i GENETIC ANALYSIS OF DROUGHT TOLERANCE IN COWPEA [VIGNA UNGUICULATA (L.) WALP] By IBITOYE, DORCAS OLUBUNMI (10359705) THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF DOCTOR OF PHILOSOPHY PLANT BREEDING DEGREE WEST AFRICA CENTRE FOR CROP IMPROVEMENT SCHOOL OF AGRICULTURE COLLEGE OF AGRICULTURE AND CONSUMER SCIENCES UNIVERSITY OF GHANA LEGON DECEMBER 2015 University of Ghana http://ugspace.ug.edu.gh ii 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. ....................................................................... Dorcas Olubunmi IBITOYE Student ....................................................................... Prof. Pangirayi TONGOONA Supervisor ……………………………………………… Prof. Samuel Kwame OFFEI Supervisor …………………………………………........ Prof. Essie T. BLAY Supervisor …………………………………………….... Dr Ousmane BOUKAR Supervisor University of Ghana http://ugspace.ug.edu.gh iii ABSTRACT Agriculture in Sub Saharan Africa (SSA) is under serious threat due to water shortage, population pressure and climate change. Cowpea, a protein-rich legume crop complements staple cereal and tuber crops in the diets of rural and urban people of the tropical and sub- tropical regions of the world. It therefore, plays a significant role in the sustainability of food and nutrition security in SSA. Cowpea, though reported to be inherently drought tolerant; but because it is mostly grown under rain-fed conditions towards the end of the rainy season in the drier parts of Nigeria, its productivity is still being adversely affected by the erratic pattern of rainfall which occurs frequently in these areas. Increasing the level of drought tolerance in existing cowpea varieties that will possess farmers’ preferred traits will increase farmers’ adoption of these varieties and ensure high and stable yield from farmers’ fields under the ever changing climatic conditions. The objectives of this study were therefore, to: (i) identify the impact of drought on cowpea production and farmers’ preferred traits in new cowpea varieties (ii) assess the diversity of cowpea germplasm for drought tolerance (iii) assess the combining ability of cowpea lines under drought and well-watered conditions and (iv) determine the gene action controlling drought tolerance in cowpea. The results of a Participatory Rural Appraisal (PRA) conducted in fifteen cowpea growing communities of Kano State, Nigeria established that drought, pests and diseases were major constraints to cowpea production. Drought reduced grain yield and fodder yield to about 62% and 56% of realizable yield under normal condition respectively. Fifty-eight percent of the farmers confirmed drought at the flowering / pod-filling stage was more devastating than drought occurring at the vegetative stage (32%) while 10% of the farmers confirmed that both growth stages were both growth stages are susceptible to University of Ghana http://ugspace.ug.edu.gh iv drought. Consumer-based traits such as large seed, short cooking time and dual-purpose varieties which increase farmers’ income were identified as important preferred traits as well as traits for biotic and abiotic tolerances in new cowpea varieties. Ninety-one cowpea varieties were screened for tolerance to drought using the wooden box screening technique with the aim to identify parents to be used for genetic analysis studies. Twenty lines were selected based on their responses to the screening and were mated in a North Carolina Design II to generate 100 single F1 crosses. The F1 progenies and their parents were evaluated under drought and well-watered conditions at two locations. Grain yield of the F1 progenies ranged between 2533 kg ha-1 for TVu6707 x TVu9797 and 18 kg ha-1 for TVu11986 x TVu2736 under drought stress, 3786 kg ha-1 for TVu6707 x TVu9797 and 45 kg ha-1 for TVu633 x TVu2736 under well-watered conditions. General Combining Ability (GCA) and Specific Combining Ability (SCA) mean squares were significant for grain yield and other traits across all research environments indicating that both additive and non-additive gene effects were important in the control of grain yield and other drought adaptive traits across all research environments. The contribution of GCA (71%) to the total sum of squares was higher than that of SCA (21%) for grain yield under drought stress indicating that additive gene action was more important in the inheritance of grain yield under drought stress. Similarly, the superior positive GCA (GCA-female and GCA-male) effects for 100-seed weight, number of seeds per pod, Normalized Difference Vegetation Index (NDVI) measured at three different growth stages, the number of pods and seeds per plant under drought stress indicated that additive gene action was more important in the inheritance of these yield related traits under drought stress. The lines TVu79, TVu6707, TVu9693 and TVu9707 were identified as general good combiners with outstanding University of Ghana http://ugspace.ug.edu.gh v positive GCA effects for grain yield under drought stress. These can be used as parents to generate improved cowpea varieties for drought tolerance. Considering both mean yield and stability performance, TVu8670 x Sanzi, IT89D-288 x TVu8670, TVu6707 x TVu79 and TVu8670 x TVu79 can further be advanced for development of novel drought tolerant varieties. University of Ghana http://ugspace.ug.edu.gh vi DEDICATION To my husband – Omoniyi David Ibitoye To my lovely girls – Anjolaoluwa Lois and Tiwaladeoluwa Sharon To the loving memory of my late father – Pastor Jacob Ogunleye Ajisafe University of Ghana http://ugspace.ug.edu.gh vii ACKNOWLEDGEMENTS My greatest appreciation is to Jesus, my ever living source of life for His mercies, goodness and faithfulness from the beginning of my life till now. To Him alone be glory and honour for ever and ever. I am grateful to Alliance for Green Revolution in Africa (AGRA) for providing the full scholarship for this programme. I appreciate the management of West Africa Center for Crop Improvement (WACCI), University of Ghana for selecting me to be one of the beneficiaries of AGRA scholarship. To all the academic and administrative staff of WACCI, I say a very big thank you. You have all been wonderful. Worthy of mention is Prof. Eric Y. Danquah (WACCI Director), a great leader worthy of emulation you are. Indeed my contact with you has left an indelible mark in my life. I am grateful to my WACCI supervisors – Prof. Tongoona, Prof. Blay and Prof. Offei for taking their time to review all the chapters of this thesis. I appreciate my in-country supervisors, Dr Ousmane Boukar and Dr C.A. Fatokun for their supervision and support. I cannot forget the tremendous assistance I received from all the staff of cowpea breeding unit, IITA, Ibadan and Kano stations. They worked with me as part of the cowpea team. Guys, it has been wonderful working with you all. I appreciate my mother, Alice O. Ajisafe for bringing me up to believe in my dreams and supporting me with prayers – mum, you are the best. To my siblings (I.K, Phillips, Omolola, G. Leke, Oluwaseun and Oluwafemi), their wives and husbands; my nieces and nephews, ---- thank you all for believing in my dreams and supporting me in all ways till they are achieved. Worthy of special mention is the support I received from my brother, I.K and his amiable wife, Gbemisola to continue with my academic pursuit even when my University of Ghana http://ugspace.ug.edu.gh viii life was threatened and I was almost quitting. Thank you for your understanding, patience and love. I appreciate my in-laws for their understanding, prayers and love throughout the study period. I am grateful to my cohort 4 colleagues: Priscilla, Perpetua, Maureen, Sissoko, Fafa, Nofou and Mahamadou, you all have been wonderful comrades. My childhood friends: Tunrayo and Olaide, thank you. My mentor, friend and colleague -- Dr Akin-Idowu; Elohor (small) and Adesike, I cannot forget your love, care and support, you all have been wonderful sisters. To our family friends: The Dadas, Odetokuns, Ayenis, Ojos, Oyewoles, Adewoyins --- the Lord will reward your labour of love. To all my friends and brethren in the Lord, I thank you for your prayers and words of encouragement throughout this journey. Remain blessed. To everyone who has contributed in many ways to my success, names are just too numerous to mention, I say a very big thank you. Worthy of mention is the assistance I received from Dr Adebayo, Dr Beatrice Ifie (‘mami’--- thanks), Rofiat (Alhaja) and Mr Oyelakin (IITA) on data analysis. Thank you so much. I appreciate the management of National Horticultural Research Institute then under the leadership of Dr A.A. Idowu which granted me study leave to come for this study. I also appreciate Dr (Mrs) A.O. Olufolaji (the incumbent executive director) for the continued support. Finally, I appreciate my merry-heart, Omoniyi David Ibitoye who supported my vision and agreed that I follow my dream despite the huge sacrifice it demanded on him. “Ọkọ ẹ” you filled the gaps during my long absences from home, you encouraged me to remain focused ---- a great encourager you are, darling, thank you. To my God-given angels, Anjolaoluwa and Tiwaladeoluwa, thank you for persevering with mummy. Mummy loves you all. University of Ghana http://ugspace.ug.edu.gh ix TABLE OF CONTENTS DECLARATION .......................................................................................................... i ABSTRACT ................................................................................................................ iii DEDICATION ............................................................................................................ vi ACKNOWLEDGEMENTS ....................................................................................... vii TABLE OF CONTENTS ............................................................................................ ix LIST OF TABLES .................................................................................................... xiii LIST OF FIGURES .................................................................................................. xvi LIST OF PLATES .................................................................................................. xviii LIST OF ABBREVIATIONS ................................................................................... xix CHAPTER ONE .......................................................................................................... 1 1.0 GENERAL INTRODUCTION ............................................................................ 1 CHAPTER TWO ....................................................................................................... 10 2.0 LITERATURE REVIEW ................................................................................... 10 2.1 Cowpea taxonomy, origin, domestication and economic importance ............... 10 2.2 Cowpea production, productivity and production constraints ........................... 11 2.3. Impact of drought on agriculture and food security ........................................... 15 2.4. Mechanisms of drought tolerance in plants........................................................ 17 2.4.1 Consequences of drought adaptation mechanisms in plants ....................... 20 2.5 Mechanisms of drought tolerance in cowpea ..................................................... 21 2.6 Advances in conventional breeding for drought tolerance in cowpea ............... 25 2.6.1 Tolerance to vegetative stage drought ........................................................ 25 2.6.2 Tolerance to mid-season drought ................................................................ 27 2.6.3 Adaptive traits for drought tolerance in cowpea ......................................... 29 2.6.4 Drought tolerance genes in cowpea ............................................................ 31 2.6.5 Molecular breeding for drought tolerance in cowpea ................................. 32 2.7 Quantitative trait loci (QTL) mapping in cowpea .............................................. 34 University of Ghana http://ugspace.ug.edu.gh x 2.7 Genetics of drought tolerance in cowpea ........................................................... 36 2.8 Participatory Rural Appraisal (PRA) ................................................................. 39 CHAPTER THREE ................................................................................................... 42 3.0 Participatory rural appraisal in cowpea growing areas in Kano State, Nigeria .. 42 3.1 Introduction ........................................................................................................ 42 3.1.1 Site selection ............................................................................................... 46 3.1.2 Selection of farmers .................................................................................... 48 3.2 Data collection techniques and data analysis ..................................................... 49 3.3. Results .................................................................................................................... 50 3.3.1 Socio-economic characteristics of respondents ................................................ 50 3.3.2 Cropping systems and frequency of cultivation.......................................... 52 3.3.3 Farmers’ sources of information on cowpea seeds, climate change and other agricultural related technologies. ............................................................................... 53 3.3.4 Prominent climatic factors limiting cowpea production in surveyed areas 56 3.3.5. Farmers’ perceptions of drought, mitigation measures and impact of drought on cowpea production ..................................................................................................... 58 3.3.6 Cowpea production constraints ................................................................... 66 3.3.7 Traits preferred in improved cowpea varieties ........................................... 70 3.3.8 Farmers’ willingness to participate in specific aspects of in cowpea breeding and other agricultural development research ............................................................. 72 3.4. Discussions ............................................................................................................. 74 3.5. Conclusions ............................................................................................................ 80 CHAPTER FOUR ...................................................................................................... 82 4.0 Evaluation of cowpea genotypes for tolerance to seedling and terminal drought 82 4.1 Introduction ........................................................................................................ 82 4.2 Materials and Methods ....................................................................................... 85 4.2.1 Plant material .............................................................................................. 85 4.2.2 Phenotyping cowpea genotypes for shoot drought tolerance ..................... 85 4.2.3 Field screening of cowpea genotypes for tolerance to terminal drought .... 86 4.2.3 Data collection ............................................................................................ 89 University of Ghana http://ugspace.ug.edu.gh xi 4.2.4 Data Analysis .............................................................................................. 91 4.3 Results ................................................................................................................ 94 4.3.1 Climatic conditions ..................................................................................... 94 4.3.2 Screening experiment.................................................................................. 95 4.3.3 Cluster analysis ............................................................................................... 103 4.4 Discussion ........................................................................................................ 105 4.5 Conclusion ....................................................................................................... 107 CHAPTER FIVE ..................................................................................................... 108 5.0 Combining ability study of selected cowpea lines and performance of their first filial generation under managed stress. ....................................................................... 108 5.1 Introduction ...................................................................................................... 108 5.2. Materials and Methods ......................................................................................... 111 5.2.1 Development of F1 crosses........................................................................ 111 5.2.2 Evaluation of F1 crosses and parents for yield and drought tolerance under managed stress conditions ....................................................................................... 116 5.2.3 Data collection .......................................................................................... 117 5.2.4 Data Analyses ........................................................................................... 120 5.3. Results .............................................................................................................. 123 5.3.2 Soil moisture content ................................................................................ 123 5.3.7 Performance of single F1 crosses for yield and drought tolerance ............ 129 5.3.4 Genetic analysis of the performance of cowpea F1 crosses under contrasting environments ........................................................................................................... 133 5.3.5 Relative contributions of male and female general combining ability effects 138 695.3.6 Estimate of general combining ability effects ....................................... 142 5.3.8 Inter-relationship among traits .................................................................. 148 5.3.9 GGE biplot analysis of grain yield of F1 crosses ...................................... 149 5.4 Discussion ........................................................................................................ 152 5.5 Conclusion ........................................................................................................ 158 CHAPTER SIX ........................................................................................................ 160 6.0 CONCLUSIONS AND RECOMMENDATIONS .......................................... 160 6.1 Recommendations ............................................................................................ 162 University of Ghana http://ugspace.ug.edu.gh xii BIBLIOGRAPHY .................................................................................................... 164 APPENDICES ......................................................................................................... 213 University of Ghana http://ugspace.ug.edu.gh xiii LIST OF TABLES Table 3. 1: PRA sites, coordinates and the number of farmers interviewed in each community. .......................................................................................................... 47 Table 3. 2: Distribution of farmers based on socio-economic characteristics. ......... 51 Table 3. 3: Types of farming system and frequency of cultivation of farmers in Tofa, Bichi, Gwarzo and Garko LGAs of Kano State, Nigeria in the year 2014. ......... 52 Table 3. 4: Drought incidence, its impact on cowpea production and mitigation measures employed by farmers in the 15 surveyed communities of Kano State in the year 2014. ....................................................................................................... 60 Table 3. 5: Devastation of drought expressed by extent to which the stage of occurrence of drought affects cowpea plant in 15 surveyed communities of Kano State in the year 2014. .......................................................................................... 61 Table 3. 6: Economic impact of drought on grain yield of cowpea in 15 surveyed communities of Kano State, 2014 ........................................................................ 62 Table 3. 7: Economic impact of drought on fodder yield of cowpea in 15 surveyed communities of Kano State, 2014 ........................................................................ 64 Table 3. 8: Constraints expressed by extent to which factors limit cowpea production in four local government areas of Kano State. ..................................................... 67 Table 3. 9: Ranking of production constraints in 15 communities surveyed nested within each local government area. ..................................................................... 69 Table 3. 10: General criteria for preferred cowpea varieties as ranked by farmers in the 15 surveyed communities of Kano State, 2014.............................................. 71 Table 3. 11: Farmers’ willingness to participate in crop development research ....... 73 University of Ghana http://ugspace.ug.edu.gh xiv Plate 4. 1: Mixing sand and top soil in ratio 3:1 Plate 4. 2 Marking out the wooden boxes for planting ................................................................................................ 87 Plate 4. 3 Cowpea seedlings 14DAP (A) and at 4 weeks after planting (B) during drought stress. ...................................................................................................... 87 Plate 4. 4 Cowpea seedlings recovery after 4 weeks of drought stress followed by 2 weeks every other day watering. The susceptible parent, TVU7778 had 0% recovery rate while tolerant genotypes showed variable percentage survival rates. ..................................................................................................................... 88 Table 5. 1: Line code, group and maturity period of 20 cowpea inbred lines selected for NCD II hybrid. ............................................................................................. 112 Table 5. 2: Mean square of 20 parents for grain yield and other measured traits under drought stress, well-watered conditions and across research environments. ..... 127 Table 5. 3: Proportion of sum of squares of genotypes, environment and genotype x environment interaction to variation for grain yield. ......................................... 128 Table 5. 4: Mean grain yield of 20 parental lines used in design II crosses under drought and well-watered environments. ........................................................... 129 Table 5. 5: Means of grain yield and secondary traits of top 20 and bottom 10 crosses under drought stress environment. ..................................................................... 131 Table 5. 6: Means of grain yield and secondary traits of top 20 and bottom 10 crosses under well-watered condition. ........................................................................... 132 Table 5. 7: Mean squares of grain yield and other traits of F1 crosses measured across drought stress environments. ............................................................................. 134 University of Ghana http://ugspace.ug.edu.gh xv Table 5. 8: Mean squares of grain yield and other traits measured across well- watered environments. ....................................................................................... 135 Table 5. 9: Mean squares of grain yield and other traits measured across all research environments, Ibadan and Minjibir. ................................................................... 137 Table 5. 10: Estimate of general combining effects of lines evaluated across drought stress environments. ........................................................................................... 143 Table 5. 11: Estimates of general combining ability effects of lines evaluated across well-watered environments. ............................................................................... 146 Table 5. 12: Correlation coefficients of traits under drought stress. ........................ 148 Table 5. 13: Correlation coefficients of traits under well watered condition. ......... 148 University of Ghana http://ugspace.ug.edu.gh xvi LIST OF FIGURES Figure 2. 1: Share of Africa in world cowpea production ......................................... 12 Figure 3. 1: Map of Kano State showing the 15 communities where the PRA was conducted. ............................................................................................................ 48 Figure 3. 2: Farmers' sources of cowpea seeds in 4 LGAs of Kano State in the year 2014...................................................................................................................... 54 Figure 3. 3: Sources where farmers in the 4 LGAs surveyed in Kano State get information on climate change and related agricultural technologies. ................ 55 Figure 3. 4: Prominent climatic factors limiting cowpea production in 15 cowpea growing communities of Kano State, .......................................... Nigeria in the year 2014...................................................................................................................... 57 Figure 4. 1: Means of monthly temperature, relative humidity of the glass house and rainfall during screening experiment ................................................................... 94 Figure 4. 2: Relationship between recovery rate and wilting (A) and stem greenness (B) for cowpea genotypes evaluated under seedling drought stress in the glass house. ................................................................................................................. 102 Figure 4. 3: Dendrogram of 90 cowpea germplasm revealed by average linkage cluster analysis based on 3 discriminant phenotypic characters. ....................... 104 Figure 5. 1: Means of monthly rainfall (mm), maximum and relative humidity readings for Minjibr (A) and Ibadan (B) for the period of experiment in each location. .............................................................................................................. 115 Figure 5. 2: Volumetric water contents measured under well-watered (WW) and drought stress (DS) in Ibadan (A) and Minjibir (B), Nigeria ............................ 125 University of Ghana http://ugspace.ug.edu.gh xvii Figure 5. 3: Percentage contributions of GCA effects from GCAf and GCAm and their interactions for selected traits across drought stress environments. .......... 139 Figure 5. 4: Percentage contributions of GCA effects from GCAf and GCAm and their interaction for selected traits across well-watered environments. ............. 140 Figure 5. 5: Percentage contributions of GCA effects from GCAf and GCAm and SCA of selected traits across all research environments. .................................. 141 Figure 5. 6: A "Which wins Where" GGE biplot of grain yield for 50 F1 crosses evaluated across drought stress and well watered environments at Ibadan and Minjibir, Nigeria. ............................................................................................... 151 Figure 5. 7: An entry/tester view of genotype main effect plus genotype x environment biplot of 50 F1 crosses across drought and well-watered environments in Ibadan and Minjibir. ................................................................ 152 University of Ghana http://ugspace.ug.edu.gh xviii LIST OF PLATES Plate 3. 1: Cross section of farmers being interviewed in Tofa and Garko villages. . 47 Plate 4. 1: Mixing sand and top soil in ratio 3:1 Plate 4. 2 Marking out the wooden boxes for planting ................................................................................................ 87 Plate 4. 3 Cowpea seedlings 14DAP (A) and at 4 weeks after planting (B) during drought stress. ...................................................................................................... 87 Plate 4. 4 Cowpea seedlings recovery after 4 weeks of drought stress followed by 2 weeks every other day watering. The susceptible parent, TVU7778 had 0% recovery rate while tolerant genotypes showed variable percentage survival rates. ..................................................................................................................... 88 Plate 5. 1 (a-c): Hand emasculation, pollination of cowpea and resulting crosses .. 114 Plate 5. 2: Schematic representation of how data was captured using the Greenseeker® handheld sensor .......................................................................... 119 Plate 5. 3: Taking NDVI measurements on cowpea plants using greenseeker® handheld crop sensor.......................................................................................... 120 University of Ghana http://ugspace.ug.edu.gh xix LIST OF ABBREVIATIONS ABL Advanced breeding line ASL Above sea level DAP Days after planting DLSC Delayed Leaf Senescence DS Drought stress FAO Food and Agricultural Organization GCA General Combining Ability IITA International Institute of Tropical Agriculture LDR Landrace LG Linkage group NCII North Carolina mating design II NDVI Normalized Difference Vegetation Index SCA Specific Combining Ability SSA Sub-Saharan Africa TVu Tropical Vigna unguiculata WECA West and Central Africa WW Well-watered University of Ghana http://ugspace.ug.edu.gh 1 CHAPTER ONE 1.0 GENERAL INTRODUCTION Cowpea [Vigna unguiculata (L.) Walpers] is the most economically important indigenous African grain legume producing a source of economic livelihood and nutritional well-being for rural poor and urban consumers (Agbicodo et al., 2009; Langyintuo et al., 2003; Timko, 2006). Cowpea plays a critical role in the lives of millions of people in Africa and other parts of the developing world, where it is a major source of dietary protein that nutritionally complements staple low-protein cereal and tuber crops, and is a valuable and dependable commodity that produces income for farmers and traders (Singh, 2002; Langyintuo, et al., 2003). Cowpea is a valuable component of farming systems in many areas because of its ability to restore soil fertility through nitrogen fixation, for succeeding cereal crops grown in rotation with it (Carsky et al., 2002; Tarawali et al., 2002; Sanginga et al., 2003). The N contribution to a cropping system by a cowpea cover crop was reported to be about 145.7 kg N/ha per season if the crop is turned under (Valenzuela and Smith, 2002). Early maturing cowpea varieties can provide food earlier than any other crop (in as few as 55 d after planting), thereby shortening the “hunger period” that often occurs prior to harvest of other crops in farming communities in the developing world. Cowpea haulms and chaff are used as livestock feeds and are also beneficial in maintaining soil fertility thus making it an important component of any cropping system (Sanginga et al., 2000, Muchero et al., 2008). Dry grain for human consumption is the most important product of the cowpea plant; these grains can either be boiled or converted into other food products such as moin- moin, akara, bean soup etc. in Nigeria. The green leaves/twigs, are also used in preparing University of Ghana http://ugspace.ug.edu.gh 2 nutritious vegetable soup; the fresh pods and peas are used for salad in vegetarian diets (Timko et al., 2007). The estimated world cowpea production area is over 14.5 million ha, with an annual production estimated at about 7.64 million tonnes. Out of this estimate, West and Central Africa (WECA) account for over 9 million ha and 3 million tonnes. West Africa is the key cowpea production zone, mainly from the dry savanna and semi-arid agro-ecological zones. In West Africa, Nigeria and Niger Republics are the major cowpea producers with Nigeria contributing over 60% of the total production (FAOSTAT, 2015). Compared with many other crops, cowpea is reported to thrive in places considered too dry for the production of other grain legumes but because it is mostly grown under rain-fed conditions on sandy soils having low water-holding capacity in the drier regions that receive between 300 - 600 mm annual rainfall, its productivity is adversely affected by erratic rainfall patterns which occur frequently in these areas (Belko et al., 2013). Drought can cause direct reduction of about 50 - 67% in cowpea grain yield (Fatokun et al., 2012; Sanda and Maina, 2013). In addition to the direct effect on yield, many aspects of plant growth are affected by drought stress (Hsaio, 1973), including leaf expansion, which is reduced due to the sensitivity of cell growth to water stress. Water stress also reduces leaf production by promoting senescence and abscission (Karamanos, 1980), resulting in decreased total leaf area per plant. Reduction in leaf area reduces crop growth and thus biomass production. Seed production, which is positively correlated with leaf area (Rawson and Turner, 1982), may also be reduced by reduction in leaf area caused by drought stress. University of Ghana http://ugspace.ug.edu.gh 3 There are various ways of reducing the effect of drought or addressing the problem of drought stress including irrigation and breeding. However, irrigation requires large capital outlay and availability of water throughout the growing season, especially at flowering and pod filling stages. This makes it less feasible especially for small scale farmers in Africa. Developing drought tolerant varieties is a more sustainable option of managing drought since there would be no additional cost to the farmer once drought tolerant seeds are available. Breeding for drought tolerance and grain yield however is complex because they are governed by minor genes whose effects are often confounded by interaction of morphological, physiological and biochemical characters of the crop with the environment thus making genetic improvement of these traits in crops a slow and difficult process (Fatokun et al., 2012; Mir et al., 2012). In cowpea research, drought tolerant factors have been separated into shoot and root tolerance using simple, rapid and cheap screening methods (Singh and Matsui, 2002; Hall et al., 2003). For selection, two classical approaches are followed when breeding for drought tolerance: (i) utilization of grain yield as selection criteria, and (ii) identification of physiological traits that might contribute to yield production under drought (Singh et al., 2003; Hamidou et al., 2007; Badu-Apraku et al., 2011). In order for a rapid progress to be made in the development of more drought-tolerant cowpea varieties, it would be necessary to identify easily recognized and measurable characteristics that are associated with the physiological traits upon which selection could be applied. Numerous efforts in this direction have led to discovery of some traits such as stomatal conductance, lower leaf area development and lower canopy conductance, stay University of Ghana http://ugspace.ug.edu.gh 4 green or delayed-leaf-senescence (DLSC) that are associated with drought tolerance (Agbicodo et al., 2009; Belko et al., 2013; Muchero et al., 2013). Studies on shoot tolerance in cowpea using wooden boxes placed in the greenhouse revealed positive significant correlation between drought tolerance at the seedling stage and drought tolerance in the field (Ewansiha and Singh, 2006; Muchero et al., 2010). They concluded that lines possessing shoot tolerance should also perform well under drought in the field. However, this does not exclude field evaluation in order to identify lines with reproductive-stage drought tolerance and stay-green characteristics, both of which could enhance better performance of cowpea under drought. Breeding for earliness is important in cowpea adaptation in water-limited areas since early maturing crops are bred to escape terminal drought stresses. However, they are susceptible to water deficits occurring during the flowering and reproductive stages (Cisse et al., 1995, 1997; Ehlers et al., 2000). They can suffer significant reductions in plant stature that lower potential yield when stress occurs at the seedling stage. Because it has become more frequent for farmers’ fields to experience irregular rainfall during cropping seasons, varieties that combine earliness and stay-green characteristics should be able to give farmers some grain yield even with irregular rainfalls. While early flowering is a drought escape mechanism, drought tolerance at the seedling stage and ‘delayed leaf senescence’ (DLSC) should enhance the plants’ ability to survive drought during early, mid-season and at pod filling stages. In Senegal, an early DLSC cowpea variety was reported to produce higher yield because of its ability to produce two flushes of pods (Hall et al., 2003). The prolonged life span that DLSC confers on plants would also add to the plants’ ability to tolerate terminal drought better. It can as well serve as an indirect selection for grain yield University of Ghana http://ugspace.ug.edu.gh 5 and biomass under drought (Gwathmey et al., 1992a, c; Hall, 2004, Muchero et al., 2013). If cowpea varieties that combine the above attributes become available, they will enable farmers to obtain a better grain yield in those years when rainfall is irregular. To be able to develop tolerant cowpea varieties that will be adopted by farmers, their involvement at the beginning of breeding effort is very crucial for adoption of developed varieties (Thagana et al., 2009; Kaloki, 2010). Their participation will provide the demand- pull necessary to ensure that the effort in breeding work is focused on key issues of value to the farmers and consumers (Witcombe et al., 1996, 2006). In this regard, a Participatory Rural Appraisal (PRA) was conducted to identify farmers preferred traits that should be bred into drought tolerant varieties in order to increase cowpea productivity. Polygenically controlled traits such as yield, quality and drought tolerance are complex traits because they are under the control of many genes each contributing small effects to the phenotype and thus cannot be easily identified (Babu et al., 2004). The regions within genomes that contain genes associated with a particular quantitative trait are known as quantitative trait loci (QTL). Identification of these QTL based on phenotype observation is impossible because they exhibit significant genotype by environment interactions (G x E), which necessitate extensive multi-location testing and breeding efforts targeted to specific production environments (Heffner et al., 2009; Bharadwaj et al., 2011). Multi- location testing, however, usually results in genotype-by-environment interactions that often complicate the interpretation of results obtained and reduce efficiency in selecting the best genotypes (Annicchiarico and Perenzin, 1994). Better understanding of the level of G × E interaction and performance stability in crops serves as a decision making tool, University of Ghana http://ugspace.ug.edu.gh 6 particularly at the final stage of the variety development process, to generate essential information on pattern of adaptation in breeding lines as well as new varieties for release, and to determine the recommendation domains where a given cultivar would be better adapted (Yan, 2011). Rapid progress in the development of polymorphic molecular markers has led to the intensive use of QTL mapping in genetic studies for quantitative traits (Wang et al., 2007). The principle of QTL analysis is based on detecting an association between the phenotype and the genotype or the marker tightly linked to the trait of interest. QTL analysis effectively improves breeding for difficult characters (Collard et al., 2005, Muchero et al., 2013). Five stay-green QTL showing positive pleiotropic effects between delayed senescence, increased biomass, and grain yield have been reported by Muchero et al., (2013). Three of these five QTL: QTL, Dro-1, 3, and 7 were identified in RIL population and diverse germplasm suggesting they may be invaluable targets for marker-assisted breeding in cowpea. Early vegetative delayed leaf senescence was co-located with biomass and grain yield suggesting the possibility of using delayed senescence at the seedling stage as a rapid screening tool for post-flowering drought tolerance in cowpea breeding (Agbicodo et al., 2009; Muchero et al., 2013). Investigations on G x E interactions at important crop growth stages for yield components would help to develop strategies that integrate conventional plant breeding with modern molecular marker-based selection for tailoring cultivars for high yield and target environments. Although grain yield between 2500 kg/ha to 4000 kg/ha is achievable for cowpea, several constraints have kept farmers’ yields constantly low at levels between 350 and 700 kg/ha University of Ghana http://ugspace.ug.edu.gh 7 (Ajeigbe et al., 2010a). If the yield barrier is to be overcome, strategies to improve the genetic potential of cowpea plants by introducing novel genes is required. For this to be achieved, genotypes with potential for higher yield and other desirable traits are needed as parent lines to develop improved varieties (Aremu, 2005). The identification of suitable parental genotypes, potentially generating superior lines with traits contributing to the overall yield of a crop, is an important step in the development of improved varieties because if parents are precisely selected, the desired recombinants will be found in the segregating generations (Moalafi et al., 2010; Ayo-Vaughan et al., 2013). Knowledge of the genetic control of complex quantitative traits and the magnitude of genetic variability that exists among available germplasm are therefore important for selection and genetic improvement of crop plants. Selection of parents based on combining ability has been used as an important breeding approach in crop improvement. The combining ability and gene effects of yield and its components have been studied by many researchers. Basbag et al. (2007) suggested that combining ability analysis is an important tool for selection of desirable parents together with the information regarding nature and magnitude of gene action controlling quantitative traits. Combining ability study provides useful information on how two inbred lines can be combined to produce a productive hybrid or breed novel varieties. Selection and development of parental lines with high combining ability is one of the most important breeding objectives whether the goal is to create a hybrid with strong vigour or develop a pure line cultivar with improved characteristics compared to their parents (Kadam et al., 2013). The genetic variability among the crosses is partitioned into effects due to additive (GCA) or non-additive (SCA) variances (Shiri et al., 2010). In self-pollinated crop species like cowpea, lines with high University of Ghana http://ugspace.ug.edu.gh 8 and positive GCA estimates for a character will be good candidates to be used as parents. Preponderance of GCA was reported for hundred seed weight, pod per plant and grain yield of cowpea under moisture stress (Carvalho et al., 2012; Alidu et al., 2013), for inheritance of resistance to cowpea-aphid borne mosaic virus (Orawu, 2013). These results showed that these traits can be improved by exploiting additive gene effects through selection schemes such as recurrent selection capitalizing on favourable additive variation. Rupela and Johnansen (1995) used pure line selection to improve nodulation in pigeon pea as a result of large GCA effects. Cho and Scott (2000) reported predominance of additive effects for seed vigour and yield in soybean. Nkalubo et al. (2009), who studied the genetic control of anthracnose resistance in common bean, obtained both additive and non-additive effects controlling this trait, but with a slight predominance of additive genetic effects over dominance effects. Information regarding combining ability and nature of gene action governing the inheritance of desirable traits are therefore efficient ways to achieving maximum genetic gain when developing high yielding cowpea cultivars with higher and more stable yields across drought prone regions where it is grown. This study is among the few studies to provide information on the combining abilities of cowpea lines of diverse germplasm under contrasting soil moisture regimes in order to be able to efficiently develop novel varieties that meet farmers’ demand and high yielding under varying moisture stress. University of Ghana http://ugspace.ug.edu.gh 9 The objectives of this study were to: 1. Elicit farmers’ understanding of drought and its impact on cowpea production and productivity. 2. Assess the genetic diversity for drought tolerance in cowpea germplasm. 3. Assess the performance of the F1 crosses under drought and well-watered conditions. 4. Determine the gene action controliing yield and drought tolerance in cowpea. University of Ghana http://ugspace.ug.edu.gh 10 CHAPTER TWO 2.0 LITERATURE REVIEW 2.1 Cowpea taxonomy, origin, domestication and economic importance Cowpea, [Vigna unguiculata (L.) Walp] is a Dicotyledoneae belonging to the order Fabales, family Fabaceae, subfamily Faboideae, tribe Phaseolaeae, subtribe Phaseolinae, genus Vigna and section Catiang (Verdcourt, 1970; Marechal et al., 1978). The genus Vigna has many species varying according to authors but all cultivated cowpeas are under Vigna unguiculata. The species unguiculata is subdivided into four culti-groups viz: (1) unguiculata which is the common form, (2) biflora or catjang with small erect pods; (3) sesquipedalis or yard-long beans characterized with very long pods and consumed as green snap bean; (4) textilis characterized with its long peduncles which is used for fibers (Padulosi and Ng, 1997). Information based on range of variation and number of varieties found in wild cowpea as well as their primitive characteristics suggested that cowpea originated from the southern Africa regions encompassing Namibia, Zambia, and Zimbabwe but further distributed through the Western Africa regions (Ng, 1995). Cowpea is considered to have been domesticated in Africa from its wild ancestral form, V.unguiculata subsp. dekindtiana (Harms) Verdc. (Ng and Marechal, 1985). The center of maximum genetic diversity of cultivated cowpea is considered to encompass region of Nigeria, southern Niger, and part of Burkina Faso, northern Benin, Togo and northwestern part of Cameroon (Ng, 1995). Cowpea is a vegetable legume which provides an inexpensive source of protein complementing staple cereal and starchy tuber crops and minerals for the urban and rural populations of SSA where it is predominantly cultivated and consumed. According to University of Ghana http://ugspace.ug.edu.gh 11 Ehlers and Hall (1997), dry grain for human consumption is the principal product of the cowpea plant, but leaves (many parts of eastern Africa), fresh peas (the southern USA and Senegal) and fresh green pods (humid regions of Asia and Caribbean) are consumed. The crop is also used for green manure (southern USA and Australia) and fodder (parts of the Sahel). Cowpea cultivars differ in nutritional composition and cooking characteristics. For example, seed protein ranged from 23 to 33% on dry weight basis (Nielsen et al., 1993); fat content ranged from 1.4 to 2.7% and cooking time ranged from 21 to 62 minutes (Ehlers and Hall, 1997). Cowpea is favoured by farmers for its diverse uses which include: rich protein source feed for livestock feed, its ability to improve soil fertility through its nitrogen fixing ability and its ability to control erosion and as an intercrop with cereals like millet and sorghum thus making it an important integral part of crop farming system in West Africa (WA). It also provides household benefits in the form of cash and income diversity for farmers (Eaglesham et al., 1992; Fabunmi et al., 2012). Demand for cowpea and low-cost nutritive food is increasing because of population increase and more knowledge on healthy nutrition (Singh et al., 2003; Yewande and Thomas, 2015). 2.2 Cowpea production, productivity and production constraints At present, cowpea is grown throughout the tropic and subtropic areas around the whole world where rainfall resources are characteristically low (300-600 mm) (Fatokun et al., 2012) and variable (Fussell et al., 1991). Information available on the Food and Agriculture Organization (FAO) database estimated that cowpea is now cultivated on at least 11.3 million hectares worldwide with an annual production of 5.7 million tonnes with 95% University of Ghana http://ugspace.ug.edu.gh 12 being produced in Africa (Figure 2.1). Although cowpea is widely cultivated throughout the tropics, West and Central Africa (WCA) account for over 64% of the area (9.2 million hectares) followed by about 2.4 million hectares in Central and South America, 1.3 million hectares in Asia and about 0.8 million hectares in Eastern and South Africa. A substantial part of cowpea production from WCA comes from the drier parts of northern Nigeria (about 3.2 million ha from 2.5 million tonnes), and southern Niger Republic (about 4.7 million ha from 1.3 million tonnes) (FAOSTAT, 2015). Figure 2. 1: Share of Africa in world cowpea production Source: Adapted from FAOSTAT (2015) Cowpea in WCA is traditionally often intercropped with cereals like sorghum, maize or millet by small-holder farmers. Fertilizers and pesticides are generally not used and when used, farmers do not apply the adequate dosage needed for optimum yield potential of the Africa, 95% Asia, 3% Carribean, 1% North America, 1% South America, 0% Europe, 0% University of Ghana http://ugspace.ug.edu.gh 13 crop because these inputs are expensive and/or not readily available for the small-holder farmers (Ajeigbe et al., 2010b). The large differences between on-farm yield of cowpea in WCA (0.025 to 0.3 tonnes/ha) and the potential yield reported from experimental stations (1.5 to 3 tonnes/ha) (Ajeigbe et al., 2010a) showed that the high production estimate arising from these regions mainly comes from increase in land area rather than the genetic potential of the crop (Singh et al., 1997; van Ek et al., 1997). For instance, average cowpea yield in the United States of America is 1.9 tonnes/ha while it is 0.97 tonnes/ha for WCA (FAOSTAT, 2015). Several biotic and abiotic factors such as insect pests, diseases (fungal, viral, bacterial and parasitic weeds), poor soil fertility, metal toxicity and drought contribute to the reduction of cowpea yield potential in SSA (Singh and Tarawali, 1997; Wang et al., 2001; Emechebe and Lagoke, 2002; Singh and Ajeigbe, 2002). Other factors contributing to low yield in SSA include lack of improved varieties that can withstand these stresses and lack of adequate production practices and inputs needed for higher productivity and profitability. This yield gap therefore can be bridged if improved varieties and production practices are available to farmers through participatory on-farm training and evaluation with farmers (Ajeigbe et al., 2010a, b). Drought is one of the most important abiotic constraints threatening food security in the world. This is so because the economies of African nations depend largely on rain-fed agricultural systems which are seriously affected during periods of severe drought thus making drought a serious natural disaster in Africa (Oladipo, 2008). Drought conditions can either be intermittent when they occur at one or more intervals due to limited periods University of Ghana http://ugspace.ug.edu.gh 14 of inadequate rain or irrigation during the crops’ growing period or terminal when there is progressive decrease in available soil water resulting in severe drought stress at the later period of crop growth at grain-filling stage. Crop response to water stress at various stages of growth is related to crop species, stage of growth, economic portion of the crop, the duration and intensity of the stress (Shouse, 1979, 1981). Cowpea is reported to be more tolerant to drought (Whitbread and Lawrence, 2006) however, it still suffers considerable damage due to frequent drought in the Savanna and Sahel sub-region. Studies have indicated that cowpea could maintain good seed yield when subjected to drought at vegetative stage provided subsequent conditions were conducive for flowering and pod set (Ziska and Hall, 1983a, b; Singh et al., 1997; Akyeampong, 2012). However, Akyeampong (1986) showed that the crop is highly sensitive to water deficits during flowering and pod filling stages. To this end, early maturing varieties have been developed to escape terminal drought (Singh, 1987) but they have been reported to perform poorly if exposed to intermittent moisture stress occurring at the vegetative growth stage (Mai-Kodomi et al., 1999; Fatokun et al., 2012). Moreover, these early varieties tend to be sensitive to drought that occurs during the early stages of the reproductive phase (Thiaw et al., 1993). Cowpea is a dual-purpose crop; it is grown for the purpose of green manure as much as for its grains. Early moisture stress that can reduce the quantity of green manure is sometimes unavoidable because cowpea solely grown under this system is terminated at the vegetative (Fabunmi et al., 2012). Therefore, genetic enhancement of cowpea for tolerance to both vegetative and terminal drought will be a most effective method for ensuring sustainable and improved yield under variable and changing climate. University of Ghana http://ugspace.ug.edu.gh 15 2.3. Impact of drought on agriculture and food security Agricultural production remains the main source of livelihood for rural communities in Sub- Saharan Africa, providing employment to more than 60% of the population and contributing about 30% of gross domestic product. Agriculture production and productivity are highly sensitive to changes in climate and weather conditions. Therefore, the climatic variability has been implicated in affecting local as well as global food, fiber and forest production (Easterling et al., 2007). Food security in its most basic form is the access of all people to the food needed for a healthy life at all times. It refers to the availability of food and one's access to it. A household is considered food secure when its occupants do not live in hunger or fear of starvation while a country is considered as food-secure when food is not only available in the quantity needed by the population consistent with decent living, but also when the consumption of the food does not pose any health hazard to the citizen. (Baro and Deubel, 2006; Pinstrup-Andersen, 2009). Drought is a naturally occurring phenomenon that exists when precipitation is significantly below normal recorded levels, causing serious hydrological imbalances that adversely affect land resource production systems (UNCCD, 2011). It can be attributed to inadequate seasonal precipitation, a prolonged dry season or a series of sub-average rainy seasons (Sheikh and Soomro, 2006). In agricultural terms, drought is said to occur when there is not enough moisture available at the right time for the growth and development of crops. It often results from insufficient and/or poor distribution of rainfall when crops are still growing in the field (Nhlane, 2001; Ganapathy and Ganesh, 2008). As a result, yields and/or absolute production decline (Sharfiq-ur-Rehman et al., 2005). Drought is especially problematic in developing countries where agriculture is predominantly rain-fed. It is University of Ghana http://ugspace.ug.edu.gh 16 considered the most serious threat to world agriculture and food security because it is the main cause of desertification, major cause of severe food shortages resulting in malnutrition and famine (Mir et al., 2012). It therefore affects the four pillars of food security: availability, stability, access and utilization (FAO, 2009). In the agricultural sector, its effects include: crop losses, lower yields in both crop and livestock production, increased livestock deaths, increases in insect infestation and plant and animal diseases, damage to fish habitat, forest and range fires, desertification and soil erosion. Its impacts on human health include increased risk of food and water shortages, increased risk of malnutrition and higher risk of water‐ and food‐borne diseases. Economic impacts include: income losses, high prices of food products as supplies are reduced, with severe effects on the poorest and most vulnerable. Shortfalls in food production leads to substantial increases in imports to meet local needs, which can result in increased fiscal pressure on national budgets (Oladipo, 2008). Twelve million hectares are lost globally annually (23 hectares/minute), where 20 million tonnes of grain could have been grown as a result of drought and desertification (http://www.un.org/en/events/desertificationday/background.shtml accessed 28/10/2014 at 1.15pm). For instance, during the drought of 1972–73 in the northeastern Nigeria about 300,000 animals representing 13% of the livestock population of the region were reported dead, while agricultural yield dropped to between 12% and 40% of the annual averages (Fagbemi, 2002). The effects of drought in terms of reduced food production have been even more severe during 1982 - 84 than 1972 - 74. In some parts of Borno State (then, comprising Borno & Yobe States) nearly 100% crop losses were recorded (Enabor, 1987). Agriculture consumes the largest available water through irrigation and in 2004 it was University of Ghana http://ugspace.ug.edu.gh 17 reported that 80% of world available water was consumed alone by irrigated agriculture (Condon et al., 2004). With expected increase in world population by 2025 to 9.6 billion, agricultural water consumption will have to decrease which in turn will affect agricultural production and productivity (Ribaut et al., 2009). 2.4. Mechanisms of drought tolerance in plants Drought is by far the most important environmental stress in agriculture which reduces the productivity of crops (Parameshwarappa and Salimath, 2007; Abdou-Razakou et al., 2013) preventing them from expressing their full genetic potential. Drought stress reduces leaf size, stems extension and root proliferation, disturbs plant water relations and reduces water-use efficiency. Many efforts have been made to improve crop productivity under water-limiting conditions. While natural selection has favoured mechanisms for adaptation and survival, breeding activity has directed selection towards increasing the economic yield of cultivated species. Many years of breeding activities have led to some yield increase in drought environments for many crop plants. Meanwhile, fundamental research has provided significant gains in the understanding of the physiological and molecular responses of plants to water deficits, but there is still a large gap between yields under optimal and stress conditions. Minimizing the ‘yield gap’ and increasing yield stability under different stress conditions are of strategic importance in guaranteeing food for the future. In agriculture, drought resistance refers to the ability of crops to achieve economic production with minimum loss in water-deficit environments compared to the water- constraint-free management (Mitra, 2001). Nevertheless, direct selection for grain yield University of Ghana http://ugspace.ug.edu.gh 18 under water-stress conditions has been hampered by low heritability, polygenic control, epistasis, significant genotype by environment (G x E) interaction and quantitative trait loci (QTL) by environment (QTLx E) interaction (Piepho, 2000). The complexity of drought tolerance mechanisms explains the slow progress in yield improvement in drought prone environments. In recent years, crop physiology and genomics have led to new insights in drought tolerance providing breeders with new knowledge and tools for plant improvement (Tuberosa and Salvi, 2006). The occurrence of morphological and physiological adaptation to drought stress may vary considerably among species. When plants are subjected to drought stress, a number of physiological and morphological responses have been observed and the magnitude of the responses varies among species and between varieties within a crop species (Kramer, 1981). Different mechanisms may make a plant tolerant to drought such as drought escape, avoidance or drought tolerance. Plants may use more than one mechanism to cope with drought (Ludlow, 1989; Mitra, 2001; Yue et al., 2006). Drought escape is the ability of the plant to complete its life cycle before the onset of serious water deficit (Yue et al., 2006; Bhatnagar-Mathur et al., 2010). This mechanism involves rapid phenological development (early flowering, early podding and early maturity) (Gaur et al., 2008). Although studies have revealed that there is yield penalty for any reduction of crop maturity duration below the optimum (Caliskan et al., 2008), selection for earliness is still a common practice in breeding crops for drought tolerance because early cultivars provide quick income at the beginning of cropping season; they can be grown in diverse cropping systems and they can escape some insect infestation (Ehlers and Hall, 1997). University of Ghana http://ugspace.ug.edu.gh 19 Drought avoidance is plants’ capacity to sustain high plant water status or cellular hydration under the effects of drought (Blum, 2005). Under this mechanism, plants minimize water loss through stomata closing and lenticular conductance (Chaves et al., 2003), reducing absorption of solar radiation through leaf rolling or folding/paraheliotropism (Fatokun et al., 2012) and reducing evapotranspiration surface (leaf area) and development of dense trichome layer that increases reflectance (Yue et al., 2006; Taiz and Zeiger, 2006). They maximize water uptake through increase of the capacity of the root system (Jackson et al., 2000) and root characteristics such as thickness, depth, length and density e.g. in rice (Ekanayake et al., 1985). This mechanism has effects on the effective water use and control of evapotranspiration. Drought tolerance is the ability of plants to withstand water-deficit with low tissue water potential. The mechanism involves maintenance of turgor through osmotic adjustment (accumulation of solutes in the cell), increased cell elasticity and decreased cell size, production of other stress relieving agents and desiccation tolerance by protoplasmic resistance (Agbicodo et al., 2010). It involves the resurrection and survival of genotypes after extended and intense internal water deficit. These genotypes can be still alive when there is 95% of leaf water loss (Scott et al., 2000). Dehydration tolerance enables the plants to survive a long and harsh periods of water deficit and regrow when rain falls. It allows also plants to maintain metabolic activities longer and transport assimilates to the storage tissues (Fukai and Cooper, 1995). Biochemical processes which involve accumulation of compatible solutes mainly nitrogen compounds, result in dehydration avoidance in plants (McCue and Hanson, 1990). Plants contain these solutes and antioxidants at varying levels and in different types. These solutes work together with antioxidants which intervene to University of Ghana http://ugspace.ug.edu.gh 20 eliminate reactive oxygen species (ROS) and to repair their damages; adaptive role by osmotic adjustment and protection of cellular compounds; control the equilibrium between the production and the elimination of free radicals (McCue and Hanson, 1990; Lin et al., 2006; Brosché et al., 2010). Crop varieties that are drought resistant or tolerant express a higher quantity of antioxidants than sensitive varieties (Herbinger et al., 2002). Although the quality and quantity of these molecules are crop species dependent, their expression is affected by environmental conditions (Blokhina et al., 2003; Lin et al., 2006). Understanding the expression, mechanisms and mode of functioning of these molecules will help to identify and develop tolerant crop varieties and improve cowpea productivity in dry lands. 2.4.1 Consequences of drought adaptation mechanisms in plants Reitz (1974) classified plants into three based on their responses to drought stress: (i) those with superior yield across diverse production environments (ii) those with better yield performance in stress environments (iii) those with better performance under optimum environment (Reitz, 1974). Breeders are interested in the first category because they are stable and will have better performance across diverse environments. It has been reported that plants adopt more than one mechanism at a time to cope with moisture deficit (Mitra, 2001; Yue et al., 2006). However, none of these adaptations exists without its undesirable consequences on the plants’ growth and productivity (Larcher, 2000; Acquaah, 2012). For instance, escape adaptation characterized by short life cycle; these genotypes usually yield less compared to the genotypes that attain normal maturity cycle. On the contrary, if these genotypes are exposed to intermittent water stress at the vegetative period they perform University of Ghana http://ugspace.ug.edu.gh 21 very poorly (Muchero et al., 2008). Avoidance adaptation such as stomata closure and reduced leaf area decrease carbon assimilation due to reduction in physical transfer of carbon dioxide molecules and increase leaf temperature thus reducing biochemical processes which negatively affects yield (Lawlor and Tezara, 2009). Dehydration tolerance with the accumulation of compatible solutes, the synthesis of antioxidants and the process of ROS scavenging depletes assimilates and energy. Consequently, these mechanisms reduce the ability of crop genotypes to synthesize organic end-products (Mitra, 2001). It is therefore important to focus on the type of drought prevalent in the target environment when incorporating drought tolerance mechanisms into improved cultivar and breeding for drought tolerance should combine a balance among escape, avoidance and tolerance while maintaining good productivity (Mitra, 2001). 2.5 Mechanisms of drought tolerance in cowpea Cowpea is inherently more tolerant to drought than other crops which is why it is cultivated in low-rainfall areas with annual rainfall less than 400 mm (Fatokun et al., 2012). Early maturing cowpea cultivars are better adapted in many cowpea production zones of Africa than late maturing ones in some dry environments because they reach maturity in as few as 60-70 days ( Singh, 1994). IT84S-2246 and Bambey 21 are examples of early cowpea cultivars already released and adopted by farmers, they however perform poorly under intermittent drought at the vegetative or reproductive stages. Late maturing fodder-type varieties planted as sole crops or relay intercrops on the other hand may suffer severe drought stress due to early cessation of rains. Efforts now are being geared towards University of Ghana http://ugspace.ug.edu.gh 22 breeding cowpea varieties with enhanced drought tolerance for early-, mid- and terminal- season drought stresses (Watanabe et al., 1995). Two types of drought tolerance have been described in cowpea at the seedling stage using the wooden box technique (Mai-Kodomi et al., 1999). At fifteen days after termination of water supply, the “Type 1” drought tolerant genotypes stopped growth after the onset of drought stress and maintained uniformity, but displayed a declining turgidity in all tissues of the plants including the unifoliates and the emerging tiny trifoliates for over 2 weeks and death of all plant parts such as the growing tip, unifoliates and epicotyl gradually almost at the same time. Suggested mechanism for type 1 was drought avoidance with the display of closure of stomata to reduced water loss through transpiration and cessation of growth was suggested for this type of tolerance (Lawan, 1983; Boyer, 1996). On the contrary, the ‘‘Type 2’’ drought tolerant lines remained green for a longer time and continued slow growth of the trifoliates under drought stress. With continued moisture stress, the trifoliates of these varieties started wilting as well and died about 4 weeks after drought stress started. Three combinations of mechanisms were suggested to interplay in the type 2: stomatal regulation (partial opening), osmotic control and selective mobilization with distinct visible differences in the desiccation of lower leaves compared to the upper leaves and growing tips (Mai-Kodomi, 1999; Muchero et al., 2008). University of Ghana http://ugspace.ug.edu.gh 23 Association between crop performance carbon isotope discrimination (Δ) was reviewed for cowpea, common bean and peanut (Condon and Hall, 1997). The authors positively related the differences in the grain yield with Δ, indicating that more productive genotypes have a higher photosynthesis rate resulting in higher internal carbon dioxide concentration in their leaves. Similarly, studies on Pima cotton (Gossypium barbadense) and bread wheat (Triticum aestivum) showed a positive correlation between yield increases and increases in stomata conductance (Lu et al., 1998). These authors argued that the higher Δ in more productive genotypes of cowpea, cotton and wheat was probably due to their having more open stomata which could have resulted in greater rates of photosynthesis effects (Condon and Hall, 1997), or beneficial effects on the plant resulting from greater evaporative cooling (Lu et al., 1998). Cruz de Carvalho et al. (1998) in his study to compare physiological responses of cowpea and common bean reported that cowpea genotypes kept their stomata partially opened and thus had a lower decrease in their net photosynthetic rates than the common bean. Further study was therefore recommended in order to demonstrate whether partial opening of stomata under drought conditions would have significant effects on grain yield Ability to maintain low water potential of about -18 bar (-1.8 MPa) has been reported as a mechanism through which cowpea is able to withstand vegetative-stage drought stress even under extreme drought (Turk and Hall 1980; Hall and Schulze 1980). This mechanism has also been reported in peanut with as low as -82bar (-8.2Mpa) (Turner, 2000). Cowpea leaves under drought stress display paraheliotropism, a drought avoidance mechanism where the leaves are orientated parallel to the sun rays in order to reduce evapotranspiration (Fatokun et al., 2012). University of Ghana http://ugspace.ug.edu.gh 24 Cowpea displays tolerance mechanism by exhibiting delayed leaf senescence (DLSC) which is similar to the stay-green characteristics in cereals such as (Gwathmey and Hall 1992; Hall et al., 1997). Their studies revealed that cowpea plants that exhibit DLSC trait were able to survive the mid-season drought caused by intermittent rainfall and were able to produce second flush of pods for additional grain yield (Hall et al., 2003). Drought can occur at the onset of the growing season in which rainfall ceases after planting; or during mid-season before the plants flower or during the reproductive to the pod-filling stage. While early maturity is a drought escape mechanism, drought tolerance at seedling stage and DLSC will enhance the plants’ ability to survive drought during early and mid-season and at pod filling stages. It has been reported in sorghum, that genotypes with DLSC (stay green) are able to remain physiologically active during the late stages of grain filling under terminal drought (van Oosterom et al., 1996). Ziska et al. (1985) reported that in cowpea, grain yield is strongly dependent upon the water available to the plant during the reproductive stage for pod-filling. A detailed study by Belko et al. (2012) on water-saving traits in cowpea observed that traits such as lower early vigour, lower transpiration rate under well-watered conditions during vegetative stage, lower leaf area development, sustained transpiration until soil was drier and lower canopy conductance under high vapour pressure deficit (VPD) conditions discriminated drought tolerant from susceptible cowpea genotypes. Similar results were observed in pearl millet (Kholova et al., 2010) and chickpea (Zaman-Allah et al., 2011). They reported that these genotypes will be desirable in low humidity environments with high atmospheric VPD, limited available water, and where water deficits commonly develop later in the growing season. University of Ghana http://ugspace.ug.edu.gh 25 Limited progress is still recorded so far in increasing drought tolerance in cowpea because (1) the basis for drought tolerance in cowpea is yet to be well established and (2) information on combining abilities of tolerant genotypes that can be hybridized together in order to be able to produce drought tolerant segregating populations on which breeders can make efficient selection is still limited. 2.6 Advances in conventional breeding for drought tolerance in cowpea Although conventional approach for breeding for drought tolerance is challenging because the occurrence of drought over time and space is unpredictable, breeding drought tolerant crops still remains the most viable and economical strategy to improve productivity under drought conditions (Blum, 2005; Carena et al., 2009). 2.6.1 Tolerance to vegetative stage drought Survival of vegetative-stage drought by cowpeas has been associated with the maintenance of higher leaf water status than pearl millet (Petrie and Hall, 1992a, b, c). Cowpeas also have stomata that are very sensitive to soil drying, partially closing before any changes in leaf water potential are detected (Bates and Hall, 1981). When cowpea plants are subjected to drought under field conditions, their leaves do not usually wilt but tend to orient more vertically, tracking the sun in a manner that minimizes the interception of solar radiation (Shackel and Hall, 1979). These mechanisms contribute to the unique ability of cowpeas to survive extreme vegetative-stage droughts that kill most other crop plants. Screening technique for survival under drought at the seedling stage has been developed that uses a shallow soil layer in boxes (Singh and Matsui, 2002). This method eliminates University of Ghana http://ugspace.ug.edu.gh 26 the influences of the root system on drought tolerance, and permits nondestructive visual identification of shoot dehydration tolerance (Singh et al., 1999a, b). Wooden box, field and pot testing of different cowpea varieties demonstrated a close correspondence between drought tolerance at the seedling stage and reproductive stage (Singh and Matsui, 2002). These authors concluded that the wooden box technique was a faster way of identifying tolerant varieties which can be saved and transplanted for further progeny testing and selection. Muchero et al. (2008) reported stem greenness, survival and recovery dry weights as the most reliable greenhouse-based assayable traits in separating tolerant cowpea genotypes from susceptible genotypes. Breeding strategies that involve selection for early and extra- early cowpea varieties to escape terminal drought have been exploited by cowpea breeders for some years. Early variety “Ein El Gazal” bred and released in UC-Riverside by crossing an erect early California cultivar that had tolerance to vegetative stage drought with an erect line from Senegal performed very well across different environments (Hall and Patel, 1985). “Melakh”, an extra-early cowpea variety which has resistance to two seed-borne diseases: cowpea aphid, and partial resistance to flower thrip was released in Senegal (Cisse et al., 1997) and “Vuli-1” and early variety was bred for cultivation during the short rains in Tanzania (Mligo and Singh, 2007). Although these varieties can survive vegetative-stage droughts and produce significant quantities of grains under very short rainy season, they produce little hay and can be devastated by mid-season drought (Thiaw et al., 1993) and some biotic stresses. Two conditions have been identified that can render vegetative drought tolerance ineffective namely: condition where Macrophomina phaseolina organism that causes ashy stem blight University of Ghana http://ugspace.ug.edu.gh 27 disease of cowpea is present in the soil and the presence of the lesser corn stalk borer (Elasmopalpus lignosellus) (Hall, 2012). It is important to incorporate resistance to these pests in cowpea varieties that are being bred for drought tolerance at the vegetative-stage. 2.6.2 Tolerance to mid-season drought Many approaches have been taken to breed cowpea varieties for tolerance to mid-season drought compared to the extra-early cultivars because these varieties are dual purpose; producing significant hay and grains. The variety “Mouride” combines high yield with tolerance to mid-season drought and resistance to Striga gesnerioides and cowpea weevil was released in Senegal (Cisse et al., 1995). A delayed-leaf-senescence (DLSC) trait has been discovered that enables cowpeas to maintain a green canopy after the first flush of pods is mature and more consistently produce a second flush of flowers and pods (Gwathmey et al., 1992a). When genotypes with this trait were subjected to a mid-season drought during the first flowering period, they had the ability to survive and then produce second flush of pods and grain (Gwathmey and Hall, 1992a). The mechanism of the DLSC trait is complex. It results in the accumulation of greater reserves of carbohydrates in the base of the stem (Gwathmey et al., 1992b) and probably also in the roots. DLSC is strongly expressed when Fusarium solani f. sp. phaseoli a soil borne fungus which causes the senescence of cowpea during pod maturation is present in the soil. Presumably, roots with greater carbohydrate reserves are more resistant to this root rotting organism (Hall, 2012). Irrespective of the complexity of the trait, it has been consistently expressed by plants in soils where cowpeas have been grown in rotations for several years in many locations in California and also at Bambey in Senegal. DLSC dual purpose cowpea cultivars have been University of Ghana http://ugspace.ug.edu.gh 28 developed and released (Dingkhun et al., 2006). DLSC has been reported to be a useful indirect selection criterion in breeding for post-flowering drought tolerance in crops (Rosenow et al., 1983). The relatively high heritability, low genotype by environment (GxE) interactions in cowpea could enable its successful incorporation in improved varieties (Hall et al., 1997; Ismail et al., 2000). Root systems cannot be overlooked because it is through them that the plant obtains its water and mineral requirements for growth and unavailability of these resources often impose limit on productivity. Screening for root characteristics confirming drought tolerance have been studied in cowpea using the ‘pin-board root-box’ (Matsui and Singh 2003), herbicidal band screening (Robertson et al., 1985; Khalfaoui and Havard, 1993) and polyethylene glycol (PEG) (Badiane et al., 2004) methods. However, ‘pin-board root-box’ is not as widely accepted as the wooden box technique for vegetative screening probably because it is not practical for screening large numbers of plants (Matsui and Singh, 2003). Limited progress has been made on root system and drought tolerance in cowpea perhaps because some studies have reported that deeper and larger root systems may not be of additional advantage for the areas of cowpea production that are characterized by sandy soils with only about 8% of soil water and secondly, possession of larger roots require additional carbohydrates and energy for their construction and maintenance (Hall et al., 2012). Studies have, however, been more directed towards the water saving traits in cowpea (Belko et al., 2013); pearl millet (Kholova et al., 2010) at the vegetative growth stage which helps the crop to retain water in the soil profile to support plant growth at pod- filling stage. No root characteristics for drought tolerance were studied in the work. University of Ghana http://ugspace.ug.edu.gh 29 2.6.3 Adaptive traits for drought tolerance in cowpea Breeding improved genotypes for the arid and semiarid tropics by selection solely for seed yield is difficult, because of the variability in amount and temporal distribution of available moisture from year to year. The relatively low heritability for grain yield under drought conditions has resulted in the use of other efficient traits that are indirectly related to grain yield (Omae et al., 2007; Singh et al., 2004, 2007; Sharma et al., 2007). These traits are referred to as secondary traits and are highly heritable, cheap and easy to measure thus making selection for drought tolerance easier and faster. In cowpea, traits such as stem greenness at vegetative stage (Muchero et al., 2008) or DLSC (during pod-filling) stage (Fatokun et al., 2012); number of pods and seeds per plant (Ajibade and Morakinyo, 2000) are frequently used as secondary traits to identify tolerant genotypes under water limited conditions. Recently, precision phenotyping for drought tolerance using more sophisticated instruments (Mir et al., 2012) that can give accurate measurement of the health of green tissues of plants are now being employed to complement visual scoring for the stay green trait which though convenient is subjective (Joshi et al., 2007). The instruments provide measurements of the normalized difference vegetation index (NDVI) of the crop growing under stress. NDVI is a mathematical formula derived to form a single spectral-based number, which is more sensitive than just a single wavelength and it is a measure of greenness (Sembiring et al. 2000; Hazratkulova et al., 2012). University of Ghana http://ugspace.ug.edu.gh 30 2.6.3.1 Stem greenness or Delayed leaf Senescence Maintenance of green stem was shown to be an important criterion for seedling stage drought tolerance in cowpea (Muchero et al., 2008) while maintenance of DLSC trait has been shown to be important for mid-season and terminal drought tolerance in cowpea (Fatokun et al., 2012). Studies have demonstrated a link between late-season DLSC, a trait similar to ‘stay-green’ in cereals and grain yield in cowpea (Gwathmey, 1992a, b). Recent studies have also described field and greenhouse protocols to screen for DLSC at seedling stage in cowpea (Mai-Kodomi, 1999; Muchero et al., 2008; Muchero et al., 2009). Plants exhibiting this phenotype are characterized by maintenance of green leaf area under drought stress. It is believed that the maintenance of green leaf area contributes to continued carbohydrate formation during drought and faster recovery following a rainfall event (Borrell et al., 2000). Therefore, selection for DLSC in cowpea genotypes allows more photosynthates to be synthesized and distributed leading to production of higher yields. 2.6.3.2 Number of pods per plant and number of seeds per pod In cowpea, variations in yield due to environmental stresses were mainly due to variations in number of pods per unit area but drought occurring at pod-filling stage reduced the number of pods per plant and poor pod-filling (Turk et al., 1980; Bala Subramanian and Maheswari 1992). Decrease in number of pods per plant is mainly due to the abscission of flowers and pods of cowpea under drought stress. This detrimental effect at flowering and pod-filling stage is rather not reversible by re-watering (Ziska et al., 1985). Higher number University of Ghana http://ugspace.ug.edu.gh 31 of pods per plant and seeds per pod and good pod-filling (big seed size) is therefore a reflection of tolerance to drought (Gwathmey et al., 1992a). 2.6.3.3 Normalized Difference Vegetation Index (NDVI) NDVI which measures leaf greenness (chlorophyll content) is considered to be correlated with crop productivity under abiotic stress. NDVI has been used in drought improvement for drought tolerance (Hazratkulova et al., 2012); maize for drought tolerance (Adebayo et al., 2014). This may also be useful in cowpea improvement for drought tolerance. Because NDVI is a measure of chlorophyll content in leaves (Hu et al., 2010), maintaining higher NDVI longer during pod-filling would contribute to grain development through photosynthesis. Hence, maintaining higher NDVI under stress, such as terminal drought occurring during pod-filling in the semi-arid regions of SSA could be considered a sign of stress tolerance with potential use in cowpea germplasm screening (Hazratkulova et al., 2012). 2.6.4 Drought tolerance genes in cowpea Candidate gene approach and differential expression of mRNA has been used in cowpea to identify genes that are involved in the drought response. Iuchi et al. (1996) isolated 24 cDNA clones that corresponded to dehydration-induced genes from cowpea variety IT84S- 2246-4 by a differential screening method. The cDNA clones represented ten different genes collectively named cowpea clones responsive to dehydration (CPRD). Nine of the CPRD genes were induced by drought. Two additional novel drought-inducible genes were reported from the same cowpea variety (IT84S-2246-4) by Iuchi et al. (2000). Drought- stressed cowpea plants accumulated ABA to a level that was 160 times higher than that in University of Ghana http://ugspace.ug.edu.gh 32 unstressed plants. The regulation of protein degradation through the use of protease- specific inhibitors is a common mechanism in metabolic processes and adaptive processes, including adaptation to drought stress in cowpea (Fernandes et al., 1993; Diop et al., 2004). Expression of the cowpea cystatin gene was studied at the mRNA and protein levels, using Northern blot and Western blot analysis (Diop et al., 2004). In cowpea seeds, multiple minor cystatin- like polypeptides were identified in addition to the major cystatin-like polypeptides of 25 kDa (Flores et al., 2001). Water deficit usually triggered by drought and desiccation is known to induce the production of reactive oxygen species (ROS). Among these, H2O2 is produced mainly in the chloroplasts and mitochondria of stressed cells and is the source of major cell damage (Foyer et al., 1994; Dat et al., 2000). Recently, sequencing and analysis of the gene-rich, hypo-methylated portion of the cowpea genome has been initiated (Timko et al., 2008). Over 250,000 gene-space sequence reads (GSRs) with an average length of 610 bp were generated from 183,000 expressed sequence tags (EST) developed at University of California Riverside (UCR). Sixty-two (62) out of Sixty- four (64) well characterized plant transcription factor (TF) gene families are represented in the cowpea GSRs. The generated GSRs may provide a source for functional markers in genes linked to drought tolerance traits in cowpea which could be used for marker-assisted selection. 2.6.5 Molecular breeding for drought tolerance in cowpea Most agronomically important traits are quantitatively inherited. Improvement of these traits through marker assisted breeding would have the greatest impact because traditional plant breeding for quantitative traits is usually slow and difficult. Marker assisted selection University of Ghana http://ugspace.ug.edu.gh 33 could potentially improve selection of traits that have low heritability by using markers with high heritability (Chapman et al., 2003). The use of molecular markers in plant breeding for indirect selection of agronomically important traits can have an impact on the efficiency of plant breeding systems. Some aspects of plant breeding that can be improved by use of molecular markers include: identification and elimination of undesirable individuals in the early stages of selection, identification of individuals prior to flowering that will be useful to transfer genes governing the favourable expression of quantitative traits, accelerating generations of new varieties, especially for traits that are difficult to score and facilitation of selection for several traits simultaneously (Allen, 1994; Bharadwaj et al., 2011). Selection of traits by molecular markers is based on molecular marker(s) tightly linked to the trait of interest, unlike direct selection of traits in conventional breeding which is based on visual observation. The selection can be beneficial when the genotype x environment interaction is significant but marker x environment interaction is not significant, allowing a stable selection of genotype (Chapman et al., 2003). Molecular breeding (genomics- assisted breeding) uses several modern breeding strategies such as marker assisted selection (MAS) and genomics selection (GS) (Varshney et al., 2005). Marker assisted selection includes marker assisted backcrossing (MABC), marker assisted recurrent selection (MARS) and most currently genome-wide selection (GWS) (Ribaut et al., 2010). Molecular markers serve as efficient and powerful tools for MAS of agronomically important traits (Bharadwaj et al., 2011) and also in biotic and abiotic tolerance traits. Marker-enhanced breeding helps breeders to discriminate genotypes directly and offers a great opportunity for developing drought tolerant varieties (Xiao et al., 2005; Romano et University of Ghana http://ugspace.ug.edu.gh 34 al., 2011). Complex traits such as drought tolerance can be analyzed as Mendelian factors with molecular markers (Paterson et al., 1988). Many DNA-based molecular marker technologies can now be accessed for constructing dense genetic maps for any crop species (Quarrie et al., 1999). Hence, molecular mapping and genomics techniques present unique opportunities for dissecting major genes and quantitative trait loci (QTL). Drought is controlled by multiple genes, hence, is a quantitative trait. Regions within genomes that contain genes associated with a particular quantitative trait are known as quantitative trait loci (QTL) (Collard et al., 2005). QTL could be detected by employing molecular markers in genetic linkage analysis, i.e. QTL mapping. With the help of molecular markers linked to QTL, the heredity of some related QTL could be tracked and the ability of genetic manipulation of the QTL is greatly enhanced, thus improving the accuracy and predictability to select genotypes with superior quantitative traits. 2.7 Quantitative trait loci (QTL) mapping in cowpea QTL analysis as applied to plant breeding typically involves a two steps process. First step is QTL discovery. Parental lines are identified which differ for one or more quantitative traits of agronomic importance. The parents are hybridized and a segregating population(s) is created in which markers can be used to identify linked QTL. The second step is to utilize knowledge of QTL map locations to create a superior variety using appropriate breeding methods (Tanksley and Nelson, 1996; Bernardo, 2008; Lucas et al., 2013). Varshney et al. (2012) have suggested that for successful introgression of QTL in elite breeding materials or varieties, the targeted QTL should be major QTL that contribute >20% phenotypic variation. University of Ghana http://ugspace.ug.edu.gh 35 In cowpea, many researchers have utilized different genetic maps based on molecular markers to locate many QTL associated with cowpea yield and other traits. Andargie et al. (2011) identified the QTL of cowpea agronomic traits related to domestication (seed weight, pod shattering) by SSR markers. Six QTL for seed size were revealed with phenotypic variation ranging from 8.9%-19.1%. Four QTL for pod shattering were identified with phenotypic variation ranging from 6.4%-17.2%. The QTL for seed size and pod shattering mainly clustered in two areas of LGs 1 and 10. QTL associated with seed size were revealed with phenotypic variation ranging from 8.9%-19.1%. One major and six minor QTL were identified for pod length variation between yard-long bean and wild cowpea (Kongjaimun et al., 2012). Agbicodo et al. (2010) used a SNP (single nucleotide polymorphism) genetic map with 282 SNP markers constructed from the recombinant inbred line (RIL) population to perform QTL analysis. Three QTL, CoBB-1, CoBB-2 and CoBB-3 were identified on linkage group LG3, LG5 and LG9, respectively explaining between 4.3-22.1% of the phenotypic variation. QTL for Macrophomina phaseolina resistance and maturity in cowpea with SNP markers were identified by Muchero et al. (2011). QTL for resistance to Thrips tabaci and Frankliniella schultzei based on an amplified fragment length polymorphism (AFLP) genetic linkage map were mapped on LG 5 and 7 accounting for between 9.1% and 32.1% of the phenotypic variance respectively (Muchero et al., 2010). QTL associated with tolerance to flower bud thrips (Megalurothrips sjostedti) was detected in five LGs of RILs. The QTL together explained 77.5% of the phenotypic variance (Omo-Ikerodah et al., 2008). In a study on leaf morphology, QTL for leaf shape, Hls (hastate leaf shape) locus accounting for 71.5% -74.7% phenotypic variance for greenhouse and field conditions University of Ghana http://ugspace.ug.edu.gh 36 respectively was identified on linkage group (LG) 4 using SNP markers (Portoff et al., 2012). Muchero et al. (2009) reported the mapping of 12 QTL associated with seedling drought tolerance and maturity in a cowpea RIL population. Regions harboring drought- related QTL were observed on linkage groups 1, 2, 3, 5, 6, 7, 9, and 10 accounting for between 4.7% and 24.2% of the phenotypic variance. Two more QTL for maturity were mapped on linkage groups 7 and 8 separately from drought-related QTL. The main application of QTL identification in breeding is to aid conventional method for selection and subsequently reduce time taken to release improved lines. It is therefore important to introgress identified QTL into elite germplasm through the use of appropriate breeding methods to develop improved varieties (Bernardo, 2008). In a study to breed cowpea varieties with large seeds, QTL Css-1from an African buff seed type cultivar, IT82E18 (18.5g/100seeds), into a black eye seed type cultivar, CB27 (22g/100seed) was successfully introgressed with the goal of stacking large seed haplotypes into a CB 27 background. Results of the field performance trials identified breeding lines with large seeds and desirable traits such as yield, maturity and plant architecture (Lucas et al., 2013, 2015). 2.7 Genetics of drought tolerance in cowpea Understanding the nature of genes controlling a specific trait of interest is the baseline of any breeding programme. Genetic information regarding the gene effects and magnitude of gene action controlling such traits must be determined and well understood (Hinkossa et al., 2013). Traits like drought and yield are polygenically controlled and affected by environmental factors which are not transmissible from parents to offspring. It is therefore University of Ghana http://ugspace.ug.edu.gh 37 important to determine the genetic factors affecting these traits for an efficient breeding programme. There are three types of gene effects i.e. additive, dominance and epistatic (Gamble, 1961). The dominance and epistatic constitute the non-additive part. The dominance can either be ambidirectional, a situation of positive and negative dominance at different gene loci or unidirectional, dominance in one direction (Kearsey and Pooni, 1998). Epistasis refers to interaction of alleles at different loci. Epistatic gene action occurs when the additive- dominance model cannot explain variation alone (Derera, 2005). The additive gene effects reflect the degree to which progenies are likely to resemble their parents, as reflected in narrow-sense heritability (Derera, 2005). Estimation of the relative proportion of additive genetic effects (or general combining ability of a line, GCA) and non-additive genetic effects (or specific combining ability of a cross, SCA) controlling the drought-adaptive traits and their interactions with the environment is useful for designing breeding programmes and assembling germplasm for population advancement (Shahi and Singh, 1985). Information on the GCA and SCA of parents is helpful in the analysis and interpretation of the genetic basis underlying inheritance pattern of the trait of interest. It estimates the average performance of a line in series of hybrid combination (GCA) and the contribution of a line to hybrid performance in a cross with a specific other line in relation to its contribution in crosses with an array of other lines (SCA) (Ogunbodede et al., 2000; Rajani et al., 2001; Acquaah, 2012). Alidu et al. (2013) reported a significant GCA effect for cowpea grain yield under drought stress suggesting that yield under drought can be improved by exploiting the preponderance of additive gene effects. In the study on combining ability for pod and seed traits in cowpea, University of Ghana http://ugspace.ug.edu.gh 38 Ayo-Vaughan et al. (2013) reported that additive gene effects significantly controlled number of pods per plant, pod length, seeds per pod and 100-seed weight. In self-pollinating crops such as cowpea, ability to accurately identify parental combinations which will generate superior pure lines for farmers’ adoption is very crucial to the success of the breeding programme. For this reason, many studies have been devoted to developing and evaluating methods of predicting cross potential in early generations (Thurling and Ratinam, 1987). Selection of suitable parents and proper choice of good mating designs are important to successful plant breeding schemes depending on the objectives of the study, time, space, cost and other biological limitations (Khan et al., 2009; Acquaah, 2012; Nduwumuremy et al., 2013). Mating design refers to the procedure of producing the progenies on which further selection is carried out. Breeders and geneticists theoretically and practically use different types of mating designs and arrangements for targeted purposes. The mating designs have four main uses: (1) to provide information on the genetic control of the character under investigation; (2) to generate a breeding populations to be used as a basis for the selection and development of potential varieties; (3) to provide estimates of genetic gain and (4) to provide information for evaluating the parents used in the breeding programme (Acquaah, 2012; Nduwumuremyi et al., 2013). Combining ability depends on the gene action controlling the trait to be improved. General combining ability (GCA) is the average performance of a line in hybrid combinations and is due to additive gene action. The estimation of GCA for a particular line depends upon the mating design, but essentially, it is the deviation of its progeny mean from the mean of all lines included in the trial (Acquaah, 2012). There is no and/or limited information on the combining ability of lines University of Ghana http://ugspace.ug.edu.gh 39 that have been used in previous cowpea breeding programmes in IITA for the development of drought tolerant lines. This may in a way have contributed to the slow progress that has been recorded in enhancing the drought tolerance level of the improved cowpea varieties. 2.8 Participatory Rural Appraisal (PRA) Farmer participatory research in agriculture is a systematic dialogue between farmers and scientists to solve agricultural problems. Farmers will choose varieties that overcome their challenges and meet their needs. Several instances exist where huge investments have been made to develop improved agricultural technologies that were not eventually adopted by the target population (Emad, 1995; Becker et al., 1995; Kormawa et al., 1999). Many such situations have often been associated with technologies developed using the top bottom approach, characterized by the involvement of the target population only when the development of the technology has been finalized by scientists and would not normally involve the farmers. Involving farmers from the beginning in breeding programme engages method such as participatory rural appraisal (PRA) in order to identify their needs and preferences, to develop a programme together with them that will suit their needs and hence encourage their adoption (Bellon, 2001). Developed and tested methods of PRA studies include: participatory mapping and modeling transect walks, matrix scoring, well-being grouping and ranking, institutional diagramming, seasonal calendars, trend and change analysis and analytical diagramming all undertaken by local people (Chambers, 1994). Other methods of involving farmers at other breeding stages include: participatory plant breeding (PPB) and participatory variety selection (PVS) (Kiiza et al., 2012). Participatory Rural Appraisal (PRA) started in the late 1980s and spread at an astonishing pace across the world over the course of the 1990s (Cornwall and Pratt, 2010). If University of Ghana http://ugspace.ug.edu.gh 40 participatory techniques are appropriately employed in plant breeding they can have an impact by quickly and cost-effectively producing much-improved crop varieties. These varieties may be for resource-poor farmers in marginal environments who previously were entirely dependent on landraces (Virk et al., 2003; Witcombe et al., 2003) or for farmers in more productive environments where they were dependent on very old improved varieties (Witcombe et al., 2001). The aim of involving farmers in plant breeding activity is to empower farmers develop their skills and knowledge of using crop genetic diversity and processes of maintaining and exchanging varieties (Witcombe et al., 2006). Farmers’ knowledge about the crops in their areas has a high potential to strengthen participatory varietal selection (PVS) / participatory plant breeding (PPB) programs. It would serve well to empower scientists’ knowledge for designing site, - crop- and farmer- gender - specific activity. Since the adoption of the PRA technique in Nigeria, many PRAs have been conducted both in animal and crop production. PRA conducted on Bambara groundnut production in Nigeria labour and lack of finance ranked the highest considerations for constraints to production (Alhassan and Egbe, 2013). Kamara et al. (2006) identified variation in the preferred choices of farmers for improved maize varieties. He observed that in the market-driven production systems in the communities in Borno State, earliness and high yield were preferred while in resource-poor sorghum-based production systems in Kano State; extra-earliness was preferred over high yield because it provided food security during the food scarcity period. In Malawi, findings from PRA and PVS studies were used by breeders to select and release six elite cowpea accessions (Nkogolo et al., 2009) that are still being grown in Malawi because these varieties were well adapted to their environment and also met the need of the different farmer gender University of Ghana http://ugspace.ug.edu.gh 41 groups and consumers in the country (Nkongolo et al., 2009). In Benin, sustainable integrated pest management technologies were developed with cowpea farmers using a participatory approach to identify the biotic constraints they experienced in cowpea production (Kossou et al., 2001). Even within the same locality, farmers’ preferences regarding which cowpea variety to plant may differ as a result of wealth class, gender, farming systems and other livelihood factors. It is therefore appropriate to obtain information on these factors before setting up an improvement programme. This understanding will help breeders develop varieties that will be acceptable by all categories of farmers. PRA was conducted among cowpea farmers in four local government areas of Kano State, Nigeria. This study was carried out in order to identify farmers’ information on the impact of drought on cowpea production its productivity and to identify their preferred traits in new drought tolerant varieties. University of Ghana http://ugspace.ug.edu.gh 42 CHAPTER THREE 3.0 Participatory rural appraisal in cowpea growing areas in Kano State, Nigeria 3.1 Introduction Cowpea has the potential to make a significant contribution to food and nutritional security and poverty reduction in Sub-Saharan Africa (SSA) as it provides nutritious grain and a less expensive source of protein for both the rural poor and urban consumers (Coulibaly and Lowenberg-Deboer, 2002). In addition to food for humans, it is a valuable source of livestock fodder (Singh et al., 2003) making it very attractive to farmers. As a leguminous crop, cowpea improves soil fertility through its ability to fix atmospheric nitrogen (Sanginga et al., 2000). Although the production trend of cowpea shows a significant increase of 410% in production, this has resulted mainly from 440% increase in land area planted (Oritz, 1998). The challenges of meeting the rapidly growing food needs of SSA cannot be successfully overcome without due consideration of the capacity and limitations of the natural environment which include land tenure system and increasing evidence of climate change (Abaje et al., 2014). Kano State in Nigeria is located in the Sudano-Sahelian Ecological Zone (SSEZ). This zone suffers from seasonal and inter-annual climatic variability. There have been droughts and effective desertification processes, particularly since the 1960s (FRN, 2005) in this zone. The Sahelian droughts of the 1970s and the 1980s that ravaged this zone left farmers impoverished (Ati et al., 2007). Frequent occurrences of drought in this zone (Alatise and Ikumawoyi, 2007) and increase in human population (FRN, 2005) have been implicated as part of the factors for the low quality of living among the people of the zone who are mostly farmers. University of Ghana http://ugspace.ug.edu.gh 43 Effects of drought on cowpea include impaired germination and poor plant establishment (Harris et al., 2002; Kaya et al., 2006) if it occurs at the seedling stage, reduced flowers (Kawakami et al., 2006), small seed size (Samarah, 2005) and poor pod filling (Ogbonnaya et al., 2003) if mid-season or terminal droughts are experienced. Since rain has become less reliable and growing seasons shorter, development and release of cowpea varieties that tolerate drought that can occur at any period within the growing season is inevitable. However, it is important that these improved varieties meet farmers’ preferences in order to facilitate adoption. This therefore means that farmers’ preferences must be included in the drought tolerant varieties. According to Jalleta (2007), generating new technology alone does not provide solutions to help farmers increase agricultural productivity. Despite the efforts of national and international development organizations, low adoption of technology and concomitant low agricultural productivity are still major concerns. One cause of low adoption of new technology appears to be that new innovations from researchers don’t address the constraints of the smallholder farmers as well as being inappropriate to their physical, cultural and economic environment (Jones, 2000; Mulatu and Beleke, 2001). According to Ceccarelli et al. (2001), these factors clearly highlight the need to involve farmers at a very early stage through to the advanced stages of varietal selection. By responding closely to farmers’ concerns and conditions, researchers can develop technologies that are adopted more widely and that respond to important social issues such as equity and sustainability (Bellon, 2001; Chambers, 1994). Their participation in breeding can be achieved in various ways namely: farmer field schools (FFS), participatory plant breeding (PPB), participatory varietal development (PVD), farmer participatory University of Ghana http://ugspace.ug.edu.gh 44 varietal selection (FPVS), participatory research and extension (PRE) and participatory rural appraisal (PRA) among others. The use of any of these approaches has been reported to help reduce possibility of developing varieties or technologies that do not meet farmers’ needs (Ellis-Jones et al., 2004, Kiiza et al., 2012). PRE was used in the three agro- ecological zones in the northern Nigeria to encourage farmers to test new IITA-bred cowpea varieties, to identify well adapted varieties for their conditions and to promote adoption (Kamara et al., 2010). In the study based on improving productivities of cowpea- cereal systems in the savannas of West Africa, farmers participated in selecting for seed type and plant type and the combination of the two traits (Ajeigbe et al., 2010b). In Southern Guinea Savanna of Nigeria, labour and lack of finance ranked the highest consideration by farmers as constraints to the production of Bambara groundnut (Alhassan and Egbe, 2013). Another survey conducted among cowpea farmers in Upper East region of Ghana on farmers’ knowledge and cultivation of cowpea showed that insect pests were the major constraints to production and the land tenure system of cowpea which involved its cultivation on small areas of land (Akpalu et al., 2014). In another study on the on-farm evaluation of drought tolerant maize in Guinea Savannah of Nigeria, farmers laid more emphasis on earliness in crop maturity than on high yield (Kamara et al., 2006). In a PVD conducted for sorghum in Burkina Faso, it was observed that farmers’ criteria for selection for a trait were more encompassing than breeders understanding of the same. This clearly showed that farmers can effectively select for traits on the basis of progeny and single plants while pursuing specific agronomic aims such as adaptation (vom Brocke et al., 2006). Farmers identified grain yield, cob size, grain size and earliness as the most important criteria for adoption of maize varieties in an evaluation study of drought tolerant University of Ghana http://ugspace.ug.edu.gh 45 maize varieties in the guinea savanna of Ghana using a mother and baby trial design (Buah et al., 2013). PRA in plant breeding is designed to bridge the gap between breeders and farmers (Banziger and de Meyer, 2000) and also ensure that new varieties satisfy farmers’ preferences and suit their socioeconomic situation (Abedi and Vahidi, 2011; Kiiza et al., 2012). Farmers’ participation in agricultural research is therefore more than talking to six farmers or putting ten experiments in their fields but a systematic dialogue between farmers and scientists to solve problems related to agriculture and ultimately increase the impact of agricultural research. Sequences of events include: learning from farmers, identification of technological options to test, designing methods to test and evaluate their impact. These activities are jointly carried out by farmers and scientists to achieve more productive, stable, equitable and sustainable agricultural systems (Witcombe et al., 1996; Bellon 2001; Odendo et al., 2002; Ellis-Jones et al., 2004). This study was therefore conducted in cowpea growing regions of Kano, Nigeria with the following objectives: 1. Identify farmers’ sources of information on climate change, seed and relevant agricultural related technologies. 2. Identify prominent climatic factors limiting cowpea production. 3. Assess farmers’ perception of drought, mitigation measures and their impact on cowpea production. 4. Identify cowpea farmers’ production constraints University of Ghana http://ugspace.ug.edu.gh 46 5. Identify farmers’ preferred traits in improved cowpea varieties. 6. Obtain information on farmers’ perceptions of their involvement in development of improved varieties and new agricultural technologies. 3.1 Materials and Methods 3.1.1 Site selection Participatory Rural Appraisal (PRA) study was conducted in fifteen cowpea growing communities sampled from four Local Government Areas (LGA) of Kano State (Gwarzo, Tofa, Bichi and Garko) (Table 3.1). Co-ordinates of the communities were captured using the geographical positioning system (GPS). ArcGIS software was used to draw Kano State map with showing the position of each community visited (Figure 3.1) Kano State lies approximately between latitudes 10o 33’N and 12o 23’N and longitudes 7o 45’E and 9o 29’E and 473 metres above sea level (masl). Kano has a subtropical steppe/ low-latitude semi-arid hot climate. It has an annual mean rainfall of 600mm ± 30%. It has an estimated land size of 21,276.872 km2 with 1,754,200 hectares agricultural and 75,000 hectares forest vegetation and grazing land. The mean annual temperature is about 26oC (Falola, 2002; Olofin, 2008; http://www.kano.climatemps.com/ accessed 09/05/2016 at 5.33pm).). University of Ghana http://ugspace.ug.edu.gh 47 Table 3. 1: PRA sites, coordinates and the number of farmers interviewed in each community. Local Government Area Selected communities Latitude (N) Longitude ( E ) *Elevation (asl) (m) Gwarzo Katsinawa 11o52.900' 007o53.587' 587 Kogon Kura 11o55.546 007o54.007' 591 Kutama 11o52.039' 007o52.064' 564 Katsira 11o55.764' 007o58.740' 554 Tofa Kwami 12o01.269' 008o18.470' 509 Wangara 11o58.863' 008o19.602 509 Tofa 12o03.592' 008o16.377' 513 Doka 11o59.625' 008o18.096' 511 Bichi Yakasai 12o12.135' 008o17.599 528 Mintsira 12o11.468' 008o13.304' 516 Badume 12o11.484' 008o18.520' 532 Jobe 12o09.473' 008o12.325' 492 Garko Kafin Malami 11o39.788' 008o52.914' 472 Kogon Doki 11o31.509' 008o46.576 527 Garko 11o39.355 008o48.190' 468 *asl= above sea level Plate 3. 1: Cross section of farmers being interviewed in Tofa and Garko villages. University of Ghana http://ugspace.ug.edu.gh 48 Figure 3. 1: Map of Kano State showing the 15 communities where the PRA was conducted. 3.1.2 Selection of farmers One group of 15 farmers was formed for focus group discussions (FGD) in one community per LGA using interview guide. The information gathered from the FGD was used to develop semi-structured questionnaires. Members of the group were selected based on recommendations from the Agricultural Extension Agent in the enumeration area. University of Ghana http://ugspace.ug.edu.gh 49 One hundred and fifty individual farmers comprising of 135 males and 15 females were interviewed using semi-structured questionnaires (Plate 3.1). The state was experiencing social unrest at the time of questionnaire administration hence the few number of the female participants. Male and female farmers were separately interviewed in line with cultural and religious beliefs of the communities. 3.2 Data collection techniques and data analysis Interview guide was used for focus group discussion and questionnaires were administered to individual respondents at various locations. Questions were put to individual farmers with translators clarifying issues in their local language and enumerators recording the information where necessary. The responses from farmers in all the study areas were coded and compiled in SPSS. Ranking was done using excel software. Criteria for evaluation were placed on the vertical axis of the matrix while the elements generated were placed on the horizontal matrix. Farmers’ responses were then ranked using a scale of 1 (highest value or most important) to 5 (lowest value or least important). Derived scores were calculated for the different criteria by assigning the criterion/rank a value that was inversely proportional to the rank i.e. a rank of 1 received a score of 5, 2 received 4, 3 received 3, 4 received 2 and 5 and above received 1. Mean Derived Scores (MDS) were calculated from the derived scores (De Groote et al., 2002). The socio-economic characteristics of respondents observed in the study area were age, measured in years, sex, level of education (measured in years invested in formal education) and marital status. Others included primary occupation, land ownership type and area of their total farmland cultivated to cowpea the cropping season prior the survey. University of Ghana http://ugspace.ug.edu.gh 50 3.3. Results 3.3.1 Socio-economic characteristics of respondents Table 3.2 presents the summary of the socio-economic characteristics of respondents. The mean age of respondents was 47 years and males dominated farming activities in the surveyed areas. Farming was the primary occupation of the respondents (57%). Most of the respondents belonged to one farmer group or the other (86%). The number of respondents with formal education was above two-thirds of the total respondents across surveyed areas. Respondents owned their farmlands either through family inheritance or purchase. Only a few of them used rented farmland for farming. Mean area of land cultivated to cowpea during the season prior to the survey was 1.5 ha with 87% of them using between 0.5 – 2 ha for growing cowpea. University of Ghana http://ugspace.ug.edu.gh 51 Table 3. 2: Distribution of farmers based on socio-economic characteristics. Characteristics Frequency (N=150) Percentage (%) Age Range 20 - 40 58 38.7 41 - 50 42 28 51 - 60 30 20 61 and above 20 13.3 Mean age = 47years *Educational Attainment FSCL 33 22.5 "O Level" 80 54.4 Tertiary Education (NCE, BSc etc) 34 23.1 Marital Status Married 145 97 Single 3 2 Widow / widower 1 1 Gender Male 135 90 Female 15 10 Membership of farmer group Yes 129 86 No 21 14 ‡Primary occupation of respondents Farming 89 59 Trading 67 45 Civil service 18 12 Artisan 17 11 ‡Land ownership type Owned through inheritance 135 90 Rent 24 16 Owned through purchase 70 47 Area of farmland cultivated with cowpea < 0.5ha 4 2.7 0.5 - 2ha 128 87.1 > 2ha 15 10.2 Mean land area = 1.5ha *FSLC=first school leaving certificate; "O Level" = Secondary school leaving certificate. ‡multiple responses. No response = where N < 150 Source: Field survey conducted by author in 2014 University of Ghana http://ugspace.ug.edu.gh 52 3.3.2 Cropping systems and frequency of cultivation Results in Table 3.3 show the farming calendar of farmers interviewed in the four LGAs. Eighty three percent of the farmers grew cowpea in mixed cropping with other crops such as maize, millet, sorghum and onions. Growing cowpea as a rotational crop after maize and other cereals was highlighted as important by interviewed farmers because it restores soil fertility. Although some respondents indicated that they grew cowpea as a sole, these respondents were identified to be the direct owners of their farmlands and so were able to grow cowpea as a sole crop while others who hired their farmlands did mixed cropping so as to maximize production per unit area. Frequency of cropping was mainly dependent on availability of irrigation facilities since many of them grew cowpea under rain-fed conditions and irrigation is an additional expense for the farmers during the growing season. Most of the respondents therefore grew the crop once in a year (63%) while some that were close to natural sources of water or irrigation schemes grew the crop twice in a year (34%). Table 3. 3: Types of farming system and frequency of cultivation of farmers in Tofa, Bichi, Gwarzo and Garko LGAs of Kano State, Nigeria in the year 2014. Frequency (N=150) Percentage (%) Cropping system Mono-cropping 24 16 Mixed cropping 125 83.33 No response 1 0.67 Total 150 100 Frequency of cultivation per year Once 95 63.33 Twice 51 34.00 Thrice 2 1.33 No response 2 1.33 Total 150 100 University of Ghana http://ugspace.ug.edu.gh 53 3.3.3 Farmers’ sources of information on cowpea seeds, climate change and other agricultural related technologies. The majority of farmers, averaged over all the communities indicated that they sourced their seeds during the growing season from the open market (31.69%), followed by seeds from farmer friends or relatives (25.73%). Considering the trend on LGA basis however, most farmers in Garko LGA sourced their seeds from seed companies or agro dealers (37.93%). During the focus group discussions held in this LGA, it was observed that extension agents worked very closely with the farmers in this LGA and secondly Garko LGA had a formidable farmers’ association where they shared information on new technologies (Figure 3.2). There was a decline in the use of own saved local seeds across all LGAs. The farmers confirmed that they had good crops and bountiful harvests which translated to high income whenever they sourced fresh seeds from agro-dealers and IITA. This confirmed the readiness of farmers to embrace new varieties as long as they met their needs. Figure 3.3 shows that most farmers got their information on climate change and other related technologies from extension agents (81.75%) followed by mass media (10.14%) especially the transistor radios. University of Ghana http://ugspace.ug.edu.gh 54 Figure 3. 2: Farmers' sources of cowpea seeds in 4 LGAs of Kano State in the year 2014 0 5 10 15 20 25 30 35 40 45 Bichi Garko Gwarzo Tofa % r es p o n d en ts Local Government Areas open market seed company/agro dealer farmer friend/relative own local saved seed own improved saved seed government/NGO University of Ghana http://ugspace.ug.edu.gh 55 Figure 3. 3: Sources where farmers in the 4 LGAs surveyed in Kano State get information on climate change and related agricultural technologies. 121 5 7 15 81.75 3.38 4.73 10.14 0 20 40 60 80 100 120 140 Extension agent Friends/relative Co-farmers Mass media Number / percentage of respondents So u rc es o f in fo rm at io n Percentage Number N= 148 University of Ghana http://ugspace.ug.edu.gh 56 3.3.4 Prominent climatic factors limiting cowpea production in surveyed areas There are two seasons in Kano, the wet/rainy season which occurs between May and September with temperatures ranging between 24oC - 29oC and the dry season between October and April with maximum temperature between 28oC - 34oC and minimum temperature between 25oC - 27oC. For farmers growing cowpea under rain-fed conditions, the planting season starts in June/July with harvesting in September/October. However, farmers growing cowpea with irrigation facilities have the advantage of growing twice in a year, first in December to March/April and the second planting in June/July. Figure 3.4 shows the responses of farmers on community basis and across all communities on climatic constraints to production. Rainfall was the most prominent climatic factor limiting production (62%) followed by high temperature (35.3%). From the focus group discussions held in all the LGAs, farmers reported that the irregularity in rainfall pattern hindered their farming activities which at times led to total crop failure whenever there was shortage in precipitation during the growing season. University of Ghana http://ugspace.ug.edu.gh 57 Figure 3. 4: Prominent climatic factors limiting cowpea production in 15 cowpea growing communities of Kano State, Nigeria in the year 2014. 0 10 20 30 40 50 60 70 80 90 R e sp o n d e n ts ( % w it h in c o m m u n it y) Cowpea growing communities Rainfall Temperature Wind Relative humidity University of Ghana http://ugspace.ug.edu.gh 58 3.3.5. Farmers’ perceptions of drought, mitigation measures and impact of drought on cowpea production One of the objectives of this study was to find out from the cowpea farmers the effect drought has on cowpea production. Tables 3.4 - 3.7 show various questions posed to farmers in order to assess their perception of drought and its impact on cowpea production. From Table 3.4, 88% of farmers responded to have experienced drought during growing seasons; 96.93% declared that the effect was severe on grain yield. When farmers were asked for the growth stage of cowpea that is more susceptible to drought, 58.11% affirmed that the flowering/grain filling stage was more susceptible to drought, 32% said vegetative while 10% said both stages were susceptible to devastation by drought. The majority of the farmers interviewed (55%) did not have access to irrigation facilities. Ninety five percent planted improved varieties as mitigation to drought because the extension agents gave them adequate information on new varieties. Ninety three percent manipulated planting and harvesting times as a drought avoidance measures. Seventy two percent of respondents said that they got information on tolerant varieties from extension workers, followed by information through radio (58%) and farmer friends / relatives (28%). The results in Table 3.5 show the responses of farmers on the extent of drought devastation on plant aspect. Eighty seven percent and 77% of farmers said that the incidence of pests and diseases increased to a great extent whenever drought occurred at vegetative and grain filling stages respectively. Eighty one percent and 66% affirmed that the incidence of drought at vegetative and grain filling stages resulted in stunted growth of cowpea University of Ghana http://ugspace.ug.edu.gh 59 respectively. Seventy seven percent and 75% confirmed the increase in flower abortion and poor pod-filling to a great extent when drought occurred during the reproductive stage. Table 3.6 and Table 3.7 show the economic impact of drought on farmers’ realized yield of harvestable parts of cowpea. Reduction in grain yield ranged between 50-73% and 40- 73% for drought stress occurring at vegetative and grain-filling stages respectively while it ranged between 56-81% and 36-79% for fodder yield for drought occurring during vegetative and grain-filling stages respectively. From the information gathered from interviewed farmers, it was deduced that under optimum condition, a farmer could generate a net income of about N230, 860 ($1282) on a hectare from grain and fodder yields for instance if he has/she has 1,027 kg ha-1 of cowpea grains and 460 rolls ha-1 of fodder. However, he/she could lose up to N131, 140 ($728) and 144,300 ($801) in the event of drought occurring at vegetative and grain-filling stages respectively (Table 3.6 and 3.7). University of Ghana http://ugspace.ug.edu.gh 60 Table 3. 4: Drought incidence, its impact on cowpea production and mitigation measures employed by farmers in the 15 surveyed communities of Kano State in the year 2014. Question Response Frequency Percentage (%) Experienced drought during cowpea growing season No 18 12.0 Yes 132 88.0 N=150 Effect of the drought experienced on grain & fodder yield Very severe 97 74.62 Moderately severe 29 22.31 Not severe 4 3.08 N=130 Crop growth stages susceptible to drought devastation Vegetative stage 47 31.76 Flowering & grain-filling stage 86 58.11 All growth stages 15 10.14 N=148 Drought mitigation measures Irrigation No 80 54.79 Yes 66 45.21 N=146 Planting improved varieties No 8 5.33 Yes 142 94.67 N=150 Manipulation of planting and harvesting times No 8 5.33 Yes 140 93.33 N=150 Other measures No 8 72.73 Yes 3 27.27 N=11 Access to cowpea varieties tolerant to drought Yes 122 83.6 No 24 16.4 N=146 Sources of information on tolerant varieties NGOs 20 13.3 Radio 88 58.7 Television 3 2 Farmer field school 5 3.3 Extension agents 108 72 Farmer friend/relatives 42 28 Other sources 4 2.7 *N=270 *Multiple responses University of Ghana http://ugspace.ug.edu.gh 61 Table 3. 5: Devastation of drought expressed by extent to which the stage of occurrence of drought affects cowpea plant in 15 surveyed communities of Kano State in the year 2014. % respondents Plant aspect great extent Some extent Little extent No extent Vegetative Grain- filling Vegetative Grain- filling Vegetative Grain- filling Vegetative Grain- filling Yellowing of leaves 77 76 19 5 4 11 - 1 Stunted growth 81 66 17 15 2 11 - 1 Plant wilting 77 74 19 9 3 9 - 1 Increase in pest & disease incidence 87 77 8 13 3 2 - 1 Flower abortion 82 77 9 10 4 6 3 1 Poor pod formation 77 74 13 15 7 4 1 2 Poor pod filling/seed set 82 75 12 15 4 3 1 1 Low grain yield 79 71 17 14 2 9 1 1 Low fodder yield 69 66 25 15 6 12 - - - = no response University of Ghana http://ugspace.ug.edu.gh 62 Table 3. 6: Economic impact of drought on grain yield of cowpea in 15 surveyed communities of Kano State, 2014 *Grain yield (Kg ha -1) Communities Optimum condition Drought stress % grain yield loss under drought stress Vegetative Grain- filling Vegetative Grain- filling Badume 587 215 191 63 68 105660 38700 34335 -66960 -71325 Doka 638 255 213 60 67 114840 45900 38340 -68940 -76500 Garko 727 254 257 65 65 130860 45720 46260 -85140 -84600 Jobe 967 441 421 54 56 174060 79380 75780 -94680 -98280 Kafin malami 639 307 263 52 59 115020 55260 47340 -59760 -67680 Katsinawa 803 270 213 66 73 144540 48600 38340 -95940 -106200 Katsira 826 294 317 64 62 148680 52920 57060 -95760 -91620 Kogon doki 594 186 188 69 68 106920 33480 33840 -73440 -73080 University of Ghana http://ugspace.ug.edu.gh 63 Table 3.6 continued *Grain yield (Kg ha -1) Communities Optimum condition Drought stress % grain yield loss under drought stress Vegetative Grain- filling Vegetative Grain- filling Kogon kura 1027 479 397 53 61 184860 86220 71460 -98640 -113400 Kutama 726 314 342 57 53 130680 56520 61560 -74160 -69120 Kwami 730 295 265 60 64 131400 53100 47700 -78300 -83700 Mintsira 792 283 299 64 62 142560 50940 53820 -91620 -88740 Tofa 515 256 308 50 40 92700 46080 55440 -46620 -37260 Wangara 678 181 183 73 73 122040 32580 32940 -89460 -89100 Yakasai 592 227 251 62 58 106560 40860 45180 -65700 -61380 *First row indicates harvested grain or fodder per hectare; row 2 indicates income generated and row 3 indicates loss in income in the event of drought. 1bag of cowpea grain =100Kg sells for N18, 000. $1= N180 at survey period. University of Ghana http://ugspace.ug.edu.gh 64 Table 3. 7: Economic impact of drought on fodder yield of cowpea in 15 surveyed communities of Kano State, 2014 Fodder yield (roll ha -1) Communities Optimum condition Drought stress % fodder loss under drought stress Vegetative Grain- filling Vegetative Grain- filling Badume 400 94 105 77 74 40000 9400 10475 -30600 -29525 Doka 279 116 122 58 56 27900 11600 12200 -16300 -15700 Garko 339 125 167 63 51 33900 12500 16700 -21400 -17200 Jobe 404 151 159 63 61 40400 15100 15900 -25300 -24500 Kafin malami 385 173 166 55 57 38500 17300 16600 -21200 -21900 Katsinawa 323 102 138 68 57 32300 10200 13800 -22100 -18500 Katsira 476 89 102 81 79 47600 8900 10200 -38700 -37400 Kogon doki 282 120 142 57 50 28200 12000 14200 -16200 -14000 University of Ghana http://ugspace.ug.edu.gh 65 Table 3.7 continued Fodder yield (roll ha -1) Communities Optimum condition Drought stress % grain yield loss under drought stress Vegetative Grain- filling Vegetative Grain- filling Kogon kura 460 135 151 71 67 46000 13500 15100 -32500 -30900 Kutama 339 104 130 69 62 33900 10400 13000 -23500 -20900 Kwami 406 180 172 56 58 40600 18000 17200 -22600 -23400 71 Mintsira 493 126 142 74 49300 12600 14200 71 -36700 -35100 Tofa 375 97 108 74 36 37500 9700 10800 -27800 -26700 Wangara 288 148 184 49 36 28800 14800 18400 -14000 -10400 Yakasai 277 123 178 56 36 27700 12300 17800 -15400 -9900 *First row indicates harvested grain or fodder per hectare; row 2 indicates income generated and row 3 indicates loss in income in the event of drought. 1roll of fodder sells for N100. $1= N180 at survey period. University of Ghana http://ugspace.ug.edu.gh 66 3.3.6 Cowpea production constraints Farmers were asked about problems faced in cowpea production and were asked to rank using extent of limitation each constraint had on cowpea production. Results are presented in Tables 3.8 – 3.9. In Table 3.8, 89.33% and 83.33% of farmers indicated that insect pests and drought constrained cowpea production to a very great extent respectively. Floods were also identified by farmers as a constraint to production. Educational level (74%) and gender with respect to land ownership (84.67%) did not have any negative effects on cowpea production across the communities surveyed. Results in Table 3.9 shows the differential ranking of production constraints across the communities nested within respective local government areas. The same trend was observed for insect pests and drought as they were both ranked first among other constraints in all the LGAs (Table 3.8). Different constraints however were ranked 2nd in each LGA. For instance, In Bichi LGA, lack of improved varieties was ranked 2nd while inadequate marketing channels, high cost of land preparation and maintenance and diseases were ranked 2nd in Garko, Gwarzo and Tofa LGAs respectively. Overall, the first three major constraints identified were drought, insect pests and diseases. University of Ghana http://ugspace.ug.edu.gh 67 Table 3. 8: Constraints expressed by extent to which factors limit cowpea production in four local government areas of Kano State. Production constraints Local Government Areas *Number of responses ‡Great Extent Some Extent Little Extent No Extent Lack of improved varieties Bichi 25(23.6) 6(26.10) 2(28.6) 6(42.9) Garko 19(17.9) 6(26.10) 2(28.6) 3(21.4) Gwarzo 30(28.3) 5(21.7) 2(28.6) 3(21.4) Tofa 32(30.2) 6(26.1) 1(14.3) 2(14.3) ¥Total (N=150) 106(70.67) 23(15.33) 7(4.67) 14(9.33) Drought Bichi 32(25.6) 5(31.2) 2(22.2) --- Garko 23(18.4) 4(25) 3(33) --- Gwarzo 35(28) 3(18.8) 2(22.2) --- Tofa 35(28) 4(25) 2(22.2) --- Total (N=150) 125(83.33) 16(10.66) 9(6) --- Diseases Bichi 26(22.4) 10(43.5) 3(37.5) --- Garko 22(19) 6(26.1) 2(25) --- Gwarzo 36(31) 1(4.3) --- 1(100) Tofa 32(27.6) 6(15.5) 3(37.5) --- Total (N=148) 116(78.38) 23(15.54) 8(5.41) 1(0.68) Insect pest Bichi 37(27.6) 2(15.4) --- --- Garko 27(20.1) 2(15.4) 1(33.3) --- Gwarzo 37(27.6) 2(15.4) 1(33.3) --- Tofa 33(24.6) 7(53.8) 1(33.3) --- Total (N=150) 134(89.33) 13(8.67) 3(2) --- Poor soil fertility Bichi 14(24.6) 4(17.4) 14(34.1) 39(26.4) Garko 9(15.8) 1(4.3) 11(40.7) 9(22) Gwarzo 15(26.3) 16(69.6) 6(22.2) 3(7.3) Tofa 19(33.3) 2(8.7) 3(11.1) 15(36.6) Total (N=148) 57(38.51) 23(15.54) 27(18.24) 41(27.7) Educational level Bichi 3(42.9) 3(25) 2(10) 31(27.9) Garko --- 1(8.3) 8(40) 21(18.9) Gwarzo 1(14.3) 4(33.3) 5(25) 30(27) Tofa 3(42.9) 4(33.3) 5(25) 29(26.1) Total (N=150) 7(4.67) 12(8) 20(13.3) 111(74) University of Ghana http://ugspace.ug.edu.gh 68 Table 3.8 continued Production constraints Local Government Areas *Number of responses ‡Great Extent Some Extent Little Extent No Extent Gender and land ownership Bichi --- 2(14.3) 2(28.6) 35(27.6) Garko --- 2(14.3) 2(28.6) 26(20.5) Gwarzo 1(50) 5(35.7) 1(14.3) 33(26) Tofa 1(50) 5(35.7) 2(28.6) 33(26) ¥Total (N=150) 2(1.33) 14(9.33) 7(4.67) 127(84.67) High cost of land rent Bichi 4(17.4) 2(15.4) 4(25) 27(28.1) Garko 1(4.3) 2(15.4) 7(43.8) 20(20.8) Gwarzo 5(21.7) 3(23.1) 2(12.5) 30(31.2) Tofa 13(56.5) 6(46.2) 3(18.8) 19(19.8) Total (N=148) 23(15.54) 13(8.78) 16(10.81) 96(64.86) Flood Bichi 22(22) 1(14.3) 7(53.8) 8(27.6) Garko 16(16) --- 2(15.4) 12(41.4) Gwarzo 31(31) 3(42.9) 1(7.7) 5(17.2) Tofa 31(31) 3(42.9) 3(23.1) 4(13.8) Total (N=149) 100(67.11) 7(4.7) 13(8.72) 29(19.46) High cost of land preparation and maintenance Bichi 14(20.3) 9(28.1) 6(24) 10(41.7) Garko 16(23.2) 7(21.9) 4(16) 3(12.5) Gwarzo 18(26.1) 8(25) 13(52) 1(4.2) Tofa 21(30.4) 8(25) 2(8) 10(41.7) Total (N=150) 69(46) 32(21.33) 25(16.67) 24(16) Inadequate marketing channels Bichi 11(19) 1(4.5) 12(30.8) 15(48.4) Garko 8(13.8) 8(36.4) 11(28.2) 3(9.7) Gwarzo 15(25.9) 8(36.4) 11(28.2) 6(19.4) Tofa 24(41.4) 5(22.7) 5(12.8) 7(22.6) Total (N=150) 58(38.67) 22(14.67) 39(26) 31(20.67) *Percentages in parentheses; ‡=percentage within extent of constraint; ¥ =percentage across total respondents; --- = no response University of Ghana http://ugspace.ug.edu.gh 69 Table 3. 9: Ranking of production constraints in 15 communities surveyed nested within each local government area. Bichi LGA Garko LGA Gwarzo LGA Tofa LGA Constraints ‡Responses Rank Responses Rank Responses Rank Responses Rank *MDS Overall rank Lack of improved varieties 84.6 2 90.0 3 92.5 4 95.0 3 3.0 3 Drought 100.0 1 100.0 1 100.0 1 100.0 1 5.0 1 Diseases 100.0 1 100.0 1 97.3 3 99.0 2 4.3 2 Insect pests 100.0 1 100.0 1 100.0 1 100.0 1 5.0 1 Poor soil fertility 64.1 5 70.0 5 92.5 4 61.5 7 1.3 6 Educational level 20.5 8 30.0 8 25.0 7 29.3 9 1.0 7 Gender and land ownership 10.2 9 13.4 9 17.5 8 19.5 10 1.0 7 High cost of land rent 27.0 7 33.3 7 25.0 7 53.6 8 1.0 7 Flood 78.9 3 60.0 6 87.5 5 90.2 4 1.8 5 High cost of land preparation and maintenance 74.4 4 89.9 4 97.5 2 75.6 6 2.3 4 Inadequate marketing channels 61.6 6 90.1 2 85.0 6 82.9 5 1.8 5 ‡Percentage respondents within LGA. *MDS= mean derived scores of ranks obtained across LGAs. A rank of 1 received a score of 5, 2 received 4, 3 received 3, 4 received 2 and 5 and above received a score of 1. University of Ghana http://ugspace.ug.edu.gh 70 3.3.7 Traits preferred in improved cowpea varieties Farmers were asked to rank cowpea traits they preferred to be incorporated into cowpea varieties that would enhance easy and high adoption by them (Table 3.10). In Bichi, high market acceptability was ranked first followed by tolerance to drought and large seeded varieties. In Gwarzo, early maturity and high market acceptability were tied in rank as their major preferred traits while in Tofa, early maturity, short cooking time and dual purpose were tied as their major preferred traits. When the scores for the criteria were computed across all the communities nested within their respective LGAs, it was observed that resistance to pest and diseases, large seeds, tolerance to drought, dual-purpose, short cooking time and high market acceptability were tied in rank as the major preferred traits followed by high yield (Table 3.10). Responses between male and female farmers were similar although data was not captured based on gender therefore no gender analysis was carried out. University of Ghana http://ugspace.ug.edu.gh 71 Table 3. 10: General criteria for preferred cowpea varieties as ranked by farmers in the 15 surveyed communities of Kano State, 2014 Local Government Areas Bichi Garko Gwarzo Tofa Across Criteria ‡MDS Rank MDS Rank MDS Rank MDS Rank MDS Rank High yield 1.27 5 1.64 3 1.18 6 1.64 3 1.82 2 Pest and disease resistance 1.36 4 1.45 4 1.45 3 1.73 2 1.91 1 High palatability 1.36 4 1.45 4 0.64 8 1.27 5 1.46 3 Early maturing 1.73 2 1.73 2 1.64 1 1.82 1 1.82 2 High storability 0.72 6 0.54 8 0.72 7 1.46 4 1.82 2 Large seeds 1.73 2 1.36 5 1.36 4 1.27 5 1.91 1 Drought tolerance 1.73 2 1.73 2 1.27 5 0.91 6 1.91 1 Dual-purpose 1.45 3 0.91 6 1.18 6 1.82 1 1.91 1 Short cooking time 1.27 5 1.64 3 1.55 2 1.82 1 1.91 1 High market acceptability 1.81 1 1.81 1 1.64 1 1.73 2 1.91 1 Late maturing 0.18 7 0.64 7 0.27 9 0.55 7 1.18 4 ‡MDS=Mean Derived Score; Every time a criterion is ranked first it receives a score of 5, each second ranking scores 4, each third ranking scores 3, each fourth ranking scores 2, and each other ranking scores 1.0=no response. University of Ghana http://ugspace.ug.edu.gh 72 3.3.8 Farmers’ willingness to participate in specific aspects of in cowpea breeding and other agricultural development research The results in Table 3.11 reveal farmers’ readiness to be involved in plant breeding research from formulation of objectives to selection. Ninety-seven percent of the farmers responded in the affirmative that their involvement in the formulation of demand-driven plant breeding objectives would enhance the adoption of varieties or technologies. The majority of respondents disagreed (95.6% with the idea of breeders/researchers pushing out new varieties on them without seeking their input on the objectives and also involving them in the on-farm trials (Table 3.11). University of Ghana http://ugspace.ug.edu.gh 73 Table 3. 11: Farmers’ willingness to participate in crop development research *Objective formulation On-farm trials, evaluation and selection No involvement Communities No Yes No Yes No Yes Badume 0(0) 10(100) 0(0) 10(100) 9(100) 0(0) Doka 0(0) 11(100) 0(0) 7(100) 4(100) 0(0) Garko 2(25) 6(75) 0(0) 10(100) 8(100) 0(0) Jobe 0(0) 10(100) 0(0) 10(100) 9(100) 0(0) Kafin malami 1(12.5) 7(87.5) 0(0) 10(100) 8(100) 0(0) Katsinawa 0(0) 10(100) 0(0) 10(100) 9(100) 0(0) Katsira 0(0) 10(100) 0(0) 10(100) 9(100) 0(0) Kogon doki 1(12.5) 7(87.5) 0(0) 9(100) 8(100) 0(0) Kogon kura 0(0) 10(100) 0(0) 10(100) 6(85.7) 1(14.3) Kutama 1(10) 9(90) 0(0) 9(100) 7(100) 0(0) Kwami 0(0) 11(100) 0(0) 11(100) 6(85.7) 1(14.3) Mintsira 0(0) 9(100) 0(0) 9(100) 9(100) 0(0) Tofa 0(0) 9(100) 0(0) 8(100) 5(100) 0(0) Wangara 0(0) 9(100) 0(0) 8(100) 5(62.5) 3(37.5) Yakasai 0(0) 10(100) 0(0) 10(100) 8(100) 0(0) Across 5(3.5) 138(96.5) 0(0) 141(100) 109(95.6) 5(4.4) *Percentage responses within community in parentheses University of Ghana http://ugspace.ug.edu.gh 74 3.4. Discussions The approach of the PRA used in this study yielded meaningful results as farmers willingly shared their experiences and knowledge. Relevant information gathered from farmers can assist breeders to incorporate farmers’ varietal preferences into new varieties in order to enhance adoption. With farmers’ active participation, it is believed that research will develop technologies that farmers will play a key role in the diffusion which will result in more productive, stable, and sustainable agricultural systems (Odendo et al., 2002; Ellis- Jones et al., 2004). Age is considered as a basic population characteristic. The age of a person influences his needs, occupation and the pattern of public demand of him (Okafor et al., 1994). The major proportion of the respondents fell within the range of 20–50 years and the average age was 47 years. They are classified as young adults that still possess both strong mental alertness and physical strength needed for their work. This finding agrees with Awotide et al. (2015) who reported 47 years as the mean age of farmers they interviewed from their study on the input use and profitability of arable crops in Nigeria. Similarly, Musa et al. (2013) reported 42 years as the average age of farmers they interviewed and Mazza et al. (2012) reported that 82.59% of the Fadama users they interviewed were within the range of 40-49 years. This finding revealed the active participation of Nigerian young adults in farming and disagrees with the general belief that they are not interested in agricultural activities. Education has been reported to be very important as it helps refine a person’s perceptions of issues and help him/her make reasonable decisions based on information. Level of education of farmers will contribute to adoption of improved technologies as well as facilitates their participation in research activities (Krueger, 1993; Mazza et al., 2012). The University of Ghana http://ugspace.ug.edu.gh 75 number of respondents found to have formal education in this study was above two – third of the total respondents. Similar results were reported by Awotide et al. (2015) and Mazza et al. (2012). This high rate of literacy might be due to the existence of free basic education in the country which has improved school enrolment. Owing to the communal co-operation among the farmers, information on new technologies were shared among all and therefore there was no difference in the responses of the one-third illiterates with the literate farmers. The size of land (farm size) of an individual usually determines his production capacity. Under normal circumstances, it is expected that the larger the land size, the greater the output that can be produced. In addition, the adoption of some agricultural technologies (such as those related to mechanization) which improve efficiency are related to farm size. Results from this study showed that the average farm size grown to cowpea was 1.5 ha out of an average of 4.5 ha of total farm size for all crops. This implies that there is high level of land fragmentation to accommodate cultivation of other crops for security reasons which hinders full mechanization which could boost cowpea productivity. Farmers’ groups are socio-economic groups because they are formed to accomplish some common social and economic goals in relation to their farming activities. Farmers join these groups because they use the membership to access credit facilities and agricultural information (Ofuoku and Urang, 2009; Ofuoku and Chukwuji, 2012; Kolade and Harpham, 2014). Ofuoku and Urang (2009) asserted in their study that farmers will only remain in their various groups as long as the group still supports their needs. Although, most of the farmers interviewed in this study belonged to one farmer group or the other, 14% of respondents said they pulled out of farmer groups because their individual goals were no longer being met by the group as a result of poor leadership and financial mis-management. University of Ghana http://ugspace.ug.edu.gh 76 This finding agrees with Ofuoku and Chukwuji (2012) who reported that most farmers group in South-Eastern Nigeria experienced membership withdrawal because of mis- management and that the groups no longer satisfy their needs. Eighty-six percent that still belong to farmers’ associations because they believe that their membership will enable to access loans as a group and also make them benefit from other interventions from government or non-governmental organizations (NGOs). Cowpea was grown as an intercrop or in rotation with cereal crops like maize, millet and sorghum. Cowpea was often planted about 4-6 weeks after planting the first crop using a wider spacing of 75 cm x 50 cm. Norman (1960) reported that it was relatively more profitable to grow crops in mixtures than sole cropping in Northern Nigeria based on the fact that there was no significant difference between the marginal value product of resource used and opportunity cost of the resource. Also, Ibeawuchi (2007) and Rashid et al. (2000) reported that factors such as population pressure, climatic conditions, risk management and high economic returns from efficient use of land resources, soil improvement were among other reasons why farmers intercrop (Ibeawuchi, 2007; Rashid et al., 2007; Ajeigbe et al., 2010b; Marinus, 2014). It has been reported that higher yields of cowpea can be obtained using furrow irrigation (Hussain et al., 2004). Findings from this study however revealed that most farmers could not afford the high cost associated with irrigation and therefore cultivated cowpea once in a year under rain-fed conditions, although, 34% of the respondents indicated that they cultivated cowpea twice a year under irrigation. They however complained of associated problems such as shortage of irrigation water or pressure, sudden shut-down of irrigation by authorities without prior notice and high cost. University of Ghana http://ugspace.ug.edu.gh 77 Availability of seeds is an important criterion that cowpea farmers consider together with size and colour of the variety before adopting the varieties. The environment or community they are growing and the market they are targeting are also important factors that drive adoption (Dugje et al., 2009). In this study, most of the farmers obtained their seeds from the open market except in Garko LGA where the majority of the respondents indicated that they bought their seeds from agro dealers. Most of the farmers however said they had access to drought tolerant cowpea varieties through the extension services. Farmers indicated that they got information on climate change and other agricultural related developments from the extension agents followed by mass media such as radios and television. The use of local languages on media enables all the categories of farmers to have access to information on agricultural technologies (Musa, 2011). Changes in climate are threatening agricultural productivity. Farmers interviewed in this study indicated that rainfall was becoming very erratic and unpredictable and that it was negatively affecting their productivity. Ajetomobi and Abiodun (2010) reported reduction in cowpea yield due to climate change and its impact on cowpea productivity. The negative impact of reduced precipitation affected both cowpea grain and fodder yields. Most of the farmers in the surveyed communities have experienced drought at one time or the other during cowpea growing seasons. They indicated that drought at vegetative stage affected leaf production, abscission, and leaf yellowing, stunted growth and wilting of the whole plant. This agrees with findings by other researchers on cowpea and soybean who reported that drought stress at vegetative stage reduced rate of leaf expansion, followed by cessation of new leaf production and eventually, leaf increment was reduced to zero (Akyeampong, 1986; Mustapha et al., 2014). Farmers indicated from their experience that drought stress University of Ghana http://ugspace.ug.edu.gh 78 occurring at the reproductive stage was more devastating than that occurring at the vegetative stage. In their responses, they stated that there was increased flower abortion, poor pod filling and eventually low grain yield. Farmers’ observations are supported by the findings of Sakamoto et al. (2012) who reported that reduction of flower buds in cowpea under drought stress is caused by rapid stomatal closure which leads to reduced assimilation of photosynthates. Similarly, Daneshnia et al. (2013) reported lower grain and biological yield of cowpea varieties when irrigation treatment ended at flowering stage. Drought was ranked overall as the first major constraint to cowpea production. Cowpea is predominantly grown towards the end of the rainy season which is characterized by erratic pattern of rainfall and high temperatures. Although, cowpea is considered drought tolerant compared with other legume crops, its yield is highly reduced under terminal drought and high temperature. Studies have reported that a combination of high temperature, drought and long days can slow down or inhibit floral bud development (Nielsen and Hall, 1985; Patel and Hall, 1990), resulting in fewer flowers being produced and substantially reduced cowpea productivity (Ahmed et al., 1992; Suliman, 2007). Generally, drought has been reported to cause grain yield reduction between 58 – 95% in chickpea (Leport et al., 2006) and 67% in cowpea (Fatokun et al., 2012). Pest infestation was tied in ranking with drought across the four LGAs surveyed followed by diseases. Hall (2012) reported that diseases like ashy stem blight caused by Macrophomina phaseolina and pests such as lesser corn stalk borer (Elasmopalpus lignosellus) destroyed cowpea seedlings under hot, dry soil conditions. Some authors indicated that the aggressiveness of pests to extract water from weak plants contributes to University of Ghana http://ugspace.ug.edu.gh 79 the increased damage they cause during drought stress (Popov et al., 2006). Farmers also indicated that the incidence of Striga gesnerioides was increased under drought stress. Muranaka et al. (2011) reported probable restriction of water uptake by Striga which magnifies the effect of drought stress in cowpea. Ishiyaku and Aliyu (2013) in their study reported that cowpea genotype IAR-07-1050 showed high resistance to S. gesnerioides thus had higher grain yield under drought stress than susceptible genotypes. Although drought tolerance, resistance to pest and diseases were ranked first among production constraints, farmers ranked consumer-based quality traits such as large seeds, short cooking time, dual purpose and high market acceptability before high yield and early maturity on the same level as the former while high yield and early maturity were ranked lower. From this study, it was observed that farmers sold two-thirds of their cowpea produce and thus they preferred varieties with high demand in the market which is a direct indication of consumer preference. Farmers’ ranking of large seeds as an important trait is supported by studies by Langyintuo et al. (2004), Mishili et al. (2007) and Egbadzor et al. (2013) that traders in cowpea grains, whether for food or seed, prefer large seeds to small ones because consumers in West Africa regions are ready to pay premium prices on them. Therefore, improving cowpea grain size while other traits remain intact will mean better varieties for farmers to accept. A study by Egbadzor et al. (2013) in Ghana revealed that farmers chose easy to cook/ short cooking time trait above other traits. Farmers indicated that seeking their input in formulating plant breeding objectives will ensure their full participation and easy adoption of the resulting varieties. Farmers’ knowledge about the crops in their areas has a high potential to strengthen PVS/PPB programmes. It would serve well to empower scientists’ knowledge for designing site-, University of Ghana http://ugspace.ug.edu.gh 80 crop- and farmer-specific activities. For instance in Malawi during a PVS, an accession of maize that ranked high by scientists using only data from multi-locational trials was not selected at all by farmers because of its long cooking time and poor taste when the grains are processed (Nkongolo et al., 2009). Witcombe et al. (2005) reported that the rapid acceptance of some varieties in Nepal was as result of participatory trial approach system which illustrated the success of a highly client-oriented breeding approach. 3.5. Conclusions Based on the evidence provided in this study, farmers gained knowledge on climate change, seeds and other related agricultural developments through extension agents and farmer friends. This information is also aired in their local languages through mass media. Impact of drought is severe on all the harvestable parts of the crop as it adversely affects yield and monetary returns on investment. Farmers were interested in cowpea varieties with the following characteristics: high market acceptability, dual purpose, short cooking time, large seeds and tolerance to biotic and abiotic stresses. This study revealed that farmers have multiple criteria apart from yield for evaluating cowpea varieties for adoption, it therefore reiterates the need to involve farmers at the appropriate stages of a plant breeding programme in order to capture farmers’ desired traits in the new varieties. This will increase the selection of these varieties and increase the adoption by farmers. Whereas breeders cannot incorporate all desired traits in one variety, many varieties could be bred focusing on the demands of different farmer groups based on their peculiar socio-economic and agro-ecological needs. Since farmers had indicated their willingness to be part of plant University of Ghana http://ugspace.ug.edu.gh 81 breeding development process, their indigenous knowledge will empower breeders’ scientists’ knowledge for designing target specific varieties. University of Ghana http://ugspace.ug.edu.gh 82 CHAPTER FOUR 4.0 Evaluation of cowpea genotypes for tolerance to seedling and terminal drought 4.1 Introduction Cowpea (Vigna unguiculata L. Walp.) is one of the most important legumes cultivated for pulse and forage production in arid and semi-arid regions in sub-Saharan Africa (SSA) (Singh and Awika, 2010; Hall, 2012). The crop is grown under rain-fed conditions but the recent pattern of rainfall in the sub-region which either comes late, sparse at the beginning of the season or stops earlier than usual requires that efforts be made to enhance the level of drought tolerance in the currently available improved varieties (Fatokun et al., 2012; Kutama et al., 2014). Drought is the most important constraint threatening food security in the world. It has been reported that change in temperature and rainfall will have dramatic effects on agricultural production in Africa because only 7% of the total agricultural land in Africa (barely 3.7% for SSA) is irrigated, given that 40% of the total irrigated area is in North Africa (http://www.fao.org/docrep/005/y6831e/y6831e-03.htm accessed 19/10/2015 at 11.31am). Studies have reported that the yield of cowpea obtained in the West African sub-region is lower than that in the USA and in Australia (Quin, 1997) and very often inconsistent (Krasova-Wade et al., 2006). The low productivity has been attributed to drought, among other factors. Although, cowpea is said to be relatively drought tolerant, it still suffers from considerable yield loss when exposed to severe drought during seedling, flowering and pod-filling stages. Drought affects anatomical, morphological, physiological and biochemical aspects (Pimentel, 2004; Ewansiha and Singh, 2006; Costa et al., 2008) of University of Ghana http://ugspace.ug.edu.gh 83 plant development. Drought severity on the crop is directly related to the duration, intensity and stage of crop development (Lobato et al., 2008; Bastos et al., 2011; Fatokun et al., 2012). Water stress occurring at the early stage of plant development can have negative impact on final crop yield by causing delay in plant development through inhibition of growth during the water deficit period and slow or incomplete photosynthetic recovery which affects growth and development of sink size and source supply when the plant resumes growth after early drought (Blum, 1996, 2011; Chaves et al., 2011; Vadez et al., 2014). For instance in rice (Oryza sativa), it was discovered that the retention of green leaf area and the number of leaves surviving at the end of drought stress occurring at the seedling stage affects the ability of rice seedlings to recover and tiller (Kamoshita et al., 2008). In soybean (Glycine max), seedling stage drought stress reduced the number of nodes, internode length, overall biomass and the number of flowers produced, yield and yield components (Desclaux et al., 2000; Passioura , 2012). Drought stress during flowering and pod filling is particularly important since it impacts directly and negatively on flower development, pollination (Boyer and McPherson, 1975), reduction in leaf area (Akyeampong, 1986), pod setting and grain filling leading to reduced number of pods per plant and seed weight, and consequently low seed yield (Suliman and Ahmed, 2010; Mustapha et al., 2014). Studies have shown that root and shoot growth are closely coordinated (Jackson, 1993; Palta et al., 2011), and that abscisic acid (ABA) likely plays a major role in their regulation for drought tolerance (Munns and Cramer, 1996). In cowpea, drought tolerance has been attributed to several mechanisms that include deep rooting, stomatal sensitivity, reduced growth rate, leaf area reduction (Turk and Hall, 1980a,b; Lawn, 1983; Mai-Kodomi et al., University of Ghana http://ugspace.ug.edu.gh 84 1999a; Singh et al., 1999a), delayed leaf senescence, hastened or delayed reproductive cycle (Gwathmey and Hall, 1992c), osmotic adjustment (Mai-Kodomi et al., 1999b; Chiulele and Agenbag, 2004) and selective moisture remobilization with major dedication to the upper leaves and growing tips (Mai-Kodomi et al., 1999b). Genetic variability existing in germplasm can only be identified and selected only when they are screened for the phenotypic expression of the genes controlling the trait of importance (Vigouroux et al., 2002). Selection of lines to serve as parents to produce the next generation is important in breeding and good parents must be selected from genetically variable populations. The present levels of drought tolerance in commonly grown cowpea varieties could be further enhanced through genetic improvement. It is conceivable that genes that could contribute to these higher levels of drought tolerance exist in some of the unexploited cowpea germplasm lines maintained in the genetic resources unit at the International Institute of Tropical Agriculture (IITA). The aim of this study was to screen different cowpea germplasm sources for tolerance to drought and select parents for genetic studies and for use in developing drought tolerant improved cowpea lines. University of Ghana http://ugspace.ug.edu.gh 85 4.2 Materials and Methods 4.2.1 Plant material A collection of ninety-one cowpea genotypes comprising of advanced breeding lines, tropical Vigna unguiculata (TVu) tvuaccessions, improved cowpea varieties and landraces (highly inbred) were chosen to represent a wide range of cowpea genotypic diversity (Table 4.1). Some of these lines were previously reported to be tolerant to water stress (DanIla, TVu 11986) and susceptible to water stress (TVu 7778) (Agbicodo et al., 2009). However, a larger evaluation was necessary to assess the wide diversity of cowpea for additional sources of drought tolerance genes. 4.2.2 Phenotyping cowpea genotypes for shoot drought tolerance A simple “wooden box screening technique” (Figure 4.1) described by Singh et al. (1999) was used to screen the seedlings of the ninety test genotypes plus one susceptible check in the glass house in two splits because of availability of wooden boxes. The dimension of each wooden box was 42 cm x 57 cm. Each wooden box was filled with a 34 kg mixture of sand and top soil in the ratio of 3: 1 (Plate 4.1). In split 1, each wooden box consisted of four test plants and a susceptible check arranged in an 10 x 5 alpha (0, 1) lattice design while a 10 x 4 alpha (0, 1) lattice design was used for split 2; each box serving as an incomplete block with three replications. Each box was watered thoroughly to field capacity and allowed to drain for 2 days before planting. Four / five hills were later marked out for each genotype, 9.5 cm between rows and 8.2 cm between holes, 1.5 cm deep and one seed per hill (Plate 4.2). After planting, boxes were watered to field capacity and thereafter watering was completely stopped for 4WAP (weeks after planting). Stress was University of Ghana http://ugspace.ug.edu.gh 86 measured by observing that all the plants for the susceptible check in each box were dead (Plate 4.3) and thereafter watering was resumed every two days for two weeks. After resumption of watering, variable numbers of recovered seedlings were rated for recovery (Plate 4.4). Daily temperature and humidity of the glasshouse were logged on hourly basis using Tinytag Ultra 2 TGU-4500. Mean readings were computed for the months. Rainfall data was collected by the automated weather station of the Geographic Information System (GIS) unit of IITA, Ibadan. The surviving seedlings that exhibited significant genetic variation to drought during the seedling stage was transferred to plastic pots in the screen house in order to separate the plant root systems and to eliminate competition among the genotypes for water. They were watered till maturity (Muchero et al., 2008). 4.2.3 Field screening of cowpea genotypes for tolerance to terminal drought The experiment was conducted at IITA experimental station in Ibadan (07o03´N, 3o9´E, 212 m above sea level (asl) during the dry season of 2013 under irrigation. The experimental design was 13 x 7 alpha (0, 1) lattice with three replications. Experiments were planted in two adjacent blocks that received different irrigation regimes. Block 1 was the drought stressed (DS) block and block 2 was the well-watered (WW) block. Irrigation water was supplied to both blocks with an overhead sprinkler irrigation system designed to dispense 17 mm of water twice a week until 35 DAP (days after planting) for the DS block while WW block continued receiving water twice a week until physiological maturity. DS block was separated from WW block by an alley that was 20 m wide to restrict lateral movement of water from the fully irrigated block to the drought stressed block. DS blocks University of Ghana http://ugspace.ug.edu.gh 87 did not receive water from 35 DAP (days after planting) until harvesting while the WW block received water twice a week until physiological maturity. Except for the different irrigation treatments, all field management practices were uniform for both the well- watered and water-stressed blocks. No data could however be obtained from the field screening owing to the interference of rain during the critical stage of imposition of terminal drought stress. Therefore, parental selection for genetic studies was only based on the response of the genotypes to seedling stage drought. Plate 4. 1: Mixing sand and top soil in ratio 3:1 Plate 4. 2 Marking out the wooden boxes for planting Plate 4. 3 Cowpea seedlings 14DAP (A) and at 4 weeks after planting (B) during drought stress. A A B B A B University of Ghana http://ugspace.ug.edu.gh 88 Plate 4. 4 Cowpea seedlings recovery after 4 weeks of drought stress followed by 2 weeks every other day watering. The susceptible parent, TVU7778 had 0% recovery rate while tolerant genotypes showed variable percentage survival rates. University of Ghana http://ugspace.ug.edu.gh 89 4.2.3 Data collection Data collection i. Temperature and humidity conditions of the glasshouse Daily minimum and maximum temperature and humidity of the glasshouse were captured using the Tinytag Ultra 2 TGU-4500 temperature loggers hung in the glasshouse. The logger was placed in the glasshouse and was set to log temperature and humidity daily on an hourly interval for the whole period of the experiment in the glasshouse. Logged data were stored in the device memory following the manufacturer’s instruction for set-up. On completion of the experiment, the logger was removed and connected to the computer to transfer logged data. Rainfall data for the period of experiment was collected from the IITA, Ibadan weather station unit. ii. Agronomic traits During drought stress, data were collected on number of days to seedling emergence, stem greenness and wilting at 14, 21 and 30 days after planting (DAP) on a scale of 0-4 (Muchero et al., 2008) and on plant height at 14 and 21DAP. Data taken during drought stress: Stem greenness 0 = leaves and stem completely yellow 1= 75% of the leaves yellow, brown either from the base or tip of stem University of Ghana http://ugspace.ug.edu.gh 90 2 = 50% yellow or pale green, stem not turgid 3 = 25% yellow, 75% green but less turgid 4 = completely green and stem turgid Wilting 0 = no sign of wilting 1 = 25% of wilting 2 = moderate wilting, 50% 3 = yellow and brown leaves with 75% wilting 4 = completely wilted After re-watering, data were collected on: Survival count (SC): number of surviving plants per genotype Recovery type: was rated on a score of 0-5 0 = no recovery 0.5 = recovery from basal meristem 1 = recovery from the apical meristem Recovery rate (RR) was computed as: 𝑁𝑜 𝑜𝑓 𝑑𝑒𝑎𝑑 𝑝𝑙𝑎𝑛𝑡𝑠 𝑇𝑜𝑡𝑎𝑙 𝑛𝑜 𝑜𝑓 𝑒𝑚𝑒𝑟𝑔𝑒𝑑 𝑝𝑙𝑎𝑛𝑡𝑠 × 100 (Fatokun, 2014 pers. Comm.) University of Ghana http://ugspace.ug.edu.gh 91 4.2.4 Data Analysis All variables were analyzed using SAS (Version 9.3; SAS Institute, Cary, NC). Phenotypic correlation and regression analyses were performed with PROC CORR and PROC REG. Significant means were separated using the least significant difference at 5% probability level (LSD0.05). Cluster analysis was performed to group observations together using the Euclidean distance method using three discriminant phenotypic characters. A dendrogram was computed from the cluster to show relationships among the genotypes. University of Ghana http://ugspace.ug.edu.gh 92 Table 4. 1: Status and designation of lines screened for seedling stage and terminal drought tolerance S/N Lines Status Designation S/N Lines Status Designation 1 13-309 Advanced Breeding Line ABL01 51 IT99K-573-1-1 Improved variety IMV17 2 14-62 Advanced Breeding Line ABL02 52 Tvu1007 Tropical Vigna unguiculata TVu01 3 15-145 Advanced Breeding Line ABL03 53 Tvu10100 Tropical Vigna unguiculata TVu02 4 16-109 Advanced Breeding Line ABL04 54 Tvu11800 Tropical Vigna unguiculata TVu03 5 19B-35 Advanced Breeding Line ABL05 55 Tvu11982 Tropical Vigna unguiculata TVu04 6 19C-8 Advanced Breeding Line ABL06 56 Tvu11984 Tropical Vigna unguiculata TVu05 7 21A-40 Advanced Breeding Line ABL07 57 Tvu11986 Tropical Vigna unguiculata TVu06 8 21A-54 Advanced Breeding Line ABL08 58 Tvu12791 Tropical Vigna unguiculata TVu07 9 22A-85 Advanced Breeding Line ABL09 59 Tvu1436 Tropical Vigna unguiculata TVu08 10 23B-136 Advanced Breeding Line ABL10 60 Tvu1438 Tropical Vigna unguiculata TVu09 11 24A-23 Advanced Breeding Line ABL11 61 Tvu14539 Tropical Vigna unguiculata TVu10 12 28-25A Advanced Breeding Line ABL12 62 Tvu14553 Tropical Vigna unguiculata TVu11 13 31-181 Advanced Breeding Line ABL13 63 Tvu14632 Tropical Vigna unguiculata TVu12 14 40-210 Advanced Breeding Line ABL14 64 Tvu14676 Tropical Vigna unguiculata TVu13 15 45-111 Advanced Breeding Line ABL15 65 Tvu148 Tropical Vigna unguiculata TVu14 16 52-13A Advanced Breeding Line ABL16 66 Tvu15055 Tropical Vigna unguiculata TVu15 17 55-261 Advanced Breeding Line ABL17 67 Tvu15058 Tropical Vigna unguiculata TVu16 18 55-71 Advanced Breeding Line ABL18 68 Tvu15450 Tropical Vigna unguiculata TVu17 19 57-109 Advanced Breeding Line ABL19 69 Tvu15866 Tropical Vigna unguiculata TVu18 20 58-246 Advanced Breeding Line ABL20 70 Tvu16646 Tropical Vigna unguiculata TVu19 21 58-60 Advanced Breeding Line ABL21 71 Tvu2672 Tropical Vigna unguiculata TVu20 22 58-66 Advanced Breeding Line ABL22 72 Tvu2722 Tropical Vigna unguiculata TVu21 23 5A-55 Advanced Breeding Line ABL23 73 Tvu2731 Tropical Vigna unguiculata TVu22 24 60-321 Advanced Breeding Line ABL24 74 TVu2736 Tropical Vigna unguiculata TVu23 25 60-374 Advanced Breeding Line ABL25 75 Tvu294 Tropical Vigna unguiculata TVu24 26 61-317 Advanced Breeding Line ABL26 76 Tvu3562 Tropical Vigna unguiculata TVu25 27 61-88 Advanced Breeding Line ABL27 77 Tvu441 Tropical Vigna unguiculata TVu26 28 64-25 Advanced Breeding Line ABL28 78 Tvu4574 Tropical Vigna unguiculata TVu27 University of Ghana http://ugspace.ug.edu.gh 93 Table 4.1 continued 29 64-68 Advanced Breeding Line ABL29 79 Tvu522 Tropical Vigna unguiculata TVu28 30 6-67 Advanced Breeding Line ABL30 80 Tvu5415 Tropical Vigna unguiculata TVu29 31 7-11 Advanced Breeding Line ABL31 81 Tvu557 Tropical Vigna unguiculata TVu30 32 71-10 Advanced Breeding Line ABL32 82 Tvu6333 Tropical Vigna unguiculata TVu31 33 8A-131 Advanced Breeding Line ABL33 83 Tvu6443 Tropical Vigna unguiculata TVu32 34 8B-46 Advanced Breeding Line ABL34 84 Tvu6707 Tropical Vigna unguiculata TVu33 35 CB27 Improved variety IMV01 85 Tvu7778 Tropical Vigna unguiculata TVu34 36 Ife Brown Improved variety IMV02 86 Tvu79 Tropical Vigna unguiculata TVu35 37 IT81D-985 Improved variety IMV03 87 Tvu8670 Tropical Vigna unguiculata TVu36 38 IT81D-994 Improved variety IMV04 88 Tvu9693 Tropical Vigna unguiculata TVu37 39 IT84S-2246-4 Improved variety IMV05 89 Tvu9797 Tropical Vigna unguiculata TVu38 40 IT85F-3139 Improved variety IMV06 90 Sanzi Landrace LDR01 41 IT89D-288 Improved variety IMV07 91 Danila Landrace LDR02 42 IT90K-277-2 Improved variety IMV08 43 IT90K-76 Improved variety IMV09 44 IT93K-452-1 Improved variety IMV10 45 IT96D-610 Improved variety IMV11 46 IT97K-207-15 Improved variety IMV12 47 IT97K-499-35 Improved variety IMV13 48 IT97K-573-2-1 Improved variety IMV14 49 IT97K-819-132 Improved variety IMV15 50 IT98K-1111-1 Improved variety IMV16 ‡All genotypes are true-breeding; SC = susceptible check University of Ghana http://ugspace.ug.edu.gh 94 4.3 Results 4.3.1 Climatic conditions On the average, temperature varied between 22.8oC and 39.8oC and relative humidity ranged between 43.89% and 94.35%. Average rainfall during the screening experiment was 131 mm (Figure 4.1). Figure 4. 1: Means of monthly temperature, relative humidity of the glass house and rainfall during screening experiment 155 179 46 169 107 0 20 40 60 80 100 120 140 160 180 200 0 20 40 60 80 100 120 June July August September October Months of measurement R ai n fa ll (m m ) Te m p er at u re (o C ) / R e la ti ve h u m id it y (% ) MinTemp (oC) MinHumidity (%) MaxTemp (oC) MaxHumidity(%) Rainfall (mm) University of Ghana http://ugspace.ug.edu.gh 95 4.3.2 Screening experiment Results for the screening experiment for tolerance of cowpea germplasm to drought at seedling stage are presented in Tables 4.2 – 4.4. There were highly significant differences among the genotypes screened for tolerance to drought for all the parameters studied (p < 0.0001) (Table 4.2). Stem greenness was reduced over time during the stress period and average stem greenness scores were 3.93, 2.79 and 1.66 at days 14, 21 and 30 days after planting respectively (Table 4.4). Stem greenness at 30 DAP was negatively correlated with wilting at days 21 and 30 after planting (r = - 0.19, - 0.54 respectively) but positively correlated with recovery (r = 0.66) (Table 4.3a). Stem greenness at 21 days after planting showed significant negative correlation with wilting at days 21 and 30 after planting (r = -0.84 and r = -0.57 respectively). The range of stem greenness scores differed among the different categories of cowpea germplasm screened. In the advanced breeding lines, stem greenness ranged from 3.87 – 2.17 for genotypes 15-145 and 58-60, respectively. Among the improved varieties, stem greenness score ranged from 3.81 – 2.3 for Ife Brown and IT97K- 573-1-1. Among the TVu lines, it ranged from 3.72 – 1.82 for TVu11982 and TVu14632 respectively. Stem greenness scores for the landraces, Danila and Sanzi were 3.36 and 1.63 respectively. University of Ghana http://ugspace.ug.edu.gh 96 Table 4. 2: Mean squares of measured traits for cowpea genotypes evaluated in the greenhouse for tolerance to drought at seedling stage. Source of variation Df ƚDTE PHT14 PHT21 SG14 DAP SG21 DAP SG30 DAP W14 DAP W21 DAP W30 DAP Recovery Rep 2 0.28ns 12.28** 10.04* 0.49** 7.79*** 5.39** 1.21*** 2.19** 4.67** 0.01ns Wooden box(Rep) 45 0.63* 9.59*** 9.71*** 0.29*** 2.78*** 3.88*** 0.44*** 2.55*** 2.37*** 0.1** Genotypes 89 2.81*** 29.77*** 13.15*** 0.19*** 8.13*** 5.62*** 1.01*** 17.02*** 2.94*** 0.22*** Error 888 0.46 2.3 2.42 0.06 0.72 0.95 0.07 0.45 0.73 0.05 *, **, ***, Significant at P < 0.05, 0.01 and 0.0001 respectively, ns=non-significant. ƚDTE = Days to seedling emergence, PHT14, PHT21= plant height at 14and 21 days after planting, SG14DAP and SG30DAP = Stem greenness(0-4) at 14, 21 and 30days after planting, W14DAP, W21DAP and W30DAP= Wilting (0-4) at 14, 21 and 30 days after planting University of Ghana http://ugspace.ug.edu.gh 97 Wilting: significant differences in wilting existed among the evaluated genotypes and measurement periods (P < 0.0001). Wilting increased over time for the period of the drought stress. Average wilting scores were 0.25, 1.97 and 2.83 at days 14, 21 and 30 after planting, respectively (Table 4.4). Wilting showed a significant negative correlation with recovery at days 14 and 30 after planting. The wilting scores ranged from 0.37 – 2.23 in the advanced breeding lines for 7-11 and 19B-35. Among the improved varieties, the range was 2.17 – 1.04 for IT81KD-985 and IT93K-452-1; among the TVu lines, it ranged from 2.67 – 1.31 for TVu14553 and TVu8670 respectively. The landraces Danila and Sanzi had wilting scores of 1.78 and 2.5 respectively. Plant height measured at 14 DAP was significant and positively correlated with height measured at 21 DAP (r = 0.97). There was a non-significant negative correlation between plant height and wilting (r = 0.0004) while it had a positive and significant relationship with stem greenness and recovery (r = 0.21, r = 0.28) respectively (Table 4.3b). Recovery from drought: Genotypes showed highly significant differences for recovery from drought at seedling stage (P < 0.0001). Correlation analysis done on the average of the measurement days for wilting, plant height and stem greenness revealed a significant negative relationship between recovery and wilting (r = -0.26) but positive significant relationship for stem greenness (r = 0.53). Stem greenness showed a strong negative significant correlation with wilting (r = -0.74) (Table 4.3b). Because recovery from drought stress is important for survival and subsequent growth and reproduction, stepwise regression analysis of recovery on wilting, plant height and stem greenness was used to University of Ghana http://ugspace.ug.edu.gh 98 determine which shoot variables could be used to predict seedling recovery from drought. Only stem greenness and wilting fitted into the model that explained 30.9% of variation to recovery. Stem greenness alone contributed 24.5% while wilting accounted for 6.4% of the variation in recovery (Figure 4.2). Recovery ratings illustrated the ability of some genotypes to recover from early drought stress while others succumbed to the stress. Those genotypes with high percentage recovery may be of interest because they might possess mechanisms that sustained them through the early drought stress. In this study, Ife Brown had the highest recovery percentage of 83.33% (Table 4.4). University of Ghana http://ugspace.ug.edu.gh 99 Table 4. 3: Spearman correlation among plant height, stem greenness, wilting and recovery for cowpea genotypes evaluated in the glass house for tolerance to drought at seedling stage. a ƚDTE PHT14 PHT21 SG14 DAP SG21 DAP SG30 DAP W14 DAP W21 DAP W30 DAP Recovery DTE 1 -0.20* -0.15ns 0.06ns -0.15ns -0.02ns -0.15ns 0.17ns 0.17ns 0.06ns PHT14 1 0.97*** 0.12ns -0.12ns 0.17ns -0.25ns 0.19ns 0.18ns 0.28** PHT21 1 0.21* -0.24* 0.16ns -0.38** 0.34** 0.27** 0.26* SG14DAP 1 -0.18ns 0.18ns -0.67*** 0.24* 0.08ns 0.15ns SG21DAP 1 0.37** 0.41*** -0.84*** -0.57*** 0.23* SG30DAP 1 -0.37* -0.19ns -0.54*** 0.66*** W14DAP 1 -0.46*** -0.1ns -0.24* W21DAP 1 0.68*** -0.12ns W30DAP 1 -0.25* 1 b PHT SG W Recovery PHT 1 -0.0004ns 0.21* 0.28** SG 1 -0.74*** 0.53*** W 1 -0.26* ƚDTE = Days to seedling emergence, PHT14, PHT21= plant height at 14and 21 days after planting, SG14DAP and SG30DAP = Stem greenness(0-4) at 14, 21 and 30days after planting, W14DAP, W21DAP and W30DAP= Wilting (0-4) at 14, 21 and 30 days after planting * , **, ***, Significant at P < 0.05, 0.01 and 0.0001 respectively, ns=non-significant University of Ghana http://ugspace.ug.edu.gh 100 Table 4. 4: Performance of best 20 and 10 worst cowpea lines screened for drought tolerance at seedling stage based on percentage rate of recovery. Genotypes *DTE Plant height Stem greenness (0-4) Wilting (0-4) ¥RR(%) 14DAP 21DAP 14DAP 21DAP 30DAP 14DAP 21DAP 30DAP Ife Brown 3.4 11.9 12.3 4.0 4.0 3.4 0.0 1.3 3.2 83.3 IT99K-573-1-1 3.4 10.2 10.6 4.0 3.0 1.9 0.0 2.1 3.1 57.1 5A55 3.3 7.8 7.3 4.0 3.8 2.7 0.3 0.7 1.9 50.0 IT90K-76 3.1 12.1 12.7 4.0 2.7 2.3 0.0 2.2 2.4 50.0 15-145 3.4 8.7 7.5 4.0 4.0 3.6 0.0 0.2 1.7 45.5 CB27 4.1 8.2 8.2 3.6 3.3 2.5 0.7 0.8 1.7 40.0 IT84S-2246-4 3.5 9.3 9.9 4.0 2.9 2.7 0.0 1.9 3.1 36.4 IT90K-277-2 3.3 13.0 13.7 4.0 2.4 3.1 0.0 2.9 3.1 36.4 19B-35 3.3 8.5 7.7 4.0 4.0 3.5 0.0 0.1 1.0 33.3 IT97K207-15 4.0 11.4 11.1 3.8 3.3 1.8 0.3 0.9 2.5 25.0 TVu15055 3.5 7.1 7.4 4.0 2.5 2.5 0.0 3.0 3.0 25.0 Tvu11984 3.8 9.6 10.1 4.0 1.8 1.8 0.0 3.1 3.0 25.0 IT89D-288 3.2 14.9 15.2 4.0 2.2 2.1 0.0 2.8 3.2 22.2 IT97K-819-132 3.9 8.4 8.3 3.9 3.9 2.3 0.2 0.6 2.3 20.0 IT81KD-985 3.0 9.6 10.3 4.0 3.5 2.2 0.0 2.4 3.0 18.2 IT93K-452-1 3.7 14.8 15.5 4.0 1.9 1.3 0.0 3.0 3.0 18.2 19C-8 3.0 8.1 8.7 4.0 3.9 3.3 0.1 0.2 1.3 16.7 64-25 3.1 9.6 9.2 4.0 3.5 1.9 1.0 1.3 2.3 16.7 6-67 3.0 7.4 7.9 3.9 3.8 2.0 0.3 0.5 2.1 16.7 University of Ghana http://ugspace.ug.edu.gh 101 Table 4.4 continued Genotypes *DTE Plant height Stem greenness (0-4) Wilting (0-4) ¥RR(%) 14DAP 21DAP 14DAP 21DAP 30DAP 14DAP 21DAP 30DAP Danila 3.0 9.7 10.3 4.0 3.3 2.8 0.0 2.2 3.2 16.7 TVu79 3.0 9.6 10.2 4.0 1.5 1.5 0.0 2.9 3.0 0.0 TVu8670 3.5 9.0 9.6 4.0 2.2 1.7 0.0 3.1 3.3 0.0 TVu9797 3.4 8.4 8.8 4.0 1.2 0.6 0.0 2.8 3.1 0.0 Tvu1007 3.7 10.1 10.5 4.0 2.7 1.8 0.0 2.4 3.0 0.0 Tvu14553 3.1 9.2 9.5 4.0 2.0 1.3 0.0 2.8 3.0 0.0 Tvu15450 3.2 9.5 10.2 4.0 2.6 1.2 0.0 2.2 2.8 0.0 Tvu15866 3.1 9.3 9.6 4.0 2.4 2.0 0.0 2.3 2.9 0.0 Tvu2736 3.6 6.1 6.5 4.0 1.4 1.4 0.0 2.7 2.2 0.0 Tvu9693 3.9 7.0 7.5 4.0 1.6 1.3 0.0 2.8 2.9 0.0 TVu7778 (SC) 3.1 7.7 8.3 4.0 1.3 0.9 0.0 3.1 3.4 0.0 ‡Mean 3.36 8.96 9.22 3.93 2.79 1.66 0.25 1.97 2.83 0.08 SE 0.03 0.07 0.08 0.01 0.04 0.04 0.01 0.04 0.03 0.08 LSD (0.05) 0.62 1.37 1.42 0.23 0.78 0.89 0.24 0.62 0.78 0.21 ‡Values derived from both wooden box screening; LSD = Least significant difference; SC= susceptible check; DTE=days to emergence; ¥RR = Recovery rate = no of dead / no of survival *100 University of Ghana http://ugspace.ug.edu.gh 102 Figure 4. 2: Relationship between recovery rate and wilting (A) and stem greenness (B) for cowpea genotypes evaluated under seedling drought stress in the glass house. Measurement for stem greenness and wilting were means of all the measurement periods during stress while recovery (0-1) was recorded 2 weeks after resuming water. Square symbols are the predicted values based on the regression model and the diamonds are the observed values. Each point represents average of four plants per genotype replicated thrice. y = -0.7321x + 1.721 R² = 0.0637 0.0 0.5 1.0 1.5 2.0 2.5 3.0 0 0.2 0.4 0.6 0.8 1 W il ti n g ( 0 -4 ) Recovery B y = 1.4572x + 2.7271 R² = 0.2445 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 0 0.2 0.4 0.6 0.8 1 S te m g re en n es s (0 -4 ) Recovery A University of Ghana http://ugspace.ug.edu.gh 103 4.3.3 Cluster analysis Cowpea genotypes screened were grouped into three clusters. Stem greenness, wilting and recovery rate with sufficient discriminative power were used to cluster the genotypes into three main cluster groups. Cluster I consisted of 14 genotypes, cluster II had 48 and cluster III had 28. The largest cluster (II) comprised of all the lines from all the germplasm sources used in this study. Cluster I comprised mainly of the improved and the advanced breeding lines (Figure 4.3). The main clusters were further subdivided into smaller clusters that were closer in relationship. University of Ghana http://ugspace.ug.edu.gh 104 Figure 4. 3: Dendrogram of 90 cowpea germplasm revealed by average linkage cluster analysis based on 3 discriminant phenotypic characters. I II III University of Ghana http://ugspace.ug.edu.gh 105 4.4 Discussion The objective of this study was to identify parents that are tolerant to drought that can serve as parental sources of favourable alleles for development of pure cowpea lines tolerant to drought. Results of the seedling screening could only be used for selection in this study owing to the interference of rain during the field trial. The use of wooden boxes or pots for screening for drought tolerance in the screen or glass house have however been reported to help quickly in identifying cowpea plants that would show drought tolerance especially at the vegetative stage. Studies by Watanabe et al. (1997) and Ewansiha and Singh (2006) have shown positive and significant correlation between drought tolerance in the seedling stage as observed using wooden boxes in a screen house and drought tolerance screening on the field. They therefore concluded that cowpea lines found to be drought tolerant at the seedling stage using wooden box should also perform well under drought in the field. The results of this experiment confirmed that despite the inherent comparative tolerance to drought, cowpea exhibits significant genetic variation in response to drought stress. The drought-stress responses were manifested in a range of discrete physiological and morphological characteristics apparently related to the ability of the cowpea plants to tolerate and recover from seedling stage drought stress. It was observed that within genotypes, individual plants expressed varying levels of survival and ultimate recovery in response to drought stress. A similar result was reported by Watanabe et al. (1997). In this study, genotypes like Ife Brown, 15-145 and Danila that wilted slowly and maintained green stems till the resumption of watering were able to conserve water in leaves and stem tissues, survive the dry period and resume growth, transferred into pots till maturity. These genotypes may have a mechanism to slow their transpiration rate and not University of Ghana http://ugspace.ug.edu.gh 106 deplete their soil moisture reserve as quickly as genotypes that have high wilting score. However, genotypes such as IT93K-452-1 and TVu 1505 that wilted fast had relatively lower recovery rate. Similar findings were observed in soybean (Ries et al., 2012), common bean (Phaseolus vulgaris) (Mukeshimana et al., 2014). The slow wilting observed in this experiment was not associated with deep-water extraction by the root because the genotypes were planted in boxes with shallow soil profiles; it therefore meant that this trait is associated with water saving mechanism in the shoot. In grain legumes such as peanut (Arachis hypogaea), chickpea (Cicer arietinum), soybean and cowpea, traits such as lower transpiration rate under lower vapour pressure deficits (VPD) has been recognized as soil water-saving strategy in tolerant genotypes (Sinclair et al., 2008; Devi et al., 2010; Zaman- Allah et al., 2011; Belko et al., 2012). Stem greenness was found to be an important seedling trait associated with drought tolerance in cowpea. From this study, stem greenness was positively correlated to recovery and genotypes such as Ife Brown that maintained high stem greenness scores during the drought stress period had higher recovery rates. Similar observations were made in sorghum (Sorghum bicolor (L.) Moench); where maintenance of stem greenness was found to be highly correlated with leaf chlorophyll content and drought tolerance (Xu et al., 2000). In other crops, various shoot characteristics have been used to evaluate drought tolerance at the seedling stage. In cotton, seedling recovery after stress was used as a criterion to assess drought tolerance in cotton (Gossypium hirsutum) (Longenberger et al., 2006; Rauf, 2008). Tolerance to seedling leaf death and recovery was used in rice (Mitchell et al., 1998; De Datta et al., 1988). In soybean, slow wilting was reported to be associated with seedling drought tolerance (Sadok et al., 2012). In cowpea, significant correlations University of Ghana http://ugspace.ug.edu.gh 107 were reported between stem greenness, survival and recovery dry weight (Thomas and Howart, 2000). The fundamental method to develop variation for selection is crossing genotypes where selecting the parents may be very important and existing genetic differences between the crossing parents be required. Grouping the genotypes can give breeder the opportunity to select appropriate parents for crossing. Cluster analysis was used to group white beans accessions (Koij and Saba, 2015) and common bean (Kahraman et al., 2015). Genotypes screened for seedling stage drought tolerance were grouped into three main clusters from where the lines for genetic studies were selected. 4.5 Conclusion This study was conducted to determine the shoot traits associated with drought tolerance at an early development stage and select parents for genetic studies. The significant genotypic variation observed among the genotypes for all the traits measured indicated that significant progress can be made in selecting for drought tolerance using the wooden boxes. Shoot traits such as stem greenness, wilting and recovery from drought were identified from this study to be associated with drought tolerance at seedling stage in cowpea. Plant growth and productivity after drought stress are dependent on the degree of recovery from stress. Stem greenness and wilting were found to be positively and negatively correlated with recovery respectively. The assay that allows for a separation of root and shoot traits functional in drought tolerance study in cowpea would provide breeders a faster means to combine these traits into new cultivars to enhance performance under a broad range of soil moisture conditions. Twenty genotypes were selected as parental lines for genetic studies using the result obtained from the cluster analysis. University of Ghana http://ugspace.ug.edu.gh 108 CHAPTER FIVE 5.0 Combining ability study of selected cowpea lines and performance of their first filial generation under managed stress. 5.1 Introduction In African countries, agriculture remains the most important sector of the economy given its multiple roles in food security, employment and contribution to national Gross Domestic Products (GDPs). Despite this, however, agriculture in Sub-Saharan Africa (SSA) is almost 80% dependent on rainfall which has become less reliable resulting in shorter growing seasons in many regions (Kandji et al., 2006). Some of the most important impacts of global climate change including reduced or inconsistent rainfall are predicted to occur more frequently in the tropical regions and the impact will be more among “subsistence” or “smallholder” farmers in developing countries, thus threatening food security in the region (Morton 2007; Cavalieri et al., 2011). Drought is defined in agricultural terms as a condition of insufficient moisture caused by a deficit of precipitation over a given period. It is the most important environmental factor, which induces significant alterations in plant physiology and biochemistry leading to reduction in crop productivity (McClean et al., 2011; Lauer et al., 2012). Research directed at improving crop resistance to production stresses, especially drought, is needed to complement other efforts under way to alleviate crop failure and malnutrition problems in the developing countries. The cowpea crop plays a critical role in providing nutritional security to millions of people in Africa and other parts of the developing world, since it is one of the few available food sources that combines essential minerals and high quality protein (~ 26%) that nutritionally University of Ghana http://ugspace.ug.edu.gh 109 complements staple low-protein cereal crops and tuber crops predominant in the region (Santos and Boiteux, 2015; Santos et al., 2015; Yewande and Thomas, 2015). It is also a valuable component of cropping systems in the drier regions because of its ability to fix atmospheric nitrogen (Sinclair et al., 2015). Although, cowpea is considered to be more drought tolerant compared with other legume crops grown in the semi-arid tropics, its productivity is adversely affected by drought particularly because production areas are subject to water deficits causing substantial yield losses. This necessitates more effort to enhance its resilience to water deficits (Agbicodo et al., 2009; Belko et al., 2012). Availability and adoption of such cowpea varieties by farmers will contribute positively to ensuring food security and improving human nutrition in the SSA region. Efforts made in the past to develop drought-tolerant cowpea varieties have met with limited success. This limitation has been attributed to the complexity of factors at play in the determination of drought tolerance and high yield in the crops and limited information on the genetic basis for drought tolerance in cowpea (Fatokun et al., 2012). Extensive research has been carried out on the screening for mid- and late-season drought tolerance in cowpea focusing on morphological, biochemical and physiological responses of cowpea to drought (Singh and Matsui 2002; Ogbonnaya et al., 2003; Hamidou et al., 2007; Belko et al., 2012; Muchero et al., 2013). Nevertheless, only very few studies have used the resultant indices from these studies to select parental genotypes for further genetic studies. It has been reported that genes that could contribute to higher levels of drought tolerance exist in some of the under-exploited cowpea germplasm maintained in the genetic resources unit at the International Institute of Tropical Agriculture (IITA) (Watanabe et al., 1997; Fatokun et al., 2012). In order to be able to efficiently utilize these lines as parental University of Ghana http://ugspace.ug.edu.gh 110 lines to enhance the level of drought tolerance in the improved cowpea varieties, information on their performance in cross combinations to produce superior progenies that will combine desirable agronomic traits and the drought tolerance genes in the segregating generations is pertinent in addition to the study of nature of gene action controlling yield and other related traits under drought stress. Alidu et al. (2013) reported the importance of GCA for seed weight, number of pods per plant and grain yield under moisture stress in a diallel mating analysis of improved and adapted cowpea varieties. However, there is limited information on the combining abilities of the cowpea germplasm that can guide efficient choice of tolerant lines that will combine well to produce desirable segregants on which selection can be applied for the development of novel drought tolerant varieties. The objectives of this study therefore were to (i) determine good general combiners among the cowpea lines for high yield and drought tolerance (ii) determine the gene action controlling grain yield and drought tolerance in the cowpea germplasm under managed stress conditions (iii) identify potential F1 hybrids that can be advanced through selection towards the development of novel cowpea varieties with enhanced drought tolerance and high yield. University of Ghana http://ugspace.ug.edu.gh 111 5.2. Materials and Methods 5.2.1 Development of F1 crosses i. Genetic materials Twenty lines out of the 91 screened for drought tolerance were selected based on how they clustered on the dendrogram (Chapter 4). The 20 lines made up of seven improved lines, hereafter referred to as improved, released lines (IMR), two landraces (LDR) and eleven Tropical Vigna unguiculata lines (TVu) were assigned into four groups of five lines each (Table 5.1). Using the modified North Carolina design II mating scheme, inter-group crosses were made in four sets and each line from one group was mated with all the five lines in another group to generate twenty-five single F1 crosses from each set and a total of 100 single F1 hybrids (Comstock and Robinso, 1948; Dhliwayo et al., 2009). Each line was used as a male or female once in different sets. The mating scheme for the four sets is as follows: SET 1: GROUP I X GROUP II SET 2: GROUPII X GROUP IV SET 3: GROUP III X GROUP I SET 4: GROUP IV X GROUP III University of Ghana http://ugspace.ug.edu.gh 112 Table 5. 1: Line code, group and maturity period of 20 cowpea inbred lines selected for NCD II hybrid. S/N Line Line code Group Pedigree Maturity 1 Ife Brown IMR01 1 TVu59 x TVu53 Medium 2 IT90K-76 IMR03 1 (IT84S-2246-4 X B301) X IT84S-2246-4 Medium 3 IT81KD-985 IMR02 1 [(TVu1190 x TVu76) x TVu2027] x TVu625 Medium 4 IT90K-277-2 IMR04 1 (IT87F-1777-2 X IT84S-2246-4) x TVx3236 Medium 5 DANILA LDR01 1 Farmers' local landrace selection Late 6 IT89KD-288 IMR09 2 (IT87F-1777-2 X IT84S-2246-4) X IT87F-1777-2 Medium 7 IT99K-573-1-1 IMR07 2 IT93K-596-9-12 X IT86D-880 Early 8 CB27 IMR16 2 Selection Early 9 TVu11986 TVu07 2 Tropical Vigna unguiculata population Medium 10 TVu6333 TVu06 2 Tropical Vigna unguiculata population Medium 11 TVu7778 TVu34 3 Tropical Vigna unguiculata population Medium 12 Sanzi LDR02 3 Farmers' landrace Medium 13 TVu3562 TVu26 3 Tropical Vigna unguiculata population Medium 14 TVu9797 TVu38 3 Tropical Vigna unguiculata population Medium 15 TVu79 TVu35 3 Tropical Vigna unguiculata population Medium 16 TVu6707 TVu33 4 Tropical Vigna unguiculata population Medium 17 TVu9693 TVu37 4 Tropical Vigna unguiculata population Medium 18 TVu2736 TVu11 4 Tropical Vigna unguiculata population Medium 19 TVu8670 TVu36 4 Tropical Vigna unguiculata population Medium 20 TVu10100 TVu13 4 Tropical Vigna unguiculata population Medium University of Ghana http://ugspace.ug.edu.gh 113 ii Crossing nursery The nursery was set up in the screen house of the International Institute of Tropical Agriculture, Ibadan. Five 25 cm diameter pots were arranged in a row. Three rows were planted for each parent for a cross and arranged by sets. Six seeds were planted per pot and were later thinned to three seedlings after two weeks of planting. Cultural practices such as manual weeding were regularly done and spraying of λ -Cyalothrin (Karate) 120 ml in 20L of water to control insect pests was done a month after planting and later as the need arose. Hand pollination commenced from April 15 2014 and lasted eight months. To ensure continuous availability of flowers so as to be able to develop enough F1 seeds for evaluation; two steps were taken: (i) cowpea plants already flowering were propagated vegetatively through the vine. This was fast as flowering resumed soon after the vine established roots and (ii) at 1 month interval, new sets of pots were planted to ensure continuous availability of fresh flowers throughout the pollination period. The flowers were prepared for pollination by cutting two-thirds of the petals of unopened mature flower buds of the female parent opposite the stylar and staminal section with the aid of forceps to expose the pistil and stigma. The anthers were also carefully removed with the aid of the forceps. Pollination was done by gently rubbing the anther from freshly opened male parent on the exposed stigma of the female parent and labeled appropriately (Plate 5.1a – c). Harvesting pods of the F1 was done as they matured and later bulked, dried to 13% moisture content and shelled (Yakubu et al., 2012). University of Ghana http://ugspace.ug.edu.gh 114 Plate 5. 1 (a-c): Hand emasculation, pollination of cowpea and resulting crosses iii. Location and climate The evaluation experiments were planted in IITA experimental stations as follows: in Ibadan, Oyo State on the 2nd December 2014 during the dry season and Minjibir, Kano State on the 19th February 2015 during the dry season following the climatic peculiarity of each location. The experimental field at Ibadan (derived savanna) is located at an altitude of 212 m above sea level (asl), latitude of 07o03´N and longitude of 3o9´E with annual rainfall of 1,400 mm. Ibadan experiences rain-free or sparing rainfall from the end of November to March annually. This makes it suitable for drought screening experiments during these months. More importantly, there was no rain interference especially during the critical periods of stress imposition in the years of study (2014-2015) (Figure 5.1). Minjibir (Sahel savanna) has an altitude of 431 m asl, latitude of 12o 13´N and longitude 3o55´E. Minijbir, Kano State falls in the Sahel region, characterized by a long and dry season between October and June with short erratic rainy season between July and September and a total annual rainfall between 400-600 mmyr-1. Rainfall, maximum and minimum, and relative humidity readings of Minjibir and Ibadan for the period of experiments were recorded daily by an automated weather station situated at each location. Means of monthly weather readings for both locations are presented in Figure 5.1. A C B University of Ghana http://ugspace.ug.edu.gh 115 Figure 5. 1: Means of monthly rainfall (mm), maximum and relative humidity readings for Minjibr (A) and Ibadan (B) for the period of experiment in each location. 0 10 20 30 40 50 60 70 80 90 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 RHx (%) 46.94 44.58 36.14 31.89 56.91 77.62 RHm (%) 11.22 7.15 8.1 5.86 10.73 30.91 Rainfall (mm) 0 0 0 0 0.21 1.31 A 0 10 20 30 40 50 60 70 80 90 100 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 RHx (%) 89.97 85.97 74.84 86.86 83.74 85.53 87.32 83.97 RHm (%) 50.13 30 24.87 34.36 31.68 36.9 51.19 57 Rainfall (mm) 1.64 0 0 1.36 2.03 3.85 5.14 6.92 B University of Ghana http://ugspace.ug.edu.gh 116 5.2.2 Evaluation of F1 crosses and parents for yield and drought tolerance under managed stress conditions The 100 F1 crosses and the 20 parental lines were evaluated in two locations during the season of 2014 in Ibadan and 2015 in Minjibir under irrigation. The test materials were arranged in a 12 x 10 alpha (0, 1) lattice with three replications. Each experimental plot consisted of 1 m single row with test materials spaced at 0.20 m within rows and 0.75 m between rows. Three cowpea plants were planted per hills which were later thinned to 2 plants per hill two weeks after planting (WAP) giving a plant population density of 133,333 plants per hectare at each location. Experiments were planted in two adjacent blocks that received different irrigation regimes. Block 1 is the drought stress (DS) block while block 2 is the well-watered (WW) block. The blocks were separated by an alley that was 20 m wide to restrict lateral movement of water from the fully irrigated block to the drought stressed block. Irrigation water was supplied to both blocks with an overhead sprinkler irrigation system designed to dispense 17 mm of water twice a week until 35 DAP (days after planting). Thereafter, DS blocks did not receive water until harvesting while the WW blocks received water twice a week until physiological maturity. Except for the different irrigation treatments, all field management practices were uniform for both the well-watered and water-stressed experiments. Insect pests were controlled at when due using 60-80 ml of λ-cyhalothrin (Karate 5EC) in 20 L of water. University of Ghana http://ugspace.ug.edu.gh 117 The experiment was therefore conducted under four environments: i. IITA, Ibadan experimental station + drought stress = environment 1 (E1) ii. IITA, Ibadan experimental station + well-water = environment 2 (E2) iii. IITA, Minjibir experimental station + drought stress = environment 3 (E3) iv. IITA, Minjibir experimental station + well –water = environment 4 (E4) 5.2.3 Data collection i. Soil moisture content Access tubes were installed in both well-watered and moisture stressed blocks to monitor volumetric soil moisture content during the crop growth period, particularly during the critical periods of moisture stress. Six soil profiles were dug diagonally across each block for each water regime using an auger hammer. The length for the soil probes were different for both locations owing to the peculiarities in the soil profile for the two locations. For Ibadan, probe length of 0.6 m long was hammered into the soil while probe length of 1.2 m long was hammered into the soil at Minjibir using the specialized installation kits that came with the instrument (HH2 moisture meter by Delta Devices). The procedures for installation and moisture reading as explained in the installation guide were followed. ii. Agronomic data Data were recorded on plot basis on both water-stressed and fully irrigated plots at both locations. Days to 50% flowering and to maturity were recorded as number of days from flowering to when 50% of the plants in the plot had opened their flowers and had matured pods respectively. At harvest, numbers of pods per plant, number of seeds per pod and University of Ghana http://ugspace.ug.edu.gh 118 hundred-seed weight were taken as average of five randomly selected plants within the plot excluding the border plants. Delayed leaf senescence (DLSC) was scored on a scale of 1- 9, where 1 = almost all leaves are green and 9= almost all leaves are dead (Muchero et al., 2013). Data on grain yield were recorded on plot basis (five plants per plot) in grams and later converted to kgha-1 using the following formula: 𝐺𝑟𝑎𝑖𝑛 𝑦𝑖𝑒𝑙𝑑 (𝐾𝑔 ℎ𝑎−1) = [ 10,000 × 𝑡𝑜𝑡𝑎𝑙 𝑔𝑟𝑎𝑖𝑛 𝑤𝑒𝑖𝑔ℎ𝑡 𝑝𝑒𝑟 𝑝𝑙𝑜𝑡 𝑝𝑙𝑜𝑡 𝑎𝑟𝑒𝑎 ℎ𝑎𝑟𝑣𝑒𝑠𝑡𝑒𝑑 × 1000 ] Data on Normalized Difference Vegetation Index (NDVI) was taken using the handheld Greenseeker® crop sensor which evaluates traits such as early vigour and stay green which are important for adaptation to drought stress. NDVI measurements were taken at different growth stages: vegetative (1), flowering (2) and pod-filling (3). The device computed the NDVI according to this formula: 𝑁𝐷𝑉𝐼 = [ 𝑅𝑁𝐼𝑅 − 𝑅𝑅𝐸𝐷 𝑅𝑁𝐼𝑅 + 𝑅𝑅𝐸𝐷 ] where RNIR = the fraction of emitted NIR radiation returned from the sensed area (reflectance), and RRED = the fraction of the emitted red radiation returned from the sensed area (reflectance) (Cabrera-Bosquet et al., 2011; Islam et al., 2011). Upon pulling the trigger, the sensor turns on, emits brief bursts of red and infrared light, and then measures the amount of each type of light that is reflected back at the sensor. The strength of the detected light is a direct indicator of the vigor or health - of the crop. The sensor displays the measured value in terms of an NDVI reading on its LCD display screen (Plate 5.2 and University of Ghana http://ugspace.ug.edu.gh 119 5.3) NDVI readings range from 0.00 to 0.99; the higher the reading, the healthier the plant under stress. Plate 5. 2: Schematic representation of how data was captured using the Greenseeker® handheld sensor University of Ghana http://ugspace.ug.edu.gh 120 5.2.4 Data Analyses Analysis of Variance (ANOVA) was performed separately for drought stress and well- watered environments for the crosses and their parents to generate entry means adjusted for block effects according to the lattice design (Cochran and Cox, 1960; Menkir et al., 2003). The pooled error mean square was calculated for each block ANOVA by dividing the sum of the error sums of square by the corresponding sum of the error degrees of freedom. Combined analysis of variance was then computed across environments using the adjusted means. In the combined analysis, environment, replications and blocks were treated as random effects while crosses were considered as fixed effects. All analyses were Plate 5. 3: Taking NDVI measurements on cowpea plants using greenseeker® handheld crop sensor. University of Ghana http://ugspace.ug.edu.gh 121 carried out using PROC GLM in SAS (SAS Institute, 2009) using a RANDOM statement with TEST option. The ANOVA of NC II for each environment was performed on the entries using PROC GLM in SAS using a RANDOM statement with the TEST option (SAS, 2008). The crosses (sets) component of variation was divided into variation due to male (sets), female (sets), and female x male (sets) interaction. The main effects of male (sets) and female (sets) represent the general combining ability (GCA) while the female x male (sets) interaction represents specific combining ability (SCA) effect (Hallauer and Miranda, 1988). The F tests for male (sets), female (sets), and female x male (sets) mean squares were computed using the mean squares for their respective interaction with environment. The mean square attributable to environment x female x male (sets) was tested using the pooled error mean squares. The general linear model for NC II mating design with set is: 𝑋𝑖𝑗𝑘𝑙𝑚 = 𝜇 + 𝑔𝑖(𝑆𝑙) + 𝑔𝑗(𝑆𝑙) + ℎ𝑖𝑗(𝑆𝑙) + 𝐸𝑚 + 𝑟𝑘 (𝑆𝐸)𝑙𝑚 + (𝑆𝐸)𝑙𝑚 + (𝐸𝑔)𝑖𝑚(𝑆𝑙) + (𝐸𝑔)𝑗𝑚(𝑆𝑙) + (𝐸ℎ)𝑖𝑗𝑚(𝑆𝑙) + 𝑒𝑖𝑗𝑘𝑙𝑚 Where; 𝑋𝑖𝑗𝑘𝑙𝑚 = the observed value of the progeny of the 𝑖 𝑡ℎ female, 𝑗𝑡ℎ male in the 𝑘𝑡ℎ replication with set l and in the 𝑚𝑡ℎ environment. 𝜇 = population mean 𝑆𝑙 = average effect of the 𝑙 𝑡ℎ set 𝑔𝑖(𝑆𝑙) = GCA effect common to all crosses of the 𝑖 𝑡ℎ female nested within 𝑙𝑡ℎ set 𝑔𝑗(𝑆𝑙) = GCA effect common to all crosses of the 𝑗 𝑡ℎ male nested within 𝑙𝑡ℎ set University of Ghana http://ugspace.ug.edu.gh 122 ℎ𝑖𝑗(𝑆𝑙) = SCA effect of the cross from the 𝑖 𝑡ℎ female and 𝑗𝑡ℎ male nested within 𝑙𝑡ℎ set 𝐸𝑚 = average effect of the 𝑚 𝑡ℎenvironment 𝑟𝑘 (𝑆𝐸)𝑙𝑚 = the effect of the 𝑘 𝑡ℎ replication nested within the 𝑙𝑡ℎ set and 𝑚𝑡ℎenvironment (𝑆𝐸)𝑙𝑚 = Interaction between the set effect and the environment (𝐸𝑔)𝑖𝑚(𝑆𝑙) and (𝐸𝑔)𝑗𝑚(𝑆𝑙) = Interaction between environment and GCA nested within sets (𝐸ℎ)𝑖𝑗𝑚(𝑆𝑙) = Interaction between environment and SCA nested within sets 𝑒𝑖𝑗𝑘𝑙𝑚 = Experimental error General combining ability (GCA) was estimated as: 𝐺𝐶𝐴𝑓 = 𝑋𝑓 − 𝜇, 𝐺𝐶𝐴𝑚 = 𝑋𝑚 − 𝜇 Where, 𝐺𝐶𝐴𝑓 𝑎𝑛𝑑 𝐺𝐶𝐴𝑚 = General combining ability (GCA) of female and male parents respectively 𝑋𝑓 𝑎𝑛𝑑 𝑋𝑚 = Mean of female and male parents respectively 𝜇 = Overall mean of crosses in the experiment Specific combining ability (SCA) was estimated as: 𝑆𝐶𝐴𝑋 = 𝑋𝑋 − 𝐸(𝑋𝑋) = 𝑋𝑋 − [𝐺𝐶𝐴𝑓 + 𝐺𝐶𝐴𝑚 + 𝜇] Where, 𝑆𝐶𝐴𝑋 = Specific combining ability of the cross X 𝑋𝑋 = Observed mean value of the cross 𝐸(𝑋𝑋) = Expected mean value of the cross based on the GCA value of both parents. The yield data was further subjected to genotype main effects plus genotype x environment interaction (GGE) bi-plot analysis to evaluate the G x E interaction of each experiment University of Ghana http://ugspace.ug.edu.gh 123 using the GGE bi-plot application (Yan et al., 2000; Yan, 2001). The bi-plot model equation is: 𝑌𝑖𝑗 − 𝑌𝑗 = 𝜆1𝜉𝑖1𝜂𝑗1 + 𝜆2𝜉𝑗2𝜂𝑗2 + 𝛴𝑖𝑗 Where, 𝑌𝑖𝑗 = average yield of genotype i in environment j 𝑌𝑗 = average yield of all genotypes in all environments j 𝜆1and 𝜆2 = singular values for principal component PC1 and PC2 respectively 𝜉𝑖1 and 𝜉𝑗2 = PC1 and PC2 scores for genotype i 𝜂𝑗1 and 𝜂𝑗2 = PC1 and PC2 scores for environment j 𝛴𝑖𝑗 = residual of the model associated with the genotype i in environment j The data was not transformed (Transform = 0), not standardized (Scale =0) and environment centered (Centering = 2). Drought tolerance index (DTI) or yield reduction was calculated as the percentage of grain yield loss due to drought stress on the yield realized under well-watered conditions (Ogbonaya et al., 2003; Derera et al., 2008) was calculated as: 𝐷𝑇𝐼( %) = [ (𝑌𝑖𝑒𝑙𝑑 𝑢𝑛𝑑𝑒𝑟 𝑤𝑒𝑙𝑙𝑤𝑎𝑡𝑒𝑟𝑒𝑑 − 𝑦𝑖𝑒𝑙𝑑 𝑢𝑛𝑑𝑒𝑟 𝑑𝑟𝑜𝑢𝑔ℎ𝑡) 𝑌𝑖𝑒𝑙𝑑 𝑢𝑛𝑑𝑒𝑟 𝑤𝑒𝑙𝑙𝑤𝑎𝑡𝑒𝑟𝑒𝑑 ] × 100 5.3. Results 5.3.2 Soil moisture content Results of the measured soil moisture content taken at one week intervals between 10/1/2015 to 7/2/2015 for Ibadan and between 10/4/2015 to 8/5/2015 for Minjibir respectively during the flowering periods of cowpea under drought stress and well-watered conditions are shown in Figure 5.2. The measuring tubes were marked at interval of 100 University of Ghana http://ugspace.ug.edu.gh 124 cm. The 100 cm marking corresponds to the volumetric water reading closest to the base of the access tube in the soil while the 400 cm or 1 m marking corresponds to the soil reading close to the soil surface. At every 100 cm interval, volumetric water content at the well-watered blocks was higher than that of the drought stressed blocks (Figure 5.2). University of Ghana http://ugspace.ug.edu.gh 125 Figure 5. 2: Volumetric water contents measured under well-watered (WW) and drought stress (DS) in Ibadan (A) and Minjibir (B), Nigeria 0 20 40 WW-100 cm DS-100 cm 0 50 WW-200 cm DS-200 cm 0 20 40 WW-300 cm DS-300 cm 0 10 20 30 40 Wk-1 Wk-2 Wk-3 Wk-4 Wk-5 Weeks of measurement WW-400 cm DS-400 cm 0 10 WW-100 cm DS-100 cm 0 10 20 WK-1 WK-2 WK-3 WK-4 WK-5 Weeks of Measurmentes WW-1 m DS-1 m 0 10 20 WW-200 cm DS-200 cm 0 10 20 WW-300 cm DS300 cm 0 20 WW-400 cm DS-400 cm 0 20 WW-600 cm DS-600 cmA B University of Ghana http://ugspace.ug.edu.gh 126 5.3.3 Performance of parental lines under drought and well-watered conditions Significant differences were observed among the parental lines for grain yield and other measured traits across drought and well-watered conditions (Table 5.2). Genotype x environment interaction effects were significant for all traits except for hundred seed weight, number of seeds per plant and number of pods per plant under DS and for number of seeds per pod under WW (Table 5.2). Under DS, genotypes accounted for 68% of the total sum of squares while genotype x environment interactions accounted for 20% and environment contributed 12%. Same trend was observed under WW environment where genotypes contributed 51%, genotype x environment contributed 39% and environment 0.1%. Across research environments, genotype contributed 50%, genotype x environment interaction 42% and environment contributed 7% (Table 5.3). Grain yield across drought environments ranged between 116 kg ha-1 for TVu11986 and 1,788 kg ha-1 for TVu6707 with a trial mean of 587 kg ha-1. Across well-watered environments, the trial mean yield was 728 kg ha-1 while grain yield among parental lines ranged between 191 kg ha-1 for Ife Brown and 2,127 kg ha-1 for TVu6707 (Table 5.4). University of Ghana http://ugspace.ug.edu.gh 127 Table 5. 2: Mean square of 20 parents for grain yield and other measured traits under drought stress, well-watered conditions and across research environments. ENTRY ENV ENTRY *ENV SE ± ǂTraits aDS WW AC DS WW AC DS WW AC DS WW AC GY 550963*** 815231*** 78.42*** 1799511** 26809ns 30.12*** 162359ns 511869** 8.63*** 48.08 55.88 37.07 SW100 23.77* 33.64** 1179844*** 50.57*** 60.55*** 1087767*** 7.2ns 10.98*** 330928*** 0.35 0.31 0.24 SDPOD 75.81** 25.96*** 57.63*** 33.79*** 47.40*** 44.48*** 8.46ns 5.01ns 5.63** 0.32 0.27 0.21 DFL 35.48*** 35.42*** 50.89*** 359.91*** 670.89*** 346.62*** 19.65*** 18.51*** 19.93*** 0.39 0.41 0.28 NDVI_1 0.06*** 0.05*** 0.06*** 2.34*** 2.46*** 1.60*** 0.03** 0.04*** 0.04*** 0.02 0.02 0.02 NDVI_2 0.04*** 0.04*** 0.07*** 0.16*** 0.18*** 0.12*** 0.02** 0.03*** 0.02*** 0.01 0.01 0.01 NDVI_3 0.03* 0.02*** 0.03** 1.18*** 2.35*** 1.23*** 0.04** 0.04*** 0.03*** 0.02 0.02 0.01 PODPLT 589.33* 1270.10** 1387.08*** 615.08ns 1665.54ns 1766.78** 444.26ns 1102.35** 756.82*** 1.85 2.46 1.55 SDPLT 169317* 196352*** 235101*** 91298ns 20210ns 140781* 92448* 130601** 100695*** 23.03 28.15 18.25 DLSC(1-9) 2.61ns - 2.12ns - 2.41ns - - 0.14 - - df 19 19 19 1 1 3 19 19 57 Error df 50 50 126 50 50 126 50 50 126 *, **, *** = significant at P < 0.05, 0.01 and 0.0001 respectively, ns=not significant. ǂGY=grain yield, SW100=hundred seed weight, SDPOD= seed per pod, NDVI 1, 2, 3= normalized difference vegetation index measured at vegetative, flowering and pod-filling stages, PODPLT=pod per plant, SDPLT=seed per plant, DLSC=delayed leaf senescence (scale of 1-9).aDS, WW, AC= drought stress, well-watered and across all environments respectively. University of Ghana http://ugspace.ug.edu.gh 128 Table 5. 3: Proportion of sum of squares of genotypes, environment and genotype x environment interaction to variation for grain yield. Sum of squares DS WW ACROSS Genotypes 68.19 61.36 50.33 Environment 11.72 0.11 7.33 G x E 20.09 38.53 42.35 On the average, grain yield obtained under drought stress was 19% of the yield obtained under well-watered conditions. Yield reduction expressed as the percentage of yield under drought stress relative to well-watered conditions ranged from -94% to 76%. The negative sign indicated that the lines had higher grain yield under drought stress than under well- watered conditions. Some of the lines that suffered less yield penalty had almost similar yield under both water regime. TVu6707 which yielded highest under both DS and WW had a yield reduction of 16% (Table 5.4). University of Ghana http://ugspace.ug.edu.gh 129 Table 5. 4: Mean grain yield of 20 parental lines used in design II crosses under drought and well-watered environments. Grain yield (kgha-1) Seed per plant Cowpea parental lines 2DS WW 1Yield reduction (%) DS WW TVu6707 1788.1 2127.4 15.9 852.8 954.8 TVu9693 890.4 861.4 -3.4 434.1 437.1 TVu8670 869.7 561.2 -55.0 443.4 378.5 IT89KD-288 826.0 682.5 -21.0 420.1 292.2 DANILA 778.2 904.7 14.0 427.5 372.7 TVu9797 769.5 1261.2 39.0 420.4 718.8 TVu10100 592.8 520.0 -14.0 389.0 371.7 IT81D-985 585.9 1433.5 59.1 364.4 840.4 IT99K-573-1-1 510.2 649.8 21.5 268.0 350.3 IT90K-76 495.9 571.1 13.2 385.8 403.4 TVu3562 461.3 473.7 2.6 398.6 417.2 IT90K-277-2 457.9 345.5 -32.5 277.7 215.6 TVu6333 450.0 232.3 -93.7 300.3 135.0 TVu79 433.3 845.7 48.8 304.4 557.5 SANZI 402.9 547.6 26.4 308.1 518.7 TVu7778 382.8 284.4 -34.6 324.3 350.4 CB-27 339.4 273.9 -23.9 161.2 94.2 TVu2736 219.8 903.8 75.7 268.1 223.0 IFEBROWN 201.8 190.7 -5.8 111.0 117.1 TVu11986 115.9 330.5 64.9 328.6 283.8 Mean 586.6 727.9 358.8 417.2 SE 48.1 55.9 23.0 28.1 Min 0.4 20.2 16.0 28.0 Max 2083.1 3003.5 988.6 1372.8 1Drought stress relative to well-watered conditions, 2DS=drought stress, WW= well-watered 5.3.7 Performance of single F1 crosses for yield and drought tolerance The means of grain yield and selected agronomic and drought adaptive traits of top 20 and 10 bottom crosses under drought stress and well-watered environments are given in Tables 5.5 and 5.6 respectively. Under drought stress conditions, grain yield ranged between 2,533 kg ha-1 to 18 kg ha-1 for crosses between lines TVu6707 x TVU9797 and TVu11986 x TVu2736 respectively (Table 5.5). Negative sign in the drought tolerance index signified that the crosses had higher yield under stress than under well-watered conditions while University of Ghana http://ugspace.ug.edu.gh 130 absence of negative sign indicated that the crosses had higher yield under well-watered environments than when evaluated under the stress. This is important for the identification of drought tolerant genotypes without compromising yield performance under well- watered environments. Out of the 100 single F1 crosses evaluated, 86 had positive tolerance indices while 14 had negative indices. Among the best 20 F1 crosses under drought stress, yield reduction relative to their performance under well-watered condition ranged from - 90.4% to 57.5% and from 30% to 470% among the 10 worst F1 crosses. The trial mean yield of 738 kg ha-1 under drought stress is about 42% of the trial mean of 1279 kg ha-1 under well-watered conditions. Under well-watered conditions, grain yield ranged between 3786.22 kg ha-1 and 45 kg ha- 1 for crosses between TVu6707 x TVu9797 and TVu6333 x TVu2736 respectively (Table 5.6). Overall, drought stress reduced grain yield by 49% (Table 5.5 and 5.6). University of Ghana http://ugspace.ug.edu.gh 131 Table 5. 5: Means of grain yield and secondary traits of top 20 and bottom 10 crosses under drought stress environment. Crosses Grain yield Secondary traits under DS DS Rank (WW) ǂDTI (%) DFL (days) Pod per plant (no) Seed per plant (no) Seed per pod (no) Top 20 TVu6707 x TVu9797 2532.9 1 33.10 51.72 78.28 1144.49 13.17 TVu79 x DANILA 1914.1 18 -6.75 48.10 78.09 1051.13 12.85 TVu79 x IT90K-277-2 1685.9 72 -90.37 49.10 46.18 670.95 12.15 TVu9693 x TVu9797 1522.1 2 53.24 49.39 58.80 961.63 15.81 IT99K-573-1-1 x TVu9693 1417.2 10 37.64 46.59 53.77 771.16 14.28 TVu9693 x TVu79 1403.6 12 36.29 49.10 42.66 698.70 14.04 TVu79 x IT81D-985 1343.6 25 18.16 48.08 45.83 642.25 12.60 IT89D-288 x TVu6707 1312.1 51 -15.34 46.58 64.34 688.53 10.11 TVu79 x IT90K-76 1256.3 26 21.63 50.22 42.46 601.38 14.28 TVu6707 x TVu7778 1234.9 38 9.25 57.33 48.24 629.40 10.86 TVu8670 x TVu3562 1225.4 17 33.99 45.26 51.72 622.54 11.95 TVu3562 x IT81D-985 1217.3 14 42.92 50.58 42.69 605.34 12.86 TVu6707 x TVu3562 1203.5 4 57.53 50.74 52.59 727.81 12.58 TVu3562 x IFEBROWN 1192.2 67 -25.96 48.11 88.57 1214.96 13.37 CB-27 x TVu6707 1188.6 39 11.36 44.36 53.94 568.45 8.82 IT89D-288 x TVu8670 1181.5 19 33.17 46.88 43.69 527.97 11.10 TVu9693 x TVu7778 1139.0 20 34.34 52.67 36.81 570.32 10.36 TVu8670 x TVu79 1119.4 30 25.91 48.00 44.70 677.40 14.49 CB-27 x TVu9693 1115.7 21 34.92 47.44 57.26 704.07 12.36 CB-27 x TVu8670 1111.7 8 53.95 46.70 47.22 564.46 10.48 Bottom 10 TVu6333 x TVu9693 263.1 89 49.71 52.88 12.03 126.28 11.81 Sanzi x IT90K-76 260.4 60 75.17 48.33 22.51 201.69 8.73 IT81D-985 x TVu11986 246.7 63 75.28 48.13 18.11 149.67 7.77 TVu7778 x IT81D-985 243.9 86 62.56 52.96 21.02 190.68 9.38 IT90K-76 x TVu6333 201.2 97 30.21 52.12 11.01 92.94 9.04 TVu6333 x TVu6707 177.5 92 62.32 51.76 17.42 183.99 10.94 TVu6333 x TVu2736 168.2 99 470.64 55.75 12.23 128.54 10.69 TVu6333 x TVu8670 60.2 91 87.28 52.03 3.72 5.58 9.71 TVu11986 x TVu10100 25.8 94 93.62 53.54 -1.22 -37.00 8.33 TVu11986 x TVu2736 17.6 66 98.14 51.16 11.58 103.16 9.41 Mean 738.2 48.57 49.58 35.70 426.53 10.73 LSD(0.05) 810.0 3.97 29.47 421.92 2.81 ǂDTI=Drought tolerance index in % = [(Yield in well-watered - yield under drought)/yield in well-watered]*100. DLSC= delayed leaf senescence University of Ghana http://ugspace.ug.edu.gh 132 Table 5. 6: Means of grain yield and secondary traits of top 20 and bottom 10 crosses under well-watered condition. Crosses Grain yield Secondary traits under WW WW Rank (DS) ǂDTI (%) DFL (days) Pod per plant (no) Seed per plant (no) Seed per pod (no) Top TVu6707 x TVu9797 3786.2 1 33.10 49.23 124.73 1944.23 14.64 TVu9693 x TVu9797 3255.3 4 53.24 52.77 80.54 1393.71 17.86 TVu8670 x TVu9797 2892.3 39 72.26 49.37 75.71 1249.05 16.37 TVu6707 x TVu3562 2833.9 13 57.53 50.02 109.16 1694.38 15.57 TVu6707 x TVu79 2692.2 35 68.59 48.75 71.93 1115.19 14.97 TVu9693 x TVu3562 2602.0 21 58.01 48.47 108.33 1434.87 13.02 TVu9693 x Sanzi 2427.0 22 55.46 48.74 121.86 1533.82 13.29 CB-27 x TVu8670 2414.3 20 53.95 44.50 95.43 1192.55 11.34 IT99K-573-1-1 x TVu6707 2330.9 26 56.47 50.19 77.63 1015.05 13.17 IT99K-573-1-1 x TVu9693 2272.7 5 37.64 50.54 59.00 852.14 13.20 TVu3562 x IT90K-277-2 2215.4 44 65.66 51.73 109.05 1437.99 13.11 TVu9693 x TVu79 2203.1 6 36.29 48.79 75.21 1205.22 15.48 TVu9797 x IT90K-277-2 2148.8 30 56.16 48.62 69.71 963.47 13.51 TVu3562 x IT81D-985 2132.8 12 42.92 49.79 68.21 861.23 12.43 TVu3562 x DANILA 1929.4 28 51.02 47.60 90.45 1092.09 12.16 IT89D-288 x TVu9693 1904.5 59 69.14 50.79 61.35 838.75 13.02 TVu8670 x TVu3562 1856.4 11 33.99 51.03 69.55 964.82 13.82 TVu79 x DANILA 1793.1 2 -6.75 49.71 64.19 758.88 10.82 IT89D-288 x TVu8670 1767.8 16 33.17 47.79 68.04 820.85 11.79 TVu9693 x TVu7778 1734.8 17 34.34 50.77 69.54 938.03 11.89 Bottom TVu6333 x TVu8670 473.5 98 87.28 53.11 31.39 413.29 11.65 TVu6333 x TVu6707 471.1 96 62.32 59.61 40.18 415.09 9.80 IT90K-277-2 x CB-27 413.7 37 -99.01 50.85 28.32 193.34 7.32 TVu11986 x TVu10100 403.9 100 106.38 54.61 19.03 220.52 9.21 Sanzi x IT90K-277-2 380.0 76 -3.43 48.27 30.47 315.36 9.86 TVu7778 x DANILA 329.6 83 7.20 50.16 27.43 348.16 12.52 IT90K-76 x TVu6333 288.3 95 30.21 55.61 17.88 178.59 9.41 TVu7778 x IFEBROWN 101.3 69 -369.08 56.78 19.65 154.27 5.88 IT81D-985 x IT89D-288 81.4 52 -741.34 49.27 10.67 66.61 7.38 TVu6333 x TVu2736 45.4 97 -270.64 54.86 3.55 12.50 13.27 Mean 1278.8 48.57 50.20 54.71 666.60 11.62 LSD(0.05) 1135 3.75 34.89 489.05 2.43 ǂDTI=Drought tolerance index in % = [(Yield in well-watered - yield under drought)/yield in well-watered]* University of Ghana http://ugspace.ug.edu.gh 133 5.3.4 Genetic analysis of the performance of cowpea F1 crosses under contrasting environments The F1 crosses showed highly significant differences for all measured traits (P < 0.0001) under both irrigation regimes (Tables 5.7 and 5.8). Under drought stress, effect due to environment (P < 0.0001) and environment x crosses interaction (P < 0.01) were highly significant for all traits. Similar trend were observed under well-watered environments for the crosses (P < 0.0001) and environment x crosses interaction (P < 0.05) was also significant. Although, the effect due to environment was found to be highly significant for all traits under stress (Table 5.7), it was not significant for number of pods per plant under well-watered conditions (Table 5.8). Similarly, the effect due to sets which was significant for all traits under drought stress was not significant for NDVI_1 and NDVI_2 under well- watered conditions. Negative drought tolerance indices (DTI) indicated that the crosses had higher yield under drought stress than the yield under well-watered condition. The cross between TVu6707 x TVu9797 had the highest grain yield under both DS and WW with 33% yield reduction. Combined analysis of variance over all the research environments showed significant differences for all the measured traits except for the effect due to set for NDVI_1 and environment x set interaction for number of pods per plant (Table 5.9). University of Ghana http://ugspace.ug.edu.gh 134 Table 5. 7: Mean squares of grain yield and other traits of F1 crosses measured across drought stress environments. Drought stress ǂGY SW100 SDPPOD DFL PODPLT SDPLT Normalized Difference Vegetation Index (NDVI) DLSC Source df Kgha-1 g no days no no 1 2 3 (1-9) Env 1 77586454*** 356.61*** 2181.6*** 4793.41*** 11528.52*** 6760612.08*** 31.53*** 1.40*** 22.86*** 588.13*** Sets 3 7443193** 602.23*** 204.11ns 78.94*** 6690.36*** 2418045.58*** 0.02* 0.19*** 0.11*** 57.76*** Env x Sets 3 4028355* 21.72*** 14.79ns 117.32*** 1586.16*** 173006.99ns 0.01ns 0.05** 0.02* 19.36** Crosses/sets 96 84891447*** 15.70*** 13.92*** 36.89*** 1198.69*** 240309.39*** 0.01*** 0.03*** 0.02*** 12.47*** Env x crosses 96 68683390** 7.89*** 8.28** 20.47*** 1154.71*** 227648.19*** 0.01** 0.02*** 0.02*** 8.15*** GCA f /sets 16 52764638*** 40.65*** 30.20*** 96.32*** 3262.50*** 700742.42*** 0.03*** 0.07*** 0.05*** 28.14*** GCA m /sets 16 9525449ns 20.86*** 22.95*** 57.64*** 1471.50** 181103.6ns 0.01ns 0.03*** 0.02*** 24.89*** SCA/sets 64 25547510ns 7.18*** 7.42ns 16.69** 648.86ns 144046.96ns 0.01* 0.02*** 0.01*** 4.67* Env x GCA f /sets 16 31628934*** 13.55*** 8.42ns 38.56*** 1749.98*** 339237.15*** 0.02** 0.04*** 0.03*** 16.98*** Env x GCA m /sets 16 8710419ns 6.48** 9.16ns 22.70** 1078.76* 230138.28* 0.01* 0.02** 0.02*** 10.94*** Env x SCA/sets 64 31604828ns 6.91*** 7.86* 14.53** 1025.58*** 204007.66** 0.01ns 0.02*** 0.01* 5.31** Pooled error 342 444069 2.47 5.46 8.97 529.45 112603.90 0.01 0.01 0.01 3.35 *, **, ***, = significant at P < 0.05, 0.01, 0.0001 respectively and ns= not-significant. ǂGY= grain yield, SW100=hundred seed weight, SDPPOD= no of seed per pod, PODPLT, no of pods per plant, SDPLT=no of seeds per plant, NDVI 1-3= measured at vegetative, flowering and pod filling stages respectively, DLSC=delayed leaf senescence where 1= almost all leaves are green, 9 virtually all leaves are dead University of Ghana http://ugspace.ug.edu.gh 135 Table 5. 8: Mean squares of grain yield and other traits measured across well-watered environments. Well-watered ǂGY SW100 SDPPOD DFL PODPLT SDPLT Normalized Difference Vegetation Index Source df Kgha-1 g no days no no 1 2 3 Env 1 102494605*** 262.31*** 645.59*** 2626.26*** 73.32ns 2233981.5** 10.80*** 4.57*** 0.11** Sets 3 15002232*** 596.80*** 275.15*** 38.36** 14740.49*** 5900877.39*** 0.01ns 0.01ns 0.08*** Env x Sets 3 5814338*** 29.77*** 0.71ns 65.78*** 298.83ns 171315.62ns 0.07** 0.03* 0.05** Crosses/sets 96 2329462*** 16.75*** 14.99*** 45.61*** 2501.31*** 501975.83*** 0.03*** 0.03*** 0.02*** Env x Crosses 96 1734211*** 6.35*** 6.61** 19.00*** 1350.94** 242943.85* 0.03*** 0.02*** 0.02** GCA f /sets 16 6358426*** 44.95*** 15.25*** 113.69*** 6896.84*** 1167580.15*** 0.04*** 0.04*** 0.03*** GCA m /sets 16 2711384*** 31.31*** 29.01*** 57.66*** 2379.49** 645940.47*** 0.02* 0.04*** 0.05*** SCA/sets 64 1111636* 5.35*** 11.62*** 23.63*** 1331.68** 281818.92** 0.02*** 0.02*** 0.01* Env x GCA f /sets 16 3915481*** 10.89*** 9.04** 22.48** 2253.24** 489999.12** 0.03** 0.03*** 0.02** Env x GCA m /sets 16 1826835** 6.5*** 6.11ns 22.67** 1304.76ns 191110.33ns 0.03** 0.03*** 0.02** Env x SCA/sets 64 1149591** 4.98*** 6.27ns 18.37*** 1178.24* 196177.95ns 0.02*** 0.02*** 0.01ns Pooled error 342 748554 2.09 4.08 8.19 836.00 169697.40 0.01 0.01 0.01 *, **, ***, = significant at P < 0.05, 0.01, 0.0001 respectively and ns= not-significant. ǂGY= grain yield, SW100=hundred seed weight, SDPPOD= no of seed per pod, PODPLT, no of pods per plant, SDPLT=no of seeds per plant, NDVI 1-3= measured at vegetative, flowering and pod filling stages respectively. University of Ghana http://ugspace.ug.edu.gh 136 Partitioning the sources of variation observed among the crosses into its genetic components, GCAf was highly significant for all traits (P < 0.0001) under both irrigation regimes (Tables 5.7 and 5.8). The interaction effect of GCAf x Env was also significant (P < 0.01) under both regimes for all traits except for number of seeds per pod under drought stress. Effect due to GCAm was significant for all traits under well-watered and drought stress but not for grain yield, number of seed per plant and NDVI_1 under drought stress. Under well-watered conditions, GCAf, GCAm, SCA and interaction effect of GCAf x environment were significant for all traits. On the other hand, the interaction of GCAm x environment was not significant for number of seeds per pod, pods per plant and number of seeds per plant under well-watered conditions (Table 5.8). Interaction effect of SCA x environment was found to be significant for all traits except for grain yield and NDVI_1 under drought stress and number of seeds per pod, number of seed per plant and NDVI_3 under well watered conditions. Across all the research environments, significant differences were observed for effects due to all the genetic effects of GCAf, GCAm, SCA and all interactions with the environment for all traits measured (Table 5.9). The GCAf and GCAm mean squares were observed to be larger than SCA effects for most of the measured traits. University of Ghana http://ugspace.ug.edu.gh 137 Table 5. 9: Mean squares of grain yield and other traits measured across all research environments, Ibadan and Minjibir. All environments GY SW100 SDPPOD DFL PODPLT SDPLT Normalized Difference Vegetation Index Source df Kgha-1 g no days no no 1 2 3 Env 3 88237441*** 364.34*** 1015.01*** 2514.61*** 38735.09*** 8533316.55*** 14.26*** 2.46*** 11.65*** Sets 3 14189803*** 1194.19*** 449.61*** 103.51*** 20472.75*** 7829746.68*** 0.02ns 0.13*** 0.18*** Env x Sets 9 3574691*** 18.97*** 15.79** 65.35*** 1001.24ns 302046.67*** 0.03** 0.05*** 0.03** Crosses/sets 96 2493519*** 25.41*** 21.06*** 66.80*** 2733.01*** 540101.4*** 0.02*** 0.04*** 0.03*** Env x Crosses 288 1039062*** 6.86*** 7.69*** 17.93*** 1143.42*** 223446.8*** 0.02*** 0.02*** 0.01*** GCAf/sets 16 8891782*** 73.83*** 38.11*** 198.27*** 9340.74*** 1730365.95*** 0.04*** 0.09*** 0.06*** GCAm/sets 16 2581033*** 44.38*** 44.82*** 96.55*** 2879.28*** 578476.47*** 0.02** 0.05*** 0.06*** SCA/sets 64 864590* 7.81*** 10.75*** 25.60*** 1048.48** 226762.44** 0.02** 0.02*** 0.02*** Env x GCAf/sets 48 2157248*** 12.17*** 8.57** 24.05*** 1576.95*** 315439.58*** 0.03*** 0.03*** 0.03*** Env x GCAm/sets 48 1010269** 6.85*** 7.38* 21.26*** 1106.75** 220052.25* 0.02*** 0.02*** 0.02*** Env x SCA/sets 192 767969* 5.44*** 7.51*** 15.79*** 1052.20*** 201625.28** 0.02*** 0.02**** 0.01* Pooled error 684 596311 2.28 4.77 8.58 682.72 141150.60 0.01 0.01 0.01 *, **, ***, = significant at P < 0.05, 0.01, 0.0001 respectively and ns= not-significant. ǂGY= grain yield, SW100=hundred seed weight, SDPPOD= no of seed per pod, PODPLT, no of pods per plant, SDPLT=no of seeds per plant, NDVI 1-3= measured at vegetative, flowering and pod filling stages respectively. University of Ghana http://ugspace.ug.edu.gh 138 5.3.5 Relative contributions of male and female general combining ability effects The relative contributions of GCA and SCA were determined through the ratio of the sum of squares of the GCA or SCA to the total genetic effects. Under drought stress environment, the overall contributions of GCA (GCAf and GCAm) sum of squares to the total variation observed among the crosses ranged between 64% for number of seeds per pod to 74% for delayed leaf senescence (Figure 5.3). Contribution of SCA ranged between 26% for delayed leaf senescence to 40% for number of seeds per pod. For grain yield, contribution of GCAf (60%) was larger than the GCAm (11%) while SCA (29%) was larger than the GCAm. This similar trend was observed for other traits except for delayed leaf senescence where both GCAf (39%) and GCAm (35%) were larger than the SCA (26%) sum of squares. Under well-watered conditions, the contribution of GCA sum of squares ranged between 49% for number of seed per pod to 78% for hundred seed weight while SCA ranged from 22% for hundred seed weight to 51% for number of seed per pod (Figure 5.4). The contribution of GCAf, GCAm and SCA was 47%, 20% and 33% respectively for grain yield. The contribution of SCA was larger (51%) for number of seed per pod than GCAf (17%) and GCAm (32%). University of Ghana http://ugspace.ug.edu.gh 139 GY SW100 SDPPOD PODPLT SDPLT DFL DLSC SCA 29 32 36 35 40 30 26 GCAm 11 23 28 20 12 26 35 GCAf 60 45 36 45 48 44 39 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% C o n ti b u ti o n o f G C A f, G C A m an d S C A Figure 5. 3: Percentage contributions of GCA effects from GCAf and GCAm and their interactions for selected traits across drought stress environments. University of Ghana http://ugspace.ug.edu.gh 140 GY SW100 SDPPOD PODPLT SDPLT DFL SCA 33 22 51 36 38 36 GCAm 20 32 32 16 22 22 GCAf 47 46 17 47 40 43 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% C o n tr ib u ti o n o f G C A f, G C A m a n d S C A Figure 5. 4: Percentage contributions of GCA effects from GCAf and GCAm and their interaction for selected traits across well-watered environments. University of Ghana http://ugspace.ug.edu.gh 141 Overall, GCA accounted for >60% of the total genotypic variation observed among the F1 crosses across all research environments (Figure 5.5). Contribution of GCAf was larger than GCAm for all traits except for number of seeds per pod. GCAf, GCAm and SCA sum of squares were 60%, 17% and 23% respectively for grain yield. Contribution of SCA sum of squares was larger than GCAm for grain yield, number of pods per plant and number of seeds per plant. GY SW100 SDPPOD PODPLT SDPLT DFL DLSC SCA 23 21 34 26 28 26 26 GCAm 17 30 36 18 18 24 35 GCAf 60 49 30 57 54 50 39 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% C o n tr ib u ti o n o f G C A f, G C A m a n d S C A Figure 5. 5: Percentage contributions of GCA effects from GCAf and GCAm and SCA of selected traits across all research environments. University of Ghana http://ugspace.ug.edu.gh 142 5.3.6 Estimate of general combining ability effects Under drought stress environments, the GCAf effect ranged from -569 for TVu6333 to 649 for TVu79 while GCAm effect ranged from -402 for TVu10100 to 497 for TVu9797 for grain yield (Table 5.10). Out of the twenty parents, only ten of them had positive GCAf and GCAm effects for grain yield. Of the ten, TVu79, TVu6707 and TVu9693 had positive and significant GCAf; TVu6333 and TVu11986 had negative and significant GCAf effects for grain yield. TVu9797 had significant positive GCAm effect while TVu10100 had significant and negative GCAm for grain yield. CB-27 and IT89KD-288 had superior positive GCA effects (significant GCAf and GCAm) for hundred-seed weight while IT81D- 985, Danila and IT90K-277-2 had only GCAf to be positive and significant for same trait. TVu6333 had superior negative GCA effects (negative GCAf and GCAm effects) for number of pods per plant and number of seeds per plant and delayed leaf senescence. TVu3562 had superior negative GCA effects for hundred-seed weight and superior positive GCA effects for number of seeds per pod, number of pods per plant and number of seeds per plant. TVu10100 had superior negative GCA effects for delayed leaf senescence. GCAm and GCAf effects for NDVI measured at flowering stage and pod-filling stages were significant and positive for TVu9693. TVu6707 had significant and positive GCAf and GCAm effects for NDVI measured at flowering stage but significant and positive GCAm effects only for NDVI at pod-filling stage (Table 5.10b). CB-27, IT89KD-288, IT99K-573- 1-1 and TVu11986 had significant negative GCAm for NDVI measured at flowering stage. CB-27 had superior negative GCA effects (significant GCAf and GCAm) for number of days to 50% flowering. University of Ghana http://ugspace.ug.edu.gh 143 Table 5. 10: Estimate of general combining effects of lines evaluated across drought stress environments. Lines GY 100-SW SDPPOD PODPLT SDPLT GCAf GCAm GCAf GCAm GCAf GCAm GCAf GCAm GCAf GCAm TVu7778 -327.47 13.78 -1.28* -1.21*** -1.76** -0.9 -8.74 -2 -153.65 10.68 TVu10100 -286.14 -402.30** -1.40* 0.13 0.37 -0.72 -4.44 -18.37** -62.78 -237.18** SANZI -371.71 -6.75 -0.28 -2.88*** -1.22* 0.37 -3.62 16.76* -105.1 227.44** TVu3562 255.59 180.76 -1.31* -2.56*** 1.36** 1.34** 13.63* 13.83* 206.54* 205.00** TVu9797 47.09 496.52*** -0.48 -2.31*** 2.16*** 2.41*** -4.5 12.73* -4.81 260.27** TVu79 648.80** 155.09 -0.05 -2.34*** 1.50** 2.87*** 15.23* 1.6 262.20** 130.48 TVu6707 567.66* 114.23 -1.52* 1.39** 1.47** -0.32 16.80* 5.01 282.00** 3.96 TVu9693 513.79* -46.44 -3.29*** -0.24 2.42*** 1.06* 14.46* -1.96 337.72** -4.96 TVu2736 -186.18 -217.7 -2.87*** -1.24** 0.42 0.31 3.63 -8.64 84.25 -112.92 TVu8670 230.27 -51.39 -2.21** 0.17 1.40** 0.05 12.46 -7.18 192.68* -96.93 IFEBROWN 56.48 18.76 2.31** -0.45 -1.36** 0.13 3.96 11.14* -19.15 151.07 TVu6333 -569.93* -196.87 -2.49*** 2.15*** 0.36 -1.20* -23.92** -13.90** -304.49** -186.35* IT90K-76 -184.49 26.85 2.09** -0.56 -0.49 0.78 -5.67 1.4 -114.98 32.04 IT81D-985 -229.45 -15.9 3.73*** -0.98* -2.33*** 1 -10.27 -3.07 -196.63* -9.95 DANILA -30.5 124.35 4.29*** -1.04* -2.30*** 0.34 -4.6 2.26 -114.21 40.42 IT90K-277-2 -100.14 98.25 2.07** -0.37 -2.04*** -0.2 -7.2 0.26 -146.07 -8.4 CB-27 226.49 -7.66 1.70** 4.37*** 0.17 -1.71** 8.78 -0.4 96.67 -84.31 IT89KD-288 170.29 45.43 1.84** 3.59*** -0.08 -1.68** 5.46 1.87 62.51 -54.82 IT99K-573-1-1 45.45 -137.2 0.67 3.16*** 0.44 -2.56*** -0.2 -4.2 -22.74 -141.11 TVu11986 -475.90* -191.8 -1.51* 1.23** -0.49 -1.37** -21.25** -7.14 -279.98** -124.45 SE ± 229.60 120.49 0.60 0.42 0.47 0.49 6.83 5.36 95.11 78.34 GCAf, GCAm =GCA effects of the line used as a female and male parents respectively; *, **, ***, Significant at probability level of 0.05, 0.01 and 0.0001 probability levels, respectively, ns not significant University of Ghana http://ugspace.ug.edu.gh 144 Table 5.10b: Estimates of general combining effects of lines evaluated across drought stress environments. Normalized Difference Vegetation Index Lines 1 2 3 DLSC GCAf GCAm GCAf GCAm GCAf GCAm GCAf GCAm TVu7778 0.050* 0.039* -0.008 0.062** -0.053 0.022 1.12 0.12 TVu10100 0.04 -0.014 0.032 0.003 0.049 0.021 -2.25** -1.88** SANZI 0.002 0.001 -0.046 0.041 -0.014 0.043 0.99 0.46 TVu3562 0.016 0.004 0.014 0.037 0.014 0.036 0.49 -0.51 TVu9797 -0.037 -0.012 -0.031 0.018 -0.027 0.003 0.86 -0.55 TVu79 0.03 0.002 0.054 0.008 0.015 -0.006 0.66 -0.55 TVu6707 0.007 -0.001 0.060* 0.060** 0.03 0.056* 0.09 -0.71 TVu9693 0.002 0.004 0.088** 0.063** 0.063* 0.047* 0.22 -0.71 TVu2736 -0.047 -0.025 -0.091** -0.036 -0.070* -0.034 -0.08 -0.58 TVu8670 0.031 0.019 0.076* 0.034 0.027 0.048* 0.99 -0.15 IFEBROWN -0.003 0.038* -0.024 0.004 -0.039 -0.011 1.12 1.32* TVu6333 -0.03 0.021 0.03 -0.005 0.079** 0.036 -2.25** -2.71*** IT90K-76 0.037 0.002 -0.014 -0.021 -0.004 -0.01 0.39 0.32 IT81D-985 -0.043 -0.016 -0.1 -0.026 -0.048 -0.021 -0.48 0.52 DANILA -0.017 0.011 -0.092** -0.003 -0.064* -0.012 0.56 1.16* IT90K-277-2 -0.053* 0.026 -0.043 0.028 -0.013 -0.01 -0.65 0.79 CB-27 -0.007 -0.033 -0.014 -0.083** 0.018 -0.04 -0.18 1.12* IT89KD-288 -0.004 -0.019 -0.024 -0.066** -0.04 -0.056* 0.26 1.59** IT99K-573-1-1 0.04 -0.021 0.074* -0.073** 0.012 -0.080** 0.76 1.39* TVu11986 -0.016 -0.027 0.059 -0.046* 0.069* -0.029 -2.61** -0.45 SE ± 0.022 0.018 0.034 0.023 0.029 0.023 0.67 0.54 GCAf, GCAm =GCA effects of the line used as a female and male parents respectively; *, **, ***, Significant at probability level of 0.05, 0.01 and 0.0001 probability levels, respectively, ns not significant University of Ghana http://ugspace.ug.edu.gh 145 GCAf effect for grain yield under well-watered conditions ranged from -786 for TVu10100 to 1220 for TVu6707 and ranged from -548 for TVu10100 to 1241 for TVu9797 for GCAm effect (Table 5.11). TVu6707 and TVu9693 had significant and positive GCAf while TVu3562 and TVu9797 had significant positive GCAm for grain yield. TVu10100, TVu2736 and CB-27 had significant and negative GCAm for grain yield. CB- 27, IT89KD-288 and IT99K-573-1-1 had superior positive GCA (GCAf and GCAm) effects for hundred seed weight while Sanzi and TVu7778 had superior negative GCA effects for the same trait. TVu9693 had superior positive GCA (significant GCAf and GCAm) effects for number of seeds per pod and positive GCAf for number of pods per plant and number of seeds per plant. TVu6333 had significant negative GCAm effects for number of seeds per pod; significant negative GCAf and significant positive GCAm for hundred-seed weight (Table 5.11). TVu79 had superior GCA effects (GCAm and GCAf) for NDVI measured at pod-filling stage and a significant GCAm for number of seeds per plant (Table 5.11b). University of Ghana http://ugspace.ug.edu.gh 146 Table 5. 11: Estimates of general combining ability effects of lines evaluated across well-watered environments. Lines GY 100-SW SDPPOD PODPLT GCAf GCAm GCAf GCAm GCAf GCAm GCAf GCAm TVu7778 -598.06 -16.39 -1.30* -2.74*** -1.69** 0.48 -10.81 -2.76 TVu10100 -168.88 -548.32* -1.98** 1.09** 1.29** -0.66 -2.16 -20.04** SANZI -476.72 227.2 -1.59** -3.64*** -0.98* 0.64 -7.91 16.55** TVu3562 257.22 799.96** -0.61 -2.74*** 0.3 2.11*** 19.66* 32.22*** TVu9797 76.75 1241.61*** -0.01 -1.64*** 0.43 3.76999 -9.59 27.89*** TVu79 263.76 291.77 0.8 -2.22*** -0.27 2.62*** 9.86 1.81 TVu6707 1220.26** -9.29 -1.65** 1.70*** 1.84** -0.82* 32.96*** 0.41 TVu9693 1162.99** 166.86 -3.05*** -0.39 2.81*** 1.62*** 36.06*** -7.81 TVu2736 -220.42 -434.92* -3.91*** -1.81*** 1.23* -0.32 -0.84 -12.09* TVu8670 550.21 103.27 -2.40*** 1.14** 2.44*** 0.58 9.69 -0.41 IFEBROWN -39.27 -114.66 2.83*** -0.84* -1.23* -1.19** 10.99 -3.38 TVu6333 -785.97 -349.24 -2.16*** 1.71*** 0.46 -0.86* -29.21** -9.36 IT90K-76 -393.63 -180.48 3.20*** -0.15 -1.02* -0.32 -11.96 2.77 IT81D-985 -395.07 -28.49 1.47** -0.86* -1.27* 0.2 -20.44** -1.13 DANILA -138.75 -213.73 3.57*** -0.92* -2.23*** -0.52 -5.01 -5.48 IT90K-277-2 -377.97 60.31 2.90*** 0.05 -2.04*** -0.39 -10.54 8.41 CB-27 35.76 -471.09* 1.70** 2.99*** -0.99* -2.20*** 1.42 -17.21** IT89KD-288 110.55 -357.13 2.14*** 3.19*** -0.53 -2.44*** 4.06 -5.74 IT99K-573-1-1 332.15 -190.38 1.61** 4.65*** 0.55 -2.08*** 4.39 -3.11 TVu11986 -414.9 23.14 -1.56** 1.44** 0.9 -0.2 -20.61** -1.54 SE ± 323.13 220.72 0.54 0.42 0.49 0.40 7.75 5.90 GCAf, GCAm =GCA effects of the line used as a female and male parents respectively; *, **, ***, Significant at probability level of 0.05, 0.01 and 0.0001 probability levels, respectively, ns not significant University of Ghana http://ugspace.ug.edu.gh 147 Table 5.11b: Estimates of general combining ability effects of lines evaluated across well-watered environments. Normalized Difference Vegetation Index Lines 1 2 3 SDPLT GCAf GCAm GCAf GCAm GCAf GCAm GCAf GCAm TVu7778 -0.002 0.003 -0.039 -0.012 0 -0.027 -197.57 2.66 TVu10100 -0.046 -0.001 -0.015 -0.01 -0.011 -0.01 14.26 -290.95*** SANZI 0.053 -0.007 -0.002 -0.029 -0.048 0 -157.97 240.57** TVu3562 0.001 -0.013 0.042 0.016 0.023 0.018 239.87 528.67*** TVu9797 0.000 -0.011 -0.055* 0.008 0.01 -0.012 -61.01 619.20*** TVu79 -0.013 0.008 0.044 -0.004 0.072** 0.048* 90.7 177.67* TVu6707 0.009 -0.018 0.029 0.038 0.009 0.053* 602.58*** -67.9 TVu9693 0.060* -0.012 0.019 0.018 0.045 0.035 638.23*** -34.58 TVu2736 -0.062* 0.011 -0.080** -0.044 -0.02 -0.016 44.1 -238.66** TVu8670 0.019*** 0.036 0.026 0.090** 0.004 0.052* 269.60* -43.55 IFEBROWN 0.013 -0.018 0.003 -0.035 -0.062* -0.016 57.91 -65.18 TVu6333 0.033 0.048 0.009 0.029 -0.004 0.068** -383.77** -159.02* IT90K-76 0.005 0.032 -0.005 0.046 -0.005 0.01 -201.53 -19.68 IT81D-985 0.042 -0.029 0.025 -0.047 -0.087** 0.016 -291.37* -20.09 DANILA -0.023 0.034 -0.069* 0.025 -0.056* 0.028 -158.66 -76.56 IT90K-277-2 -0.071* 0.019 -0.017 0.002 0.017 0.016 -213.49 95.53 CB-27 0.000 -0.042 0.007 -0.033 0.02 -0.107*** -40.71 -293.15*** IT89KD-288 0.009 -0.064* -0.01 -0.065* -0.004 -0.079** -1.19 -180.82* IT99K-573-1-1 0.023 0.006 0.047 -0.043 0.019 -0.092*** -28.16 -161.85* TVu11986 -0.049 0.019 0.039 0.049 0.082** 0.017 -221.81 -12.3 SE ± 0.030 0.029 0.027 0.026 0.025 0.023 114.31 71.39 GCAf, GCAm =GCA effects of the line used as a female and male parents respectively; *, **, ***, Significant at probability level of 0.05, 0.01 and 0.0001 probability levels, respectively, ns not significant University of Ghana http://ugspace.ug.edu.gh 148 5.3.8 Inter-relationship among traits Under drought stress, significant and negative correlation was observed between grain yield and number of days to 50% flowering and NDVI measured at pod-filling stage (r = - 0.43) and (r= -0.35) (Table 5.12). The relationship between grain yield and number of pods per plant and number of seeds per plant was significant and positive under both drought stress and well-watered condition (Tables 5.12 and 5.13). Similarly, number of days to 50% flowering had significant and negative relationship with number of pods per plant and number of seeds per pod under both water regimes (Tables 5.12 and 5.13). Table 5. 12: Correlation coefficients of traits under drought stress. Traits SDPOD DFL NDVI_1 NDVI_2 NDVI_3 PODPLT SDPLT GY 0.59** -0.43** 0.49** 0.31** -0.35** 0.77** 0.85** DFL 0.60** -0.29** 0.51*** -0.34** -0.34** NDVI_1 0.49** - 0.78** 0.27** 0.35** NDVI_2 -0.03ns 0.32** 0.34** NDVI_3 -0.05ns -0.14* PODPLT 0.96** * , ** ,indicates significance at P < 0.05 and 0.01 respectively, ns=not significant GY= grain yield, DFL=days to flowering, NDVI_1,NDV_2,NDVI_3 = normalized difference vegetation index taken at vegetative, flowering and pod-filling stages respectively, SDPLT=seed per plant, PODPLT=pod per plant, SDPOD=seed per pod Table 5. 13: Correlation coefficients of traits under well watered condition. Traits SDPOD DFL NDVI_1 NDVI_2 NDVI_3 PODPLT SDPLT GY 0.56** -0.32** 0.32** 0.31** 0.11* 0.74** 0.83** DFL -0.55** -0.49** 0.09* -0.18** -0.22** NDVI_1 0.74** -0.02ns 0.05ns 0.15* NDVI_2 0.21** 0.16ns 0.23** NDVI_3 0.19** 0.19** PODPLT 0.93** University of Ghana http://ugspace.ug.edu.gh 149 5.3.9 GGE biplot analysis of grain yield of F1 crosses The GGE biplot analysis for grain yield of fifty crosses that had good yield across all the research environments revealed that both PC1 and PC2 together accounted for 86.3% of the total variation for grain yield with PC1 explaining 71.7% and PC2 explaining 14.6%. The combined analysis of variance performed across all research environments revealed highly significant differences among the crosses and environment x crosses interaction for grain yield. In Figure 5.6 which showed the “which wins where” of the mean grain yield of the crosses plus the environment x crosses interaction, there are 8 vertices with entries 38,37,18,13,45,8, 9 and 35 as the vertex cultivars. This shows that these vertex cultivars were most responsive in the environment (s) found within the same vector as the cultivar (s). From Figure 5.6, only TVu6707 x TVu3562 (38) fell within the same sector as environment IBWW (Ibadan well-watered conditions). Vertex cultivar TVu79 x Danila (35) fell within the sector where environments MJBWW (well-watered condition in Minjibr) and IBDS (drought stress condition in Ibadan) fell. Although, entries 37, 18, 13, 45, 8 and 9 were vertex cultivars, they did not fall within any sector with any test environments suggesting that they were not responsive at any of these environments. Based on Figure 5.7, it is possible to assess both mean yield and stability performance through a biplot. An average tester coordinate (ATC) horizontal axis passes through the biplot origin and the average location and the oval shows the positive end of the ATC horizontal axis. The average yields of genotypes are estimated by projections of their markers on to the ATC horizontal axis. Thus TVu6707 x TVu9797 (38) had the highest average yield while TVu2736 x TVu79 (45) had the lowest average yield (Figure 5.7). Stability of each genotype is explored by its projection onto the ATC vertical axis. The University of Ghana http://ugspace.ug.edu.gh 150 smaller the absolute length of projection of a genotype, the more stable it is. Thus, TVu6707 x TVu3562 (37), TVu6707 x TVu9797 (38), TVu9693 x TVu3562 (40) and TVu8670 x TVu9797 were high yielding but highly unstable while TVu8670 x Sanzi (47), IT89D-288 x TVu8670 (19), TVu6707 x TVu79 (39), TVu9693 x TVu79 (42) and TVu8670 x TVu79 (49) were the most stable (Figure 5.7). Considering both mean yield and stability performance, TVu8670 x Sanzi (47), IT89D-288 x TVu8670 (19), TVu6707 x TVu79 (39) and TVu8670 x TVu79 (49) are most favourable. University of Ghana http://ugspace.ug.edu.gh 151 Code Crosses Code Crosses Code Crosses 1 IFEBROWN x CB-27 21 IT99K-573-1-1 x TVu6707 41 TVu9693 x TVu9797 2 IFEBROWN x IT89D-288 22 IT99K-573-1-1 x TVu9693 42 TVu9693 x TVu79 3 IFEBROWN x IT99K-573-1-1 23 TVu11986 x TVu2736 43 TVu2736 x TVu3562 4 IFEBROWN x TVu11986 24 TVu7778 x IT90K-76 44 TVu2736 x TVu9797 5 IFEBROWN x TVu6333 25 Sanzi x IT90K-76 45 TVu2736 x TVu79 6 IT90K-76 x CB-27 26 TVu3562 x IFEBROWN 46 TVu8670 x TVu7778 7 IT90K-76 x IT89D-288 27 TVu3562 x IT90K-76 47 TVu8670 x Sanzi 8 IT90K-76 x IT99K-573-1-1 28 TVu3562 x IT81D-985 48 TVu8670 x TVu9797 9 IT90K-76 x TVu11986 29 TVu3562 x DANILA 49 TVu8670 x TVu79 10 DANIILA x CB-27 30 TVu9797 x IFEBROWN 50 TVu10100 x TVu9797 11 DANILA x IT89D-288 31 TVu9797 x IT81D-985 12 DANILA x IT99K-573-1-1 32 TVu79 x IFEBROWN Code Environment 13 IT90K-277-2 x IT99K-573-1-1 33 TVu79 x IT90K-76 MJBDS Minjibir drought stress 14 CB-27 x TVu2736 34 TVu79 x IT81D-985 MJBWW Minjibir well-watered 15 CB-27 x TVu8670 35 TVu79 x DANILA IBDS Ibadan drought stress 16 CB-27 x TVu10100 36 TVu79 x IT90K-277-2 IBWW Ibadan well-watered 17 IT89D-288 x TVu6707 37 TVu6707 x TVu3562 18 IT89D-288 x TVu9693 38 TVu6707 x TVu9797 19 IT89D-288 x TVu8670 39 TVu6707 x TVu79 20 IT89D-288 x TVu10100 40 TVu9693 x TVu3562 Figure 5. 6: A "Which wins Where" GGE biplot of grain yield for 50 F1 crosses evaluated across drought stress and well watered environments at Ibadan and Minjibir, Nigeria. University of Ghana http://ugspace.ug.edu.gh 152 5.4 Discussion Soil moisture content measured for both drought and well-watered blocks showed that the volumetric water content within the root zone between 100 cm to 300 cm in the well- watered block was higher than that in the drought stress block. Yield reduction under drought stress has been reported to be between 42% (Abayomi et al., 2001) and 67% (Fatokun et al., 2012). The average yield reduction obtained for the hybrids under drought in this study was 48% of that under well-watered conditions. Figure 5. 7: An entry/tester view of genotype main effect plus genotype x environment biplot of 50 F1 crosses across drought and well-watered environments in Ibadan and Minjibir. University of Ghana http://ugspace.ug.edu.gh 153 Genetic variation is a basic criterion for measuring germplasm genetic diversity on which selection acts (Ferriol et al., 2003). The significant variation among the parental lines and their F1 crosses under drought and well-watered conditions indicated that adequate genetic variation existed among the cowpea germplasm and their crosses. This implies that significant progress can be made from selection for development of productive novel drought tolerant cowpea varieties. The significant environmental variation observed for all traits under drought and well-watered conditions indicates that the environments were unique and variable which emphasizes testing of genotypes in multi-environments. The significant genotype x environment interaction effect observed for all the traits across the research environments indicates that the F1 crosses varied in their responses to the different environment. This is in agreement with findings of Akande et al. (2009) and Ddamulira et al. (2015). Combining ability analysis is an important tool for the selection of desirable parents together with the information regarding nature and magnitude of gene effects controlling quantitative traits. The success of the hybridization programme depends on the ability of the parents used in hybridization to produce superior segregants / recombinants (Hallauer and Miranda 1981). Combining ability studies help to define the pattern of gene effects in the expression of quantitative traits by identifying potentially superior parents and hybrids (Goyal and Kumar 1991; Ahuja and Dhayal 2007). In the combining ability study, females within sets (GCAf) was significant for grain yield and other measured traits under both watering regimes. Under drought stress, males within sets (GCAm) was not significant for grain yield, number of seeds per plant and NDVI_1. The male x female within set interaction (SCA) was not significant for grain yield, number of seeds per pod, number of University of Ghana http://ugspace.ug.edu.gh 154 pods per plant and number of seeds per plant. The significant GCAf, GCAm and SCA for traits under stress indicated that there were differences in the performance of the lines used as parents. The percentage sum of squares due to GCA effects for grain yield, flowering traits, number of seeds and pods per plant and delayed leaf senescence under drought stress was larger than that due to SCA effects indicating the preponderance of additive gene effects controlling the inheritance of these traits. This result indicates that genetic gains can be achieved through selection for grain yield under drought stress. This agrees with the findings of Jatasra, 1980; Hallauer et al., 2010; Rashwan et al., 2015. The GCAf and GCAm mean squares for grain yield were two times larger than those of SCA under drought stress further confirming the importance of additive gene effects over non-additive gene effects in the inheritance of grain yield in the parental lines used. The result of this study contradicts the findings of Alidu et al. (2013) who reported that non-additive gene action was more preponderant for the control of grain yield under drought stress. This disparity may be due to the different set of cowpea germplasm used in the two studies. None of the parental lines had superior GCA (both significant GCAf and GCAm estimates) effects for grain yield under drought stress; however lines TVu6707, TVu79, TVu8670 and TVu9693 had positive GCAf effect while TVu9797, TVu6707, TVu79 and TVu3562 had positive GCAm effect for grain yield under drought stress. The results suggest that these lines contribute positively to the grain yield of their crosses either as female or male parents, respectively, under drought stress. It is not surprising that these lines produced the best 20 hybrids under drought and well-watered conditions. TVu3562 had superior GCA effects (GCAf and GCAm) for number of seeds per pod and number of pods per plant under drought stress. This suggests that this TVu3562 will contribute favourable alleles for this University of Ghana http://ugspace.ug.edu.gh 155 trait to its progeny. This is in agreement with findings of Ayo-Vaughan, (2013) who reported predominance of additive genetic variance for number of pods per plant in cowpea. NDVI readings taken with the Greenseeker® handheld crop sensor device which measures leaf greenness (chlorophyll content) is considered to be correlated with crop productivity under moisture stress. Although this device has gained wide acceptance in maize and wheat breeding for precision phenotyping under water stress (Hazratkulova et al., 2012; Lu et al., 2012), this study appeared to be the first to employ the use of the device for phenotyping cowpea under water stress. The device was used to measure the vegetation index of the parental lines and their crosses at vegetative, flowering and pod-filling stages. From the result of this study, TVu9693 had a superior GCA (GCAf and GCAm) effects for NDVI at flowering and grain filling stages under drought stress. TVu6707 had superior GCA (GCAf and GCAm for NDVI_2. This indicates that these lines contributed positively to NDVI values of their crosses during these stages of cowpea growth thus enhancing their increased productivity under drought stress. Under well-watered conditions, TVu9693 and TVu6707 had significant positive GCAf for grain yield indicating that they contributed positively to the grain yield of their crosses as female parents. TVu3562 and TVu9797 had significant positive GCAm for grain yield indicating positive allelic contribution for grain yield as male parent. This indicates that hybrids from the crosses of these lines will produce high yield under well-watered conditions. From this study, the hybrids produced from TVu6707, TVu9693, TVu3562 and TVu8670 were among the best 20 under both watering regimes indicating that these University of Ghana http://ugspace.ug.edu.gh 156 lines are good combiners for grain yield under both watering regimes and will be good sources of drought tolerance genes to develop new cowpea varieties that are high yielding and tolerant to drought. Correlation analysis was used to determine the degree of association between the measured traits under both watering regimes. Under the watering regimes, number of pods and seeds per plant showed high and significant positive relationships with grain yield (r =0.77, r=0.85 and r= 0.74, r =0.83). This is in agreement with findings of Ayo-Vaughan, (2013). It has been suggested that NDVI could be used to predict grain yield in cereals (Wang et al., 2010). The result from this study showed a significant and positive correlation between grain yield and NDVI measured at vegetative and flowering stages under drought stress (r = 0.49, r =0.31) and under well-watered conditions (r = 0.32, r =0.31 respectively). This is in agreement with results of other workers (Islam et al., 2011; Lu et al., 2011) who reported positive but weak correlation between NDVI and grain yield in maize (r= 0.38 – 0.49). This indicates that a high NDVI value which translates to maintaining green photosynthetic surface during flowering stage will allow cowpea varieties to survive mid-season drought stress. Accumulation of abundant biomass at vegetative stage under drought stress has been reported to be correlated with increase in grain yield at harvest (Adebayo et al., 2014). In cowpea, varieties that combine water saving traits at vegetative stage with delayed leaf senescence will be able to withstand mid-season and terminal drought stress (Belko et al., 2012; Fatokun et al., 2012). Negative and significant correlations were observed between number of days to 50% flowering and grain yield under drought stress as well as under well-watered conditions. This is in agreement with findings of Alidu et al. (2013). This shows that to increase grain yield under both watering regimes, in cowpea, earliness will University of Ghana http://ugspace.ug.edu.gh 157 be a good direction to exploit but Muchero et al. (2013) reported that early maturing genotypes do not give good yield when exposed to intermittent mid-season drought. The evaluation of G and GE interaction is an important component of the cultivar selection process in multi-environment trials (METs). The yield of each cultivar in each test environment is a mixture of environment main effect (E), genotype main effect (G) and genotype × environment interaction (GEI) (Pourdad and Moghaddam, 2013). GGE biplot analysis is an effective method which is based on the principal component analysis (PCA) in order to fully explore METs. For this reason, instead of trying to separate G and GE, Yan et al. (2000) combined G and GE and referred to the mixture as GGE. The GGE biplot is thus a powerful tool for identifying best performing genotypes in a given environment and the most suitable environment for each genotype, average yield of the genotype and the stability of the genotypes in given environments. The PC1 and PC2 of the GGE biplot captured 86.3% of the total variability observed in grain yield with PC1 and PC2 explaining 71.7% and 14.6%, respectively. TVu8670 x Sanzi, IT89D-288 x TVu8670, and TVu6707 x TVu79 were thus identified by the GGE biplot analysis as the most stable crosses across all environments. Although, the GGE biplot identified crosses between TVu6707 x TVu3562 and TVu6707 x TVu9797 as high yielding, they were unstable across environments. The findings from this study confirmed the report by Fatokun et al. (2012) that the under-exploited cowpea germplasm (TVu lines) possesses favourable alleles for drought tolerance. University of Ghana http://ugspace.ug.edu.gh 158 5.5 Conclusion The ultimate aim in cowpea breeding is to produce pure lines that are highly productive under the ever changing climate conditions. Choosing parents that will contribute favourable alleles for these traits of interest and the knowledge of the mode of gene action controlling the traits are important prerequisites that will ensure efficient utilization of available genetic resources. In this study, F1 crosses were evaluated under drought stress and well-watered conditions in two locations in order to examine the combining ability of parents covering diverse array of cowpea germplasm (landrace, advanced breeding lines and tropical Vigna unguiculata accession) for drought tolerance; to identify the mode of gene action conditioning drought tolerance and to identify putative parents with good combining ability that will give good hybrids that can further be advanced through appropriate breeding methodology for cowpea to develop high yielding pure lines for drought prone production areas. The findings of this study are summarized below: The drought and well-watered environments at both experimental locations were unique. High variability exists among the parents and their F1 progenies to allow selection of F1 crosses tolerant to drought stress. Additive gene action played a predominant role in the inheritance of grain yield and most traits measured under drought stress. TVu6707, TVu79, TVu96963 and TVu9797 are good general combiners identified from this study. Lines TVu6707 and TVu9693 had significant GCAf effect for grain yield under drought stress and well-watered conditions while TVu79 had significant GCAm effect under drought stress. TVu3562 and TVu9797 had significant GCAm effects for grain yield under the contrasting environments. Advanced progenies resulting from crosses involving these University of Ghana http://ugspace.ug.edu.gh 159 parents will contribute favourable alleles for drought tolerance and high yield when used as male and female parents in cowpea hybridization programme. These high yielding crosses: TVu6707 x TVU9797, TVu9693 x TVu9797 and TVu79 x DANILA found to be more productive under both watering regimes identified from this study resulted from cross combinations of these lines. The result on GGE identified cross between TVu6707 x TVu9797 and TVu6707 x TVu3562 were high yielding but were unstable across contrasting environments. TVu8670 x Sanzi, IT89D-288 x TVu8670, TVu6707 x TVu79 and TVu8670 x TVu79 were however high yielding and stable across all the environments. Advancing these crosses could provide new drought tolerant varieties that are high yielding University of Ghana http://ugspace.ug.edu.gh 160 CHAPTER SIX 6.0 CONCLUSIONS AND RECOMMENDATIONS West African countries (Nigeria and Niger) account for over 68% of world’s cowpea grain production. However, information from the Food and Agricultural Organization (FAO) database revealed that the production realized per hectare of land in the United States of America surpasses what is realized in SSA. This indicates that increased production quantity in SSA is mainly as a result of increase of land under cultivation and not from the actual yield potential of the crop. Drought stress is the single most important abiotic factor that reduces cowpea production in semi-arid regions where it is mainly cultivated. With the ever changing global climate, productivity may continue to decline in these regions. The development of high yielding drought tolerant cowpea varieties through breeding will be more rewarding for smallholder cowpea farmers in SSA. In order to achieve this, it is important that cowpea breeders exploit existing variability in cowpea germplasm and identify superior parental lines with good combining ability for pure line development. Although several studies have reported cowpea to be more tolerant to drought when compared to other grain legumes, in this study, the hypothesis that drought stress reduces cowpea production was tested. From the PRA conducted, the tested hypothesis was accepted. The result of this study confirmed the set out hypothesis and revealed that drought was still ranked as the major abiotic stress limiting cowpea productivity. Farmers interviewed in this study indicated that the unreliable and erratic pattern of rainfall they witness during cropping seasons reduce cowpea productivity through reduction in leaf University of Ghana http://ugspace.ug.edu.gh 161 production, flower abscission, poor pod-filling and increase in the incidence of some devastating pests and diseases. The PRA was able to identify reasons why at times new varieties are never adopted by farmers. They indicated that sometimes many of the high yielding varieties they get don’t meet consumer preferences which drive their farming business. It was identified in this study that consumer-based traits such as large seeds, short cooking time and high market acceptability were of importance to the farmers as well as the biotic and abiotic constraints and so should be incorporated in breeding programmes. In order to identify lines with tolerance to vegetative and terminal drought, ninety-one cowpea lines comprising of advanced breeding lines, improved released lines and tropical Vigna unguiculata accessions belonging to the primary gene pool of cowpea were screened for seedling and terminal drought tolerance. The objective of this study was to identify parental lines that will combine well to produce superior progenies. The result from the field screening could not be used for selection because of rain interference on the field during the year of evaluation. In the wooden box study for seedling stage tolerance, maintenance of stem greenness was found to be an important trait for tolerance to drought at seedling stage. The genotypes showed significant differences in their responses to drought imposed at seedling stage. The result therefore confirms that the genotypes possess desirable genes for tolerance to drought at seedling stage and that there will be gain in selection for this trait in cowpea using these germplasm. From this study, cowpea was successfully propagated like vegetative crops using the vines of already flowering plants. This success enabled continuous production of flowers for hybridization, thus the number of F1 seeds needed for evaluation at two locations was possible. University of Ghana http://ugspace.ug.edu.gh 162 Significant genotype, environment and genotype x environment variation observed among the crosses and the parents for grain yield and in this study confirmed that variability in the germplasm for drought tolerance and the need for multi-environment testing. The significant GCAf and GCAm for grain yield and other traits under drought stress indicated that the performance of the parents in cross combinations was influenced by additive gene action under drought stress. The larger proportion of GCA sum of squares over SCA for grain yield, number of pod per plant and delayed leaf senescence indicates that additive gene action played a major role in the inheritance of these traits under drought stress. Some lines showed positive GCA effects for grain yield under drought stress (TVu6707 and TVu79) and (TVu9797 and TVu9693) under well-watered conditions. These lines could be used to develop new varieties that will be highly productive under diverse environmental conditions. Among the one hundred crosses evaluated under drought and well-watered conditions, eight out of the top 20 yielders under both irrigation regimes were crosses involving lines from TVu accessions while others were crosses involving either a landrace or an improved line. This confirmed that these accessions are repository of favourable alleles for drought tolerance and grain yield. These favourable genes can be exploited for improvement of adapted lines and development of novel varieties. 6.1 Recommendations  Climate change is a global phenomenon that needs coordinated measures by government, breeders and farmers in order to provide food and nutrition for the ever increasing population in the SSA. University of Ghana http://ugspace.ug.edu.gh 163  Farmers’ preferences are driven by consumers; therefore, breeders have to incorporate these preferences in newly developed climate-smart cowpea varieties so as to increase adoption.  The crosses that showed positive GCA effects for grain yield under both irrigation regimes can be advanced for the development of pure lines.  Multi-environment testing should be done for the new varieties from the identified parents for stability and grain yield before release.  The use of crop sensors for precision cowpea phenotyping under water stress would enhance selection efficiency for drought tolerant cowpea varieties.  Pyramiding of drought tolerant QTL for vegetative and terminal drought stress in new cowpea varieties would be a major breakthrough.in developing drought tolerant cowpea varieties. University of Ghana http://ugspace.ug.edu.gh 164 BIBLIOGRAPHY Abaje, I.B., Ndabula, C. & Garba, A.H. (2014). 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Increasing genetic diversity by participatory varietal selection in high potential production systems in Nepal and India. Euphytica, 122:575–588. Xiao, Y. N., Li, X. H., George, M.L., Li, M. S., Zhang, S. H. & Zheng, Y. L. (2005). Quantitative trait locus analysis of drought tolerance and yield in maize in China. Plant Molecular Biology Reporter, 23:155-165. University of Ghana http://ugspace.ug.edu.gh 211 Xu, W., Rosenow, D.T. & Nguyen, H.T. (2000). Stay green trait in grain sorghum: relationship between visual rating and leaf chlorophyll concentration. Plant Breeding, 119(4): 365-367. Yakubu, B.L., Mbonu, O.A. & Nda, A.J. (2012). Cowpea (Vigna unguiculata) pest control methods in storage and recommended practices for efficiency: A Review. Journal of Biology, Agriculture and Healthcare, 2(2): 27-33. Yan, J.B., Warburton, M., & Crouch, J. (2011). Association mapping for enhancing maize (Zea mays L.) genetic improvement. Crop Science, 51: 433-449. Yan, W. (2000). 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University of Ghana http://ugspace.ug.edu.gh 212 Zaman-Allah, M., Jenkinson, D.M. & Vadez, V. (2011). A conservative pattern of water use, rather than deep or profuse rooting, is critical for the terminal drought tolerance of chickpea. Journal of Experimental Botany, 62:4239–4252. Ziska, L. H., & Hall, A. E. (1983a). Seed yields and water use of cowpeas (Vigna unguiculata [L.] Walp.) subjected to planned-water-deficit irrigation. Irrigation Science, 3(4): 237-245. Ziska, L. H., & Hall, A. E. (1983b). Soil and plant measurements for determining when to irrigate cowpeas (Vigna unguiculata [L.] Walp.) grown under planned-water- deficits. Irrigation Science, 3(4): 247-257. Ziska, L.H. & Hall, A.E. (1985). Irrigation management methods for reducing water use of cowpea (Vigna unguiculata [L.] Walp) and lima bean (Phaseolus lunatus L.) while maintaining seed yield at maximum levels. Irrigation Science, 6: 223-2339. Ziska, L.H., Hall, A.E & Hoove, R.M. (1985). Irrigation management methods for reducing water use of cowpea (Vigna unguiculata (L.) Walp.) and lima beans (Phaseolus lunatus L.) while maintaining seed yield at maximum levels. Irrigation Science, 6: 223–239. University of Ghana http://ugspace.ug.edu.gh 213 APPENDICES APPENDIX 3.1 SURVEY GUIDE QUESTIONS FOR FOCUS GROUP DISCUSSION General questions 1. Geographic boundaries of the district 2. Major towns and villages 3. Major crops grown 4. Minor crops grown 5. The ranking of cowpea among other crops in the area 6. Other economic activities of the area Questions on cowpea 1. Varieties of cowpea grown 2. Sources of seeds 3. Preferred seed colour 4. Sources of information on new cowpea technologies 5. Number of times cowpea seeds are used for planting before discarding 6. Seed treatment for storage 7. Storage receptacles ( i.e. sacks) 8. Criteria for selecting cowpea for home consumption 9. Criteria for selecting cowpea for marketing 10. Farmers recommended traits into future cowpea varieties a. Yield related b. Preference and quality related c. Labour related 11. Farmers involvement in cowpea research a. Goal setting and objective formulation b. Variety selection c. On-farm evaluation d. Variety release e. Seed multiplication and promotion f. No involvement 12. Production constraints (list and rank) University of Ghana http://ugspace.ug.edu.gh 214 Questions of drought impact on yield 1. Sources of information on climate change and mitigation techniques (drought) 2. Are varieties grown drought tolerant? 3. Symptoms of drought on cowpea 4. Realized yield of cowpea under normal growing conditions 5. Yield as a result of pre flowering drought 6. Yield as a result of post flowering drought 7. Which stage of cowpea is drought very devastating? 8. Causes of drought 9. Controlled options employed 10. Access to drought tolerant cowpea varieties? APPENDIX 3.2. PRA Semi-structured questionnaire for individual farmer State………………………. Local Government Area …………………… Village ………………….. Community ………………………. Latitude ………………… Longitude ……………. Interviewed by ………………………………. Date ……………………… A. Background information 1. What is your name? ……………………………………………………………………… 2. What is your relationship to the household? (a) Household head (b) Spouse (c) Son/daughter aged 15 and above, (d) Others 3. What is the gender of the household head? (a) Male (b) Female 4. What is age of the household head? _____________ 5. Marital Status 1= Single[ ] 2= Married[ ] 3= Separated [ ] 4=widow/widower [ ] 6. What is the highest schooling years attained by any member of the household? _________ 7. How many household members do you have? ___________________ 8. In the last agricultural year (2013/14), how many household members engaged in agricultural activities? _______________ 9. Primary occupation (Tick as many as applicable) University of Ghana http://ugspace.ug.edu.gh 215 1= Farming [ ] 2= Trading [ ] 3=Civil service [ ] 4= Artisan [ ] 5=others (specify) 10. Are you a member of any farmer group? 1= Yes [ ] 0=No [ ] 11. If yes please specify …………………………. If No, why not? .......................................... B. General information on farming practices 12. Years of farming experience. ________ 13. Total farm size (ha) ____________ 14. What was the total cowpea area planted in the last agricultural season?_____________ 15. What was the area planted to improved cowpea varieties in 2013/14 agricultural season?___ 16. In 2013/14, did you apply chemical fertilizer to cowpea? _______________ 17. Land ownership. 1= rent [ ] 2= inherited through family [ ] 3= bought [ ] 18. How many times do you cultivate cowpea in a year? 1=once [ ]2=twice [ ] 3=thrice[ ] 19. What type of cropping system do you use? 1=mono-cropping [ ] 2= mixed cropping [ ]. If answer in 12 above is 2, list crops------------------------------------------------------- ------------------------------------------------------------------------------------------------------ 20. What kind of labour do you use for cowpea farming? (Tick as many as applicable) 1= immediate family [ ] 2=hired [ ] 3=others (specify) 21. Do you use any motor-powered equipment for cowpea farming? 1= Yes 0= N 22. If yes, which machines do you use? 1= plough[ ] 2= seed planter[ ] 3= combine harvester [ ] 4=other (specify)______________________ 23. Does your household currently own any of the following? (a) Cattle, Donkey, or horses (b) Goatsor sheep (c) Poultry (b) If yes, How many? (a) Cattle, Donkey or horses_________ (b) Goats or Sheep_________ (c) Poultry _________________ 22. Please provide the following information on labor and other power inputs for other crops Farm operation Labour input in person day Oxen inputs in days Tractor No. of persons No. of days No of Oxen pairs No of days No. of days Amount paid/day Land clearing 1st ploughing/ Hoeing 2nd ploughing/ Harrowing Furrowing/ ridging Planting 1st weeding 2nd weeding University of Ghana http://ugspace.ug.edu.gh 216 Remolding Herbicide/insecticid e application Farm operation Labour input in person day Oxen inputs in days Tractor No. of persons No. of days No of Oxen pairs No of days No of days Amount paid/day Manure application Inorganic fertilizer application Harvesting *State the number of oxen used ________ C. Varietal preferences 24. Cowpea varieties grown and their yield estimates Variety Seed colour 1=white, 2=brown, 3=red 4=black Seed size 1=small, 2=medium, 3=large Yield under normal condition (kg/ha) Yield under drought(kg/ha) 25. Type of cowpea seeds grown 1= local landrace [ ] 2= improved seeds from research [ ] 26. Sources of seeds 1= open market [ ] 2= Seed company/ agro-dealer [ ] 3= farmer friend/relative [ ] 4=own saved local seed [ ] 5= own saved improved seed [ ] 6= Government /NGO [ ] D. Climate change (drought) and impact on cowpea production 27. Prominent climatic factors that affect cowpea production 1= rainfall [ ] 2= temperature [ ] 3=wind [ ] 4= relative humidity [ ] 28. Sources of information on climate change 1 = Extension agent [ ] 2=Friends[ ]Relatives [ ] 3= Co-farmers [ ] 4= Mass Media [ ] 5=Other sources[ ] 29. Have you ever experienced drought during cowpea growing seasons 1= Yes [ ] 2=No [ ] 30. If yes, how devastating was the effect on grain yield? 1=very severe [ ] 2=moderately severe [ ] 3=not severe [ ] 31. If yes in 29, how devastating was the effect on fodder yield? 1=very severe [ ] 2=moderately severe [ ] 3=not severe [ ] University of Ghana http://ugspace.ug.edu.gh 217 32. At which stage of cowpea growth can drought be devastating? 1= vegetative stage [ ] 2= flowering stage [ ] 3= pod setting [ ] 4=all growth stages [ ] 33. Effects of drought on cowpea growth and yield: Tick one option for one effect under both headings. 34. Realized yield (kg/ha) as a result of drought at different growth stages of cowpea. Varieties Pre-flowering drought Post-flowering drought grain fodder grain fodder E. Adaptation measures employed Adaptation measures Yes No Use of irrigation Planting improved varieties tolerant to drought Manipulation of planting times and harvesting dates Other measures (specify) 35. Production constraints Constraints Great extent Some extent Little extent No extent Lack of improved varieties Drought Effects Pre-flowering drought Post- flowering drought Great extent Some extent Little extent No extent Great extent Some extent Little extent No extent Late emergence of seed Plant wilting Flower abortion Poor pod formation Poor pod filling/seed set Increase in Incidence of pests and diseases Stunted growth Yellowing of leaves Low grain yield Low fodder yield University of Ghana http://ugspace.ug.edu.gh 218 Problems of diseases Problems of pests High cost of land preparation and maintenance Inadequate marketing channels Floods High cost of land rent Gender in respect of land ownership Religion Educational level Poor soil fertility Land size F. Improved cowpea varieties 36. Do you have easy access to improved cowpea varieties that are tolerant to drought? 0=No [ ] 1= Yes [ ] 37. How do you get information on these improved seeds? NGOs [ ]Radio [ ]Television [ ] farmer field school [ ]Agric. Extension [ ] other farmers[ ] friends [ ]other sources [ ] 38. Have you ever grown these improved varieties? 0= No [ ] 1=Yes [ ] 39. If yes, give reasons for your adoption of the improved varieties. -------------------------- -------- 40. Factors limiting adoption of improved varieties Limiting factors Great extent Some extent Little extent No extent Expensive seeds Information dissemination Lack of demonstration/field days Steady source of seeds/access to seeds Educational status Age Perception Gap between technology and adoption Threshing quality Cooking time Market and consumer acceptability Others 41. Do the improved varieties yield more than local varieties in times of drought? 0=No [ ] 1= Yes [ ] 42. If yes, please give comparison Variety name Type: (local =1; improved = 2) Yield (kg/ha) University of Ghana http://ugspace.ug.edu.gh 219 43. How do you get information on improved seeds? NGOs [ ] Radio [ ] Television [ ] farmer field school days [ ]Agric. Extension [ ] other farmers [ ] other sources [ ] 44. What do you think farmers’ involvement in cowpea research should be? Please tick all that apply. Farmers’ involvement in research Yes No a) Objective formulation b) On-farm trials, evaluations and selection c) No involvement 45. Please rank these traits in order of preference. G. Marketing of cowpea 46. Do you cultivate cowpea for home consumption or for selling? 1= selling 2= home consumption 3=both 47. If both, what percentage of cowpea do you sell in a growing season? ...…………. 48. Who buys your cowpea? 1= market women[ ] 2= government agency /NGO [ ] 3= Others ----------------------------------- 49. Is it difficult to sell the cowpea you produce 0= No [ ] 1=Yes [ ] 50. If Yes, what is the major reason for lack of market for the cowpea you produce?1= Bad road[ ] 2=high cost of transportation[ ] 3=Glut [ ] 4=poor storage[ ] 5=other (specify) 51. Do you make profits from growing cowpea? 0=No 1=Yes Preferred traits Rank High yielding Pest and disease resistance High palatability Early maturing High storability Large seeds Drought resistance Dual-purpose Short cooking time High market acceptability Late maturing University of Ghana http://ugspace.ug.edu.gh 220 APPENDIX 4.1 Pedigree and maturity of lines screened for drought tolerance S/N Lines Pedigree Maturity S/N Lines Pedigree Maturity 1 13-309 IT97K-499-35 x (TVu522 x TVu557) Medium 47 IT97K-499-35 IT93K-596-12 x IT93K-2046-1 Medium 2 14-62 IT97K-499-35 x (TVu522 x DANILA) Medium 48 IT97K-573-2-1 IT93K-596-9-12 x IT86D-880 Medium 3 15-145 IT97K-499-35 x (TVu1438 x TVu557 Medium 49 IT97K-819-132 IT90K-592 x IT88DM-867-11 Medium 4 16-109 IT97K-499-35 x (TVu4574 x TVu522) Medium 50 IT98K-1111-1 IT93K-452-1 x TVU 11426 Early 5 19B-35 TVu522 x IT99K-494-6 Medium 51 IT99K-573-1-1 IT93K-596-9-12 x IT86D-880 Early 6 19C-8 IT97K-499-35 x (TVu4574 x TVu6443) Medium 52 Tvu1007 TVu accession Medium 7 21A-40 TVu1438 x TVu2731 Late 53 Tvu10100 TVu accession Medium 8 21A-54 TVu1438 x TVu2731 Medium 54 Tvu11800 TVu accession Medium 9 22A-85 TVu1438 x TVu522 Medium 55 Tvu11982 TVu accession Medium 10 23B-136 IT99K-494-6 x TVu522 Medium 56 Tvu11984 TVu accession Medium 11 24A-23 IT99K-494-6 x TVu6443 Medium 57 Tvu11986 TVu accession Medium 12 28-25A IT98K-205-8 x (TVu522 x TVu4574) Medium 58 Tvu12791 TVu accession Medium 13 31-181 IT98K-205-8 x (IT99K-494-6 x TVu522) Medium 59 Tvu1436 TVu accession Medium 14 40-210 IT89KD-374-57 x (TVu6443 x IT99K-494-6) Medium 60 Tvu1438 TVu accession Medium 15 45-111 IT97K-499-38 x (IT99K-494-6 x TVu522) Medium 61 Tvu14539 TVu accession Medium 16 52-13A IT90K-372-1-2 x (TVu4574 x DANILA Medium 62 Tvu14553 TVu accession Medium 17 55-261 IT97K-1069-6 x (TVu522 x TVu557) Medium 63 Tvu14632 TVu accession Medium 18 55-71 IT97K-1069-6 x (TVu522 x TVu557) Medium 64 Tvu14676 TVu accession Medium 19 57-109 IT00K-1263 x (IT99K-494-6 x TVu522) Medium 65 Tvu148 TVu accession Medium 20 58-246 IT00K-1263 x (TVU522 x TVu557) Medium 66 Tvu15055 TVu accession Medium 21 58-60 IT00K-1263 x (TVU522 x TVu557) Medium 67 Tvu15058 TVu accession Medium 22 58-66 IT00K-1263 x (TVU522 x TVu557) Medium 68 Tvu15450 TVu accession Medium 23 5A-55 TVU 6443 x IT 99K-494-6 Medium 69 Tvu15866 TVu accession Medium 24 60-321 IT00K-1263 x (TVu4574 x TVu522) Medium 70 Tvu16646 TVu accession Medium 25 60-374 IT00K-1263 x (TVu4574 x TVu522) Medium 71 Tvu2672 TVu accession Medium 26 61-317 IT00K-1263 x (TVu6443 x TVu1438) Late 72 Tvu2722 TVu accession Medium 27 61-88 IT00K-1263 x (TVu6443 x TVu1438) Medium 73 Tvu2731 TVu accession Medium 28 64-25 IT00K-1263 x (TVu557 x TVu522) Medium 74 TVu2736 TVu accession Medium 29 64-68 IT00K-1263 x (TVu557 x TVu522) Medium 75 Tvu294 TVu accession Medium 30 6-67 TVU 6443 x TVU 1438 Medium 76 Tvu3562 TVu accession Medium 31 7-11 IT98K-506-1 x (IT98K-506-1 x TVu6443) Medium 77 Tvu441 TVu accession Medium 32 71-10 IT96D-610 x (TVu557 x TVu522) Medium 78 Tvu4574 TVu accession Medium 33 8A-131 TVu557 x TVu522 Medium 79 Tvu522 TVu accession Medium 34 8B-46 TVu557 x TVu523 Medium 80 Tvu5415 TVu accession Medium 35 CB27 California Blackeye No. 27 Early 81 Tvu557 TVu accession Medium University of Ghana http://ugspace.ug.edu.gh 221 Appendix 4.1 continued 36 Ife Brown TVu59 x TVu53 Medium 82 Tvu6333 TVu accession Medium 37 IT81D-985 [(TVu1190 x TVu76) x TVu2027] x TVu625 Medium 83 Tvu6443 TVu accession Medium 38 IT81D-994 (IT87F-1777-2 x IT84S-2246-4) x TVx3236 Late 84 Tvu6707 TVu accession Medium 39 IT84S-2246-4 IT82D-716 x IT81D-1020 Medium 85 Tvu7778 TVu accession Medium 40 IT85F-3139 IT82D-716 x IT82D-889 Medium 86 Tvu79 TVu accession Medium 41 IT89D-288 (IT87F-1777-2 x IT84S-2246-4) x IT87F-1777-2 Late 87 Tvu8670 TVu accession Medium 42 IT90K-277-2 (IT87F-1777-2 x IT84S-2246-4) x TVx3236 Medium 88 Tvu9693 TVu accession Medium 43 IT90K-76 (IT84S-2246-4 x B301) x IT84S-2246-4 Late 89 Tvu9797 TVu accession Medium 44 IT93K-452-1 (IT87F-1777-2 x IT84S-2246-4) x TVx3236 Early 90 Sanzi Selection Medium 45 IT96D-610 IT90K-48-1 x IT89KD-288 Medium 91 Danila Selection Late 46 IT97K-207-15 IT90K-48-1 x IT86D-719 Medium University of Ghana http://ugspace.ug.edu.gh 222 APPENDIX 4.2 Performance of lines screened for drought tolerance at seedling stage. Genotypes *DT E PHT1 4 PH T21 SG21 DAP SG1 4DA P SG30 DAP LW 14D AP LW 21D AP LW3 0DAP ¥RR (%) 13-309 3.3 8.1 7.8 3.9 4.0 1.6 0.3 0.3 2.3 0.0 14-62 3.8 8.9 9.6 3.6 3.9 1.6 0.4 1.0 2.2 9.1 15-145 3.4 8.7 7.5 4.0 4.0 3.6 0.0 0.2 1.7 45.5 16-109 3.1 8.5 8.2 3.5 3.6 1.8 0.7 1.1 2.1 0.0 19B-35 3.3 8.5 7.7 4.0 4.0 3.5 0.0 0.1 1.0 33.3 19C-8 3.0 8.1 8.7 3.9 4.0 3.3 0.1 0.2 1.3 16.7 21A-40 3.0 6.9 7.3 4.0 4.0 1.6 0.4 0.5 2.0 0.0 21A-54 3.4 7.6 7.7 4.0 4.0 2.3 0.3 0.3 1.4 0.0 22A-85 3.0 7.4 7.3 4.0 3.8 2.6 0.5 1.0 1.3 0.0 23B-136 3.3 6.4 6.3 3.4 3.8 1.4 0.6 1.3 2.6 0.0 24A-23 3.0 8.0 7.8 3.5 4.0 1.1 0.5 1.5 3.0 9.1 28-25A 3.0 10.0 10.3 3.6 4.0 1.3 0.0 0.1 2.9 0.0 31-181 3.3 6.3 6.4 3.6 4.0 1.4 0.3 0.8 2.6 0.0 40-210 3.2 8.8 8.2 3.9 4.0 1.8 0.3 0.3 2.3 0.0 45-111 4.0 8.0 8.3 4.0 4.0 0.5 0.5 0.5 3.5 0.0 52-13A 3.2 7.8 7.7 3.7 4.0 1.4 0.5 0.9 2.3 0.0 55-71 3.2 8.2 7.7 3.6 3.6 1.0 0.7 1.4 3.1 0.0 55-261 3.1 9.7 9.8 3.5 3.9 1.2 0.7 0.9 2.9 0.0 57-109 3.5 7.1 7.4 3.8 4.0 0.4 0.8 0.8 3.5 0.0 58-246 3.3 10.4 9.5 3.3 3.9 0.8 1.1 1.8 3.1 0.0 58-60 3.2 8.6 8.9 2.4 3.5 0.6 0.9 2.1 3.3 0.0 58-66 3.3 9.0 8.5 3.0 3.7 1.0 1.0 2.0 3.0 0.0 5A55 3.3 7.8 7.3 3.8 4.0 2.7 0.3 0.7 1.9 50.0 60-321 3.3 9.8 9.6 3.7 3.7 2.4 0.6 0.9 2.2 0.0 60-374 3.1 9.7 9.8 3.1 3.3 0.8 1.0 1.5 3.1 0.0 61-317 3.2 9.0 8.7 3.4 3.9 1.3 1.1 2.0 2.9 0.0 61-88 3.3 7.3 7.8 3.3 3.3 1.3 1.0 1.3 2.8 0.0 64-25 3.1 9.6 9.2 3.5 4.0 1.9 1.0 1.3 2.3 16.7 64-68 3.0 7.9 7.6 3.2 3.6 1.3 1.0 1.6 2.7 0.0 6-67 3.0 7.4 7.9 3.8 3.9 2.0 0.3 0.5 2.1 16.7 7-11 3.5 5.9 6.7 2.7 4.0 0.6 1.0 2.2 3.5 0.0 71-10 3.4 9.9 10.3 3.1 4.0 0.6 0.7 1.3 3.2 0.0 8A-131 3.1 9.0 8.1 3.7 4.0 1.5 0.3 0.3 2.3 0.0 8B-46 3.0 7.6 7.4 3.8 4.0 2.2 0.5 1.0 1.7 0.0 CB27 4.1 8.2 8.2 3.3 3.6 2.5 0.7 0.8 1.7 40.0 Danila 3.0 9.7 10.3 3.3 4.0 2.8 0.0 2.2 3.2 16.7 IT81D-994 3.0 12.8 13.5 3.0 4.0 3.1 0.0 2.3 3.1 10.0 IT81KD-985 3.0 9.6 10.3 3.5 4.0 2.2 0.0 2.4 3.0 18.2 IT84S-2246-4 3.5 9.3 9.9 2.9 4.0 2.7 0.0 1.9 3.1 36.4 IT85F-3139 3.4 7.2 6.6 3.1 3.5 0.5 1.3 1.8 3.5 0.0 Appendix 4.2 continued IT89D-288 3.2 14.9 15.2 2.2 4.0 2.1 0.0 2.8 3.2 22.2 University of Ghana http://ugspace.ug.edu.gh 223 IT90K-277-2 3.3 13.0 13.7 2.4 4.0 3.1 0.0 2.9 3.1 36.4 IT90K-76 3.1 12.1 12.7 2.7 4.0 2.3 0.0 2.2 2.4 50.0 IT93K-452-1 3.7 14.8 15.5 1.9 4.0 1.3 0.0 3.0 3.0 18.2 IT96D-610 4.0 9.2 8.4 3.5 3.6 2.3 0.6 0.8 2.2 0.0 IT97K-499-35 3.3 9.7 10.2 3.3 4.0 2.6 0.0 3.0 2.6 12.5 IT97K-573-1-1 3.6 12.9 13.4 1.5 4.0 1.4 0.0 2.9 3.5 0.0 IT97K-573-2-1 3.3 13.1 13.2 2.4 4.0 1.5 0.0 2.9 3.5 0.0 IT97K-819-132 3.9 8.4 8.3 3.9 3.9 2.3 0.2 0.6 2.3 20.0 IT97K207-15 4.0 11.4 11.1 3.3 3.8 1.8 0.3 0.9 2.5 25.0 IT98K-1111-1 3.4 9.8 9.5 3.5 4.0 0.7 0.6 1.4 3.2 0.0 IT99K-573-1-1 3.4 10.2 10.6 3.0 4.0 1.9 0.0 2.1 3.1 57.1 Ife Brown 3.4 11.9 12.3 4.0 4.0 3.4 0.0 1.3 3.2 83.3 Sanzi 3.0 8.9 9.3 0.7 4.0 0.2 0.0 3.6 4.0 0.0 TVu10100 3.1 12.2 12.6 2.0 4.0 1.0 0.0 2.8 3.0 0.0 TVu11800 3.0 10.4 10.7 2.6 4.0 1.8 0.0 3.0 3.3 0.0 TVu11982 3.1 8.1 8.8 4.0 4.0 3.2 0.0 2.8 3.0 16.7 TVu12791 3.2 8.7 10.1 2.4 4.0 1.8 0.0 2.5 2.7 9.1 TVu1438 4.0 8.0 8.5 4.0 4.0 1.0 0.0 2.0 3.0 0.0 TVu14539 3.0 8.5 9.1 2.5 4.0 1.5 0.0 2.4 3.1 0.0 TVu14632 4.0 8.8 9.3 1.1 4.0 0.4 0.0 2.7 3.2 0.0 TVu14676 3.2 6.4 7.4 2.2 4.0 2.5 0.0 2.1 2.6 0.0 TVu148 3.1 8.0 8.4 1.5 4.0 1.3 0.0 3.2 3.3 8.3 TVu15055 3.5 7.1 7.4 2.5 4.0 2.5 0.0 3.0 3.0 25.0 TVu15058 3.0 10.2 11.0 3.0 4.0 1.6 0.0 2.4 3.4 0.0 TVu16646 3.2 12.0 12.9 3.7 4.0 2.8 0.0 1.5 2.4 8.3 TVu2672 3.4 10.0 10.5 1.6 4.0 1.6 0.0 3.3 3.0 0.0 TVu2722 5.3 7.7 8.0 1.1 4.0 1.4 0.0 3.6 3.6 0.0 TVu2731 4.9 7.6 8.7 2.6 4.0 1.9 0.0 2.9 3.1 0.0 TVu294 3.1 11.9 12.5 2.3 4.0 2.3 0.0 2.5 2.9 0.0 TVu3562 4.7 6.0 7.5 1.0 4.0 1.0 0.0 4.0 4.0 0.0 TVu522 3.3 8.4 9.1 2.0 4.0 1.9 0.0 3.1 2.9 0.0 TVu557 3.4 10.3 10.9 3.0 4.0 1.0 0.0 2.0 3.0 0.0 TVu6333 7.1 7.2 7.7 3.3 4.0 2.6 0.0 2.1 3.0 16.7 TVu6443 4.4 7.4 8.0 2.4 4.0 1.0 0.0 2.8 3.1 0.0 TVu6707 3.3 9.1 9.7 1.8 4.0 1.5 0.0 2.8 3.1 0.0 TVu79 3.0 9.6 10.2 1.5 4.0 1.5 0.0 2.9 3.0 0.0 TVu8670 3.5 9.0 9.6 2.2 4.0 1.7 0.0 3.1 3.3 0.0 TVu9797 3.4 8.4 8.8 1.2 4.0 0.6 0.0 2.8 3.1 0.0 Tvu1007 3.7 10.1 10.5 2.7 4.0 1.8 0.0 2.4 3.0 0.0 Tvu11984 3.8 9.6 10.1 1.8 4.0 1.8 0.0 3.1 3.0 25.0 Tvu11986 3.3 7.8 8.3 3.3 4.0 2.7 0.0 2.4 2.5 8.3 Tvu1436 3.6 9.4 9.6 2.9 4.0 2.3 0.0 2.7 3.2 11.1 Tvu14553 3.1 9.2 9.5 2.0 4.0 1.3 0.0 2.8 3.0 0.0 Tvu15450 3.2 9.5 10.2 2.6 4.0 1.2 0.0 2.2 2.8 0.0 University of Ghana http://ugspace.ug.edu.gh 224 Appendix 4.2 continued Tvu15866 3.1 9.3 9.6 2.4 4.0 2.0 0.0 2.3 2.9 0.0 Tvu2736 3.6 6.1 6.5 1.4 4.0 1.4 0.0 2.7 2.2 0.0 Tvu441 3.8 8.0 8.4 2.5 4.0 1.6 0.0 2.4 3.0 0.0 Tvu9693 3.9 7.0 7.5 1.6 4.0 1.3 0.0 2.8 2.9 0.0 TVu7778 (SC) 3.1 7.7 8.3 1.3 4.0 0.9 0.0 3.1 3.4 0.0 ‡Mean 3.36 8.96 9.22 2.79 3.93 1.66 0.25 1.97 2.83 0.08 SE 0.03 0.07 0.08 0.04 0.01 0.04 0.01 0.04 0.03 0.08 LSD (0.05) 0.62 1.37 1.42 0.78 0.23 0.89 0.24 0.62 0.78 0.21 ‡Values derived from both wooden box screenings; SC= susceptible check; *DTE = Days to seedling emergence, PHT14, PHT21= plant height at 14and 21 days after planting, SG14DAP and SG30DAP = Stem greenness (0-4) at 14, 21 and 30days after planting, LW14DAP, LW21DAP and LW30DAP= Wilting (0-4) at 14, 21 and 30 days after planting. ¥Each point represents four plants per genotype replicated thrice. %RR (recovered plants) = no of dead/ no of survived*100 University of Ghana http://ugspace.ug.edu.gh 225 APPENDIX 5.1: Means of grain yield and other agronomic traits of parental lines evaluated under drought stress at Ibadan and Minjibir. Pedigree GY SW100 sdppod D50fl Ndvi_1 Ndvi_2 Ndvi_3 Podplt Pdlt sdplt DLSC CB-27 339.4 18.33 7.45 49.70 0.18 0.40 0.35 19.21 13.86 161.25 7.19 DANILA 778.2 16.07 9.97 48.71 -1.28 0.61 0.50 42.72 16.90 427.55 8.06 IFEBROWN 201.8 14.13 6.52 47.45 1.22 0.71 0.46 16.59 10.05 111.05 8.87 IT81D-985 585.9 13.14 9.15 47.75 2.12 0.65 0.41 38.86 15.51 364.41 6.71 IT89KD-288 826.0 16.93 10.28 48.61 0.69 0.48 0.38 38.55 13.83 420.09 7.55 IT90K-277-2 457.9 14.28 7.28 53.37 0.67 0.54 0.40 38.29 12.43 277.72 7.73 IT90K-76 495.9 11.49 11.15 51.90 -2.48 0.70 0.52 31.94 13.10 385.84 9.42 IT99K-573-1-1 510.2 12.30 6.34 56.08 0.86 0.70 0.45 33.27 14.57 268.00 7.89 SANZI 402.9 7.32 7.62 45.83 0.66 0.49 0.33 32.69 10.60 308.11 8.44 TVu10100 592.8 9.42 10.56 52.90 2.28 0.63 0.31 31.73 12.66 388.99 8.12 TVu11986 115.9 10.90 13.37 50.18 33.66 0.50 0.40 4.04 34.17 328.61 7.71 TVu2736 219.8 7.48 10.62 51.04 0.52 0.53 0.28 22.41 12.25 268.09 7.26 TVu3562 461.3 8.18 10.35 51.04 0.32 0.60 0.44 32.94 13.46 398.65 8.09 TVu6333 450.0 9.61 11.55 52.23 -0.59 0.68 0.48 26.15 14.59 300.32 7.82 TVu6707 1788.1 12.60 15.52 46.26 4.91 0.77 0.26 61.87 18.46 852.81 7.05 TVu7778 382.8 8.00 12.32 47.18 12.94 0.67 0.25 25.48 14.55 324.31 8.54 TVu79 433.3 8.37 12.00 47.45 -0.07 0.68 0.51 24.27 14.39 304.41 9.32 TVu8670 869.7 11.54 13.57 47.12 0.81 0.75 0.38 29.44 17.59 443.42 7.27 TVu9693 890.4 6.92 15.27 50.49 -2.45 0.75 0.41 25.17 15.82 434.14 7.76 TVu9797 769.5 9.68 13.28 46.82 -0.20 0.64 0.39 32.13 15.37 420.38 6.43 Mean 586.6 11.41 10.71 49.30 0.59 0.64 0.40 30.68 15.13 358.81 7.88 SE 48.1 0.35 0.32 0.39 0.02 0.01 0.02 1.85 0.62 23.03 0.14 University of Ghana http://ugspace.ug.edu.gh 226 APPENDIX 5.2: Means of grain yield and other agronomic traits of parental lines evaluated under well-watered conditions at Ibadan and Minjibir. pedigree GY SW100 sdppod D50fl Ndvi_1 Ndvi_2 Ndvi_3 Podplt Pdlt sdplt CB-27 273.9 14.05 7.54 47.91 0.66 0.45 0.38 13.96 14.06 94.19 DANILA 904.7 14.68 11.05 45.27 0.75 0.58 0.25 41.95 14.65 372.67 IFEBROWN 190.7 13.73 7.65 49.47 0.52 0.58 0.40 13.50 10.81 117.07 IT81D-985 1433.5 15.88 10.61 49.34 0.48 0.60 0.48 82.60 15.47 840.41 IT89KD-288 682.5 17.38 8.71 46.08 0.61 0.62 0.38 33.35 14.03 292.20 IT90K-277-2 345.5 15.04 8.07 51.40 0.55 0.58 0.44 30.69 12.61 215.63 IT90K-76 571.1 9.71 11.67 50.09 0.56 0.74 0.51 34.11 13.94 403.41 IT99K-573-1-1 649.8 13.66 7.07 51.53 0.55 0.71 0.44 42.79 14.89 350.33 SANZI 547.6 8.29 7.80 46.13 0.58 0.55 0.48 61.29 10.65 518.74 TVu10100 520.0 9.68 10.31 52.31 0.44 0.51 0.43 31.23 12.82 371.69 TVu11986 330.5 11.20 11.16 49.73 0.56 0.63 0.49 27.24 14.70 283.77 TVu2736 903.8 9.22 11.07 50.46 0.52 0.68 0.52 21.32 11.89 222.96 TVu3562 473.7 8.75 11.07 49.54 0.46 0.61 0.52 37.06 12.74 417.24 TVu6333 232.3 9.04 12.21 48.24 0.79 0.82 0.47 9.92 15.24 134.95 TVu6707 2127.4 11.51 14.97 47.48 0.77 0.79 0.32 62.46 17.93 954.82 TVu7778 284.4 8.58 11.62 53.62 0.54 0.54 0.40 32.17 14.41 350.45 TVu79 845.7 11.01 13.39 47.12 0.59 0.72 0.47 47.87 15.91 557.47 TVu8670 561.2 12.38 12.55 48.21 0.64 0.71 0.53 25.26 16.11 378.45 TVu9693 861.4 9.45 15.04 55.90 0.49 0.60 0.50 30.43 16.24 437.07 TVu9797 1261.2 11.27 14.25 51.18 0.49 0.69 0.51 50.12 15.88 718.77 Mean 727.9 11.70 11.03 49.83 0.58 0.64 0.45 37.25 14.33 417.15 SE 55.9 0.31 0.27 0.41 0.02 0.01 0.02 2.46 0.21 28.15 University of Ghana http://ugspace.ug.edu.gh 227 APPENDIX 5.3: Means of grain yield and other agronomic traits of 100 single F1 crosses evaluated under drought stress at Ibadan and Minjibir. Crosses *GY SW100 sdppod D50fl Ndvi_1 Ndvi_2 Ndvi_3 Podplt Pdlt sdplt DLSC IFEBROWN x TVu6333 668.7 16.06 9.32 50.85 0.56 0.74 0.51 20.62 14.51 257.16 3.82 IFEBROWN x CB-27 920.1 17.26 8.51 45.37 0.51 0.61 0.43 52.64 14.85 500.30 7.73 IFEBROWN x IT89D-288 1080.8 14.11 10.93 46.14 0.52 0.67 0.43 46.60 14.58 537.63 8.30 IFEBROWN x IT99K-573-1-1 610.6 12.71 7.99 48.03 0.54 0.69 0.41 40.42 15.27 326.85 8.37 IFEBROWN x TVu11986 716.4 11.60 10.93 50.93 0.46 0.51 0.36 40.68 14.42 446.32 6.62 IT90K-76 x TVu6333 201.2 13.34 9.04 52.12 0.63 0.61 0.47 11.01 13.29 92.94 2.84 IT90K-76 x CB-27 527.6 15.71 9.80 47.29 0.53 0.65 0.46 35.68 14.37 336.53 8.45 IT90K-76 x IT89D-288 270.3 13.77 10.38 48.20 0.50 0.67 0.49 35.24 14.40 353.57 7.61 IT90K-76 x IT99K-573-1-1 476.1 15.43 8.27 50.95 0.50 0.57 0.39 26.78 14.21 241.67 7.09 IT90K-76 x TVu11986 744.2 12.97 11.66 49.76 0.59 0.74 0.56 45.05 15.30 510.87 4.99 IT81D-985 x TVu6333 787.8 14.12 11.20 50.41 0.51 0.68 0.58 26.56 17.28 341.90 2.36 IT81D-985 x CB-27 296.7 15.53 9.08 44.39 0.46 0.54 0.38 23.15 15.71 218.66 6.47 IT81D-985 x IT89D-288 684.9 18.06 6.95 45.87 0.48 0.52 0.36 36.99 13.04 275.16 6.30 IT81D-985 x IT99K-573-1-1 692.1 17.11 7.51 46.51 0.46 0.51 0.41 28.59 14.20 266.79 6.09 IT81D-985 x TVu11986 246.7 14.47 7.77 48.13 0.43 0.57 0.37 18.11 13.61 149.67 5.14 DANILA x TVu6333 766.8 13.80 10.57 48.97 0.49 0.62 0.46 26.44 15.03 319.60 2.81 DANIILA x CB-27 924.2 17.19 8.33 42.81 0.52 0.54 0.37 36.24 13.51 377.12 8.05 DANILA x IT89D-288 571.6 19.15 8.23 46.20 0.48 0.52 0.37 24.55 12.89 237.45 8.64 DANILA x IT99K-573-1-1 686.9 17.67 7.98 49.21 0.49 0.56 0.37 37.51 15.32 327.47 6.92 DANILA x TVu11986 431.4 14.93 7.92 53.03 0.47 0.58 0.44 18.59 13.35 191.60 3.88 IT90K-277-2 x TVu6333 418.9 13.25 8.39 52.52 0.50 0.69 0.51 17.52 13.69 159.55 4.22 IT90K-277-2 x CB-27 823.4 16.17 9.51 46.72 0.40 0.58 0.52 37.04 14.18 386.98 4.23 IT90K-277-2 x IT89D-288 816.2 14.29 9.11 48.43 0.50 0.62 0.43 33.18 13.58 323.69 6.28 IT90K-277-2 x IT99K-573-1-1 562.3 14.37 8.10 49.02 0.44 0.56 0.38 23.15 14.49 205.16 5.44 IT90K-277-2 x TVu11986 441.9 13.02 8.63 50.41 0.51 0.65 0.45 19.14 12.26 194.25 4.95 TVu6333 x TVu10100 281.0 9.37 11.45 53.81 0.49 0.67 0.57 14.19 13.36 166.76 2.57 TVu6333 x TVu6707 177.5 10.23 10.94 51.76 0.45 0.70 0.59 17.42 14.92 183.99 2.33 TVu6333 x TVu9693 263.1 9.66 11.81 52.88 0.50 0.72 0.51 12.03 14.74 126.28 3.92 TVu6333 x TVu2736 168.2 8.89 10.69 55.75 0.49 0.69 0.53 12.23 12.82 128.54 4.45 TVu6333 x TVu8670 60.2 9.24 9.71 52.03 0.49 0.70 0.52 3.72 12.70 5.58 4.29 University of Ghana http://ugspace.ug.edu.gh 228 Appendix 5.3 continued CB-27 x TVu10100 737.8 15.04 11.32 45.19 0.52 0.64 0.51 33.31 15.30 381.72 4.83 CB-27 x TVu6707 1188.6 16.18 8.82 44.36 0.54 0.68 0.52 53.94 14.50 568.45 4.96 CB-27 x TVu9693 1115.7 13.66 12.36 47.44 0.52 0.70 0.52 57.26 16.88 704.07 6.29 CB-27 x TVu2736 799.0 10.93 11.53 46.82 0.47 0.58 0.43 35.69 12.82 464.60 6.18 CB-27 x TVu8670 1111.7 14.09 10.48 46.70 0.49 0.65 0.51 47.22 15.64 564.46 5.15 IT89D-288 x TVu10100 485.3 13.76 9.25 49.45 0.47 0.70 0.44 21.67 14.87 239.87 3.87 IT89D-288 x TVu6707 1312.1 15.39 10.11 46.58 0.59 0.76 0.47 64.34 15.59 688.53 8.14 IT89D-288 x TVu9693 587.8 14.09 10.70 48.05 0.53 0.67 0.52 30.94 16.36 408.89 4.79 IT89D-288 x TVu2736 965.2 11.67 12.15 49.38 0.41 0.39 0.22 37.83 14.32 499.83 5.27 IT89D-288 x TVu8670 1181.5 14.59 11.10 46.88 0.54 0.65 0.46 43.69 17.13 527.97 7.90 IT99K-573-1-1 x TVu10100 375.2 13.36 9.57 50.16 0.54 0.71 0.45 15.67 14.15 188.95 5.68 IT99K-573-1-1 x TVu6707 1014.6 14.76 10.27 48.28 0.51 0.69 0.52 45.78 16.84 443.76 6.50 IT99K-573-1-1 x TVu9693 1417.2 10.84 14.28 46.59 0.55 0.77 0.49 53.77 17.46 771.16 6.75 IT99K-573-1-1 x TVu2736 552.0 13.35 9.76 46.81 0.55 0.74 0.47 37.09 14.48 310.60 6.27 IT99K-573-1-1 x TVu8670 943.1 12.50 11.77 49.81 0.57 0.76 0.51 32.20 17.68 383.21 6.90 TVu11986 x TVu10100 -25.8 9.27 8.33 53.54 0.48 0.66 0.50 -1.22 13.03 -37.00 2.95 TVu11986 x TVu6707 368.4 11.72 11.69 53.05 0.47 0.77 0.56 19.05 15.99 220.19 3.61 TVu11986 x TVu9693 308.9 11.48 9.42 52.13 0.44 0.73 0.52 12.54 13.23 82.76 2.55 TVu11986 x TVu2736 17.6 9.89 9.41 51.16 0.51 0.70 0.53 11.58 13.42 103.16 2.59 TVu11986 x TVu8670 295.2 11.12 10.92 52.01 0.57 0.73 0.58 12.80 15.62 133.42 3.19 TVu7778 x IFEBROWN 475.2 11.16 9.01 48.94 0.60 0.69 0.37 32.82 12.13 356.27 7.82 TVu7778 x IT90K-76 446.2 11.90 9.55 49.58 0.54 0.66 0.46 28.60 13.02 285.65 6.21 TVu7778 x IT81D-985 243.9 11.31 9.38 52.96 0.55 0.64 0.46 21.02 12.93 190.68 6.11 TVu7778 x DANILA 305.9 9.68 9.28 49.72 0.65 0.68 0.39 22.02 13.88 224.09 7.90 TVu7778 x IT90K-277-2 609.0 10.14 8.41 55.79 0.52 0.65 0.41 39.23 12.41 465.10 6.26 Sanzi x IFEBROWN 456.8 12.19 10.54 47.67 0.52 0.59 0.47 42.49 12.22 465.90 6.66 Sanzi x IT90K-76 260.4 11.86 8.73 48.33 0.47 0.53 0.41 22.51 11.52 201.69 5.15 Sanzi x IT81D-985 290.8 11.22 9.92 47.54 0.48 0.53 0.46 31.41 13.92 313.17 6.15 Sanzi x DANILA 300.4 12.42 9.66 46.60 0.52 0.67 0.48 28.47 11.14 280.99 7.04 Sanzi x IT90K-277-2 393.1 12.99 7.86 49.37 0.57 0.67 0.45 36.00 10.34 292.90 6.38 TVu3562 x IFEBROWN 1192.2 11.05 13.37 48.11 0.60 0.75 0.52 88.57 15.28 1214.96 6.14 TVu3562 x IT90K-76 1065.3 10.31 11.89 49.36 0.56 0.67 0.43 54.23 13.77 701.14 6.84 University of Ghana http://ugspace.ug.edu.gh 229 Appendix 5.3 continued TVu3562 x IT81D-985 1217.3 11.91 12.86 50.58 0.45 0.63 0.42 42.69 16.48 605.34 6.34 TVu3562 x DANILA 945.1 9.66 11.92 47.28 0.47 0.64 0.47 37.13 15.65 458.77 6.43 TVu3562 x IT90K-277-2 760.8 11.27 11.65 54.97 0.58 0.71 0.54 26.98 13.33 272.13 5.34 TVu9797 x IFEBROWN 831.4 12.04 12.07 47.98 0.48 0.55 0.42 26.37 15.13 345.57 6.84 TVu9797 x IT90K-76 1028.5 12.10 13.42 53.69 0.47 0.59 0.43 42.60 14.76 572.99 6.49 TVu9797 x IT81D-985 673.9 10.88 14.06 51.83 0.48 0.62 0.42 26.92 14.62 379.77 5.04 TVu9797 x DANILA 784.6 11.83 12.72 53.38 0.44 0.61 0.45 31.81 15.34 411.11 6.50 TVu9797 x IT90K-277-2 942.1 10.99 12.72 47.89 0.50 0.73 0.49 34.84 15.66 465.70 7.39 TVu79 x IFEBROWN 704.2 12.01 10.35 49.62 0.51 0.70 0.51 35.08 14.08 421.48 6.13 TVu79 x IT90K-76 1256.3 11.54 14.28 50.22 0.54 0.74 0.53 42.46 17.45 601.38 6.06 TVu79 x IT81D-985 1343.6 10.62 12.60 48.08 0.57 0.76 0.50 45.83 16.49 642.25 6.67 TVu79 x DANILA 1914.1 11.51 12.85 48.10 0.58 0.71 0.47 78.09 19.29 1051.13 6.58 TVu79 x IT90K-277-2 1685.9 13.73 12.15 49.10 0.54 0.67 0.39 46.18 14.96 670.95 6.55 TVu10100 x TVu7778 275.2 12.31 8.01 49.82 0.69 0.75 0.55 18.06 12.65 190.47 4.87 TVu10100 x Sanzi 460.6 9.76 10.08 50.64 0.56 0.73 0.55 34.46 13.52 373.58 5.31 TVu10100 x TVu3562 356.2 10.39 10.66 53.64 0.47 0.64 0.48 37.60 13.52 353.50 2.67 TVu10100 x TVu9797 883.1 11.26 14.29 49.31 0.50 0.62 0.49 36.93 15.62 523.10 2.34 TVu10100 x TVu79 322.0 10.56 14.07 50.42 0.55 0.72 0.53 26.64 15.95 418.37 2.59 TVu6707 x TVu7778 1234.9 10.73 10.86 57.33 0.54 0.71 0.49 48.24 14.86 629.40 6.06 TVu6707 x SANZI 508.2 10.67 9.89 48.69 0.51 0.71 0.52 40.24 16.03 438.63 6.08 TVu6707 x TVu3562 1203.5 10.65 12.58 50.74 0.56 0.72 0.51 52.59 14.56 727.81 4.44 TVu6707 x TVU9797 2532.9 11.11 13.17 51.72 0.47 0.71 0.50 78.28 14.75 1144.49 4.43 TVu6707 x TVu79 845.7 10.17 13.26 49.22 0.52 0.67 0.50 36.98 18.62 493.21 5.55 TVu9693 x TVu7778 1139.0 8.21 10.36 52.67 0.52 0.77 0.55 36.81 15.65 570.32 6.23 TVu9693 x Sanzi 1081.1 8.53 12.37 49.43 0.51 0.79 0.58 70.30 15.16 917.86 4.51 TVu9693 x TVu3562 1092.5 9.33 13.58 49.49 0.52 0.75 0.57 60.69 16.09 897.44 5.85 TVu9693 x TVu9797 1522.1 8.22 15.81 49.39 0.53 0.79 0.52 58.80 16.84 961.63 7.09 TVu9693 x TVu79 1403.6 9.41 14.04 49.10 0.53 0.69 0.49 42.66 15.53 698.70 6.01 TVu2736 x TVu7778 381.0 13.54 8.80 52.67 0.44 0.60 0.33 25.83 11.08 328.51 5.30 TVu2736 x Sanzi 627.0 7.62 11.90 55.23 0.51 0.65 0.48 67.75 13.77 936.27 6.93 TVu2736 x TVu3562 680.1 8.04 11.67 50.15 0.47 0.55 0.50 38.79 13.03 443.85 5.60 TVu2736 x TVu9797 520.7 8.57 11.39 49.68 0.49 0.54 0.31 26.09 13.35 336.98 5.14 University of Ghana http://ugspace.ug.edu.gh 230 Appendix 5.3 continued TVu2736 x TVu79 532.2 8.68 12.06 51.34 0.46 0.57 0.37 31.07 15.75 451.38 6.10 TVu8670 x TVu7778 711.5 10.25 11.37 49.37 0.56 0.76 0.55 31.78 17.55 407.83 5.43 TVu8670 x Sanzi 919.7 9.20 11.91 47.34 0.53 0.67 0.46 64.68 15.24 830.18 8.22 TVu8670 x TVu3562 1225.4 9.15 11.95 45.26 0.58 0.80 0.49 51.72 15.47 622.54 6.85 TVu8670 x TVu9797 802.2 9.73 11.71 47.79 0.53 0.72 0.52 41.67 14.07 491.29 6.79 TVu8670 x TVu79 1119.4 10.79 14.49 48.00 0.52 0.68 0.48 44.70 16.75 677.40 5.31 Mean 738.2 12.14 10.73 49.58 0.52 0.66 0.47 35.70 14.61 426.53 5.65 SE 35.1 0.13 0.14 0.21 0.01 0.01 0.01 1.17 0.13 17.32 0.11 *GY=grain yield (kg/ha), SW100=100-seed weight, sdppod=no of seed/pod, DFL=days to 50% flower; NDVI_1-3=normalized difference vegetation indices at vegetative, flowering and pod-filling stages, podplt=no of pod/plant, pdlt=pod length, sdplt=no of seed/plant, DLSC=delayed leaf senescence APPENDIX 5.4: Means of grain yield and other agronomic traits of 100 single F1 crosses evaluated under well-watered conditions at Ibadan and Minjibir. Crosses *GY SW100 sdppod DFL Ndvi_1 Ndvi_2 Ndvi_3 Podplt Pdlt sdplt IFEBROWN x TVu6333 1382.7 14.81 11.04 48.82 0.54 0.76 0.80 84.32 15.94 962.01 IFEBROWN x CB-27 1198.9 18.41 7.99 45.40 0.51 0.72 0.63 50.68 13.96 424.58 IFEBROWN x IT89D-288 1254.1 16.46 10.17 47.84 0.60 0.73 0.62 67.46 15.22 702.95 IFEBROWN x IT99K-573-1-1 1173.3 17.31 8.67 49.17 0.65 0.78 0.57 63.58 16.53 590.30 IFEBROWN x TVu11986 1666.8 15.00 13.02 50.85 0.57 0.75 0.65 76.67 15.54 1047.45 IT90K-76 x TVu6333 288.3 14.88 9.41 55.61 0.67 0.70 0.69 17.88 14.33 178.59 IT90K-76 x CB-27 798.9 15.88 10.09 48.82 0.52 0.68 0.60 33.46 14.03 376.49 IT90K-76 x IT89D-288 1210.5 17.62 10.63 51.07 0.48 0.76 0.66 66.79 14.40 723.02 IT90K-76 x IT99K-573-1-1 820.4 19.49 11.40 49.61 0.54 0.70 0.58 32.97 18.26 355.47 IT90K-76 x TVu11986 1338.6 14.86 11.41 51.80 0.57 0.79 0.72 56.87 15.94 669.77 IT81D-985 x TVu6333 1204.0 14.95 10.30 52.43 0.59 0.77 0.72 47.81 16.78 553.89 IT81D-985 x CB-27 772.6 14.22 10.20 43.69 0.70 0.83 0.51 33.94 21.44 342.88 IT81D-985 x IT89D-288 -81.4 14.78 7.38 49.27 0.45 0.55 0.43 10.67 13.73 66.61 IT81D-985 x IT99K-573-1-1 1079.7 15.56 10.35 46.44 0.64 0.78 0.54 31.88 18.56 368.23 University of Ghana http://ugspace.ug.edu.gh 231 Appendix 5.4 continued IT81D-985 x TVu11986 998.0 14.27 13.22 47.01 0.65 0.85 0.66 31.75 17.92 439.06 DANILA x TVu6333 1152.1 15.43 10.88 49.98 0.60 0.76 0.74 51.54 16.59 581.13 DANIILA x CB-27 1131.4 16.29 9.97 43.45 0.51 0.59 0.48 47.02 16.14 521.09 DANILA x IT89D-288 956.8 19.00 8.60 47.42 0.46 0.69 0.67 39.56 14.46 339.20 DANILA x IT99K-573-1-1 1340.8 19.34 9.05 48.24 0.49 0.56 0.52 57.93 15.55 551.95 DANILA x TVu11986 1074.1 14.06 7.89 53.62 0.58 0.72 0.62 48.23 12.54 495.71 IT90K-277-2 x TVu6333 478.2 14.92 11.03 51.02 0.60 0.80 0.77 20.12 15.86 210.76 IT90K-277-2 x CB-27 413.7 17.20 7.32 50.85 0.36 0.66 0.64 28.32 13.60 193.34 IT90K-277-2 x IT89D-288 925.0 14.71 8.52 50.89 0.45 0.66 0.63 44.07 13.65 423.76 IT90K-277-2 x IT99K-573-1-1 974.5 18.27 9.16 50.81 0.51 0.63 0.65 59.87 15.78 576.66 IT90K-277-2 x TVu11986 1571.0 15.62 11.29 53.75 0.51 0.79 0.74 59.46 16.00 724.27 TVu6333 x TVu10100 686.7 11.37 13.58 51.67 0.71 0.80 0.72 19.98 14.31 259.04 TVu6333 x TVu6707 471.1 12.83 9.80 59.61 0.48 0.68 0.71 40.18 15.43 415.09 TVu6333 x TVu9693 523.3 10.86 13.37 52.02 0.59 0.75 0.63 17.90 15.56 250.01 TVu6333 x TVu2736 -45.4 10.12 13.27 54.86 0.50 0.67 0.62 3.55 15.24 12.50 TVu6333 x TVu8670 473.5 11.18 11.65 53.11 0.63 0.81 0.70 31.39 14.99 413.29 CB-27 x TVu10100 874.5 16.46 10.65 47.78 0.44 0.62 0.58 34.38 16.30 375.00 CB-27 x TVu6707 1341.0 16.93 9.09 47.11 0.52 0.74 0.69 63.57 15.42 564.46 CB-27 x TVu9693 1714.5 14.97 13.00 47.70 0.58 0.80 0.74 72.58 17.59 852.09 CB-27 x TVu2736 664.6 11.15 10.69 45.08 0.60 0.69 0.70 30.82 13.78 324.06 CB-27 x TVu8670 2414.3 16.45 11.34 44.50 0.58 0.79 0.69 95.43 16.71 1192.55 IT89D-288 x TVu10100 899.5 16.91 9.88 50.96 0.52 0.66 0.63 47.08 15.36 498.07 IT89D-288 x TVu6707 1137.6 17.12 9.75 49.70 0.62 0.78 0.67 62.77 16.57 603.25 IT89D-288 x TVu9693 1904.5 14.81 13.02 50.79 0.53 0.68 0.64 61.35 17.57 838.75 IT89D-288 x TVu2736 804.5 12.44 10.45 48.95 0.58 0.64 0.69 46.90 14.05 513.02 IT89D-288 x TVu8670 1767.8 16.53 11.79 47.79 0.61 0.82 0.75 68.04 16.64 820.85 IT99K-573-1-1 x TVu10100 1155.5 15.92 11.31 50.85 0.54 0.70 0.64 60.70 17.27 607.29 IT99K-573-1-1 x TVu6707 2330.9 16.40 13.17 50.19 0.55 0.79 0.71 77.63 17.77 1015.05 IT99K-573-1-1 x TVu9693 2272.7 13.16 13.20 50.54 0.50 0.76 0.73 59.00 18.19 852.14 IT99K-573-1-1 x TVu2736 1385.8 14.25 11.67 48.42 0.65 0.79 0.59 65.98 17.42 552.75 IT99K-573-1-1 x TVu8670 658.5 15.48 11.86 50.26 0.60 0.82 0.67 31.84 18.25 150.64 University of Ghana http://ugspace.ug.edu.gh 232 Table 5.4 continued TVu11986 x TVu10100 403.9 12.32 9.21 54.61 0.56 0.77 0.73 19.03 15.15 220.52 TVu11986 x TVu6707 1065.9 12.90 12.33 53.64 0.54 0.80 0.80 33.59 16.59 418.66 TVu11986 x TVu9693 1061.3 11.25 15.07 51.78 0.49 0.76 0.75 37.20 17.14 557.26 TVu11986 x TVu2736 949.9 10.27 12.12 53.30 0.47 0.67 0.70 41.89 16.31 559.40 TVu11986 x TVu8670 1110.7 13.20 14.41 51.41 0.49 0.85 0.79 38.86 17.46 478.82 TVu7778 x IFEBROWN 101.3 10.57 5.88 56.78 0.43 0.60 0.58 19.65 12.40 154.27 TVu7778 x IT90K-76 1040.5 12.96 10.29 48.50 0.59 0.74 0.65 64.65 14.25 723.91 TVu7778 x IT81D-985 651.5 12.24 10.76 57.02 0.58 0.75 0.72 45.45 16.35 524.63 TVu7778 x DANILA 329.6 10.45 12.52 50.16 0.52 0.68 0.67 27.43 15.97 348.16 TVu7778 x IT90K-277-2 1111.2 14.06 10.08 51.78 0.62 0.69 0.67 59.40 15.00 605.13 Sanzi x IFEBROWN 1128.9 12.55 10.54 46.64 0.65 0.77 0.66 61.43 12.96 650.03 Sanzi x IT90K-76 1048.6 12.46 10.24 47.34 0.53 0.72 0.63 52.01 12.54 546.51 Sanzi x IT81D-985 803.3 11.37 11.29 53.80 0.59 0.59 0.58 47.57 14.74 560.85 Sanzi x DANILA 643.2 10.33 10.92 45.48 0.64 0.82 0.68 45.11 13.40 500.13 Sanzi x IT90K-277-2 380.0 12.51 9.86 48.27 0.66 0.72 0.63 30.47 13.32 315.36 TVu3562 x IFEBROWN 946.4 12.63 9.73 49.63 0.56 0.75 0.65 51.70 13.45 499.26 TVu3562 x IT90K-76 847.3 12.85 11.14 49.42 0.59 0.80 0.72 57.23 13.58 639.31 TVu3562 x IT81D-985 2132.8 12.33 12.43 49.79 0.54 0.70 0.63 68.21 16.23 861.23 TVu3562 x DANILA 1929.4 12.83 12.16 47.60 0.55 0.80 0.65 90.45 15.37 1092.09 TVu3562 x IT90K-277-2 2215.4 12.78 13.11 51.73 0.54 0.84 0.83 109.05 15.86 1437.99 TVu9797 x IFEBROWN 1655.0 13.04 11.87 47.28 0.54 0.62 0.59 53.91 14.98 690.68 TVu9797 x IT90K-76 1410.0 13.63 12.90 46.27 0.68 0.81 0.65 49.26 16.13 628.77 TVu9797 x IT81D-985 1021.8 12.37 13.12 50.50 0.49 0.62 0.74 35.32 17.49 471.88 TVu9797 x DANILA 632.8 13.28 9.03 55.16 0.57 0.68 0.75 28.02 12.15 337.67 TVu9797 x IT90K-277-2 2148.8 14.44 13.51 48.62 0.52 0.70 0.70 69.71 16.08 963.47 TVu79 x IFEBROWN 1708.7 13.83 13.06 49.06 0.50 0.67 0.68 70.50 15.95 977.90 TVu79 x IT90K-76 1603.2 14.57 11.29 50.30 0.56 0.82 0.73 69.50 15.83 745.89 TVu79 x IT81D-985 1641.7 14.19 12.60 50.07 0.48 0.74 0.74 66.27 17.67 831.96 TVu79 x DANILA 1793.1 15.28 10.82 49.71 0.61 0.80 0.73 64.19 15.97 758.88 TVu79 x IT90K-277-2 885.6 13.90 9.64 50.60 0.53 0.80 0.75 54.06 14.36 524.55 TVu10100 x TVu7778 1235.9 12.43 13.36 52.49 0.52 0.78 0.70 40.91 16.47 609.68 University of Ghana http://ugspace.ug.edu.gh 233 Appendix 5.4 continued TVu10100 x Sanzi 891.7 9.38 10.84 49.49 0.46 0.62 0.65 36.46 14.96 426.33 TVu10100 x TVu3562 862.5 11.00 13.80 51.43 0.54 0.80 0.65 54.70 15.29 717.98 TVu10100 x TVu9797 1374.1 13.10 13.43 53.49 0.52 0.74 0.68 68.66 15.20 910.68 TVu10100 x TVu79 886.3 11.33 12.68 51.54 0.48 0.67 0.68 47.47 14.41 575.44 TVu6707 x TVu7778 1360.8 11.60 11.07 56.86 0.49 0.68 0.63 51.74 15.98 641.87 TVu6707 x SANZI 1424.0 11.65 11.00 52.15 0.56 0.68 0.59 67.81 14.10 762.45 TVu6707 x TVu3562 2833.9 11.80 15.57 50.02 0.55 0.84 0.77 109.16 17.37 1694.38 TVu6707 x TVU9797 3786.2 12.65 14.64 49.23 0.60 0.82 0.75 124.73 17.79 1944.23 TVu6707 x TVu79 2692.2 11.39 14.97 48.75 0.54 0.77 0.74 71.93 16.59 1115.19 TVu9693 x TVu7778 1734.8 9.57 11.89 50.77 0.74 0.82 0.73 69.54 15.02 938.03 TVu9693 x Sanzi 2427.0 9.86 13.29 48.74 0.58 0.76 0.75 121.86 14.36 1533.82 TVu9693 x TVu3562 2602.0 10.52 13.02 48.47 0.56 0.75 0.73 108.33 16.12 1434.87 TVu9693 x TVu9797 3255.3 11.41 17.86 52.77 0.50 0.62 0.64 80.54 17.81 1393.71 TVu9693 x TVu79 2203.1 10.40 15.48 48.79 0.67 0.81 0.76 75.21 16.89 1205.22 TVu2736 x TVu7778 659.1 9.21 12.08 52.96 0.40 0.56 0.67 37.89 14.48 462.70 TVu2736 x Sanzi 1248.2 7.95 13.39 50.65 0.49 0.68 0.70 67.07 13.83 867.29 TVu2736 x TVu3562 1574.1 8.31 12.86 49.93 0.50 0.66 0.63 71.06 14.63 918.14 TVu2736 x TVu9797 1420.1 10.01 14.95 50.18 0.54 0.75 0.62 69.31 14.46 987.53 TVu2736 x TVu79 693.0 12.44 11.27 51.47 0.51 0.59 0.67 36.28 15.09 461.09 TVu8670 x TVu7778 1538.6 11.60 12.05 49.14 0.62 0.77 0.70 56.95 16.49 669.34 TVu8670 x Sanzi 1449.5 10.25 13.46 48.41 0.59 0.75 0.67 63.87 16.76 922.27 TVu8670 x TVu3562 1856.4 11.50 13.82 51.03 0.48 0.67 0.69 69.55 16.91 964.82 TVu8670 x TVu9797 2892.3 12.01 16.37 49.37 0.54 0.80 0.69 75.71 16.27 1249.05 TVu8670 x TVu79 1510.9 10.30 15.20 49.03 0.64 0.76 0.66 53.50 17.47 863.41 Mean 1278.8 13.43 11.62 50.23 0.55 0.73 0.67 54.71 15.67 666.60 SE 50.2 0.13 0.12 0.19 0.01 0.01 0.01 1.52 0.12 21.79 *GY=grain yield (kg/ha), SW100=100-seed weight, sdppod=no of seed/pod, DFL=days to 50% flower; NDVI_1-3=normalized difference vegetation indices at vegetative, flowering and pod-filling stages, podplt=no of pod/plant, pdlt=pod length, sdplt=no of seed/plan University of Ghana http://ugspace.ug.edu.gh 234 APPENDIX 5.5: Specific Combining Ability (SCA) effects of the crosses for grain yield F1 Crosses DS WW AC TVu7778 x IFEBROWN -2.7 -363.8* -183.3 TVu7778 x IT90K-76 -31.8 437.7** 202.9 TVu7778 x IT81D-985 -64.5 377.2* 156.4 TVu7778 x DANILA -264.9* -153.9 -209.4 TVu7778 x IT90K-277-2 111.6 179.9 145.7 TVu10100 x TVu7778 -234 284.3 25.2 TVu10100 x Sanzi -134.4 -530.6** -332.5* TVu10100 x TVu3562 -155.3 -666.6** -410.9** TVu10100 x TVu9797 -101.7 -1161.8*** -631.8*** TVu10100 x TVu79 -214.1 -469.5* -341.8* Sanzi x IFEBROWN 99.8 296.2 198.0 Sanzi x IT90K-76 131.2 346.7* 238.9 Sanzi x IT81D-985 -56.5 -1.8 -29.1 Sanzi x DANILA -280.2* 130.5 -74.9 Sanzi x IT90K-277-2 -146.6 -294.5 -220.6 TVu3562 x IFEBROWN 120.9 -393.5* -136.3 TVu3562 x IT90K-76 -125.8 -526.3** -326.0* TVu3562 x IT81D-985 171.1 440.4* 305.8* TVu3562 x DANILA -103.3 494.5** 195.6 TVu3562 x IT90K-277-2 -315.2** 461.9** 73.3 TVu9797 x IFEBROWN 27.8 303.6 165.7 TVu9797 x IT90K-76 70.8 92.6 81.7 TVu9797 x IT81D-985 -214.1 -361.0* -287.6* TVu9797 x DANILA -72.7 -385.2* -228.9 TVu9797 x IT90K-277-2 -64.1 827.1*** 381.5** TVu79 x IFEBROWN -498.2*** 634.6** 68.2 TVu79 x IT90K-76 -296.6 126.3 -85.2 TVu79 x IT81D-985 -88.4 22.3 -33.1 TVu79 x DANILA 468.8*** 391.2* 430.0** TVu79 x IT90K-277-2 162.1 -697.3*** -267.6 TVu6707 x TVu7778 90.7 -1228.4*** -568.8*** TVu6707 x SANZI -747.4*** -1224.5*** -986.0*** TVu6707 x TVu3562 -265.6* -250.5 -258.1 TVu6707 x TVU9797 636.3*** 279.2 457.7** TVu6707 x TVu79 -553.4*** -119.9 -336.7* TVu9693 x TVu7778 -149.8 -563.4** -356.6* TVu9693 x Sanzi -142.5 -229.4 -186.0 TVu9693 x TVu3562 -344.4 -568.6** -456.5** TVu9693 x TVu9797 -274.3* -510.8** -392.6** TVu9693 x TVu79 71.6 -671.9** -300.1* TVu2736 x TVu7778 -194.3 -521.1** -357.7* TVu2736 x Sanzi 163.3 -7.2 78.1 TVu2736 x TVu3562 -54.4 -229.8 -142.1 University of Ghana http://ugspace.ug.edu.gh 235 Appendix 5.5 continued F1 Crosses DS WW AC TVu2736 x TVu9797 -614.9 -1020.0*** -817.5*** TVu2736 x TVu79 -139.1 -766.0*** -452.6** TVu8670 x TVu7778 -352.0** -515.6** -433.8** TVu8670 x Sanzi 21.6 -552.4** -265.4 TVu8670 x TVu3562 -19.7 -828.6*** -424.2** TVu8670 x TVu9797 -484.8*** -130.7 -307.7* TVu8670 x TVu79 -4.5 -516.8** -260.7 IFEBROWN x TVu6333 -44.5 286.7 121.1 IFEBROWN x CB-27 122.4 179.3 150.8 IFEBROWN x IT89D-288 425.4** 472.6* 449.0** IFEBROWN x IT99K-573-1-1 -67.7 80.8 6.5 IFEBROWN x TVu11986 52.5 325.4 188.9 TVu6333 x TVu10100 448.3** 508.5** 478.4** TVu6333 x TVu6707 -3.1 198.9 97.9 TVu6333 x TVu9693 -20.5 -283.8 -152.1 TVu6333 x TVu2736 189.1 346.6* 267.9 TVu6333 x TVu8670 -10.2 -47.9 -29.0 IT90K-76 x TVu6333 -43 -275.9 -159.5 IT90K-76 x CB-27 153.4 468.9** 311.2* IT90K-76 x IT89D-288 -93.6 698.4*** 302.4* IT90K-76 x IT99K-573-1-1 28.4 56.4 42.4 IT90K-76 x TVu11986 442.9** 396.9* 419.9** IT81D-985 x TVu6333 344.4** 958.6*** 651.5*** IT81D-985 x CB-27 -281.3* 191.4 -45.0 IT81D-985 x IT89D-288 238.1* -229.2 4.5 IT81D-985 x IT99K-573-1-1 155.4 235.6 195.5 IT81D-985 x TVu11986 31.5 188.2 109.9 DANILA x TVu6333 363.7** 486.5** 425.1** DANIILA x CB-27 309.5** 375.7* 342.6* DANILA x IT89D-288 -254.3* -9.5 -131.9 DANILA x IT99K-573-1-1 181.3 758.5*** 469.9** DANILA x TVu11986 -112.2 -266.5 -189.3 IT90K-277-2 x TVu6333 -132.5 -111.2 -121.9 IT90K-277-2 x CB-27 184 129.3 156.7 IT90K-277-2 x IT89D-288 172.6 412.4* 292.5 IT90K-277-2 x IT99K-573-1-1 190.6 213.4 202.0 IT90K-277-2 x TVu11986 73.4 700.7*** 387.1** CB-27 x TVu10100 170.6 -15.5 77.6 CB-27 x TVu6707 22.5 48.6 35.5 CB-27 x TVu9693 240.4* 113.9 177.1 CB-27 x TVu2736 60.1 -360.3* -150.1 CB-27 x TVu8670 110 935.7*** 522.9** IT89D-288 x TVu10100 -86.5 32.1 -27.2 IT89D-288 x TVu6707 338.9** -256.1 41.4 University of Ghana http://ugspace.ug.edu.gh 236 Appendix 5.5 continued F1 Crosses DS WW AC IT89D-288 x TVu9693 -186 318.9 66.4 IT89D-288 x TVu2736 217.8 235 226.4 IT89D-288 x TVu8670 319.5** 392.4* 356.0* IT99K-573-1-1 x TVu10100 -173.4 185.7 6.2 IT99K-573-1-1 x TVu6707 188.4 592.3** 390.3** IT99K-573-1-1 x TVu9693 523.6*** 552.4** 538.0** IT99K-573-1-1 x TVu2736 -71.6 59.3 -6.1 IT99K-573-1-1 x TVu8670 136.5 -667.2** -265.3 TVu11986 x TVu10100 244.5 11.6 128.1 TVu11986 x TVu6707 56.9 138.7 97.8 TVu11986 x TVu9693 46.2 20.9 33.6 TVu11986 x TVu2736 208.2 441.8* 325.0* TVu11986 x TVu8670 47.8 109.4 78.6 SE ± 114.8 175.1 143.1 SCA =SCA effects of the crosses; *, **, ***, Significant at probability level of 0.05, 0.01 and 0.0001 probability levels, respectively. SE = Standard Error of SCA. DS, WW, AC = drought stress, well-watered and across all environments respectively. University of Ghana http://ugspace.ug.edu.gh