTCAGT[G/A]CCAATTATATGGCCA 1_1141 12650698 G A TTATATTAATGTTGC[A/G]AATCATTGCAACAA 1_0853 12650709 G A CGGCGGAGGACGCC[G/A]GAGATAATGCGGCTG 1_0056 12650712 G A TCCATGAGGAAAACA[A/G]CCTCTAAGTCTGTT 1_1129 12650713 G A ATGTTCATGGTATT[G/A]TAGTCATTTATCAAC 1_1096 12650718 G A TCACTTAATCACTCA[A/G]TCACTTTCATCTTC 1_0730 12650735 G A ATGGTTTTGGTTTC[G/A]GTCTGAAGAAGCTCG 1_1117 12650741 C A GTTTGTGTGCATTG[C/A]AGTCTGGGAGTTCTG 1_0514 12650751 G A GGAATCCTCTATCA[G/A]AGGCACCCAGTAAGA 1_0923 12650752 G A GCAAGCATTAACAGT[A/G]GCGGCTGCAGTTGG 1_0397 12650767 C A TGGTTCTCTTTGTGG[A/C]CCTGTTGTTGATCA 1_0222 12650773 G A AACCTTTGACTCCR[G/A]AGATTCTTGGTGAGT 1_1038 12650777 G A TGAGGAAGAGCGTA[G/A]CCCTCATAAATGGGG 1_0014 12650685 C A CCCTTTGCAGGTTT[C/A]GTCTGCACCAAAACA 1_1492 12650785 C G ACAATCTACCGTTT[C/G]TGAAACGCGTTACCT 1_1092 12650786 G A TGATACTACTGTCAA[A/G]ATTTACAATGGGAA 1_0449 12650788 G A TGAACATTAAAATG[G/A]GAAACATCTTATTAT 1_0058 12650793 G A GGAAACTGAGGAAAA[A/G]AAGGGGTTTCTTGA 1_0421 12650794 G A ACAGCACGCAATAT[G/A]TTTGCACCAGCGCCT 1_0529 12650804 G A TCATCCTGCTGTCAA[A/G]GGCCTTCTCCCAGA 1_0482 12650805 C G AAGAATTTGCACTT[C/G]AAGGATATCTTCCAA 1_0905 12650809 G A AGATCCAAGGACAGG[A/G]GAAGTGATTACGAA 1_0232 12650812 G A GAGGAATCGTGGTC[G/A]TGGATCTTCCCGGAA 1_0957 12650816 G C TAAAACTGCAAATGT[C/G]GGAACGAAGATATG 1_0510 12650817 G A GAGATCTGGAAGTTA[A/G]TTGTCATTTTGAAC 1_0657 12650821 G A CACTGACTTGGCCA[G/A]CACGGTGTAGTCCTC 1_0773 12650823 G A ACTGATGGAAGGAAC[A/G]CTGAAGAGAAGGGA 1_0451 12650833 C G CTGCCTCTTCTGGA[C/G]GATCACTCTGTGGAG 1_0062 12650864 G C AAGGAGGTAGGGCTA[C/G]CCAATGGGYTTTTA 1_0437 9030018 G A TAGTACCCCTCTTCT[A/G]ATATCTTTTATTTG 1_0605 12650911 C A GGATAACCGGACCGT[A/C]CTGGACGGGACCTT University of Ghana http://ugspace.ug.edu.gh 130 Appendix 1: SNP ID and sequences used for MABC selection. Continued 1_1130 12650915 G A ATGATGTTGGCTTT[G/A]TGGACGGCGGTGACT 1_0319 12650924 G A GGAACCTGCTCAGC[G/A]CATGTAAGTAATTCA 1_0740 12650940 G A ATGAAGCTGCTTCT[G/A]TGTGGCTTCCTCTGG 1_0001 9030026 G A TTTAGAGATCTAAGG[A/G]ATGTGGTTTTTAAT 1_0107 12650955 G C CCGCCACAACCCCAA[C/G]CTCTCTTTCCTTCA 1_0178 12650964 C A TYTGGTTGGTGCACC[A/C]GGTGGCCTAAAAGC 1_0362 12650966 G A TGGGGTTCGATTCGC[A/G]GTTGAACCCGAACA 1_0718 12650968 G A GAGAAAAAATCGTTC[A/G]TTGTAACGTTTTCG 1_0425 12650971 G A AGATGCAAGTCCTTC[A/G]GGAAACGCTGCCGG 1_0246 12650978 G C ATTGGGCTCTYCTCT[C/G]CGCTATTAGTTTTC 1_1512 12651001 A T GCAATGATGAGCAT[A/T]CAGAGACCATTATTC 1_0834 12651004 G A AGTGCCGGCAGGGT[G/A]TTGCACAACTCCGGA 1_0699 12651008 C G CATGCAAGATACTT[C/G]GTAAACTGATCAATT 1_0663 12651013 G A GGATTCTGCTTCAA[G/A]TCGCCAAAAGACGGG 1_0878 12651014 G A TCCATTGAACCACA[G/A]GCAAGTCGTTTCCCA 1_0911 12651029 C A ACGGCTGAAACTGAG[A/C]AGAGGAGGATAGTC 1_0746 12651032 C A ATCATTTTCCTCAT[C/A]AATGTCGTCGTCGTC 1_0442 12651034 C G CGATTGATCGGCAT[C/G]GACGAGATGAAGAAC 1_0146 12651037 G A TTGACGACGAGGTT[G/A]GTGACGGAGTAGAGG 1_0876 12651065 T A TAGGATATTTTGACA[A/T]GTTATGTATCCGAT 1_0945 12651066 G A TTTCTCCTCACAGAA[A/G]CAGAGAATGCAGCG 1_1062 12651070 C A TTTAGTTAACAAAGC[A/C]TTGGTTCTCATAAC 1_0604 12651075 A T CAACCATCTATGAA[A/T]TGCCCTTTTGATGGA 1_0977 9030009 C A TGTAGTGGTCAATGG[A/C]TGTGCTCACATATA 1_0323 12651082 C A GAAACCAACTCTTA[C/A]CAAAAGGCGCAACAA 1_0128 12651083 G C GACCCTTCACCTTGT[C/G]CTCAGGCTTCGCGG 1_0588 12651090 G A TTTCGAGACTGTGTT[A/G]ATGGTTTAATGTAT 1_1367 12651092 G A TCAAAGATTAAACAT[A/G]CCTCTCATGTATCA 1_1121 12651101 G A CTGTGGGAGCTATGG[A/G]GATTATCCTGTGGA 1_0259 12651106 G A CTGCTGCACCGTTT[G/A]GAGTTATCCATTGCA 1_0889 12651110 C G TTTCAATACTGTTT[C/G]TTGTTAGTACTATCT 1_1255 12651114 T A ATCGATACAGTGTTG[A/T]GGAAGTGAAGAAAG 1_0238 12651129 G A CATCACCGATCTTAA[A/G]GGTGGCAAAGTCGG 1_0074 9030020 C A CTGGACACTTATGTG[A/C]GAGGAAATCTTGTG 1_0647 12651138 G A GAAAGAAGCTCAGG[G/A]AACTCTGTCTTCAAT 1_1037 12651147 G A ACAGACGAGATCAT[G/A]CATGACGATTTATAA University of Ghana http://ugspace.ug.edu.gh 131 Appendix 2: Full genotyping results for selecting BC2F1 line carrying yield, stay green, and nematodes QTL in the cross Moussa local /IT93K-503-1//Moussa local Nematode resistance Yield, stay-green Plant 1_1170 1_0678 1_0128 1_0157 1_0992 Moussa background (%) M503_BC2F1_54P10 AA AA AB AB AB 97 M503_BC2F1_54P9 AA -- AB AB AB 95 M503_BC2F1_82P21 AA AA AA AA AA 94 M503_BC2F1_54P15 AA AB AB AB AB 92 M503_BC2F1_54P7 AA AB AB AB AA 92 M503_BC2F1_54P17 AA AA AA AA AA 92 M503_BC2F1_54P8 AA -- AB AB AB 89 M503_BC2F1_54P13 AA AA AA AA AA 89 M503_BC2F1_54P14 AA AB AB AB AB 88 M503_BC1F2_77P61 AA AA AA AA AA 88 M503_BC2F1_82P20 AA AA AA AA AA 87 M503_BC2F1_54P11 AA AB AB AB AB 87 M503_BC2F1_82P18 AA AA AA AA AA 87 M503_BC2F1_82P19 AA AB AB -- AB 86 M503_BC1F2_92P27 AB AA AA AA AA 86 M503_BC1F2_83P34 AB AA AA AA -- 86 M503_BC1F2_83P31 AA AA AA AA AB 86 M503_BC2F1_54P12 AA AA AA AA AA 86 M503_BC1F2_83P45 AA AA AB AA AA 82 M503_BC1F2_83P49 AA AA AA AA AA 82 M503_BC1F2_83P32 AA AA AA AA -- 82 M503_BC1F2_83P39 AA AA AB AA AA 81 M503_BC1F2_77P64 AA AA AA AA AA 81 M503_BC2F1_54P16 AA AB AB AB AB 81 M503_BC1F2_92P24 AB AA AA AA AA 80 M503_BC1F2_77P55 AB AA AA AA AA 80 University of Ghana http://ugspace.ug.edu.gh 132 Appendix 3: Full genotyping results for selecting BC2F1 line carrying yield and striga QTL in the cross Moussa local /IT97K-499-35//Moussa local Yield Striga Plant 1_0022 1_1370 1_0567 1_0583 Moussa background (%) M499_BC2F1_47P2 AA AA AA AA 97 M499_BC2F1_47P15 AA AA AA AA 95 M499_BC2F1_47P13 AA AA AA AA 95 M499_BC2F1_47P6 AA AA AA AA 95 M499_BC2F1_47P11 AA AA AA AA 95 M499_BC2F1_47P1 AA AA AA AA 94 M499_BC2F1_48P94 AA AA AA AA 94 M499_BC2F1_47P14 AA AA AA AA 94 M499_BC2F1_47P9 AA AA AA AA 93 M499_BC2F1_4P67 AA AA AA AB 93 M499_BC2F1_29P64 AA AA AA AA 93 M499_BC2F1_47P3 AA AA AA AA 93 M499_BC2F1_49P28 AB AB AB AA 93 M499_BC2F1_48P84 AA AA AA AA 93 M499_BC2F1_48P90 AB AB AB AA 93 M499_BC2F1_47P12 AA AA AA AA 92 M499_BC2F1_49P33 AB AA AB AA 92 M499_BC2F1_47P7 AA AA AA AA 92 M499_BC2F1_44P17 AB AB AB AA 92 M499_BC2F1_47P16 AA AA AA AA 92 M499_BC2F1_47P5 AB AA AA AA 92 M499_BC2F1_47P8 AA AA AA AA 92 M499_BC2F1_48P81 AA AB AA AA 92 M499_BC2F1_49P32 AB AB AB AA 92 M499_BC2F1_27P4 AB AA AB AA 91 M499_BC2F1_44P20 AA AA AA AA 91 M499_BC2F1_44P21 AB AA AB AA 91 M499_BC2F1_47P4 AA AA AA AA 90 M499_BC2F1_44P19 -- AB AB AA 90 M499_BC2F1_31P55 AA AA AA AA 90 M499_BC2F1_29P61 AA AA AA AA 90 M499_BC2F1_47P10 AA AA AA AA 90 M499_BC2F1_38P79 AB AA AB AA 89 M499_BC2F1_48P83 AA AA AA AA 89 University of Ghana http://ugspace.ug.edu.gh 133 Appendix 3: Full genotyping results for selecting BC2F1 line carrying yield and striga QTL in the cross Moussa local /IT97K-499-35//Moussa local. Continued M499_BC2F1_48P86 -- AA AA AA 89 M499_BC2F1_4P72 AA AA AA AB 89 M499_BC2F1_49P31 AB AB AB AB 88 M499_BC2F1_49P25 AA AA AA AB 88 M499_BC2F1_27P2 AA AA AA AB 88 M499_BC2F1_48P92 AA AA AA AA 88 M499_BC2F1_31P48 -- AA AA AA 88 M499_BC2F1_48P85 AB AB AB AA 88 M499_BC2F1_39P66 AA AA AA AA 88 M499_BC2F1_31P44 AB AA AB AA 87 M499_BC2F1_10P41 AA AA AA AB 86 M499_BC2F1_27P1 AB AA AB AA 86 M499_BC2F1_31P45 AA AA AA AA 86 M499_BC2F1_31P49 AA AA AA AB 86 M499_BC2F1_31P50 AA AA AA AA 86 M499_BC2F1_39P65 AA AA AA AA 86 M499_BC2F1_48P82 AB AB AB AA 86 M499_BC2F1_10P40 AA AA AA -- 86 M499_BC2F1_48P88 AB AB AB AA 86 M499_BC2F1_48P91 AB AA AB AA 85 M499_BC2F1_29P58 AA AA AA AB 85 M499_BC2F1_49P36 AB AB AB AA 85 M499_BC2F1_49P22 AB -- AB AA 85 M499_BC2F1_27P3 AA AA AA AB 85 M499_BC2F1_31P56 AB AA AB AA 84 M499_BC2F1_48P93 AB AB AB AA 84 M499_BC2F1_31P53 AA AB AA AB 84 M499_BC2F1_48P87 AA AA AA AA 84 M499_BC2F1_38P80 AA AA AA AA 84 M499_BC2F1_44P18 AB -- BB AA 84 M499_BC2F1_29P59 AB AA AA AB 84 M499_BC2F1_31P46 AB AA AB AB 84 M499_BC2F1_10P39 AB -- AB AB 83 M499_BC2F1_49P26 AB AB AB -- 83 M499_BC2F1_49P35 AA AA AB AB 83 M499_BC2F1_48P89 AA AA AB AA 83 M499_BC2F1_31P47 AB AB AB AA 83 M499_BC2F1_4P71 AA AA AA AB 83 University of Ghana http://ugspace.ug.edu.gh 134 Appendix 3: Full genotyping results for selecting BC2F1 line carrying yield and striga QTL in the cross Moussa local /IT97K-499-35//Moussa local. Continued M499_BC2F1_31P52 AA AA AA AA 81 M499_BC2F1_4P69 AA -- AA AB 81 M499_BC1F2_27P51 AA AA AA AA 81 M499_BC1F2_67P80 AA AA AA AA 80 M499_BC2F1_49P27 AA AA AA AB 80 M499_BC2F1_49P37 AA AA AA AB 80 M499_BC2F1_49P34 AA AA AA AB 80 M499_BC2F1_31P54 AA AA AB AA 79 M499_BC2F1_29P57 AB AB AA AB 79 M499_BC2F1_27P5 AB AA AB AA 79 M499_BC1F2_67P86 AB AB AB AB 79 M499_BC2F1_4P68 AA AA AA AB 79 M499_BC2F1_4P70 AA AA AA AB 79 M499_BC2F1_49P23 AB AB AB AB 79 M499_BC2F1_49P24 AA AB AA AB 79 M499_BC1F2_67P85 AA BB AA AB 78 M499_BC1F2_27P50 BB AA BB AA 77 M499_BC2F1_31P51 AB AB AB AB 77 M499_BC2F1_20P42 AB AB BB AB 77 M499_BC2F1_20P43 AB -- AB AB 77 M499_BC2F1_38P78 AA AA AA AB 77 M499_BC2F1_66P73 AB AA AB AA 77 M499_BC1F2_27P73 AA AA AA AB 76 M499_BC1F2_67P87 AB AB BB BB 76 M499_BC2F1_70P38 AB AB AB AA 76 M499_BC1F2_67P95 AA AA AA AB 75 M499_BC2F1_66P74 AB AA AB AA 73 M499_BC2F1_66P77 AB AB AB AA 72 M499_BC1F2_67P91 AA BB AA AB 71 M499_BC2F1_27P6 AB AA AB AB 71 M499_BC1F2_27P77 AA BB AA BB 71 M499_BC1F2_67P94 AA AA AA AB 70 M499_BC1F2_27P68 AA AA AA AA 70 M499_BC2F1_49P30 AB AB AB BB 70 M499_BC1F2_67P89 AA AA AA BB 70 M499_BC2F1_66P75 AB AB AB AA 69 M499_BC2F1_29P63 AB AB AB AB 69 University of Ghana http://ugspace.ug.edu.gh 135 Appendix 4: LGC Genomics DNA extraction Protocol (Plant tissue, sbeade) 1. Samples are received in 96-well sample tube storage racks or 96-well plates. These racks / plates are centrifuged briefly to ensure that samples are at the bottom of the tubes / wells. 2. The seals are removed from the racks/plates, and a single ball bearing is added to each sample using a plate ball dispenser (Size of ball bearing used is adjusted based on sample type, standard size is 4 mm). 3. This step varies based on the type of sample being processed: a. Dessicated / frozen samples - Plates are resealed using a heat sealer, and transferred to the Genogrinder. b. Fresh samples – 75 μl lysis solution is added to each well. Plates are resealed using a heat sealer and transferred to the Genogrinder. 4. Samples are disrupted in the Genogrinder; the standard protocol is 1 min at 1500 rpm although this will be adjusted based on sample properties to ensure that samples are fully disrupted. Samples are then centrifuged briefly to ensure that all material is at the bottom of the tube / well. 5. The plate seal is removed, an appropriate volume of lysis solution is added to each well using a multidrop, and the plate resealed. The plate is vortexed thoroughly and centrifuged briefly. 6. Lysis plates are incubated at 65ºC for 1 hour. 7. An appropriate volume of binding solution is prepared and dispensed across the wells of a 1.2 mL process plate. University of Ghana http://ugspace.ug.edu.gh 136 8. The lysis plates are centrifuged to ensure all liquid is at the bottom of the wells. Lysate is then transferred to the corresponding wells of the process plate containing binding solution. Plates are heat sealed, briefly vortexed to mix, and incubated at room temperature for 30 min. 9. After incubation, plates are centrifuged and placed on a magnetic rack for 1 min. 10. The plate seal is removed and supernatant removed by inverting the plate and magnetic rack. 11. An appropriate volume of wash buffer PN1 is added to each well, the plate is sealed, vortexed briefly and incubated at room temperature for 5 min. 12. After incubation, plates are centrifuged and placed on a magnetic rack for 1 mm University of Ghana http://ugspace.ug.edu.gh