i GENETIC ANALYSIS OF RESISTANCE TO ROSETTE DISEASE OF GROUNDNUT (Arachis hypogaea L.) By USMAN ALHASSAN (10293978) THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF DOCTOR OF PHILOSOPHY DEGREE IN PLANT BREEDING WEST AFRICA CENTRE FOR CROP IMPROVEMENT SCHOOL OF AGRICULTURE COLLEGE OF AGRICULTURE AND CONSUMER SCIENCE UNIVERSITY OF GHANA LEGON December, 2013 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. .................................................. Usman Alhassan (Student) .................................................. Prof. Eric Yirenkyi Danquah (Supervisor) .................................................. Prof. Kwadwo Ofori (Supervisor) .................................................. Prof. Samuel Kwame Offei (Supervisor) .................................................. Prof. S.G. Ado (Supervisor) University of Ghana http://ugspace.ug.edu.gh iii ABSTRACT Groundnut rosette disease (GRD), transmitted naturally by aphids, Aphis craccivora, is the most destructive viral disease of groundnut (Arachis hypogaea L.) in Nigeria and causes serious yield losses to farmers. The narrow genetic base among groundnuts has impeded efficient utilization for development of host resistance to GRD. Studies were undertaken in Nigeria to: (i) ascertain farmers‘ knowledge of and preferences for rosette resistant genotypes; (ii) assess the genetic diversity among aphid and rosette resistant genotypes using microsatellite markers; (iii) exploit genotype x environment interaction towards improved selection efficiency to obtain high-yielding varieties; and, (iv) determine the mode of inheritance of resistance to groundnut rosette disease. A participatory rural appraisal (PRA) involving 90 farmers was conducted in two groundnut producing communities in Northern Nigeria. Early maturing genotypes and GRD resistance were the most important farmer preferred traits. Farmers ranked insect pests and inadequate rainfall as the most important causes of groundnut rosette disease. Majority of farmers across the study areas were doing nothing to avert the disease. Some farmers however rogue infected plants and use GRD resistant varieties when available. Genetic diversity and association of simple sequence repeat (SSR) markers with GRD resistance were detected in a set of 50 cultivated groundnut genotypes with different levels of resistance to GRD. Out of 170 bands amplified from 36 primers, 166 were polymorphic (97.65%). Each amplified 2 to 12 microsatellite loci, with an average of 4.74 loci per primer. The Polymorphic Information Content value of each marker ranged from 0.19 to 0.82. Average pairwise genetic distance among the 50 genotypes was 0.31. The largest distance was 0.51 (between ICGV – IS – 07812 and RS006F4B1 – 31) and the shortest distance was 0.05 between ICGV – IS – 07865 and ICGV – IS – 07864, all the four lines were GRD-resistant. Cluster analysis revealed seven clusters using disease reaction University of Ghana http://ugspace.ug.edu.gh iv to GRD. The assessment of genetic diversity of GRD-resistant groundnut genotypes will help groundnut breeders to formulate crosses by choosing parents with different genetic backgrounds and in the development of gene-mapping populations with greater marker polymorphism. The 36 F2 populations generated from 9 x 9 half diallel mating scheme were infested with veruliferous aphids, Aphis craccivora and scored three times fortnightly following inoculation. General combining ability (GCA) and specific combining ability (SCA) effects for GRD resistance were highly significant, indicating that both additive and non-additive gene effects govern the inheritance of GRD resistance. Low narrow sense heritability for Area Under Disease Progress Curve (29.29 %) along with high broad sense heritability (94.78 %) further highlight the influence of non-additive gene action in controlling resistance to GRD, suggesting effective selection of superior genotypes at advanced generations when maximum homozygosity is fixed. University of Ghana http://ugspace.ug.edu.gh v DEDICATION To my mother, wife and children, brothers and sisters for their prayers and support throughout this study. University of Ghana http://ugspace.ug.edu.gh vi ACKNOWLEDGMENT I sincerely wish to thank my principal supervisor Prof Eric Yirenkyi Danquah for offering me the opportunity to work on this project at the same time keeping me well on track. I deeply appreciate the efforts and suggestions of my co supervisors Prof. Kwadwo Ofori and Prof. S. K. Offei for their visit to Abuja to thoroughly discuss my first thesis draft despite their busy schedule. My in – country supervisor, Prof. S.G Ado became my true soul mate during the course of the study. He was always open to discussions and gave invaluable advice and contribution on my field work and thesis write up. I am indebted to Dr. Bonney Ntare for reading through all my chapters and for sharing his wealth of experience on groundnut rosette virus with me despite not being one of my supervisory team. I am grateful to WACCI administrative staff for their continued support for ensuring that my stay in Ghana was comfortable, and that the critical resources for the in-country research were received on time. I am grateful to the Alliance for Green Revolution in Africa (AGRA) for the PhD scholarship. They supported this research by making funds available for my research. Many thanks to Ahmadu Bello University, Zaria and Institute for Agriculture in particular for allowing me to pursue the programme. I would like to recognize the encouragement and useful critique provided by the members of my cohort (Cohort 2) – particularly at the proposal and final writing-up stages. Most importantly, special thanks to my wife, Hauwa Ahmed, and my children, Muhammad Sani, Hassana and Hussaina, M. Almustapha, Usman and M. Jaafar for their patience and understanding during the critical times of the research. University of Ghana http://ugspace.ug.edu.gh vii TABLES OF CONTENTS DECLARATION I ABSTRACT III DEDICATION V ACKNOWLEDGMENT VI TABLES OF CONTENTS VII LIST OF FIGURES XI LIST OF TABLES XII LIST OF ABBREVIATIONS XIV CHAPTER ONE 1 1 GENERAL INTRODUCTION 1 CHAPTER TWO 4 2 LITERATURE REVIEW 4 2.1 Groundnut (Arachis hypogaea L.) 4 2.2 Genetic Resources 6 2.3 Groundnut Rosette Disease 7 2.3.1 Origin and occurrence of Groundnut Rosette Virus 8 2.3.2 Symptoms of Groundnut Rosette Disease (GRD) 9 2.3.3 Causal agents of Groundnut Rosette Disease 10 2.3.4 Diagnosis of Groundnut Rosette Disease 13 2.3.5 Epidemiology of Groundnut Rosette Disease 13 2.3.6 Virus-vector interactions and transmission 14 2.4 Progress made in combating Groundnut Rosette Disease in Nigeria 15 2.5 Genetics of Resistance to Groundnut Rosette Disease 16 University of Ghana http://ugspace.ug.edu.gh viii 2.6 Combining Ability for traits in Groundnut 17 2.7 Genotype x Environment Interaction (GEI) in groundnut 19 2.8 Genetic Diversity in Cultivated Groundnut Based on Molecular Markers 21 2.9 Participatory breeding and varietal selection in groundnut 24 CHAPTER THREE 26 3 FARMERS‘ PERCEPTION OF PRODUCTION CONSTRAINTS AND PREFERRED TRAITS FOR RESISTANT GROUNDNUT ROSETTE VARIETIES 26 3.1 Introduction 26 3.2 Materials and Methods 27 3.2.1 Description of the study areas 27 3.2.2 Farmer survey and data analysis 28 3.3 Results 29 3.3.1 Household and demographic information 29 3.3.2 Characteristics of preferred groundnut varieties in Batsari and Nasarawa – eggon districts 35 3.3.3 Preferred groundnut varieties and associated characteristics 38 3.3.4 Perception of farmers on constraints to groundnut production 38 3.4 Discussion 39 3.5 Conclusions and Recommendations 42 CHAPTER FOUR 43 4 ASSESSMENT OF GENETIC DIVERSITY OF GROUNDNUT (ARACHIS HYPOGAEA L.) GENOTYPES FOR RESISTANCE TO ROSETTE DISEASE USING SSR MARKERS 43 4.1 Introduction 43 4.2 Materials and methods 44 4.2.1 Plant material and DNA extraction 44 4.2.2 SSR Analysis 45 4.2.3 Electrophoresis and data collection 47 4.3 Results 48 4.3.1 Allelic variation at SSR loci 48 University of Ghana http://ugspace.ug.edu.gh ix 4.3.2 Comparison of gene diversity 50 4.4 Discussion 55 4.5 Conclusions and Recomendations 57 CHAPTER FIVE 58 5 INHERITANCE ON RESISTANCE TO GROUNDNUT ROSETTE DISEASE 58 5.1 Introduction 58 5.2 Materials and Methods 59 5.2.1 Population Development and Phenotype Evaluation 59 5.2.1 Aphid and Rosette resistance evaluation 60 5.2.2 Agronomic performance 64 5.2.3 Data analysis 64 5.3 Results 71 5.3.1 Variance components and heritability of traits 71 5.3.2 Performance of the groundnut genotypes grown at Samaru and Lafia, 2012 75 5.4 General and specific combining ability for traits 87 5.5 Selection for superior genotypes for resistance to groundnut rosette disease 95 5.6 Discussion 97 5.7 Conclusions and Recommendations 103 CHAPTER SIX 105 6 MOLECULAR CONFIRMATION OF ROSETTE RESISTANCE IN PROMISING GROUNDNUT GENOTYPES BY ONE-STEP REVERSE TRANSCRIPTASE POLYMERASE CHAIN REACTION (RT – PCR) 105 6.1 Introduction 105 University of Ghana http://ugspace.ug.edu.gh x 6.2 Materials and methods 107 6.2.1 Collection of plant samples 107 6.2.2 RNA Extraction and Purification 107 6.2.3 Complementary DNA (cDNA) synthesis and Polymerase Chain Reaction (PCR) 108 6.3 Results 111 6.4 Discussion 114 6.5 Conclusions and Recommendations 116 CHAPTER SEVEN 118 7 GENERAL DISCUSSION 118 7.1 Participatory rural appraisal (PRA) 118 7.1.1 Epidemiology of GRD from the studied genotypes 119 7.1.2 Performance of the genotypes across the two contrasting locations 121 7.1.3 Inheritance of resistance to groundnut rosette disease 123 7.2 Challenges 124 7.3 Conclusions and recommendations 125 BIBLIOGRAPHY 126 APPENDIX 1: PARTICIPATORY RURAL APPRAISALS QUESTIONNAIRE 146 University of Ghana http://ugspace.ug.edu.gh xi LIST OF FIGURES Figure 3. 1: Map of Nigeria showing Batsari (Katsina state) and Nasrawa-Eggon (Nasarawa state) 28 Figure 3. 2: Distribution of traits preferred by farmers 35 Figure 3. 3: Farmers perception on the causes of groundnut rosette disease 36 Figure 3. 4: Farmers perception of yield loss due to groundnut rosette disease 37 Figure 3. 5: Groundnut rosette disease control measures adopted by farmers 38 Figure 4. 1: Hierarchical dendrogram of 50 groundnut genotypes by using similarity coefficients based on the Nei‘s (1983) original genetic distance calculated from data of 166 SSR loci using the UPGMA method: Refer to Table 4.1 for names of the corresponding codes 54 Figure 4. 2: The genetic relationships among the five groundnut populations calculated using UPGMA method based on the Nei‘s (1983) genetic distance 55 Figure 5. 1: Cross over Genotype x Location Interaction for sound kernel yield per plant across Batstari and Lafia Locations 84 Figure 5. 2: Performance of F2 groundnut for sound kernel yield per plant and AUDPC 84 Figure 6. 1: Amplification banding pattern of GRAV – CP (HRP92/93), GRV – CP (OFR3P and 4P) and Sat-RNA markers in 16 groundnut genotypes 113 University of Ghana http://ugspace.ug.edu.gh xii LIST OF TABLES Table 3. 1: Farmer and Household information for Batsari and Nasarawa – eggon LGA in Nigeria for the 2010 growing season 31 Table 3. 2: Pair wise correlation of some farmers‘ level of awareness of the groundnut rosette disease 32 Table 3. 3: Pair-wise ranking of major crops grown by farmers in Batsari and Nasarawa – eggon in 2011 34 Table 3. 4: Pair-wise ranking of the most important constraints in groundnut production in Batsari and Nasarawa – eggon 39 Table 4. 1: Groundnut genotypes with different levels of resistance and susceptibility to groundnut rosette disease (GRD) included in the study 46 Table 4. 2: Primers used in the study, gene bank ID, repeat motif, frequency and number of alleles as well as gene diversity, and polymorphic information contents (PIC) based on the analysis of 50 groundnut genotypes for 35 polymorphic SSR markers 51 Table 4. 3: Pairwise genetic distance coefficients of 50 GRD-resistant genotypes using 36 SSR primer pairs combinations analyzed by PowerMarker software 52 Table 5. 1: Pedigree, source, description and characteristics of parental genotypes used for population development 61 Table 5. 2: Table 5. 2:Format of ANOVA for individual location 65 Table 5. 3: Table 5. 3: Format of ANOVA for the combined locations 66 Table 5. 4: Format of Diallel analysis of variance for model I method II for groundnut progenies evaluated in one location 70 Table 5. 5: Mean squares of measured traits for 9 parents and 36 F2 half diallel progenies of groundnut evaluated over Samaru and Lafia Locations in 2012 73 Table 5. 6: Table 5. 6: Variance components, Heritability estimates and expected gain for groundnut traits over combined Samaru and Lafia location in 2012 74 Table 5. 7: Performance of parents and their F2 progenies for sound kernel weight per plant (g) over Samaru and Lafia environment in 2012 76 Table 5. 8: Performance of parents and their F2 progenies for aphid damage Index (DI) over Samaru and Lafia environment in 2012 79 Table 5. 9: Performance of parents and their F2 progenies for AUDPC over University of Ghana http://ugspace.ug.edu.gh xiii Samaru and Lafia environment in 2012 82 Table 5. 11: Mean squares of combined ANOVA for half 9 x 9 diallel analysis for general and specific combining abilities and their interactions with location for ten morphological traits of groundnut evaluated at two locations in 2012 89 Table 5. 12: Variance component for GCA, SCA and their interactions with location, Bakers ratio, additive and dominance variances considering random effect model for 9 parents and 36 F2 evaluated across Samaru and Lafia Locations in 2012 90 Table 5. 13: Estimates of general combining ability (GCA) effects of 9 parental lines for four important morphological characters of groundnut evaluated across Samaru and Lafia Locations in 2012 91 Table 5. 14: Table 5. 14: Estimates of specific combining ability (SCA) effects measured in the 36 F2 progenies evaluated across Samaru and Lafia Locations in 2012 93 Table 5. 15: The top 10 and 4 poorest performing F2 genotypes selected based on Rank summation Index of SKWT, PWT, DI and AUDPC 96 Table 6. 1: Primers used in amplification of various regions of causal agents of groundnut rosette disease complex primers in the 100 series represent internal primers for specified regions 110 Table 6. 2: Field resistance scored by DI and AUDPC and RT – PCR confirmation of GRD-resistance in some groundnut genotypes 112 University of Ghana http://ugspace.ug.edu.gh xiv LIST OF ABBREVIATIONS 100SKWT One hundred sound kernel weight AFLP Amplified fragment length polymorphism ANOVA Analysis of variance ATA Agricultural Transformation agenda AUDPC Area under disease progress curve BW Bacteria wilt cDNA Complementary Deoxyribo nucleic acid CP Coat protein CTAB Cetyl trimethyl ammonium bromide DAPI diamidino-2-phenylindole DI Aphid damage index DNA Deoxyribo nucleic acid EST Express sequence tag F1 First filial generation F2 Second filial generation FAO Food and Agricultural Organization GCA General combining ability GD Genetic distance GEI Genotype x environment interaction GISH Genomic in situ hybridization GLM Generalized linear model GRAV Groundnut rosette assistor virus University of Ghana http://ugspace.ug.edu.gh xv GRD Groundnut rosette disease GRV Groundnut rosette virus HCL Hydrochloric acid He Heterozygote percentage HS Half – sib IC - RT – PCR Immunocapture-reverse transcriptase-polymerase chain reaction ICRISAT International Crop Research Institute for Semi – Arid Tropics ILRI International Livestock Research Institute IAP Inoculation access period IAR Institute for Agricultural Research LGA Local Government Area LRR Leucine-rich repeats LSD Least significant difference MAb Monoclonal antibody MAS Marker assisted selection MS Mean square MT Metric ton NAICPP National Accelerated Industry Crop Production Program NARP National Agricultural Research Program NBS Nucleotide binding site NBS – LRR Nucleotide-binding-site leucine-rich repeat NCBI National Centre for Biotechnology Information NID Normally and independently distributed NOPP Number of pods per plant University of Ghana http://ugspace.ug.edu.gh xvi ORF Open reading frame PBNV Peanut bud necrosis tospovirus PCR Polymerase chain reaction PIC Polymorphic information content PRA Participatory rural appraisal PK protein kinases PWPT Pod weight per plant PWTON Pod weight in tons per hectare QTL Quantitative trait loci RAPD Random amplified fragment length polymorphism RGA Resistant gene analogue RNA Ribose nucleic acid RSI Rank summation index RT – PCR Reverse transcriptase polymerase reaction SAS Statistical analysis software Sat-RNA Satellite – RNA SCA Specific combining ability SCAR Sequence characterized amplified region SHP Shelling percentage SKWPT Sound kernel weight per plant SKWTTON Sound kernel weight ton per hectare SSA Sub – Saharan Africa SSR Simple sequence repeat Taq Thermos aquaticus University of Ghana http://ugspace.ug.edu.gh xvii TAS – ELISA Triple antibody sandwich - ELISA TIR Toll and interleukin-1 receptor TSWV Tomato Spotted Wilt Virus UPGMA Unweighted pair group method with arithmetic averaging. USDA United States Department of Agriculture UV Ultra violet WACCI West Africa Centre for Crop Improvement WB Wash buffer University of Ghana http://ugspace.ug.edu.gh 1 CHAPTER ONE 1 GENERAL INTRODUCTION Groundnut (Arachis hypogaea L.) is an important food legume highly adapted to tropical and subtropical climates of the world. It is a key crop for small scale farmers especially in Africa and Asia where the crop serve as a valuable source of dietary protein, oil, and fodder for livestock. It contains 48-50% oil and 26-28% protein, and a rich source of dietary fibre, minerals, and vitamins (Janila et al., 2013). In addition, groundnut has the ability to fix atmospheric nitrogen to the soil to help in the maintenance of soil fertility. This crop is cultivated annually on about 24.63 million hectares worldwide with annual production of 41.27 million tons in shell and productivity of about 1.85 t ha–1 (FAO, 2012). The global annual increase in production is 0.4% between 2009 and 2012 was attributed to both, an annual increase in yield by 0.1% and in area by 0.3% (Janila et al., 2013). In West Africa, Nigeria is the largest producer of groundnuts with production of 3.07 million tons on about 2.4 million hectare (FAO, 2012). Groundnut is the fifth most important oilseed in the world in terms of volume of oil production and is widely grown in more than 100 countries of tropical, subtropical, and warm temperate regions of the globe (Upadhyaya et al., 2012) Despite the economic, social and cultural importance of groundnuts, its productivity is severely constrained by several biotic and abiotic factors. Drought is the major abiotic constraint affecting groundnut productivity and quality worldwide. Two thirds of the global production occurs in rain-fed regions of the semi-arid tropics where rainfall is generally erratic and insufficient, causing unpredictable drought stress (Reddy et al., 2003). Groundnut yield and quality are severely constrained by a wide variety of fungal, University of Ghana http://ugspace.ug.edu.gh 2 bacterial, viral, and nematode pathogens. Among the fungal diseases, early leaf spot (Cercospora arachidicola) and late leaf spot (Cercosporidium personatum) are most prevalent, and occur throughout groundnut growing regions (Liu et al., 2013). Late leaf spot and rust (Puccinia arachidis) diseases often occur simultaneously and can cause 50– 70% yield loss in India and some African countries (Khedikar et al., 2010). Groundnut rosette disease (GRD) causes greater yield loss than any other viral disease affecting groundnut in the semi-arid tropics of the world (Naidu et al., 1999). The disease is caused by three interdependent viruses (causal agents) (see 2.3.3). It is the most destructive viral disease of groundnut in Africa (Naidu et al., 1999) resulting in sporadic yield loss of about 30% annually in farmers field. It is endemic to groundnut-growing regions of sub-Saharan Africa (SSA) and Madagascar (Yayock et al., 1976). The most serious yield losses were reported during 1975 when an epidemic in northern Nigeria destroyed approximately 0.7 million hectares of groundnut, with an estimated loss of US$250 million (Yayock et al., 1976). In Nigeria, farmers have identified groundnut rosette disease (GRD) as widespread and devastating, compelling some of them to abandon production in some areas. The disease is characterised by small, chlorotic, twisted and distorted leaflet with shortened internodes and thickened stems. Affected plants especially those infected at a young stage are severely stunted (Bock et al., 1990). The disease also affects both quality of the haulm and the pod. This disease can cause up to 100% yield (Adu-Dapaah et al., 2004; Waliyar, et al., 2007). Previous studies indicate that GRD could be managed by chemical control of the vector (Bock and Nigam, 1998). However, resource poor farmers seldom use chemical control measures due to lack of resources, labour constraints and cost (Dwivedi et al., 2003; Adu-Dapaah et al., 2004). The above factors coupled with health hazards associated with University of Ghana http://ugspace.ug.edu.gh 3 the use of insecticides make the use of host resistance the most cost effective and environmentally friendly alternative. Earlier attempts to control GRD using host resistance resulted in development of several resistant varieties across West Africa (Waliyar et al., 2007). However, these varieties were only tolerant to one of the components of groundnut rosette virus (GRV) but susceptible to groundnut rosette assistor virus (GRAV) indicating lack of resistance to this component of the rosette complex that serves as a helper component for aphid transmission (Subrahmanyam et al., 1998; Olorunju et al., 2001). Furthermore, resistance to GRV is not immunity and seems to be overcome under high inoculum pressure and in adverse environmental conditions (Bock et al., 1990). Therefore development of genotypes that are resistant to GRD virus is the most effective, economic and sustainable method of limiting virus inoculum build- up and spread of both the aphid and the virus. The main objective of this study was to develop groundnut breeding lines with potential for resistance to groundnut rosette disease with acceptable farmers‘ preferred agronomic and yield traits The specific objectives were to: a. ascertain farmers‘ knowledge and preferences of rosette resistant genotypes; b. assess molecular polymorphism among aphid and rosette resistant genotypes; c. asses genotype x environment interaction towards improved selection efficiency to obtain high-yielding varieties; and d. determine the mode of inheritance of resistance to groundnut rosette disease University of Ghana http://ugspace.ug.edu.gh 4 CHAPTER TWO 2 LITERATURE REVIEW 2.1 Groundnut (Arachis hypogaea L.) Groundnut (Arachis hypogaea L.) is an annual or perennial plant that is distinguished from most other species by producing aerial flowers, but fruiting below the soil level. Arachis belongs to the family Fabaceae, tribe Aeschynnomeneae, sub-tribe Stylanthinae. Arachis hypogaea L. is the only domesticated species in the genus (Tillman and Stalker, 2009), and Krapovickas (1969) concluded that A. hypogaea var. hypogaea is likely the most ancient type because it has similar branching patterns as wild species, no compound florets, and a prostrate growth habit. The centre of origin for the genus Arachis is the Mato Grosso area of Brazil, but species evolved over a wide range of habitats in South America (Gregory et al., 1980). Molecular data indicate that the centre of genetic variation also is the Mato Grosso area of Brazil to eastern Bolivia (Stalker et al., 1994). Eighty species have been described (Krapovickas and Gregory, 1994; Valls and Simpson, 2005) which have been divided into nine sections based on morphology and cross-compatibility relationships. Smartt and Stalker (1982) proposed that the A and B genomes of section Arachis may be an A1 and A2 rather than being truly different based on chromosome pairing relationships. The species of different sections have overlapping distributions in many areas. Hybrids between species in different sections are difficult to produce and are usually sterile, while intrasectional hybrids can be fertile if they have similar genomic make-up (Stalker et al., 1991). The species A. hypogaea has two subspecies hypogaea and fastigiata which are further divided into six botanical types hypogaea and hirsute, and fastigiata, vulgaris, equatoriana and peruviana botanical varieties. The subspecies are separated morphologically based on presence or absence of flowers on the main stem and regularly alternating vegetative and reproductive University of Ghana http://ugspace.ug.edu.gh 5 nodes on branches. The characteristics of these both botanical varieties shows that hypogaea has no floral axes or branches on main stem; alternating pairs of vegetative and reproductive axes on branches (alternate branching); inflorescence simple; vegetative branches moderate to profuse; primary branches longer than main stem; growth habit spreading, intermediate, or erect; usually two seeds per pod; pod beak not very prominent; seed size medium (runner market type) to large (Virginia market type); testa color generally tan (red, white, purple, or variegated also exist); cured seed dormancy moderate; maturity medium to late (Ntare et al., 2007). The fastigiata has floral axes on main stem; irregular pattern of vegetative and productive branches with reproductive branches predominating on branches (sequential branching); inflorescence usually simple; vegetative branches sparse; primary branches shorter than main stem; growth habit upright; two to four seeds per pod; pod beak absent, slight, or prominent; seed size small to medium; testa color tan, red, white, yellow, purple, or variegated; cured seed dormancy little (Ntare et al., 2007). Cultivated groundnut has an allotetraploid genome (AABB, 2n = 4x = 40). The low level of genetic variation within the cultivated gene pool and its polyploid nature limit the utilization of molecular markers to explore genome structure and facilitate genetic improvement. Nevertheless, a wealth of genetic diversity exists in diploid Arachis species (2n = 2x = 20), which represent a valuable gene pool for cultivated peanut improvement (Guo et al., 2012). Arachis hypogaea is a recent allotetraploid (David at al., 2012), most probably resulting from the hybridization of two wild species followed by natural chromosome duplication (Halward et al., 1991; Young et al., 1996; Seijo et al., 2007). The genome of A. hypogaea is large, being estimated at 2·8 Gb (Grilhuber, 2005) with a large repetitive fraction of approximately 64 % determined by DNA renaturation kinetics (Dhillon et al., 1980) Cytogenetic analyses in A. hypogaea have revealed two types of chromosomes: ten pairs of A-type chromosomes, with strongly 4', 6-diamidino-2-phenylindole (DAPI)-stained (and hence AT-rich) heterochromatin at the centromeres, including the smallest pair of all University of Ghana http://ugspace.ug.edu.gh 6 chromosomes (Robledo et al., 2010), and another ten pairs of chromosomes with more weakly staining centromeric heterochromatin bands, designated B chromosomes (Seijo et al., 2004; Robledo et al., 2010). Studies comparing the chromosomal heterochromatic banding patterns together with evidence from positions of rDNA clusters (Robledo, et al., 2010) and genomic in situ hybridization (GISH) (Seijo et al., 2007) suggest that A. hypogaea A chromosomes are similar to those in the wild diploid A. duranensis, whilst the peanut B chromosomes are similar to those in the wild diploid A. ipaënsis Other evidence such as species geographic distribution (Robledo et al.,2010) and molecular phylogenies (Kochert et al., 1996; Burow et al., 2009; Moretzsohn et al., 2013) corroborates that the most probable A and B genome donors to A. hypogaea are A. duranensis and A. ipaënsis. 2.2 Genetic Resources for Groundnut Rosette Disease Over 3400 germplasms accessions evaluated for reaction to groundnut rosette disease at Chitedze Agricultural Research Station Lilongwe, Malawi, only 89 long duration Virginia types were identified as resistance to the disease. A high percentage (76%) of them originated in West Africa (Nigeria 39%, Burkina – Faso 13.9%, Cote d‘voire 9.9%, Senegal 6.9%, Mali 30%, Gambia 2.0% and Equitoria Guinea 1.0%) and the rest were from Southern Africa (Subrahmanyam et al., 1998). In addition, 11 short duration Spanish types were identified in Africa germplasm originating from West Africa especially Burkina – Faso. Out of a total of over 2000 accession evaluated in a preliminary screening trial in 1994/95 growing season at the same station, 15 were found to be rosette disease resistance (Subrahmanyam et al., 1998). Additional resistant sources were sought from 2,301 accessions from different sources and 252 advanced breeding lines derived from crosses involving earlier identified sources of resistance to rosette (Olorunju et al., 2001). The lines were evaluated in field screening trials using an infector row technique during 1996 and 1997 growing seasons at the Institute for University of Ghana http://ugspace.ug.edu.gh 7 Agricultural Research (IAR), Samaru, Nigeria (Olorunju et al., 2001). Among the germplasm lines, 65 accessions were reported to show high levels of resistance while 134 breeding lines were resistant (Olorunju et al., 2001).The report concluded that all rosette disease resistant lines were susceptible to groundnut rosette assistor virus (GRAV) and that the identified germplasm and breeding lines will contribute to an integrated management of groundnut rosette disease. In addition to A. hypogaea collections, more than 1,300 Arachis species accessions have been collected (Stalker et al., 2002), with about 800 Arachis entries being maintained by the USDA (Stalker and Simpson, 1995). Preservation of wild Arachis species is difficult for most accessions because the long, fragile pegs break during harvest and the soil must be sifted to recover pods. Stalker and Simpson (1995) reported that nearly 25% of the species from which seed can be obtained under nursery conditions have fewer than 50 seeds in storage. Additionally, at least 25% of the Arachis species accessions in germplasm nurseries are maintained vegetatively because of their poor seed production under cultivation. A large number of disease and insect resistance, and other agronomically useful traits are present in accessions of Arachis species, which makes them potentially valuable genetic resources for crop improvement (Stalker and Moss, 1987; Stalker and Simpson, 1995). 2.3 Groundnut Rosette Disease Groundnut rosette disease is the most destructive viral disease of groundnut in Africa and can cause serious yield losses under favourable conditions. Groundnut production is constantly threatened by potential outbreaks of rosette disease epidemics. Improvement of host plant resistance to this disease provides the most effective control strategy (Olorunju et al., 2001; Herselman et al., 2004). ICRISAT and its partners have made significant contributions towards the understanding of the epidemiology of the disease and confirmation based on University of Ghana http://ugspace.ug.edu.gh 8 molecular diagnostic assays. This knowledge has provided a basis for development and utilization of groundnut cultivars with resistance to the groundnut rosette disease and impacted the lives of thousands of farmers in sub-Saharan Africa (Olorunju et al., 2001). 2.3.1 Origin and occurrence of Groundnut Rosette Virus Groundnut is the only known natural host of a complex of three agents of rosette disease (GRV sat-RNA and GRAV). It is likely that the viruses have evolved and survived in the host species native to Africa before the introduction of groundnut (Subrahmanyam et al., 1998). After its introduction in the 16th century, groundnut became an accidental host of rosette disease representing a case of the ―new – encounter‖ phenomenon (Buddenhagen and Ponti, 1984). It is possible that resistance came to Africa in some of the original introductions from South American centre (s) of origin and due to recurrent epidemics in West Africa (Olorunju et al., 2001). It was concentrated to a greater degree by natural out crossing and recombination. Groundnut rosette disease was first reported in 1907 from Tanganyika (Waliyar et al., 2007), now called Tanzania, and has since been reported in several other African countries south of Sahara. The major areas of disease occurrence include Burkina Faso, Ghana, Nigeria, Malawi, Mozambique and Uganda (Ntare et al., 2002). Symptoms similar to groundnut rosette disease have been reported in some countries of Asia and South America, but diagnostic tests to unequivocally confirm the presence of the disease have not been conducted (Reddy, 1991). Thus, it is generally assumed that groundnut rosette disease is endemic to groundnut growing countries in Africa South of the Sahara and its off-shore islands such as Madagascar (Ntare et al., 2002). During the course of evolution, as these genes did not possess any survival value in the absence of the disease, they may have been altered in the majority of genotypes (Reddy, 1991). One prerequisite for the loss of traits University of Ghana http://ugspace.ug.edu.gh 9 during ‗evolution‘ is their simple inheritance and rosette resistance is governed by two independent major recessive genes (Nigam and Bock, 1990). 2.3.2 Symptoms of Groundnut Rosette Disease (GRD) GRD occurs with two variant symptoms, chlorotic rosette and green rosette, with considerable variation within each type (Murant, 1989; Naidu et al., 1999). Both forms of the disease cause plants to be severely stunted, with shortened internodes and reduced leaf size, resulting in a bushy appearance of plants (Naidu et al., 1999). In chlorotic rosette, leaves are usually bright yellow with a few green islands and leaf lamina is curled. In the green rosette, leaves appear dark green, with light green to dark green mosaic (Naidu et al., 1999). Chlorotic rosette occurs throughout the Sub-Sahara Africa (SSA), whereas green rosette has been reported from East and West Africa (Naidu et al., 1999). A less common symptom variant, mosaic rosette, due to mixed infection of the plants by the Sat-RNA causing chlorotic variant and mottle variant, was reported from East Africa (Waliyar, et al., 2007). Variability in Sat-RNA is mainly responsible for symptom variations (Murant and Kumar, 1990; Taliansky et al., 1997). In addition, differences in genotypes, plant stage at infection, variable climatic conditions and mixed infections with other viruses also contribute to symptom variability under field conditions (Naidu et al., 2007). Infection due to chlorotic or green rosette disease occurring in young plants (prior to flowering) usually results in 100% yield loss. In contrast, plants infected during later growth stages (between flowering and pod setting) may show symptoms only in some branches or parts of branches and yield loss depends on severity of infection (Naidu et al., 2007). Infection after pod setting/maturation causes negligible effects on pod yield (Waliyar et al., 2007). An average annual yield loss due to GRD is estimated to be between 5 and 30% in non-epidemic years and epidemics often result in 100% yield loss (Waliyar et al., 2007). The deleterious impact of GRAV or GRV on University of Ghana http://ugspace.ug.edu.gh 10 host plant together with Sat-RNA in a synergistic manner is not known. Ansa et al. (1991) have reported that stunting is more severe in diseased groundnut plants containing all the three agents than in diseased groundnut plants containing only GRV and Sat-RNA. Other reports have suggested that GRAV or GRV infection alone in groundnut results in transient mottle symptoms with insignificant impact on the plant growth and yield (Taliansky et al., 2000). These results have, however, been contradicted by Naidu and Kimmins (2007) who demonstrated that GRAV infection alone affects plant growth and contributes to significant yield losses in susceptible groundnut cultivars. 2.3.3 Causal agents of Groundnut Rosette Disease Groundnut rosette disease is a viral disease, transmitted by an aphid, Aphis craccivora Koch (Insecta: Homoptera) in a persistent calculative manner (Waliyar, et al., 2007). Three causal agents are involved in GRD etiology: Groundnut rosette assistor virus (GRAV), Groundnut rosette virus (GRV) and a Satellite-RNA (Sat-RNA) (Reddy et al., 1985; Murant et al., 1988; Taliansky et al., 2000). The intimate interaction between GRAV, GRV, and sat-RNA is crucial to the development of the disease. GRV, a member of the genus Umbravirus, has a single-stranded, positive-sense RNA genome of 4,019 nt (Talianskyet al., 1996) that contains four large open reading frames (ORFs). ORF 2 is a putative RNA-dependent RNA polymerase and is likely expressed as a fusion protein with the product of ORF1 by a –1 frameshift mechanism. The 3¢ ORFs (Bock et al., 1990); Deom et al., 2000) are almost completely overlapping. The protein encoded by ORF 3 was shown to be a trans-acting long- distance movement protein that can traffic nonrelated viral RNA systemically (Ryabov et al., 1999), while analysis of the ORF 4 putative amino acid sequence suggests that it may be involved in cell-to-cell movement (Taliansky, et al., 1996). University of Ghana http://ugspace.ug.edu.gh 11 GRAV is a member of the family Luteoviridae. It was first recognized as a component of groundnut rosette disease by Waliyar et al., (2007). Casper et al. (1983) and Reddy et al. (1985) characterized the virus and identified it as a luteovirus. The virus replicates autonomously in the cytoplasm of phloem tissue. GRAV is transmitted by A. craccivora in a persistent manner, and experimentally by grafting, but not by mechanical sap inoculation, seed, and pollen or by contact between the plants (Taliansky et al., 2000). Groundnut is the only known natural host of GRAV is reported to occur wherever GRD has been reported (Waliyar et al., 2007). The virus on its own causes symptomless infection or transient mottle, and can cause significant yield loss in susceptible groundnut cultivars (Naidu et al., 1999). There are no reports on occurrence of strains of GRAV causing the disease (Waliyar et al., 2007). GRV belongs to the genus Umbravirus. It was first isolated and characterized by Reddy et al. (1985). The virus replicates autonomously in the cytoplasm of the infected tissues (Taliansky et al., 2000). GRV on its own causes transient symptoms, but a Sat-RNA associated with GRV is responsible for rosette disease symptoms. GRV depends on GRAV for encapsulation of its RNA and transmission by A. craccivora in a persistent mode (Robinson et al., 1999). Groundnut is the only known natural host, but several experimental hosts in the family Chenopodiaceae and Solanaceae have been reported (Murant et al., 1998). No strains of GRV have been reported (Waliyar et al., 2007) and the virus is restricted to SSA and its offshore islands. GRV acts as a helper virus for replication of sat-RNA. The Sat-RNA (sub-viral RNAs) of GRV belongs to the Subgroup-2 (small linear) satellite RNAs. It is a single-stranded, linear, non-segmented RNA of 895 to 903 nucleotides (Murant et al., 1988; Block et al., 1994; Taliansky et al., 2000). It totally depends on GRV for its replication, encapsulation and movement, both within and between the plants. Sat-RNA is responsible for rosette symptoms and plays a critical role in helper virus dependent University of Ghana http://ugspace.ug.edu.gh 12 transmission of GRV. Different variants of Sat-RNA have been shown to be responsible for different rosette symptoms, such as green rosette and chlorotic rosette (Murant and Kumar, 1990; Taliansky et al., 1997). It is mechanically transmissible along with the GRV and is also transmitted by aphids in the presence of GRV and GRAV. A single aphid vector acquires GRAV, GRV, and sat-RNA; however, it does not always transmit the three disease agents together to a host plant (Naidu et al., 1999). GRAV or GRV plus sat-RNA can be transmitted separately. However, for the disease to perpetuate in nature, all three agents must be transmitted by the aphid vector to a plant (Deom et al., 2000). Phylogenetic analysis of the overlapping ORFs 3 and 4 shows that the GRV isolates cluster according to the geographic region from which they were isolated, indicating that Malawian GRV isolates are distinct from Nigerian GRV isolates (Deom et al., 2000). Phylogenetic analysis also showed clustering within the sat-RNA isolates according to country of origin, as well as within isolates from two distinct regions of Malawi Deom et al., 2000). Because the GRAV CP sequence is highly conserved, independent of the geographic source of the GRAV isolates, the GRAV CP sequence represents the most likely candidate to use for pathogen-derived resistance in groundnut and may provide effective protection against groundnut rosette disease throughout SSA (Deom et al., 2000). Groundnut rosette disease has been reported in Angola, Burkina Faso, Côte d‘Ivoire, Gambia, Ghana, Kenya, Madagascar, Malawi, Niger, Nigeria, Senegal, South Africa, Sudan, Swaziland, Tanzania, Uganda, and Zaire (now DR Congo) (Gibbons, 1977; Naidu et al., 1999). The agents of GRD have not been detected elsewhere in the world, despite the fact that groundnut is grown in more than 100 countries around the world (Upadhyaya et al., 2012) and A. craccivora is found in almost all these groundnut growing regions (Naidu et al., 1999). University of Ghana http://ugspace.ug.edu.gh 13 2.3.4 Diagnosis of Groundnut Rosette Disease Groundnut rosette disease can be tentatively diagnosed in the field based on the characteristic symptoms in groundnut or by mechanical inoculation onto a suitable indicator host such as Chenopodium amaranticolor. Symptom development on C. amaranticolor indicates the presence of GRV, but this test is not always reliable when the indicator plants are subjected to the widely fluctuating temperatures of SSA (Naidu et al., 1999). Improved diagnostic methods include a triple antibody sandwich enzyme-linked immunosorbent assay (TAS- ELISA) for detection of GRAV (Rajeshwar et al., 1987) and reverse transcription- polymerase chain reaction (RT-PCR) that allows detection of each of the three agents (Naidu et al., 1999). The advantage of the RT-PCR method is that it concurrently detects all three groundnut rosette disease agents in plants and aphids (Naidu et al., 1999). 2.3.5 Epidemiology of Groundnut Rosette Disease The epidemiology of GRD is complex, involving interactions between and among two viruses and a Sat-RNA, the vector, and the host plant and environment (Naidu et al., 1998). Since none of the causal agents is seed-borne, primary infection of crops depend on the survival of infected plants (virus sources) and vectors (aphids) (Naidu et al., 1998). In the West, East and Southern Africa, A. craccivora maintains itself successfully through the dry and wet seasons on some crops and wild host plants. In Nigeria these hosts are found in the Amaranthaceae, Asteraceae, Caesalpinaceae, Compositae, Euphorbiaceae, Fabaceae, Moringaceae, Nyctaginaceae, Papilionaceae, Portulacaceae, Solanaceae and Verbenaceae (Alegbejo, 2000). The epidemics of groundnut rosette virus disease that occurred in the main groundnut producing areas of Nigeria was speculated to be due to unusual combination of weather and groundnut sowing dates, which lead to massive build-up, early dispersal and survival of University of Ghana http://ugspace.ug.edu.gh 14 aphids in the wet season (Misari et al.,1988). Also intermittent wet and dry spells in the early part of the season, without heavy rainfall, were probably responsible for the development and successful dispersal of alate aphids (Yayock et al., 1976). 2.3.6 Virus-vector interactions and transmission Aphis craccivora, commonly known as the cowpea aphid is the principal vector involved in the transmission of all the GRD agents in a persistent and circulative manner (Waliyar, 2007). GRV and Sat-RNA must be packaged within the GRAV coat protein to be aphid transmissible. Studies have shown that all the GRAV particles whether they contain GRAV RNA or GRV RNA and Sat-RNA are acquired by the aphid vector from phloem sap in 4h and 8h acquisition access feeding for chlorotic and green rosette, respectively (Misari et al., 1988). Then, there is a latent period of 26h 40min and 38h 40min for chlorotic and green rosette, respectively, and the inoculation access feeding period of 10min for both forms (Misari et al., 1988). Once acquired, aphid can transmit virus particles for up to two weeks and beyond. All stages of the aphid can acquire and transmit the disease agents. Transmission rates of 26-31% have been reported with one and two aphids per plant, and 49% with five aphids per plant (Misari et al., 1988). Aphid vector does not always transmit all the three agents together (Naidu et al., 1999). Under natural conditions, some GRD-affected plants (GRV and Sat-RNA positive) were found to be free from GRAV, and GRAV was detected in some non-symptomatic plants (no GRV and Sat-RNA) (Naidu et al., 1999). This situation was due to difference in inoculation feeding behaviour of the vector leading to transmission of (i) all the three agents together, (ii) only GRAV or (iii) GRV and Sat-RNA, as demonstrated by the electrical penetration graph (EPG) studies of aphid stylet activities (Naidu et al., 1999). This showed that during short inoculation feeding (test probe or stylet pathway phase) vector aphids probe groundnut leaves University of Ghana http://ugspace.ug.edu.gh 15 without reaching the phloem, transmitting only GRV and Sat-RNA, which multiply in the epidermal and mesophyll cells. Even if GRAV particles are deposited in the mesophyll cells, they cannot replicate, as they can replicate only in the phloem cells (Naidu et al., 1999). However, vector aphids can transmit GRAV, and GRV, Sat-RNA when the stylets penetrate sieve elements (salivation phase) of the phloem cells. Therefore, the success of transmitting all the three agents together is high when inoculation feeding period is longer or increasing the number of aphids per plant (Misari et al., 1988). Vector aphids fail to acquire or transmit GRV and Sat-RNA from diseased plants lacking GRAV and such plants become dead-end sources. However, if such plants receive GRAV later due to vector feeding, the plants again serve as source of inoculum (Waliyar et al., 2007). 2.4 Progress made in combating Groundnut Rosette Disease in Nigeria In Nigeria, research on the development of groundnut cultivars with resistance to rosette was initiated in 1986 by Institute for Agricultural Research (IAR), Ahmadu Bello University, Zaria in collaboration with ICRISAT (Olorunju et al., 1992). Concerted efforts were made to improve resistance to groundnut rosette viruses in the existing locally grown varieties. These earlier attempts resulted in the development of a number of rosette resistant varieties such as SAMNUT 10 (RMP 12), SAMNUT 11 (RMP 91), SAMNUT 16 (M554.76), SAMNUT 20 (M412.80I), and SAMNUT 21 (MDR-8-19).SAMNUT 22 and SAMNUT 23 and SAMNUT 24. Popularization of these varieties and availability of seed to farmers were made possible by the then Federal government projects (NAICPP and NARP), collaborative work between IAR and ICRISAT, ILRI and GGP/CFC using farmer participatory approach. There was a dramatic increase in production from 1.4 million MT to over 2 million MT from 1994 to 2003 in Nigeria because of the well-coordinated collaborative work between NARJs and IARCs in combating the groundnut virus disease in Africa (Ntare et al., 2002). Despite these University of Ghana http://ugspace.ug.edu.gh 16 attempts, sporadic occurrences of the disease of about 30 % from year to year and among fields were still observed in farmer‘s field (Waliyar et al., 2007). Control strategies adopted by most farmers have traditionally emphasized on vector control mainly by pesticide and cultural practices such as manipulating sowing dates and plant density (Subrahmanyam et al., 1998). Chemical control methods have been only partially effective, since aphid populations can reach very high numbers, leading to intensive pesticide application in an attempt to eliminate the vector, and when accompanied with drought may lead to epidemics. Furthermore, there are concerns that the vector may develop pesticide resistance and the intense application may have deleterious effect on the environment. Therefore the development of genotypes that are resistant to both aphids and the virus is the most effective, economic and sustainable method of limiting virus inoculums build-up (Herselman et al.,2004). 2.5 Genetics of Resistance to Groundnut Rosette Disease Breeding for resistance to groundnut rosette disease demands a good knowledge of the breeding methodologies as well as a good understanding of the disease and its causal organisms. Identification of sources of resistance and its efficient utilization require an understanding of the genetic control of resistance and knowledge of the amount of genetic variability available for selection. Determining the suitable parents to use for development of resistant genotype is particularly important. Early genetic studies on groundnut rosette disease showed that resistance was effective against GRV and its sat-RNA and was governed by 2 independent recessive genes (de Berchoux, 1960). He also stated that resistant lines were not immune and that individual plants could become infected when subjected to inoculation by massive number of aphids. This resistance was reported to operate equally against both chlorotic rosette (de Berchoux, 1960) and green rosette (Harkness, 1977). He attributed the University of Ghana http://ugspace.ug.edu.gh 17 low recovery of resistant plants from Virginia x Spanish crosses to heavy inoculum pressure at early stage of growth and suggested occurrence resistance breakdown from generation to generation. Nigam and Bock (1990) studied the inheritance of resistance to chlorotic rosette (GRV and its sat-RNA) in crosses involving botanical varieties of groundnut in Malawi and confirmed the findings of de Berchoux (1960) of two recessive genes governing the resistance in all the backgrounds. In resistant plants, the presence of GRAV was detected. Gene conferring resistance to GRV and its sat-RNA did not confer resistance to GRAV (Bock and Nigam, 1988; Bock et al., 1990). Similar findings on the inheritance of resistance to green rosette using mixed infection in the field (GRV + and its sat-RNA + GRAV) and single GRV infection under greenhouse conditions were reported from Nigeria by Olorunju et al. (1992). There was one exception from RMP12 x M124.781 crosses, where in F2 generation, the plant segregated into 1 susceptible: 3 resistant, suggesting dominant gene action governing rosette resistance (Olorunju et al., 1992). Amin (1985) reported high level of resistance to A. cracivora in some crosses under greenhouse conditions. Progenies of A. chacoense and A. villas interspecific derivatives with cultivated groundnut also showed high resistance to A. crracivora. Resistance to aphid vector identified in cultivated groundnut ICG 5240 [EC36892] (Padgham et al., 1990) is reported to be controlled by single a recessive gene (van de Merwe, 2001; Herselman et al., 2004) 2.6 Combining Ability for traits in Groundnut Cultivar improvement for yield and stress resistance requires availability of genetic resources that could act as sources of genes conferring the desire traits that could be introgressed into the present cultivars (Sitaresmi et al., 2010; Nsabiyera et al., 2013). Gene introgression could be achieved through a combination of desired traits into target genotypes using recombination breeding under local conditions. This is essential to the generation of genetic diversity and University of Ghana http://ugspace.ug.edu.gh 18 fixing genes in the progeny (Marame et al., 2009; Zecevic et al., 2011). This, however, involves a lengthy and costly process of identifying and combining superior parents into superior hybrids (Rego et al., 2009). Diallel mating systems provide plant breeders with estimates for general combining ability (GCA) and specific combining ability (SCA). The GCA effects reflect the parent‘s genetic ability to influence all of its progeny for a specific trait, which is an expression of additive genetic effects (Griffing, 1956). The SCA effects represent non-additive genetic effects such as intra-allelic (dominance) or inter-allelic (epistasis) interactions or multiplicative gene action, which can be viewed as a departure from performance, can be predicted in simple additive models (Henderson, 1952; Griffing, 1956). Breeders have largely used the diallel mating scheme to estimate the potential value of genotypes per se, their combining ability effects for resistance to foliar disease in groundnut from a fixed or randomly chosen set of parental lines (Adamu et al.,2008). The studies of combining ability provide a guideline for selecting elite parents or combiners which may later be hybridized to accumulate fixable genes through selection. Both SCA and GCA have been reported to be significant in conditioning resistance to foliar disease in groundnut (Vishnuvardhan et al., 2011). Pensuk et al. (2002) from a 6 x 6 diallel cross of resistance to peanut bud necrosis tospovirus (PBNV) reported highly significant GCA effects for PBNV incidence in F2 and F3 generations. SCA was also significant, but the relative contribution to variation among crosses was much less than those of GCA effects. In an earlier study, Anderson et al. (1990) reported significant GCA and SCA effects for peanut stripe virus (PStV) and rust incidence from a study of diallel in groundnut. Makne (1992) and Dwivedi et al. (1994) found significant SCA for seed weight per plant, number of pods per plant and pod weight per plant and concluded that these traits are controlled by non-additive gene action. Adamu et al. (2008) recommended that selection for pod yield and resistance to groundnut rosette disease should be done among progenies from RMP12/ICGV87281 and University of Ghana http://ugspace.ug.edu.gh 19 RMP12/ICGV87018 since they depicted best general combiners for these traits. He also suggested that the significance of SCA mean squares for some of the traits is an indication that non-additive gene effects played an important role in their inheritance. SCA mean square was much smaller than GCA mean squares, which indicates that additive genetic variance was more important than non-additive genetic variance for these traits. Studies on combining ability in F2 and F3 crosses of Spanish and Virginia groundnut have shown that GCA and SCA were significant for almost all traits (Ali, et al., 2001) with preponderance of SCA which implies that selection for pod yield would be more effective in later generations. However, greater magnitude of GCA effect over SCA have been reported (Dwivedi et al., 1998) indicating the importance of additive genetic variance over non-additive variance. From the available reports it is evident that information on the precise nature of genetic control of GRD in groundnut is still lacking. Appropriate experimental design that includes the GRD resistant lines should provide additional information on the gene action involved in the expression of resistance. The knowledge on combining ability and type of gene action responsible for regulation of expression of GRD would certainly help in planning appropriate breeding strategies. 2.7 Genotype x Environment Interaction (GEI) in groundnut To design an appropriate breeding program, it is important to know the proportion of phenotypic variation of a trait that is heritable (Kearsey and Pooni, 1996), since the efficiency of a selection program is mainly dependent on the magnitude of genetic variation and heritability of a trait (Falconer and Mackay, 1996). Apart from high haulm and kernel yield in groundnut, adaptation to specific environments has also been a major breeding goal for groundnut breeders. GEI is a major problem involving quantitative traits, complicates the interpretation of genetic experiments, makes predictions difficult, and reduces the efficiency University of Ghana http://ugspace.ug.edu.gh 20 of selection. For quantitative traits, this interaction can be caused by genotypic rank change or by changes in the absolute differences between genotypes without rank change (Cooper and DeLacy, 1994). Therefore, knowledge about the magnitude of GEI is important to develop cultivars with higher yields and stable performance over a wide range of environmental conditions. Studies and interpretations of GEI range from simple analysis of variance to more specific analyses of genotype performance (Amini et al., 2013). The existence of GEI in groundnut breeding has been reported by Bentur et al. (2004); Senapathi et al. (2004) and Hariprasanna et al. (2008). The expectation has been the identification of suitable genotypes having maximum GEI with moderate level of resistance or susceptible to disease would be of immense benefit to improve the production of groundnut (Mothilal et al., 2010). They further reported significant linear component of GEI for kernel yield and concluded that genotypes differed for their linear response to fluctuations in environments. The magnitude of variation due to environment for kernel yield was higher than G x E (linear) for the same trait which depicted the major part of the total variation and was considered a linear function of environment only (Mothilal et al., 2010). In an earlier study of G x E interaction for PBNV, Buiel et al. (1995) reported that Genotype x environment interaction variance was significant but small. The field resistance of the genotypes studied was equally effective in all environments. Selection in any of these environments may be possible, but is more effective in environments which are favorable for disease development. Yan and Kang (2003) described the different types of G x E interactions and highlighted the implications of these in plant breeding and crop production. Crossover interactions (change in rankings of varieties across environments) are of greatest interest to breeders as these directly affect genotype selection in specific environments. Consequently, promising selections in one environment may perform poorly in another. Such crossover interactions often compel breeders to implement multiple selection programs within industries based on the University of Ghana http://ugspace.ug.edu.gh 21 homogeneity of regions, thereby utilizing greater resources. Ignoring significant G x E in favour of resource savings can lead to reduced genetic gains from selection (Ramburan et al., 2011). Inaccurate characterization of genotype adaptability may lead to poor productivity in environments that interact negatively with specific genotypes and this has implications on industry sustainability. With regards to genetic gains from selection, large G x E interactions, as components of total phenotypic variance, affect heritability (proportion of total phenotypic variance that is due to genetic variance) negatively. The larger the G x E interaction component, the smaller the heritability estimate; thus, progress from selection would be reduced as well (Yan and Kang, 2003). 2.8 Genetic Diversity in Cultivated Groundnut Based on Molecular Markers DNA-based markers provide a reliable means for estimating the genetic relationships among genotypes or taxonomic groups as compared to the morphological markers (Sajib et al., 2012). Precise understanding of the degree of genetic relationships among genotypes, botanical varieties of peanut, and Arachis species could provide insights into the domestication and evolution of this crop. Furthermore, it would have a valuable impact on peanut improvement, through identification of appropriate parents, to ensure a broad genetic base by inter-variety and inter-species crosses. DNA-markers, such as, randomly amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), and simple sequence repeats (SSR) have been used for cultivar discrimination and to study the botanical relationships among the cultivated peanut varieties (Subramanian, et al., 2000; Raina et al., 2001; He and Prakash, 2001; He et al., 2003). AFLP and SSR techniques can be used to detect DNA polymorphism in the cultivated peanut (He and Prakash, 1997; Tang et al., 2007). AFLP and SSR are two powerful DNA fingerprinting techniques. A number of loci can be analysed in an experiment and there is a higher reproducibility of banding patterns by University of Ghana http://ugspace.ug.edu.gh 22 AFLP. SSR markers have several advantages over other molecular markers for their codominant inheritance, large number of alleles per locus, and abundance in genomes (Sajib et al., 2012). These characteristics have promoted the application of SSR as molecular markers in fingerprinting (Wang and Du, 2005), genome mapping (Yin et al., 2006), phylogenetic and genetic relationship studies (Duan et al., 2006), and marker assisted breeding (Yi et al., 2004) in many crops. However, there are few reports concerning SSR and AFLP for evaluation of genetic diversity and relationships among the Arachis species, and much remains to be discovered (Tang et al., 2007). He and Prakash (1997) reported considerable DNA polymorphism in A. hypogaea revealed by the AFLP approach, this assay has been used for molecular diversity studies in peanut by several researchers (Dwivedi et al., 2001; He et al., 2003; Jiang et al., 2007). Comparing SSR and AFLP primers Jiang et al. (2007) reported that SSR primers amplified 91 polymorphic loci in total with an average of 3.14 alleles per primer, and the AFLP primers amplified 72 polymorphic loci in total with an average of 2.25 alleles per primer. Four SSR primers (14H06, 7G02, 3A8, 16C6) and one AFLP primer (P1M62) were found to be most efficient in detecting diversity. They also noted that genetic distance between pairs of Bacteria Wilt (BW) genotypes ranged from 0.12 to 0.94 with an average of 0.53 in the SSR data and from 0.06 to 0.57 with an average of 0.25 in the AFLP data. The SSR-based estimates of the genetic distance were generally larger than that based on the AFLP data. The genotypes belonging to subsp. fastigiata possessed wider diversity than that of subsp. hypogaea. The clustering of genotypes based on the SSR and AFLP data were similar but the SSR clustering was more consistent with morphological classification of A. hypogaea (Jiang et al., 2007). Optimum diverse genotypes of both subsp. hypogaea and subsp. fastigiata can be recommended based on this analysis for developing mapping populations and breeding for high yielding and resistant cultivars. University of Ghana http://ugspace.ug.edu.gh 23 In a study of Phylogenetic Relationships in Genus Arachis based on SSR and AFLP markers, Tang et al. (2008) found genetic distance detected by the SSR markers ranged from 0.09 to 0.95, and the mean was 0.73; and the genetic distance detected by the AFLP markers ranged from 0.01 to 0.79 with an average of 0.42. They also reported that in all the tested BW resistant peanut genotypes, SSR primer pairs were multilocus ones, and the amplified fragments per SSR marker in each peanut genome ranged from 2 to 15 with a mean of 4.77. The peanut cultivars were closely related to each other, and shared a large number of SSR and AFLP fragments. Jiang et al. (2007) partitioned the BW resistant peanut genotypes into two main groups and four subgroups at the molecular level, and that A. duranensis is one of the wild ancestors of A. hypogaea. The lowest genetic variation was detected between A. cardenasii and A. batizocoi, and the highest was detected between A. pintoi and the species in the section Arachis (Tang et al., 2007). Distinct clustering pattern of wild and cultivated genotypes was also reported in genetic diversity studies through SSR and EST – derived SRR maker systems (Moretzshon et al.,2005; Kottapalli et al., 2007; Koppolu et al., 2010). In a related study using single nucleotide polymorphism–based genetic diversity in the reference set of peanut (Arachis spp.) Khera et al. (2013) reported high level of diversity between wild and cultivated peanut and affirmed that grouping pattern exhibited discrete clustering of genotypes based on subspecies, botanical varieties and genome types. Mean genetic similarity between genotype pairs was found to be 0.13 and maximum between ICG 8200 and ICG 8206 at 0.4. They also reported the average major alleles was maximum in AA genome (0.81) and minimum in EE genome (0.56) while for BB and AABB genomes, it was found to be 0.71 and 0.63, respectively. The average PIC ranged from 0.21 (AA genome) to 0.38 (EE genome) while BB and AABB genomes recorded 0.31 and 0.32 respectively (Khera et al., 2013). The narrow genetic base variation observed in cultivated tetraploid groundnut may be attributed to its very recent origin in its evolutionary time as compared to other crops University of Ghana http://ugspace.ug.edu.gh 24 and is a serious genetic bottle neck towards modern breeding effort (Khera et al., 2013). Hence tapping the maximum genetic variation in the primary gene pool is vital to groundnut improvement. From the literature reviewed so far, the genetic background of parents in breeding programs is still narrow, which may have impeded the progress of breeding (Mace et al., 2007). Hence, a better understanding of the genetic diversity amongst the available GRD resistant germplasm is a prerequisite for further efficient improvement of GRD resistance. 2.9 Participatory breeding and varietal selection in groundnut Over the three past decades groundnut production in Nigeria has declined in importance both as food and cash crop for household and National economies (Ndjeunga et al.., 2010). Prospects for regaining production and market share lie in the adoption of improved varieties and crop management techniques that will significantly increase productivity and the improvement of quality standards. The key factors that constrain farmers‘ adoption of technologies are inappropriateness of the technologies, unavailability of required inputs and farmers‘ socio-economic conditions (KARI, 1996; Martins et al., 2002). Technologies that do not meet farmers‘ preferences, objectives and conditions are less likely to be adopted (Upton, 1987). During priority setting within the KARI-Kisii mandate region, groundnut was ranked fourth in importance for arid and semi-arid areas (Andima et al., 2006). Reasons for this included, lack of improved high yielding disease tolerant varieties, organized seed production system, poor agronomic practices, pests and diseases, low producer prices, lack of markets and market information and low adoption of developed technologies (Rachier et al., 2006, Okoko et al., 1998). The matrix ranking of the groundnut varieties conducted by 20 farmers in Suba District of Kenya indicated ICGV-SM 99568 was ranked first because it is early maturing, it is easy to shell, has tasty big seeds with good colour that has high market demand University of Ghana http://ugspace.ug.edu.gh 25 (Okoko et al., 2010). They further concluded that although ICGV-SM 12991 had the highest yield it was ranked second because of its small seed size leading to low marketability. Homabay local and ICGVSM 90704 were ranked last because of their poor germination, growth vigour, late maturing. In a PRA study conducted on Bambara groundnut Alhassan and Egbe (2013) indicated that more males (52.91%) than females (47.08%) were engaged in the production of bambara groundnut. This contrasted the works of Gibbon and Pain (1985) and Mkandawire and Sibuga (2002).These reports indicated that bambara groundnut production was done mainly by female subsistence farmers. Many men might have gone into the production of bambara groundnut because the crop fetches higher income now than it did previously (Tanimu and Aliyu, 1995). Alhassan and Egbe (2013) also indicated that 83.33% of farmers in the study area planted Bambara groundnut on ridges and 65.83% of farmers intercropped cowpea and cassava. The growing of crops in mixed cropping is consistent with the goal of food security (Alhassan and Egbe, 2013). However, apart from participatory variety selection of tropical legume II project by ICRISAT, there is no information at the Institute for Agricultural Research on PRA to assess farmers‘ preferences with the view of involving them in groundnut improvement program. Thus, there is need for concerted efforts to study the problems through research and social motivation for improving sustainability of cropping system and for meeting the challenges of low adoption of improved varieties. University of Ghana http://ugspace.ug.edu.gh 26 CHAPTER THREE 3 FARMERS’ PERCEPTION OF PRODUCTION CONSTRAINTS AND PREFERRED TRAITS FOR RESISTANT GROUNDNUT ROSETTE VARIETIES 3.1 Introduction The groundnut improvement programme at the Institute for Agricultural Research, Samaru is currently developing high yielding, GRD resistant groundnut varieties that are acceptable to farmers using the participatory variety selection approach. However, this conventional breeding procedure has been cited to be more formal concentrating on researchers‘ objectives of solving problems without considering farmers‘ preferences and opinion (Assefa et al., 2005). The consequences of neglecting farmers in setting up research and policy agenda are well documented (Gupta and Lagoke, 1999; Bänziger and Cooper, 2001; Snapp, 2002; Danial, 2003; Kamara et al., 2006; Derera et al., 2006; Ceccarelli and Grando, 2007). It is important for plant breeders to understand how and why farmers choose varieties of their crops, because this will ultimately determine whether a new or improved variety will be useful. Understanding farmers‘ choice and selection practices, their knowledge and goals underlying them, and the similarities and differences with plant breeders provides a means for the two groups to work together more effectively. This understanding and collaboration is critical for supporting long-term global food security (Makanda et al., 2009). In order to ensure sustainable groundnut production, there is a need for combining farmers‘ and researchers‘ objectives. These combinations have significantly contributed to agricultural development (Bucheyeki et al., 2011). Gathering groundnut production constraints and farmers‘ varietal preferences with the view to incorporating them into breeding objective was expected to contribute to increased rate of adoption, improved food security, and reduced poverty. The objectives of the participatory rural appraisal (PRA) were to: University of Ghana http://ugspace.ug.edu.gh 27 a. identify groundnut production constraints. b. assess farmers‘ knowledge of groundnut rosette disease c. appraise farmers‘ preference for rosette resistant varieties. 3.2 Materials and Methods 3.2.1 Description of the study areas The study was conducted in Batsari Local Government Area (LGA) of Katsina State (12°45′10N 7° 14′31). Batsari LGA occupies a land area of 1,107 km2 with population of 208, 978 people (Censor, 2006). Major Socio – economic activities of the inhabitants of this area are farming and livestock keeping. Majority of them are Hausa and Hausa – Fulani. The second location was Nasarawa – eggon LGA of Nasarwa state (8° 32′N, 80 17″ 58. 78′ E,). The LGA occupy land mass of 1,208 km2 with population of 149, 129 inhabitants (Census, 2006). Major Socio – economic activities here are farming and trading. Majority of them are Eggon and Mada. Maps of these areas are shown in Figure 3.1. These areas represent the groundnut growing regions in sudano – Sahelian and Northern guinea savannah of Nigeria, respectively. These areas are characterised by mono-modal type of rainfall that falls between June and October in Batsari and April to October in Nasarawa eggon. University of Ghana http://ugspace.ug.edu.gh 28 Figure 3. 1:Map of Nigeria showing Batsari (Katsina state) and Nasrawa-Eggon (Nasarawa state) 3.2.2 Farmer survey and data analysis Preliminary visits were made to the two locations to discuss with farmers prior to the study. First visit was made on 15th and16th January 2010 to Bastari in Kastina state which was followed by visit to Nasarawa Eggon (Nasarawa state) on the 21st and 22nd February 2010. The visit provided opportunities for informal interactions with groundnut farmers and processors. During these initial visits, secondary data on groundnut production and Nas-Eggon Batsari University of Ghana http://ugspace.ug.edu.gh 29 constraints were obtained from local extension officers. In addition, enumerators, who spoke the local languages, were identified, trained and made to pre-test the questionnaires. The farmers within villages were randomly selected at different strata. In order to obtain information on specific issues covered under the PRA, a formal survey was then conducted during January to March 2011 using a structured questionnaire, and other participatory rural appraisal tools including focus group discussions and observations made during transects walks across the areas. In both Batsari, and Nasarawa – eggon, 50 farmers were interviewed. Demographic information such as general household structure, education level, wealth status (as judged by property owned), cropping enterprises, and production constraints were obtained using the structured questionnaire (Appendix 1). Group discussions were done on completion of the questionnaire interviews to confirm data obtained and to solicit new information that was not captured during the formal process. Both qualitative and quantitative data were subjected to statistical analysis using the statistical Package for Social Science (SPSS) version 15 (SPSS Inc., Chicago IL). Frequencies were determined for quality questions. Associations and t – test for comparison were determined for quantitative variables. Graphs were used to present results. Preferred and unfavoured traits, as well as the importance and severity of rosette diseases were ranked to highlight farmers‘ perceptions. 3.3 Results 3.3.1 Household and demographic information Majority (36 %) of the farmers in Batsari were above 40 years while those of Nasarawa – eggon were younger (61 %) and ranged between 20 – 35 years (Table 3.1) and majority of them were married. The sex ratios of the household were not significantly different (p ≤ 0.05) University of Ghana http://ugspace.ug.edu.gh 30 in the two districts (t = 6.31, P =0.33). The number of farmers who had tertiary education or belonged to an association did not differ in the two districts. However, the age of farmers was significantly different and so were married and single farmers across the districts. The numbers of farmers with primary and secondary education were significantly different (Table 3.1). In both areas, at least 80% of the farmers had lived in a village for more than 10 years practicing small – scale farming and had acquired experience in farming. Ninety – eight per cent of the farmers at Batsari village were males and in Nasarawa – eggon 7% were female. The majority of farmers in Batsari village were illiterate while 50% of the farmers in Nasarawa – eggon were literate. A proportion of the farmers (8%) in Batsari and 32 % in Nasarawa – eggon had primary education (Table 3.1). Farmers belonging to associations were 12.8% (Batsari) and 9.1% in Nasarawa eggon. Farmers‘ experience (farming for more than 10 years) significantly (P < 0.05) correlated with level of education (r = 0.51517**) and level of awareness of rosette diseases (r = 0.45602*) (Table 3.2) University of Ghana http://ugspace.ug.edu.gh 31 Table 3. 1:Farmer and Household information for Batsari and Nasarawa – eggon LGA in Nigeria for the 2010 growing season Variable Local Government Area t – value Probability Batsari Nasarawa Average Gender of household head (%) (%) (%) 6.31 0.50 Male head 98 93 95 Female head 2 7 5 Age of farmers 1.90 0.49 20 – 25 0 18 9 26 – 30 0 23 12 31 – 35 0 20 10 36 – 40 16 14 15 41 – 45 36 14 25 46 – 50 24 7 16 51 – 55 16 5 11 > 56 8 0 4 Marital status 2.35 0.03 Married 100 88.6 94** Single 0 6.8 3 Widowed 0 1 1 Divorce 0 1 1 Over 10 year experience in farming 94 84 89 Level of education acquired 2.35 0.50 Illiterate 88 50 69 Primary 8 32 20 Secondary 4 16 10 Tertiary 0 2 1 Membership of association Member 12.8 9.1 11 6.31 0.33 Non member 76.2 90.9 84 * and ** significant and P < 0.05 and P < 0.01, respectively University of Ghana http://ugspace.ug.edu.gh 32 Table 3. 2: Pair wise correlation of some farmers’ level of awareness of the groundnut rosette disease Gender Age Marital Status Famer Experience Membership of association Level of education Rosette disease awareness Gender 0.202 0.992** 0.462 -0.897** 0.998** 0.999** Age 0.321 0.962** -0.615* 0.262 0.196 Marital Status 0.568 -0.945** 0.998* 0.992* Famer Experience -0.807** 0.515** 0.456* Membership of association -0.922 -0.894** Level of education 0.562* University of Ghana http://ugspace.ug.edu.gh 33 Maize, sorghum, groundnut, yam, cowpea and sesame were mentioned as the most important crops grown for cash income and sources of food security, while cassava, tomatoes, water melon, onion and pepper were less cultivated by the sampled farmers. Groundnut was ranked as the most important cash crop in both Batsari and Nasarawa – eggon areas (98% and 82%, respectively). While sesame was ranked as the second most important crop in Batsari (83.7%), yam was ranked the second in Nasasrawa – eggon (86.4%) (Table 3.3). Ranking of groundnut as the most important cash crop showed farmers‘ strong interest in the crop. This was probably because of the increasing demand for cultivation to target markets, as well as alleviating poverty and food shortage at household level. The results of the study showed that farmers produced groundnut in association with other crops especially maize and sorghum. Groundnut is intercropped by most farmers and with only few farmers growing it as a sole crop. University of Ghana http://ugspace.ug.edu.gh 34 Table 3. 3: Pair-wise ranking of major crops grown by farmers in Batsari and Nasarawa – eggon in 2011 Crop Districts Batsari (%) Nasarawa – eggon (%) Average (%) Cash crop Food crop Cash crop Food crop Rainy season Groundnut 98 (1) 40 (5) 86 (2) 43 (7) 92.0 Groundnut var: SAMNUT 21 and 23 Maiyado‖ RRB Cowpea 75.5 (3) 30 (6) 64.6 (5) 51 (5) 70.1 Maize 63.4 (4) 100 (1) 75.8 (3) 93 (1) 59.6 Millet 75.3 (3) 98 (2) 53.7 (7) 23 (8) 64.5 Sesame 83.7 (2) 32 (7) 89.4 (1) 47 (6) 86.6 Yam — 86.4 (2) 82 (3) 86.4 Sorghum 54.5 (5) 74 (3) 67.3 (4) 87 (2) 50.9 Cassava 25.2 (6) 51(4) 63.0 (6) 23 (8) 44.1 Rice — 58.9 (7) 58 (4) 58.9 Dry season Tomatoes 56.8 56.5 56.7 Onion 78.0 53.6 65.8 Water – melon 78.5 45.3 61.9 Pepper 75.0 34.3 54.7 The figures are percentage responses, number in parenthesis = rank and — = crop not reported University of Ghana http://ugspace.ug.edu.gh 35 3.3.2 Characteristics of preferred groundnut varieties in Batsari and Nasarawa – eggon districts The choice of a groundnut variety in rural areas is determined by some characteristics of the plant and the environment. Although farmers‘ criteria in choosing varieties were similar across the two study areas, there were marked differences in the characteristics of the varieties preferred by farmers. These differences varied from site to site (Fig. 3.3). Most of the major traits preferred were those associated with yield, market value and those that enabled the crop to escape or produce yield, even when attacked by pests and diseases. Pest and disease tolerance and high oil content (Fig. 3.3) were considered to be the most important desired characteristics in Batsari and Nasarwa – eggon with 21% and 28% of the respondents respectively while early maturity was most important in Batsari. The other important traits preferred by famers in both locations were the pod yield. In Batsari, 14% of the interviewed farmers considered haulm to be an important trait. This is not quite an important trait in Nasarawa – eggon as only 6% use haulm. Figure 3. 2:Distribution of traits preferred by farmers University of Ghana http://ugspace.ug.edu.gh 36 The results revealed that farmers were aware of groundnut rosette disease which was commonly known by various names such as ―Kuturtan gyada‖ emphasizing the predominance of the disease in the area. The majority of the farmers reported the disease to be associated with insects and few of them recognized aphid as being responsible. Eighteen per cent of the respondents associated the disease with inadequate rainfall (Fig. 3.4). Figure 3. 3:Farmers perception on the causes of groundnut rosette disease Most farmers recorded moderate (20 – 40%) to high (50 %) yield loss due to GRD (Fig. 3.5). A total yield loss (100 %) had been experienced by some farmers University of Ghana http://ugspace.ug.edu.gh 37 Figure 3. 4:Farmers perception of yield loss due to groundnut rosette disease About 34 – 40 % of the respondents in both Batsari and Nasarawa – eggon did nothing to combat the menace of groundnut rosette disease, while many other farmers rogued out infected plants with some utilizing rosette resistant groundnut varieties (Figure 3.6) Figure 3. 5: Groundnut rosette disease control measures adopted by farmers University of Ghana http://ugspace.ug.edu.gh 38 3.3.3 Preferred groundnut varieties and associated characteristics Farmers indicated that they selected groundnut varieties for commercial production based on consumer preference. Farmers also chose groundnut varieties for production on the basis of potential pod and haulm yield, oil quality and content, and market price. For instance, in Nasarawa – eggon, farmers ranked ‗Maiyado‘ as the most preferred variety because of market demand, and high yield potential, although it is susceptible to groundnut rosette diseases and other foliar diseases, while ‗SAMNUT21‘ and ‗SAMNUT23‘ varieties were preferred in Batsari because of high pod yield, earliness, seed colour, market acceptance and tolerance to pest and foliar diseases. 3.3.4 Perception of farmers on constraints to groundnut production Several constraints were mentioned and ranked by the farmers (Table 3.4). The most important constraints reported were pests and diseases and poor quality seeds and drought. The farmers recounted these limitations to severely reduced yields. Price fluctuation for groundnut was reported to constitute a problem. Whenever there was a bumper harvest groundnut prices dropped, so they kept their produce in storage, until such a time that the price increased in the off-season. Farmers differentiated between threats due to diseases from those caused by weeds and drought. The ranking of the frequent diseases or pests across villages revealed that GRD was the most threatening constraint. The symptoms observed by respondents to describe GRD were stunting, bushy and yellowing of leaves, where groundnut plants were not suffering from water shortage. University of Ghana http://ugspace.ug.edu.gh 39 Table 3. 4:Pair-wise ranking of the most important constraints in groundnut production in Batsari and Nasarawa – eggon Constraint Score by farmers Batsari Nass – eggon Total Score Ranking Drought 2 4 6 3 Pest and Diseases 1 2 3 1 Weeds 3 4 7 4 High cost of insecticides 5 3 8 5 Poor quality seed 2 1 3 1 Price fluctuation 3 2 5 2 1 = very serious problem and 5 = minor 3.4 Discussion A participatory rural appraisal was conducted to understand farmers‘ production systems and perceptions on groundnut rosette diseases across two locations in Nigeria. The PRA helped to obtain information on auxiliary data on socio – economic aspect of farmers. Farmers faced several constraints from seed through crop production, crop protection and marketing in groundnut production. The majority of farmers were males and had been farming for more than 10 years. Most of them were in the age range of 25 – 30 years in Nasarawa-eggon but older in Batsari. The major source of income was from crop growing that accounted for more than 80% of their household income However, most farmers recorded low yield of groundnut crop owing to several constraints that called for intervention and strategies to enhanced productivity. University of Ghana http://ugspace.ug.edu.gh 40 Most farmers at Batsari location were aware of improved groundnut varieties and some grow them together with their landrace varieties. Nasarawa – eggon completely relies on their landrace variety ‗Maiyado‘. Reasons for poor adoption rate of improved varieties could be due to limitations of certified seeds, high prices and inadequate information on the improved varieties. The seeds of landrace varieties with farmers‘ preference traits were sold at reasonably affordable prices. The farmers faced similar groundnut production constraint across Batsarit and Nasarawa – eggon despite differences in geographical locations. The major challenges were poor quality seeds and prevalence of groundnut rosette disease. The findings from this study further showed that groundnut rosette disease was probably associated with insects and drought. Adu – Dapaah et al. (2004) associated insect and drought as the favourable conditions for groundnut rosette disease from the PRA studies of groundnut. The Agricultural Transformation Agenda (ATA) programme of the Federal Government of Nigeria was designed to enhance the livelihood of farming community through improving productivity and hence raising their income through supply of agricultural inputs; fertilizers, good quality seeds and credit facility. These did not reach resource poor farmers because of the requirements that include detailed information on farming activities, and marketing. For farmers with limited education and understanding of the process, the requirements are unobtainable. Furthermore, majority of farmers were not members of any cooperative society. To derive maximum benefit from the ATA programme, the Federal government should improve the extension services to facilitate the formation of farmers‘ cooperative societies at grass root levels for coordinated agricultural activities. University of Ghana http://ugspace.ug.edu.gh 41 Genotype plays a very significant role in achieving higher productivity. In general, across the locations it was noticed that there was no efficient seed system or replacement mechanism for penetration of improved cultivars of groundnut. Most of the farmers use very old landrace ‗Maiyado‘, demonstrating the poor rate of seed replacement in these parts of the country. The risk taking ability and openness of Batsari‘s farmers to new technologies (SAMNUT21 and SAMNUT23) made a big difference to their achieving high productivities, an approach that was relatively lacking in Nasarawa - eggon. ‗Maiyado‘ was preferred by majority of farmers at both locations because of its colour, high oil contents and resistance to foliar disease. These are traits that most farmers and consumers look for in groundnut (Ndjuenga et al., 2010). Until recently, researchers at IAR/ICRISAT focused mainly on earliness, yield and resistance to foliar diseases (Olorunju et al., 2001) to improve groundnut production in sub Saharan Africa. The new focus is involvement of farmers through PRA to final production of improved seed which will ultimately, enhance rapid adoption. This agrees with the findings of Nkonya and Featherstone (2001) who found that varieties with farmers preferred traits were easily adopted. This was evident with SAMNUT21 and SAMNUT23 with a high adoption rate (> 70%) by farmers at Katsina state, Kano, Jigawa and Kaduna states because the background parents of these varieties are local varieties with farmers preferred traits (Ndjeunga et al., 2003). However, the nonavailability of the two varieties in Nasarawa state, compelled the farmers to use the landrace they have and available in market as recycle seeds. The high price of certified seed if available was another reason for low adoption. University of Ghana http://ugspace.ug.edu.gh 42 3.5 Conclusions and Recommendations This study identifies groundnut varieties grown by the farmers, criteria for choice of the varieties and constraints in production, thus providing the basis for formulation of farmer-oriented groundnut breeding programme. Farmers have diverse perceptions and complex combinations of criteria they use in selecting groundnut varieties. The key criteria include high yields, early maturity, tolerance to groundnut rosette disease drought and insect pests. Groundnut production in both Batsari and Nasarawa-Eggon is constrained by related factors. The most important constraints perceived by farmers are pest and rosette diseases, poor quality seeds and drought. Farmers in both locations were aware of groundnut rosette disease as it is called by various local names. For instance ―Kutrtan- gyada‖ was the name given to GRD in Batsari. To increase groundnut production, research should take into consideration the farmers‘ circumstances and preferences and develop varieties and crop management packages meet farmers demands. Incorporation of farmers‘ preferences in selection of groundnut varieties in breeding process would increase likelihood of adoption of the varieties. Whereas groundnut breeding cannot incorporate all the desired attributes, the key attributes should be included in particular varieties and many varieties should be bred focusing the demands of different groups of farmers. Considering that farmers prefer saved seeds of local varieties as a strategy for coping with cash flow constraints, effort should be made to breed varieties that are resistant to insect pests and disease. Such varieties are likely to be highly adopted by smallholder farmers, especially when the other key criteria they apply in variety selection are also incorporated. University of Ghana http://ugspace.ug.edu.gh 43 CHAPTER FOUR 4 ASSESSMENT OF GENETIC DIVERSITY OF GROUNDNUT (ARACHIS HYPOGAEA L.) GENOTYPES FOR RESISTANCE TO ROSETTE DISEASE USING SSR MARKERS 4.1 Introduction Breeding for foliar disease resistant genotypes is the ideal solution for reducing the crop losses. Identification and utilization of a broad spectrum of genetically diverse sources of GRD resistance is critical for the development of a new generation of broad-based high-yielding GRD- resistant groundnut cultivars. Limited knowledge about the genetic diversity of the GRD- resistant germplasm and deleterious linkage drag has impeded the utilization of a wide spectrum of GRD resistance donors. Diversity studies in groundnut have generally revealed extensive phenotypic variation amongst varieties (Upadhyaya al., 2001, 2003) yet limited variation at the molecular level (Subramanian et al., 2000; Moretzsohn et al., 2004). Several approaches including molecular (Jiang et al., 2007; Milla-Lewis et al., 2010; Khera et al., 2013) and morphological characterization have been used in assessing the genetic diversity of groundnut germplasm but results of morphological characterization are highly influenced by environmental factors (Shoba et al., 2010). Molecular marker technologies are playing an increasingly important role in conservation and use of plant genetic resources in plant breeding programmes (Varshney et al., 2009). Among the DNA markers, simple sequence repeat (SSR) markers are more preferable as it is more variable within genomes than other marker types (Belaj et al., 2003). Additionally, SSRs have the advantage of being co-dominant, only requiring very small amounts of DNA and hence have been widely applied in many plant genetics studies, e.g. for evaluating genetic diversity (Zhebentyayaeva et al., 2003; Fahima et al., 1998). University of Ghana http://ugspace.ug.edu.gh 44 In Nigeria, extensive efforts have been made in groundnut breeding for GRD-resistance and several resistant cultivars have been released. However, these cultivars have been released 10 years ago have begun to show resistance breaking that is influenced by genetic variability in the pathogen population (Legrève and Duveiller, 2010). Only a few sources of GRD-resistance have been successfully used in breeding programs at the Institute for Agricultural Research (IAR) even though several resistant genotypes are available (Olorunju et al., 2001). Most GRD- resistant cultivars released in IAR are based on just three sources of resistance (RMP12, MDR-8- 19 and UGA2). Obviously, the genetic background of parents in IAR groundnut breeding programs is still narrow, which may have impeded the progress of breeding. Therefore, a better understanding of the genetic diversity amongst GRD-resistant germplasm is a prerequisite for further efficient improvement of GRD-resistance. The objectives of the present study are to use SSR markers to detect DNA polymorphism among cultivated groundnut genotypes with differential levels of GRD resistance and for selecting parents for further breeding programmes. 4.2 Materials and methods 4.2.1 Plant material and DNA extraction Fifty groundnut genotypes obtained from the IAR and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) Mali, consisting of aphid and rosette resistant genotypes were assayed in this study (Table 4.1). Total genomic DNA was isolated from young leaves of 15 – 20 days old seedlings. Each sample consisted of about 5g of leaves pooled from 2-3 seedlings and DNA was extracted using a CTAB-based procedure, with 3% (v/v) b-mercaptoethanol in a 3% (w/v) CTAB buffer (Mace et al., 2003). The quantity and quality of DNA were determined University of Ghana http://ugspace.ug.edu.gh 45 electrophoretically through comparison with known concentrations of uncut l DNA standards and spectrophotometric analysis at 260/280nm, and subsequently diluted to 5ng/ml. Laboratory analysis was done at Generation Challenge Program (GCG) Kenya between August, 2012 – November 2012. 4.2.1 SSR Analysis Forty SSR primer pairs (Table 4.2) were used to amplify the genomic DNAs. PCR reactions were carried out in 10μL reaction volume using a GeneAmp PCR System 9700 (Applied Biosystems). The PCR reaction mixtures contained between 5 and 15ng of genomic DNA, 10–30 pmol of each primer, 100–125mM of dNTP, 0.6–1.2U/ml of Taq DNA polymerase (Amersham), 1 PCR buffer (10mM Tris–HCl pH 8.3, 50mM KCl) and 0.5–2.5mM MgCl2. The fixed- temperature PCR programmes consisted of an initial denaturation step for 2 min at 940C, followed by 35 cycles of denaturation for 45 s (940C), annealing for 1 min (57–640C) and extension for 1 min 30s (720C). The PCR products were then incubated at 720C for a further 10 min to ensure complete extension. A second PCR programme using the touchdown approach was also used for selected SSRs with the following conditions: initial denaturation for 2 min at 940C, followed by 10 cycles: 940C for 45 s, 650C (210C/cycle) for 1 min and 720C for 1 min 30 s. This was then followed by 20 cycles of 940C for 45s, 550C for 1 min and 720C for 1 min 30s, followed by a final extension step of 10min (720C). University of Ghana http://ugspace.ug.edu.gh 46 Table 4. 1:Groundnut genotypes with different levels of resistance and susceptibility to groundnut rosette disease (GRD) included in the study Groundnut genotypes Seed source Aphid/rosette resistant status ICGV-IS 07812 ICRISAT Rosette Resistant ICGV-IS-07885 ICRISAT Rosette Resistant ICGV-IS-07886 ICRISAT Rosette Resistant ICGV-IS-07888 ICRISAT Rosette Resistant ICGV-IS-07890 ICRISAT Rosette Resistant ICGV-IS-07893 ICRISAT Rosette Resistant ICGV-IS-07894 ICRISAT Rosette Resistant ICGV-IS-07895 ICRISAT Rosette Resistant ICGV-IS-07899 ICRISAT Rosette Resistant ICGV-IS-07903 ICRISAT Rosette Resistant ICGV-IS-07904 ICRISAT Rosette Resistant ICGV-IS-07839 ICRISAT Rosette Resistant ICGV-IS-07842 ICRISAT Rosette Resistant ICGV-IS-07844 ICRISAT Rosette Resistant ICGV-IS-07850 ICRISAT Rosette Resistant ICGV-IS-07852 ICRISAT Rosette Resistant ICGV-IS-07859 ICRISAT Rosette Resistant ICGV-IS-07864 ICRISAT Rosette Resistant ICGV-IS-07865 ICRISAT Rosette Resistant ICGX – SM 00017/5/P10/P1 ICRISAT Aphid Resistant ICGX-SM 00020/5/P6/P2 ICRISAT Aphid Resistant ICGX-SM 00020/5/P9 ICRISAT Aphid Resistant ICGX – SM 00020/5/9 ICRISAT Aphid Resistant ICGX – SM 00020/5/P4/P1 ICRISAT Aphid Resistant ICGX-SM 00017/5/P1/P1 ICRISAT Aphid Resistant ICGX-SM 00017/5/P15/P2 ICRISAT Aphid Resistant ICGX-SM 00020/5/15/P2 ICRISAT Aphid Resistant ICGX-SM 00020/5/P2/P1 ICRISAT Aphid Resistant ICGX-SM 00020/5/P4/P1 ICRISAT Aphid Resistant ICGX-SM 00020/5/P4/P10 ICRISAT Aphid Resistant KWANKWASO SAMARU Rosette susceptible MANIPENTA SAMARU Rosette susceptible RS006F3B1-21 ICRISAT ROSDOM University of Ghana http://ugspace.ug.edu.gh 47 34 RS006F4B1-25 Source ROSDOM 35 RS006F4B1-31 ICRISAT ROSDOM 36 RS006F4B1-35 ICRISAT ROSDOM 37 RS006F3B1-27 ICRISAT ROSDOM 38 RS006F3B1-53 (B) ICRISAT ROSDOM 39 RS006F3B1-57 (B) ICRISAT ROSDOM 40 RS006F3B1-59 (R) ICRISAT ROSDOM 41 RS006F4B1-10 (B) ICRISAT ROSDOM 42 RS006F4B1-13 ICRISAT ROSDOM 43 RS006F4B1-2 ICRISAT ROSDOM 44 RS006F4B1-22 ICRISAT ROSDOM 45 SAMNUT10 IAR Rosette Tolerant 46 SAMNUT14 IAR Rosette susceptible 47 SAMNUT21 IAR Rosette Tolerant 48 SAMNUT22 IAR Rosette Tolerant 49 SAMNUT23 IAR Rosette Tolerant 50 SAMNUT24 IAR Rosette Tolerant ROSDOM=Rosette disease resistant + dormancy, IAR=Institute for Agricultural Research, ICRISAT=International Crop Research Institute for Semi-Arid Tropics 4.2.2 Electrophoresis and data collection PCR amplification products were separated on 6% nondenaturing polyacrylamide gels and revealed using a silver staining procedure based on ammoniacal solutions of silver, modified from Kolodny (1984). The size of the allele scored was determined through comparison with the 100 bp DNA ladder (Amersham) included on all gels. Estimates of similarity were based on Nei et al. (1983) definition of similarity as  cbaaSij  22 , where ijS is the similarity between two individuals, i and j , a is the number of bands present both in i and j , b is the number of bands present in i and absent in j , and c is the number of bands absent in i and present in j . Gene diversity ( eH ) was estimated according to the formula of Nei et al, (1983) for each locus as  21 ije PH , where ijP is the frequency of thj allele for thi locus summed across all allele of University of Ghana http://ugspace.ug.edu.gh 48 the locus. Nei‘s et al. (1983) genetic distance (GD) was calculated for each pair of population and for each pair of tested entries. Dendrogram based on Nei‘s genetic distances (Nei, et al., 1983) were generated using the unweighted pair group method with arithmetic average (UPGMA). Polymorphism information content (PIC) as described by Torres et al. (2008) as follows:    n i iPPIC 1 21 Where iP is the frequency of the thj allele for the thi marker, and summed over n alleles, it was used to represent the information value of a marker for detecting polymorphism within a population. It depends on the number of detectable alleles and their frequency distribution. All statistical analyses were conducted using PowerMarker 1.32 Window based computer package (Yeh and Yang, 2000). 4.3 Results 4.3.1 Allelic variation at SSR loci Diversity assessment of the 50 groundnut genotypes was performed using 40 SSR primer pairs, of which 35 primers amplified polymorphic bands. A total of 166 polymorphic alleles were recorded among the groundnut genotypes tested (Table 4.2). The 35 polymorphic SSR primers each amplified 2 to 12 microsatellite loci, with an average of 4.74 loci per primer. Several primers including IPAHM103, IPAHM287 and IPAHM524 were more efficient than the rest in detecting the diversity among groundnut genotypes since each amplified 6 to 8 loci. An allele observed in less than 10% of the 50 accessions was considered to be rare. A total of 14 rare alleles were observed. Value of each marker PICs ranged from 0.19 for marker detected by IPAHM354 to 0.82 for the marker detected by IPAHM524 (Table 4.2). The PIC values greater than 0.5 are considered highly informative, however, markers with 0.5 PIC > 0.25 were also considered to be informative (Botstein et al., 1980). The variation observed in this study was University of Ghana http://ugspace.ug.edu.gh 49 significantly associated with the number of alleles detected at each locus; hence the SSR marker revealed large amount of variation in the sampled groundnut genome. The observed frequency of the 166 alleles ranged from 0.25 for marker IPAHM524 to 0.89 for marker IPAHM345 with an average of 0.54 per marker. Based on SSR analysis, the average genetic pairwise distance among the 50 genotypes was 0.31 (Table 4.3). The largest distance was 0.51 (between ICGV – IS – 07812 and RS006F4B1 – 31) and the shortest distance was 0.05 between ICGV – IS – 07865 and ICGV – IS – 07864, all the four lines were GRD-resistant. The distances among the susceptible accessions were relatively large, ranging from 0.35 (between KWANKWASO and SAMNUT14) to 0.39 (KWANKWASO and MANIPENTA). The average distance between the aphid resistant and susceptible genotype (KWANKWASO) was 0.37 with the greatest diversity between ‗KWANKWASO‘ and (ICGX- SM 00020/5/P6/P2 and ICGX-SM 00020/5/P9) (0.41). A GRD-susceptible farmer variety (MANIPENTA) had an average distance of 0.38 from the rosette resistant genotypes with a range from 0.26 (between ‗MANIPENTA‘ and ICGV – IS – 07894) to 0.45 (between ‗MANIPENTA‘ and ICGV – IS – 07842). This susceptible farmers‘ variety had an average genetic distance of 0.37 from aphid resistant genotypes and ranged from 0.30 (between ‗MANIPENTA‘ and ICGX – SM – 000020/5/P4/P10) to 0.41 (‗MANIPENTA‘ and ICGX – SM 00017/5/P10/P1; ‗MANIPENTA‘ and ICGX – SM 00020/5/9; ‗MANIPENTA‘ and ICGX-SM 00020/5/P2/P1) . Similarly, ‗MANIPENTA‘ had an average distance of 0.38 with RS0006F series with a range from 0.27 (between ‗MANIPENTA‘ and RS0006F3B1 – 21) to 0.45 (between ‗MANIPENTA‘ and RS0006F4B1 – 22). Finally, with rosette tolerant SAMNUT series, ‗MANIPENTA‘ had an average genetic distance of 0.39 with a range of 0.13 (between ‗MANIPENTA‘ and SAMNUT22) to 0.44 (between ‗MANIPENTA‘ and SAMNUT24). University of Ghana http://ugspace.ug.edu.gh 50 Comparing the different GRD-resistant sources based on Nei, (1983) original genetic distance resulted in the dendrogram shown in Figure 4.2. The ICGV – IS series and ICGX – SM GRD- resistant sources showed the smallest genetic distant (0.06) and were grouped together. The largest genetic distances are found between Local variety and RS0006F series (0.20) and were well separated from other GRD-resistant sources (Fig 4.2). Large variation of gene diversity among loci was found. 4.3.2 Comparison of gene diversity The statistics describing the genetic diversity found at each locus in each population was calculated. Considering the genetic background of experimental groundnut genotypes, the average gene diversity for the whole sample was 0.51. The lowest gene diversity (0.20) was detected in 4 loci for all genotypes. Gene diversity of > 0.50 in all the genotypes was observed for 25 loci. The highest gene diversity in each population was found at IPAHM524 (0.84), IPAHM108 (0.76), IPAHM229 (0.75), IPAHM23 (0.74) and IPAHM219 (0.74). Eighteen loci with gene diversity > 0.6 were found in GRD-resistance sources. (Table 4.2) University of Ghana http://ugspace.ug.edu.gh 51 Table 4. 2:Primers used in the study, gene bank ID, repeat motif, frequency and number of alleles as well as gene diversity, and polymorphic information contents (PIC) based on the analysis of 50 groundnut genotypes for 35 polymorphic SSR markers Marker Gene Bank Accession ID Repeat Motif Allele Frequency No of Allele No of rare allele Gene Diversity PIC IPAHM23 ER974415 (CA)17(TA)3 0.43 4 1 0.63 0.56 IPAHM73 ER974423 (GA)13 0.62 4 0.54 0.48 IPAHM82 IPAHM 82 (GA)15 0.52 5 0.54 0.44 IPAHM92 ER974430 (GT)11 0.56 3 0.51 0.40 IPAHM93 ER974431 (CT)15 0.83 4 0.30 0.28 IPAHM103 ER974437 (CA)3(GA)17 0.69 8 0.50 0.48 IPAHM105 ER974438 (CT)18 0.43 6 1 0.68 0.62 IPAHM108 ER974439 (TC)18 0.29 8 1 0.76 0.72 IPAHM123 ER974446 (GA)18 0.39 6 1 0.71 0.66 IPAHM136 ER974452 (TC)2(CT)13 0.49 5 1 0.57 0.48 IPAHM164 ER974461 (GA)20 0.43 6 1 0.69 0.64 IPAHM165 ER974462 (GA)13 0.51 3 0.61 0.54 IPAHM171a ER974466 (TC)7TGTT(TC)9 0.59 5 0.60 0.57 IPAHM171c ER974468 (GA)16 0.52 2 0.50 0.37 IPAHM176 ER974469 (GA)18 0.57 6 0.59 0.54 IPAHM177 ER974470 (CA)11TA(CA)3 (TA)4 0.51 3 0.62 0.55 IPAHM219 ER974474 (TG)15 0.34 6 1 0.74 0.70 IPAHM229 ER974475 (CA)14TA(CA)3 0.32 6 1 0.75 0.71 IPAHM282 ER974488 (CA)14(TA)5 0.53 4 0.64 0.60 IPAHM283 ER974489 (TA)4(TG)26TTG (GT)2 0.56 3 0.58 0.51 IPAHM287 ER974492 (TG)16(AG)22 0.48 7 1 0.70 0.66 IPAHM288 ER974493 (GA)4AA(GA)2 0.86 5 0.24 0.23 IPAHM290 ER974494 (TA)3(CA)3CC(CA)5(TA)8 0.76 4 0.38 0.34 IPAHM354 ER974514 (GA)16 0.89 3 0.20 0.19 IPAHM356 ER974515 (GA)21G(GA)2 0.39 7 1 0.74 0.69 IPAHM37 ER974519 (CA)10(TA)7 0.72 3 0.44 0.40 IPAHM373 EE974518 (TTG)6CT(GTT)8 0.76 2 0.37 0.30 IPAHM395 ER974522 (GA)14 0.86 3 0.25 0.23 IPAHM407a ER974525 (TC)7TGTT(TC)9 0.53 4 0.63 0.58 IPAHM455 ER974536 (TA)5(TG)16 0.51 4 0.66 0.61 IPAHM429 ER974534 (GT)17 0.45 3 1 0.65 0.57 IPAHM468 ER974542 (GA)15 0.60 2 0.48 0.37 IPAHM524 ER974548 (GA)20AA(GA)3 0.25 12 1 0.84 0.82 IPAHM531 ER974551 (TAC)7 0.49 4 1 0.52 0.40 IPAHM475 ER974545 (GT)7(GA)12 0.48 6 1 0.61 0.53 Total 166 14 Mean 0.55 4.47 0.57 0.51 University of Ghana http://ugspace.ug.edu.gh 52 Table 4. 3:Pairwise genetic distance coefficients of 50 GRD-resistant genotypes using 36 SSR primer pairs combinations analyzed by PowerMarker software Genotypes 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 1 ICGV-IS 07812 2 ICGV-IS-07885 0.21 3 ICGV-IS-07886 0.42 0.28 4 ICGV-IS-07888 0.40 0.27 0.22 5 ICGV-IS-07890 0.42 0.29 0.25 0.36 6 ICGV-IS-07893 0.38 0.20 0.27 0.34 0.25 7 ICGV-IS-07894 0.30 0.21 0.29 0.29 0.27 0.22 8 ICGV-IS-07895 0.35 0.35 0.39 0.36 0.42 0.45 0.38 9 ICGV-IS-07899 0.37 0.35 0.39 0.48 0.33 0.28 0.33 0.35 10 ICGV-IS-07903 0.24 0.13 0.29 0.28 0.27 0.16 0.10 0.32 0.32 11 ICGV-IS-07904 0.49 0.41 0.25 0.19 0.38 0.37 0.41 0.47 0.45 0.37 12 ICGV-IS-07839 0.37 0.30 0.34 0.13 0.45 0.41 0.32 0.38 0.49 0.30 0.21 13 ICGV-IS-07842 0.32 0.23 0.32 0.35 0.23 0.29 0.17 0.33 0.36 0.14 0.46 0.36 14 ICGV-IS-07844 0.33 0.22 0.32 0.35 0.27 0.27 0.15 0.36 0.35 0.13 0.46 0.37 0.10 15 ICGV-IS-07850 0.30 0.24 0.33 0.36 0.21 0.28 0.23 0.31 0.25 0.17 0.45 0.43 0.23 0.21 16 ICGV-IS-07852 0.46 0.35 0.21 0.21 0.34 0.33 0.35 0.44 0.40 0.34 0.17 0.15 0.36 0.37 0.43 17 ICGV-IS-07859 0.29 0.27 0.24 0.37 0.22 0.23 0.22 0.39 0.31 0.23 0.34 0.36 0.25 0.28 0.36 0.28 18 ICGV-IS-07864 0.45 0.29 0.30 0.40 0.09 0.20 0.28 0.40 0.32 0.26 0.41 0.41 0.24 0.27 0.27 0.34 0.18 19 ICGV-IS-07865 0.41 0.29 0.31 0.42 0.10 0.21 0.30 0.44 0.35 0.28 0.43 0.45 0.25 0.29 0.28 0.35 0.20 0.05 20 ICGX – SM 00017/5/P10/P1 0.20 0.16 0.30 0.31 0.30 0.27 0.22 0.32 0.37 0.13 0.38 0.30 0.20 0.25 0.27 0.35 0.20 0.30 0.27 21 ICGX-SM 00020/5/P6/P2 0.36 0.33 0.32 0.36 0.34 0.25 0.31 0.49 0.25 0.32 0.36 0.42 0.32 0.32 0.33 0.33 0.27 0.36 0.37 0.34 22 ICGX-SM 00020/5/P9 0.32 0.27 0.31 0.37 0.26 0.29 0.21 0.37 0.41 0.18 0.38 0.37 0.19 0.23 0.31 0.35 0.22 0.25 0.23 0.16 0.39 23 ICGX – SM 00020/5/9 0.32 0.27 0.28 0.42 0.34 0.25 0.31 0.42 0.24 0.30 0.33 0.42 0.37 0.37 0.27 0.30 0.23 0.32 0.33 0.33 0.23 0.40 24 ICGX – SM 00020/5/P4/P1 0.24 0.24 0.36 0.39 0.31 0.33 0.17 0.36 0.27 0.18 0.44 0.37 0.19 0.18 0.27 0.40 0.23 0.31 0.28 0.17 0.33 0.21 0.36 25 ICGX-SM 00017/5/P1/P1 0.32 0.22 0.25 0.28 0.24 0.27 0.14 0.34 0.42 0.12 0.33 0.30 0.15 0.19 0.25 0.33 0.23 0.25 0.27 0.16 0.40 0.11 0.38 0.21 26 ICGX-SM 00017/5/P15/P2 0.32 0.21 0.25 0.27 0.25 0.27 0.18 0.32 0.40 0.14 0.35 0.28 0.18 0.21 0.26 0.31 0.22 0.26 0.26 0.16 0.42 0.19 0.38 0.21 0.14 27 ICGX-SM 00020/5/15/P2 0.29 0.23 0.32 0.35 0.28 0.29 0.18 0.36 0.43 0.14 0.39 0.35 0.17 0.21 0.28 0.39 0.25 0.27 0.25 0.14 0.42 0.05 0.43 0.18 0.08 0.16 28 ICGX-SM 00020/5/P2/P1 0.24 0.19 0.34 0.32 0.32 0.28 0.14 0.34 0.31 0.16 0.42 0.33 0.21 0.21 0.28 0.37 0.23 0.31 0.28 0.17 0.33 0.18 0.33 0.09 0.19 0.20 0.15 29 ICGX-SM 00020/5/P4/P1 0.24 0.24 0.36 0.39 0.31 0.33 0.17 0.36 0.27 0.18 0.44 0.37 0.19 0.18 0.27 0.40 0.23 0.31 0.28 0.17 0.33 0.21 0.36 0.00 0.21 0.21 0.18 0.09 30 ICGX-SM 00020/5/P4/P10 0.25 0.18 0.36 0.31 0.32 0.29 0.16 0.35 0.28 0.17 0.40 0.30 0.23 0.22 0.30 0.34 0.21 0.30 0.32 0.21 0.33 0.24 0.32 0.10 0.20 0.19 0.22 0.06 0.10 31 KWANKWASO 0.36 0.29 0.29 0.42 0.24 0.19 0.27 0.38 0.24 0.18 0.34 0.40 0.27 0.23 0.22 0.32 0.23 0.20 0.24 0.26 0.26 0.28 0.24 0.27 0.29 0.27 0.29 0.32 0.27 0.32 32 MANIPENTA 0.45 0.39 0.43 0.37 0.40 0.34 0.26 0.39 0.34 0.35 0.38 0.41 0.45 0.41 0.40 0.37 0.32 0.41 0.43 0.41 0.35 0.41 0.41 0.34 0.38 0.37 0.41 0.33 0.34 0.30 0.39 33 RS006F3B1-21 0.49 0.34 0.35 0.38 0.40 0.38 0.36 0.44 0.44 0.37 0.37 0.38 0.41 0.41 0.45 0.33 0.32 0.38 0.37 0.34 0.40 0.34 0.40 0.34 0.38 0.36 0.38 0.33 0.34 0.29 0.43 0.27 34 RS006F4B1-25 0.42 0.29 0.27 0.36 0.34 0.34 0.33 0.42 0.29 0.32 0.28 0.38 0.35 0.34 0.35 0.26 0.30 0.35 0.36 0.35 0.29 0.34 0.28 0.31 0.33 0.32 0.37 0.34 0.31 0.28 0.27 0.42 0.36 35 RS006F4B1-31 0.39 0.38 0.42 0.41 0.45 0.39 0.34 0.39 0.30 0.35 0.44 0.46 0.36 0.35 0.32 0.45 0.43 0.46 0.51 0.40 0.21 0.41 0.35 0.35 0.37 0.44 0.40 0.35 0.35 0.33 0.36 0.30 0.50 0.33 36 RS006F4B1-35 0.38 0.39 0.35 0.46 0.38 0.32 0.40 0.44 0.21 0.36 0.40 0.50 0.36 0.36 0.37 0.37 0.32 0.38 0.35 0.37 0.22 0.38 0.28 0.28 0.45 0.42 0.40 0.31 0.28 0.32 0.25 0.43 0.40 0.26 0.28 37 RS006F3B1-27 0.37 0.31 0.44 0.36 0.43 0.39 0.29 0.44 0.46 0.30 0.38 0.35 0.35 0.34 0.41 0.40 0.36 0.44 0.45 0.34 0.40 0.34 0.41 0.30 0.28 0.35 0.32 0.28 0.30 0.22 0.45 0.40 0.26 0.33 0.43 0.45 38 RS006F3B1-53 (B) 0.24 0.15 0.31 0.28 0.33 0.22 0.20 0.32 0.31 0.09 0.36 0.29 0.23 0.23 0.25 0.34 0.29 0.32 0.29 0.14 0.34 0.21 0.33 0.16 0.18 0.21 0.17 0.09 0.16 0.16 0.28 0.37 0.36 0.34 0.34 0.35 0.30 39 RS006F3B1-57 (B) 0.28 0.27 0.41 0.38 0.44 0.42 0.32 0.35 0.36 0.32 0.44 0.40 0.32 0.32 0.34 0.42 0.40 0.45 0.42 0.30 0.42 0.29 0.39 0.24 0.30 0.31 0.26 0.20 0.24 0.18 0.43 0.40 0.34 0.32 0.36 0.32 0.28 0.25 40 RS006F3B1-59 (R) 0.36 0.36 0.43 0.46 0.30 0.34 0.40 0.49 0.17 0.37 0.44 0.51 0.38 0.37 0.33 0.41 0.35 0.32 0.29 0.38 0.21 0.43 0.27 0.28 0.47 0.46 0.44 0.30 0.28 0.29 0.30 0.42 0.38 0.31 0.34 0.18 0.44 0.31 0.34 41 RS006F4B1-10 (B) 0.41 0.38 0.36 0.44 0.33 0.29 0.35 0.48 0.21 0.35 0.41 0.49 0.36 0.35 0.30 0.39 0.31 0.33 0.35 0.40 0.13 0.45 0.22 0.33 0.42 0.44 0.47 0.32 0.33 0.30 0.28 0.37 0.39 0.29 0.24 0.24 0.42 0.33 0.42 0.15 42 RS006F4B1-13 0.35 0.32 0.38 0.34 0.43 0.41 0.36 0.34 0.30 0.31 0.31 0.40 0.40 0.37 0.31 0.40 0.41 0.48 0.49 0.35 0.44 0.36 0.38 0.30 0.32 0.33 0.34 0.30 0.30 0.27 0.41 0.36 0.41 0.34 0.29 0.34 0.35 0.29 0.21 0.34 0.39 43 RS006F4B1-2 0.27 0.26 0.39 0.31 0.37 0.40 0.31 0.38 0.35 0.30 0.37 0.32 0.32 0.31 0.31 0.35 0.38 0.43 0.40 0.28 0.25 0.40 0.30 0.27 0.38 0.37 0.39 0.27 0.27 0.28 0.33 0.39 0.38 0.31 0.28 0.25 0.35 0.27 0.28 0.23 0.30 0.34 44 RS006F4B1-22 0.36 0.27 0.35 0.30 0.38 0.39 0.30 0.35 0.45 0.26 0.32 0.34 0.28 0.32 0.39 0.38 0.29 0.38 0.40 0.24 0.39 0.25 0.41 0.28 0.21 0.23 0.24 0.27 0.28 0.23 0.40 0.43 0.29 0.32 0.39 0.40 0.25 0.27 0.19 0.45 0.47 0.25 0.32 45 SAMNUT10 0.35 0.32 0.38 0.49 0.34 0.31 0.36 0.44 0.19 0.36 0.46 0.51 0.38 0.36 0.28 0.42 0.30 0.35 0.35 0.38 0.20 0.46 0.17 0.36 0.44 0.42 0.48 0.39 0.36 0.38 0.29 0.37 0.40 0.24 0.35 0.33 0.45 0.40 0.46 0.26 0.20 0.41 0.33 0.48 46 SAMNUT14 0.32 0.25 0.31 0.30 0.28 0.36 0.27 0.35 0.46 0.24 0.35 0.38 0.26 0.29 0.32 0.40 0.24 0.32 0.33 0.21 0.40 0.18 0.41 0.27 0.17 0.24 0.19 0.26 0.27 0.25 0.35 0.44 0.39 0.35 0.38 0.44 0.30 0.29 0.32 0.44 0.48 0.29 0.37 0.20 0.46 47 SAMNUT21 0.31 0.30 0.36 0.44 0.32 0.28 0.34 0.43 0.19 0.34 0.42 0.47 0.35 0.34 0.26 0.38 0.26 0.31 0.32 0.37 0.15 0.45 0.13 0.35 0.42 0.41 0.46 0.37 0.35 0.35 0.27 0.37 0.40 0.28 0.31 0.30 0.43 0.39 0.42 0.23 0.16 0.39 0.29 0.45 0.05 0.43 48 SAMNUT22 0.37 0.29 0.28 0.42 0.35 0.27 0.33 0.40 0.34 0.29 0.33 0.47 0.40 0.36 0.35 0.36 0.25 0.37 0.38 0.35 0.31 0.36 0.30 0.37 0.33 0.35 0.36 0.35 0.37 0.33 0.29 0.31 0.29 0.28 0.39 0.36 0.28 0.31 0.39 0.35 0.29 0.40 0.40 0.37 0.31 0.35 0.30 49 SAMNUT23 0.36 0.31 0.37 0.40 0.37 0.32 0.43 0.42 0.24 0.37 0.40 0.43 0.39 0.37 0.35 0.34 0.31 0.38 0.38 0.39 0.24 0.47 0.27 0.40 0.44 0.41 0.49 0.41 0.40 0.37 0.32 0.38 0.43 0.32 0.39 0.33 0.45 0.37 0.36 0.28 0.28 0.39 0.29 0.41 0.23 0.45 0.19 0.38 50 SAMNUT24 0.37 0.33 0.38 0.33 0.40 0.34 0.34 0.46 0.48 0.29 0.33 0.36 0.38 0.38 0.41 0.37 0.32 0.41 0.42 0.32 0.37 0.29 0.43 0.37 0.30 0.40 0.32 0.34 0.37 0.35 0.38 0.44 0.44 0.40 0.46 0.44 0.38 0.35 0.42 0.46 0.48 0.37 0.45 0.33 0.49 0.20 0.43 0.38 0.44 University of Ghana http://ugspace.ug.edu.gh 53 The cluster analysis using UPGMA based on genetic distances from SSR marker analysis clustered the 50 genotypes into 7 groups (A to G) at a genetic distance of 0.16 (Fig. 4.1). Group A comprises of 18 GRD-resistant genotypes and could further divide into 2 sub-groups (sub- group I and II) at genetic distance of 0.1. Sub-group AI consist of four rosette resistant (ICGV – IS series) and 4 aphid resistant (ICGX – SM series) genotypes. Two other members each of this group also stand as subgroup. Sub group AII is made up of 2 rosette resistant (ICGV – IS series) and 5 aphid resistance (ICGX – SM series) with RS0006F series grouped into a sub-group. Group B included only four genotypes; two RS0006F series, one IAR rosette tolerant variety (SAMNUT22) and a farmer preferred variety ‗MANIPENTA‘. Group E is also made up of 2 sub – groupings, sub-group EI consist of 4 rosette resistance with dormancy (RS0006F series), 1 aphid resistant (ICGX – SM series) and 4 rosette resistant (ICGV – IS series). All the genotypes in this group are originated from ICRISAT breeding unit. Sub – group E2 comprised of 3 IAR GRD-tolerant genotypes (SAMNUT10, 21 and 23), 8 RS0006F series and 2 ICGX – SM series. Variation in group E was wider than all other groups. University of Ghana http://ugspace.ug.edu.gh 54 ICCV5 ICCV16 ICCV4 ICCV3 ICCV15 ICCV18 ICGX6 ICGX11 ICGX4 ICGX5 ICCV10 ICCV1 ICGX1 ICGX9 ICGX7 ICGX8 ICGX3 RS3 RS1 MANI SAM22 RS2 RS7 RS4 RS9 ICCV6 ICCV19 ICCV2 ICCV12 ICCV11 RS10 RS6 ICGX10 RS11 RS5 ICCV17 RS12 SAM21 SAM10 ICGX2 SAM23 RS8 SAM24 SAM14 ICCV9 ICCV8 ICCV13 ICCV7 KWA ICCV14 0.000.050.100.15 Figure 4. 1:Hierarchical dendrogram of 50 groundnut genotypes by using similarity coefficients based on the Nei’s (1983) original genetic distance calculated from data of 166 SSR loci using the UPGMA method: Refer to Table 4.1 for names of the corresponding codes I II I II A B C D E F G University of Ghana http://ugspace.ug.edu.gh 55 LOCAL SAMNUT RS0006F ICGV-IS ICGX-SM 0.000.020.040.060.08 Genetic Distance Figure 4. 2:The genetic relationships among the five groundnut populations calculated using UPGMA method based on the Nei’s (1983) genetic distance 4.4 Discussion The primary goal of this study was to elucidate the ability of SSR markers to detect molecular polymorphism among GRD-resistant cultivars of groundnut. The 35 SSR primer pairs detected a total number of 166 alleles. This support the findings of Milla-Lewis et al. (2010) who reported that for the first time that such a large number of alleles were used to describe genetic diversity in a small collection of cultivated varieties of groundnut. Despite the fact that some SSR primers had relatively low PIC values and an average pairwise genetic similarity at 0.32, the cultivars could be separated fairly well. The study demonstrates that microsatellite markers may be useful for detecting molecular variation among GRD-resistant groundnut cultivars. Although the cultivated groundnut germplasm exhibits a high level of morphological variation, the detectable level of DNA polymorphism in this species is relatively low when compared to other crops (Upadhyaya et al., 2012). An average of 4.47 alleles per locus and a mean PIC value of 0.51 in the present study are comparable to those obtained for groundnut ( Milla-Lewis et al.,2010) and University of Ghana http://ugspace.ug.edu.gh 56 in crops such as tobacco (Moon et al., 2009) that also has a narrow genetic base. This finding illustrates the potential in introgressing genetic diversity from GRD resistance sources to broaden the genetic base of cultivated groundnut breeding. Wide variations were shown by clusters A and E along with occurrence of unrelated individuals in some sub-clusters. This SSR markers clustering pattern according to GRD reaction indicated a possible association between marker data and disease reactions of the genotypes. This supports the findings of Mondal and Badigannavar ( 2010), who found three and four SSR alleles to be associated with rust and late leaf spot resistance in groundnut, respectively. The highest genetic distance detected among GRD-resistant groundnut germplasm tested conformed with a wide variation of phenotype in the cultivated groundnut, which contradicted with some previous studies (Subramanian et al., 2000; Tang et al., 2004) but was consistent with He and Prakash (2001) research, indicating the great potential of its use in cultivated groundnut improvement program. This study detected relatively acceptable level of molecular diversity among groundnut genotypes with various levels of resistance to GRD although some markers were more informative than others. This result agrees with Jiang et al. (2007) who reported similar levels of resistance to bacterial wilt disease of groundnut using SSR markers. In this study it was shown that moderate levels of genetic variation could be detected effectively in groundnut using SSR markers. The grouping of the genotypes at molecular level indicated a clear distinction between subspecies and among groundnut with differential levels of GRD resistance. This molecular study has provided useful information toward parental selections and specific SSR marker that can be used for varietal identification. University of Ghana http://ugspace.ug.edu.gh 57 4.5 Conclusions and Recommendations The SSR markers used in this study detected a high level of polymorphism and were successful in distinguishing groundnut genotypes with various levels of GRD resistance. Genetic distances effectively grouped the germplasm according to differential level of GRD resistance thus highlighting the potential value of genetic distances for preliminary classification of poorly characterized groundnut germplasm. The clustering pattern according to disease reaction may indicate a possible association between marker data and disease resistance. The assessment of genetic diversity of GRD-resistant groundnut genotypes present in the working germplasm collection will help groundnut breeders to formulate crosses by choosing parent with different genetic backgrounds and will assist in the development of gene-mapping populations with greater marker polymorphism. In view of the above and based on the study, resistant sources such as ICGV – IS – 0784, ICGX – SM – 00020/5/P4/P10, RS0006F4B1 – 22, SMANUT22 and SAMNUT24 could be recommended as parents in crosses with the GRD-susceptible breeding such as ‗MANIPENTA with high yield and high seed oil content. University of Ghana http://ugspace.ug.edu.gh 58 CHAPTER FIVE 5 INHERITANCE ON RESISTANCE TO GROUNDNUT ROSETTE DISEASE 5.1 Introduction Breeding for resistance to diseases remains a principal focus in the groundnut breeding programme in Nigeria. Information about the mode of gene action conferring resistance to diseases is prerequisite to the development of a focused breeding programme. Information on the genetic structure of a set of parents and mode of gene action governing yield and its attributes is useful in designing suitable breeding procedures. To design an appropriate breeding programme, it is important to know the proportion of phenotypic variation of a trait that is heritable (Kearsey and Pooni, 1996) since the efficiency of a selection programme is mainly dependent on the magnitude of genetic variation and heritability of a trait (Falconer and Mackay, 1996). In breeding programmes for stress resistance, half-sib (HS) mating systems are commonly used to evaluate general combining ability of parental line development, recombine selected entries in recurrent selection programs, and obtain quantitative genetic information (Amini et al., 2013). Estimation of heritability based on HS family evaluation gives a good prediction of narrow-sense heritability since genetic variance among HS families represents primarily the additive genetic variance contained in the phenotypic variance (Kearsey and Pooni, 1996; Wricke and Weber, 1986). In general, diallel mating designs provides information on genetic effects of a fixed set of parental lines or estimates of general combining ability (GCA) and specific combining ability (SCA), variance components as well as heritability. The use of F2 progenies to study genetic analysis of GRD resistance has not been established so far. The results of this study is expected to provide detailed information on relevant quantitative genetic parameters such as GCA, SCA, the ratio of variance due to GCA and SCA and their interaction with environment. This University of Ghana http://ugspace.ug.edu.gh 59 information will provide the basis to establish breeding strategy toward genetic improvement of GRD resistance and broadening genetic base of GRD resistance breeding of the Institute for Agricultural Research (IAR), Ahmadu Bello University, Samaru, Nigeria. Further, Genotype × environment (G×E) interaction, a major problem involving quantitative traits such as GRD, complicates the interpretation of genetic experiments, makes predictions difficult, and reduces the efficiency of selection. Therefore, knowledge about the magnitude of G×E interactions is important to develop cultivars with stable performance over a wide range of environmental conditions. The objectives of this study were to: a. estimate the variance components and test the significance of G x E interaction on groundnut rosette disease; b. estimate their heritability and degree of association of GRD parameters with agronomic traits c. determine the mode of inheritance of resistance to groundnut rosette disease 5.2 Materials and Methods 5.2.1 Population Development and Phenotype Evaluation The study involved the use of nine experimental lines comprised of three aphid resistant (ICGX – SM 00020/5/9, ICGX – SM 00017/5/P10/P1 and ICGX – SM 00020/5/P4/P1 and three rosette resistant (ICGV IS 07890, ICGV IS 07899 and ICIAR-19BT) were obtained from the International Crop Research Institute for Tropical Agriculture (ICRISAT) Mali. These breeding lines were previously evaluated for three years (2008 – 2010) at the Institute for Agricultural Research (IAR), Ahmadu Bello University Zaria, Nigeria and were confirmed to have field resistance to aphids and GRD, respectively. Three farmers preferred varieties (SAMNUT14, University of Ghana http://ugspace.ug.edu.gh 60 KWANKWASO, and MANIPENTA) were also included as parents in the population development. The pedigree descriptions of the nine genotypes are presented in Table 5.1. The genotypes were manually cross-pollinated in a half diallel mating scheme at the screen house of Institute for Agricultural Research (IAR) Samaru, (11010.00ʺN and 7038.00ʺ E, 693 m), Ahmadu Bello University Zaria, Nigeria in 2011. Additional manual cross-pollinations were made at IAR research field during the 2011 rainy season. Seed limitations for multi-location evaluation were overcome by advancing F1 seeds to next generation (F2) as suggested by (Hallauer et al., 2008) The nine parental lines and 36 F2 genotypes were evaluated for disease resistance using a 9 x 5  lattice design with two replications at two locations (Samaru, Kaduna state, and Lafia, Nasarawa state (8032"N, 7042"E ) during the 2011/2012 growing seasons using an infector – row techniques as described by Olorunju et al. (2001) at the two locations. A system where infectors of susceptible genotypes (SAMNUT 14) were planted in an alternating rows with test materials. The infector rows were planted 2 weeks earlier before the test materials to allow the build-up of infestation. Two row plots 4.0 m in length with inter and intra-row spacing of 0.75 m x 0.25 m, respectively, were used. 5.2.1 Aphid and Rosette resistance evaluation Viruliferous aphid A. craccivora colonies were collected from infested cowpea Vigna unguiculata L., and groundnut A hypogaea plants at different locations in groundnut producing area in Nigeria to cover the different isolates that may be present in the country. University of Ghana http://ugspace.ug.edu.gh 61 Table 5. 1: Pedigree, source, description and characteristics of parental genotypes used for population development Genotype Pedigree Source Description ICGX – SM 00020/5/9 ICG 12991 x ICGV-SM 95713 ICRISAT Resistant to Aphis craccivora; early maturing ICGX – SM 00017/5/P10/P1 ICG 12991 x ICGV-SM 99529 ICRISAT Resistant to Aphis craccivora; early maturing ICGX – SM 00020/5/P4/P1 ICG 12991 x ICGV-SM 99574 ICRISAT Resistant to Aphis craccivora; early maturing ICGV IS 07890 ICG 12991 x ICGV-SM 95603 ICRISAT Resistant to GRD; early maturing ICGV IS 07899 ICG 12991 x ICGV-SM 95603 ICRISAT Resistant to GRD; early maturing ICIAR-19BT KH 241D/ICGV 87922 ICRISAT Resistant to GRD; early maturing SAMNUT 14 55 – 437 ex –Dakar IAR Susceptible to GRD; late maturing KWANKWASO Local collection SAMARU Susceptible to GRD; late maturing MANIPENTA Local collection SAMARU Susceptible to GRD; late maturing Source: Breeding Nurseries for Samanko Stations 2008 and Institute for Agricultural Research, Samaru – Zaria University of Ghana http://ugspace.ug.edu.gh 62 The colonies were each maintained on susceptible groundnut genotypes SAMNUT14 (susceptible farmers variety), in screen house. Two wingless (apterae) aphids were transferred onto 7 to 14-day-old seedlings of 9 parental lines and their 36 F2s grown at IAR research field (S, field). Each genotype was observed for presence or absence of aphid colonies (adults as well as nymphs) 7 days after infestation. Plants with no aphid colonies were re-infested with viable aphids 7 days after the first infestation. It is rare to find plants without aphids in choice tests because the aphids are free to roam to find suitable plant hosts. Aphids that appeared to be transient, possibly probing for feeding sites, are often observed on resistant plants in choice tests, along with dead aphids. Sometimes several viviparous aptera, surrounded by a few nymphs, may be observed on resistant plants without the development of established colonies. Based on these observations, aphids were visually rated for each plant at two weeks intervals after infestation using a scale of 0 – 4 developed by Mensah et al. (2005, 2008), where 0 = No aphid 0.5 = fewer than 10 aphids per plant, no colony formed 1.0 = 11–100 aphids per plant, plants appear healthy 1.5 = 101–150 aphids per plant, plants appear healthy 2.0 = 151–300 aphids per plant, mostly on the young leaves or tender stems, plants appear healthy 2.5 = 301–500 aphids per plant, plants appear healthy 3.0 = 501–800 aphids per plant, young leaves and tender stems are covered with aphids, leaves appear slightly curly and shiny 3.5 = more than 800 aphids per plant, plants appear stunted, leaves appear curled and slightly yellow, no sooty mould and few cast skins 4.0 = more than 800 aphids per plant, plants appear stunted, leaves appear severely curled and yellow and are covered with sooty mould and cast skins University of Ghana http://ugspace.ug.edu.gh 63 An aphid damage index (DI) for each line was calculated by the following formula: DI = ∑ (scale value x no. of plants in the category)/ (4 x total no. of plants) x 100. The DI ranges between 0 for no infestation and 100 for the most severe damage (Mensah et al. , 2005). The DI was used as an indicator of aphid resistance and was applied in the analysis. The disease severity was recorded as the amount of plant tissue that is diseased, green or chlorotic rosette. Reaction to rosette on a scale of 1 – 9 as described by GGP (2000) was used as follows: 1 = No apparent rosette symptoms 3 = 10 – 20% rosette symptoms 5 = 20 – 60% rosette symptoms 7 = 60 – 80% rosette symptoms 9 = 100% rosette symptoms The results of these observations were transformed to compute infection responses as measured by Area Under Disease Progressive Curve (AUDPC) based on Moldovan et al. (2005) according to the following function: AUDPC =        1 1 1 12 n i ii ii ttyy Where: y = disease severity at the thi observation (transformed), t = time (days) of thi observation n = Total number of assessment times University of Ghana http://ugspace.ug.edu.gh 64 5.2.2 Agronomic performance The following traits were measured on five randomly selected plants per plot: Plant height: This is the perpendicular height of the plant from the ground level to the end of the topmost leaf of the plant Number of pods per plant: Total number of healthy filled pods picked from each plant Number of pods per plot: Total number of healthy filled pods picked from each plot Pod weight per plant (g): This the weight of the pods per plant following sun drying for 3 days Pod weight per plot (g): This the weight of the pods per plot following sun drying for 3 days Sound kernel weight per plant (g): This the weight of sound kernels recorded for each plant Sound kernel weight per plot (g): This the weight of sound kernels recorded for each plot 100 – Sound kernel weight (g): This the weight of 100 – kernels for each plant Shelling percentage (%): This is the ratio of sound kernel weight to dry pod weight expressed as a percentage 5.2.3 Data analysis Analysis of variance for individual location and combined data computed to estimate the main effects of locations, genotypes, and their interaction. Genotypes were considered fixed effects, and replications and location were considered random effects. The analysis of variance using General Linear Model (GLM) of Statistical Analysis System (SAS) program were performed with the PROC MIXED procedure from SAS® 9.3.1 software (SAS Institute, 2013). Pairwise comparisons of means were made using Least Significant Differences (LSD) for multiple-means University of Ghana http://ugspace.ug.edu.gh 65 comparison method. A Type I error of 0.05 was used for all statistical comparisons when F value was significant. The analysis of the variance was done using the TYPE III model. Various variance components use derived from the expectation of means squares. Table 5. 2:Format of ANOVA for individual location Source of variation Df MS EMS F – test Replication 1r rMS Genotypes 1g gMS 22 ge r  gMS / eMS Error ( 1g )( 1r ) eMS 2e Total 1gr For individual location the variance components are computed from mean squares as follows: 222 egp   r MSMS eg g 2 ee MS2 Where: gMS = Genotypic means square eMS = Error mean square 2g = Genotypic variance 2e = Error variance University of Ghana http://ugspace.ug.edu.gh 66 Table 5. 3: Format of ANOVA for the combined locations Source of variation Df MS EMS F – test Replication 1r bMS Replication (Location) )1( lr Location 1l lMS 222 lgle rgr   lMS / geMS Genotype 1g gMS 222 ggle rlr   gMS / eMS Genotype x Location ( 1g )( 1l ) geMS 22 gle r  geMS / eMS Error ( 1gl )( 1r ) eMS 2e Total 1glr Where: rg MSMS gll l 2 rl MSMS glg g 2 r MSMS egl gl 2 ee MS2 lMS = Means square due Location gMS = Mean square due genotype glMS = Mean square due Genotype x Location eMS = Error mean square 2l Location variance 2g = Genotypic variance University of Ghana http://ugspace.ug.edu.gh 67 2gl = Genotype x Location variance 2e = Error variance r , l , g are number of replication, location, genotype respectively and e experimental error 5.2.3.1 Heritability estimates Broad-sense heritability for each trait was calculated as follows: 222 / Pgbh  , rll egl gP 22 22   as described by (Littell et al., 2006) Heritability estimates were grouped as high (> 50%), moderate (20 – 50 %) and low (< 20 %) as suggested by Stansfield (1986). Narrow-sense heritability estimates were computed using the variance of GCA and SCA as described Hallauer et al. (2008) 2 2 2 Ph A nh   , 2222 *2 ESCAGCAPh   where 2nh = the narrow sense heritability, 2ˆGCA component of variance estimates due to general combining ability which is a measure of additive effects, 2ˆ SCA is the component of variance estimates due to specific combining ability, a measure of non-additive gene effects. 5.2.3.2 Expected genetic gain Expected genetic gain as per percentage of population mean was estimated at 10% selection intensity as GCVhiG ** (Hallauer et al., 2008) where i is standardized selection intensity, University of Ghana http://ugspace.ug.edu.gh 68 2h is the heritability in a broad sense and GCV is the genotypic coefficient of variation expressed as percentage of mean. 5.2.3.3 Rank Summation Index (RSI) For the purpose of selection, an index, Rank Summation Index (RSI) Mulamba and Mock (1978) was generated from four traits namely pod weight and sound kernel weight per plant, aphid damage index and Area under Disease Progress Curve. The index was formed by ranking each trait according to the order of desired traits. Finally, the values assigned to each trait are added, obtaining the sum of the ranks, which indicates the classification of genotypes. The entry with good plant appeal and tolerance, and highest pod and sound kernel yield and low AUDPC ranked first, while the reverse ranked the last. Rank Summation Index (Mulamba and Mock, 1978) was summarized as follows;    n i i sRRSI 1 Where, RSI= Aggregate performance of a genotype using the ranking of each of the desired traits iR = the rank of the mean of each of the desired traits; 5.2.3.4 Genetic Analysis of Resistance to Groundnut Rosette Disease Analysis of the diallel for general combining ability (GCA) and specific combining ability (SCA) effects for all traits were based on the Model I, Method 2 proposed by Griffing (1956). Parents and one set of F2‘s but not reciprocal F2‘s are included given 2/)1( pp combinations, in which p is the number of parents used to derive the F1 progeny. Trait values were predicted based on traits mean value to produce a balanced data set. Diallel data were analysed using the University of Ghana http://ugspace.ug.edu.gh 69 Diallel SAS-05 program (Zhang et al., 2005). GCA and SCA effects were determined for parents and the 36F2‘s, respectively. The following linear mixed model was fitted to data to estimate variance components for single and multi-location diallel tests. The model for the analysis of variance for single location was: ijkijjikijk esggrY   Where  is the mean, kr is the replication effect, ig and jg are the GCA effects, ijs is the SCA effect, and ijke is the experimental error for the ijkY observation (k =1……… 36, r = 2, i = j = 9. Model for the analysis of variance for multi-location was: ijklmiklilikkllkijiijklm ESCALGCALGCALSCAGCAGCArLY  ***)( Where ijklmY = the thm observation of the thj replication for thk cross in thi location;  = the overall mean; L = the thi fixed (location) effect, 21i ; )(ijr = the fixed effect of the thj replication within the thi location, 21j ; University of Ghana http://ugspace.ug.edu.gh 70 lk GCAGCA , = is the random general combining ability (GCA) effect of the thk female or the thi male ~Normally Independently Distributed (NID) (0, 2G ); klSCA = is the random specific combining ability (SCA) effect of the thk and the thi parents~ (NID) (0, 2S ); ikGCAL * , ijGCAL * = is the random GCA by location Interaction effect ~ (NID) (0, 2IG ); iklSCAL * = is the random SCA by location Interaction effect ~ (NID) (0, 2IS ) and ijklmE = is the random error term ~ (NID) (0, 2E ). Table 5. 4: Format of Diallel analysis of variance for model I method II for groundnut progenies evaluated in one location Source of variation E(MS) Df MS Model I Model II Replication 1r Crosses   12/)1(( pp 2M Ce rK2ˆ 22 ˆˆ Ce r  GCA 1p 22M  22 )]1/()2/[ˆ GCAe Knnr  222 ˆ)2(ˆˆ GCASCAe nrr   SCA 2/)1( pp 21M  22 )]}1/[2{ˆ SCAe Knnr  22 ˆˆ SCAe r  Error   12/)1()1(  ppr 1M 2ˆ e 2ˆ e r and n are number of replications and parents, respectively (Hallauer et al., 2010) The variance explained by the general combining ability effects of parents (half-sibs) is a half of additive genetic variance i.e. 22 *2/1 AGCA   while the variance explained by the female and male interactions (specific combining ability) is equal to dominance genetic variance. i.e. University of Ghana http://ugspace.ug.edu.gh 71 dominance genetic variance 22 DSCA   . Phenotypic variance is the sum of the observational components of variance. 2222 *2 ESCAGCAPh   5.2.3.5 Baker’s ratio Prediction of progeny performance based on GCA and SCA was carried out by the use of Becker‘s ratio, which is the ratio of combining ability variance component described by Becker (1978) as follows:  )2/()2( 222 SCAGCAGCA  . The closer this ratio is to unity, the greater the predictability based on GCA alone. Results 5.2.4 Variance components and heritability of traits Highly significant ( )01.0( p ) differences were found around genotypes for all traits (Table 5.5). The same applied for location except for estimated shelling percentage and aphid damage index. The interaction of genotype and location (G x L) was also highly significant for most of the traits. However, there was no significant G x L interaction for one hundred sound kernel weight and aphid damage index. Estimates of variance components (Table 5.6) indicated that genetic components were a significant source of variation for all the traits except for sound kernel weight in ton per hectare. Unlike genotype, genotype x location variance was significant for traits such as plant height, number of pod per plant, sound kernel weight per plant and shelling percentage. Variance component due to location showed to be a highly significant source of variation for number of pods per plant and area AUDPC. Broad-sense heritability was quite high for all the traits except for plant height and shelling percentage. Broad sense heritability ( 2bh ) was estimated across locations for all traits considered University of Ghana http://ugspace.ug.edu.gh 72 in this study (Table 5.6). Estimates of broad-sense heritability from combined analysis ranged from 27.77% to 99.50%. The estimates was highest for 100 sound kernel weight (99.50%) followed by DI and AUDPC with estimates of 95.65 and 94.78 respectively. Most of the traits in this study had similar heritability estimates as to those reported by Sikinarum et al. (2007) and Puttha et al. (2008). Estimates of narrow sense heritability for use in selection among half – sib families and among individuals ranged from 0.90% for plant height to 67.54% for aphid damage index. A low estimate of narrow sense heritability estimate was obtained for AUDPC (29.29 %) with high estimate for DI (67.54). Genetic advance (GA) was relatively low for DI (5.88%) and AUDPC (3.75 %). This was coupled with low narrow sense heritability estimates. The estimate of GA for SKWPT was relatively low (Table 5.6). University of Ghana http://ugspace.ug.edu.gh 73 Table 5. 5:Mean squares of measured traits for 9 parents and 36 F2 half diallel progenies of groundnut evaluated over Samaru and Lafia Locations in 2012 Source of variation PHT NOPP PWPT SKWPT 100SKWT PWTON SKWTTON SHP DI AUDPC Replication 0.66 8.75 110.53 41.41 0.01 0.05 0.00 1.48 58.80 6.31 Rep(Location) 14.59 3.93 3.03 0.43 0.01 0.00 0.09 0.54 18.37 0.01 Location 757.39** 3020.08** 337.16 163.34** 189.32** 3.24** 1.77** 32.32 198.16 7865.90** Genotype 261.18** 215.29** 1033.52** 283.06** 126.25** 2.07** 1.49** 144.70** 870.23** 780.01** Genotype x Location 188.66** 67.75** 233.63** 97.15** 0.63 0.15** 3.07** 95.58* 37.82 40.72** Residual 14.89 6.88 22.66 15.86 2.11 0.03 0.09 41.96 60.70 4.34 CV (%) 9.97 6.08 12.33 15.84 5.44 12.83 25.83 9.67 22.03 6.04 R2 0.94 0.96 0.97 0.92 0.97 0.97 0.90 0.74 0.88 0.99 *and ** significance at P <0.05 and P < 0.01 respectively. PHT=Plant Height (cm), NOPP= Number of pod per plant, PWPT= Pod weight per Plant (g), SKWPT= Sound kernel weight per plant (g), 100SKWT= 100 sound kernel weight (g), PWTON= Pod weight ton ha-1, SKWTTON= Sound kernel weight ton ha-1, SHP= Shelling percentage (%), DI= Aphid damage Index and AUDPC = Area under disease progress curve University of Ghana http://ugspace.ug.edu.gh 74 Table 5. 6: Variance components, Heritability estimates and expected gain for groundnut traits over combined Samaru and Lafia location in 2012 Variance components of F2 genotypes Traits Genetic ( 2g ) Location ( 2l ) G x L ( 2gl ) Error ( 2e ) 2bH (%) 2nh (%) Genetic CV (%) )(%XG PHT 18.13** 6.32 86.89** 14.89 27.77 0.90 10.80 0.18 NOPP 36.89** 32.80** 30.44** 6.88 68.53 29.48 14.09 1.34 PWPT 199.97** 1.15 105.49* 22.66 77.39 44.28 38.63 4.51 SKWPT 46.48** 0.74 40.65** 15.86 65.68 64.25 27.12 3.82 100SKWT 31.41** 2.10 -0.74 2.11 99.50 21.00 – PWTON 0.48** 0.03 0.06 0.03 92.75 62.32 50.20 6.95 SKWTTON -0.40 -0.01 1.49 0.09 – 67.07 – – SHP 12.28* -0.70 26.81** 41.96 33.95 33.05 5.23 0.53 DI 208.10** 1.78 -11.44 60.70 95.65 67.54 40.80 5.88 AUDPC 184.82** 86.95* 18.19 4.34 94.78 29.29 39.45 3.75 PHT=Plant Height (cm), NOPP= Number of pod per plant, PWPT= Pod weight per Plant (g), SKWPT= Sound kernel weight per plant (g), 100SKWT= 100 sound kernel weight (g), PWTON= Pod weight ton ha-1, SKWTTON= Sound kernel weight ton ha-1, SHP= Shelling percentage (%), DI= Aphid damage Index and AUDPC = Area under disease progress curve. University of Ghana http://ugspace.ug.edu.gh 75 5.2.5 Performance of the groundnut genotypes grown at Samaru and Lafia, 2012 5.2.5.1 Sound Kernel weight per Plant (g) (SKWTPPT) The mean SKWTPPT for genotypes over environmental index was 25.14 g and ranged from the lowest entry KWANKWASO (9.41g) to the highest ICGV IS 07890 X ICGV IS 07899 (48.90g) (Table 5.7). In Samaru, ICGV IS 07890 X ICGV IS 07899, ICGV IS 07890, ICGX – SM 00020/5/9 X ICGV IS 07899 and ICGX – SM 00020/5/9 X ICGV IS 07890 recorded the highest sound kernel yield of 57.05g, 47.50g, 41.93 g and 40.47g, respectively. The lowest performers for this trait were ICGX – SM 00017/5/P10/P1 X MANIPENTA, MANIPENTA and KWANKWASO with sound kernel yield per plant of 11.54g, 10.24g and 9.47g respectively. In Lafia however, ICGX – SM 00017/5/P10/P1, ICGV IS 07890 X ICGV IS 07899 and ICGX – SM 00020/5/9 X ICGX – SM 00017/5/P10/P1 with sound kernel yield per plant of 42.30g, 40.76g and 39.90g respectively. The lowest yield were ICGX – SM 00020/5/9 X KWANKWASO, ICGX – SM 00020/5/9 X MANIPENTA and KWANKWASO. They have sound kernel per plant of 10.96g, 10.17 g and 9.36 g, respectively. Across the two locations, the best performing genotypes for sound kernel yield were ICGV IS 07890 X ICGV IS 07899, ICGV IS 07890 and ICGX – SM 00020/5/9 X ICGX – SM 00017/5/P10/P1(48.90g, 43.46g and 38.72g, respectively). The lowest yielders were ICGX – SM 00020/5/9 X MANIPENTA, MANIPENTA and KWANKWASO with sound kernel yield per plant of 11.99 g, 11.03 g and 9.41g (Table 5.7) University of Ghana http://ugspace.ug.edu.gh 76 Table 5. 7: Performance of parents and their F2 progenies for sound kernel weight per plant (g) over Samaru and Lafia environment in 2012 Genotypes SKWTPPT (g) Samaru Lafia Mean ICGX – SM 00020/5/9 14.66 21.29 17.98 ICGX – SM 00017/5/P10/P1 34.49 42.30 38.40 ICGX – SM 00020/5/P4/P1 32.41 38.15 35.28 ICGV IS 07890 47.50 39.43 43.46 ICGV IS 07899 25.09 25.90 25.49 ICIAR-19BT 20.35 27.13 23.74 SAMNUT 14 17.99 22.54 20.26 KWANKWASO 9.47 9.36 9.41 MANIPENTA 10.24 11.82 11.03 ICGX – SM 00020/5/9 X ICGX – SM 00017/5/P10/P1 37.54 39.90 38.72 ICGX – SM 00020/5/9 X ICGX – SM 00020/5/P4/P1 37.70 38.42 38.06 ICGX – SM 00020/5/9 X ICGV IS 07890 40.47 21.92 31.20 ICGX – SM 00020/5/9 X ICGV IS 07899 41.93 16.25 29.09 ICGX – SM 00020/5/9 X ICIAR-19BT 31.77 26.16 28.97 ICGX – SM 00020/5/9 X SAMNUT 14 21.22 21.58 21.40 ICGX – SM 00020/5/9 X KWANKWASO 24.77 10.96 17.86 ICGX – SM 00020/5/9 X MANIPENTA 13.81 10.17 11.99 ICGX – SM 00017/5/P10/P1 X ICGX – SM 00020/5/P4/P1 22.39 39.41 30.90 ICGX – SM 00017/5/P10/P1 X ICGV IS 07890 32.67 31.40 32.03 ICGX – SM 00017/5/P10/P1 X ICGV IS 07899 36.32 27.24 31.78 ICGX – SM 00017/5/P10/P1 X ICIAR-19BT 36.80 25.09 30.94 ICGX – SM 00017/5/P10/P1 X SAMNUT 14 21.25 26.63 23.94 ICGX – SM 00017/5/P10/P1 X KWANKWASO 15.74 15.22 15.48 ICGX – SM 00017/5/P10/P1 X MANIPENTA 11.54 24.46 18.00 ICGX – SM 00020/5/P4/P1 X ICGV IS 07890 31.47 19.81 25.64 ICGX – SM 00020/5/P4/P1 X ICGV IS 07899 22.93 13.73 18.33 University of Ghana http://ugspace.ug.edu.gh 77 ICGX – SM 00020/5/P4/P1 X ICIAR-19BT 33.65 17.31 25.48 ICGX – SM 00020/5/P4/P1 X SAMNUT 14 15.53 27.34 21.43 ICGX – SM 00020/5/P4/P1 X KWANKWASO 20.55 20.75 20.65 ICGX – SM 00020/5/P4/P1 X MANIPENTA 15.51 39.43 27.47 ICGV IS 07890 X ICGV IS 07899 57.05 40.76 48.90 ICGV IS 07890 X ICIAR-19BT 26.19 27.32 26.75 ICGV IS 07890 X SAMNUT 14 22.51 26.34 24.42 ICGV IS 07890 X KWANKWASO 21.32 18.95 20.14 ICGV IS 07890 X MANIPENTA 38.96 23.75 31.36 ICGV IS 07899 X ICIAR-19BT 26.97 27.53 27.25 ICGV IS 07899 X SAMNUT 14 27.41 18.08 22.74 ICGV IS 07899 X KWANKWASO 19.78 19.18 19.48 ICGV IS 07899 X MANIPENTA 28.94 11.85 20.39 ICIAR-19BT X SAMNUT 14 28.87 19.64 24.25 ICIAR-19BT X KWANKWASO 25.85 27.38 26.61 ICIAR-19BT X MANIPENTA 20.38 25.54 22.96 SAMNUT 14 X KWANKWASO 17.37 17.77 17.57 SAMNUT 14 X MANIPENTA 21.64 21.10 21.37 KWANKWASO X MANIPENTA 13.33 12.32 12.83 Mean 26.10 24.19 25.14 LSD 7.97 7.99 5.60 SKWTPPT= Sound kernel weight per plant University of Ghana http://ugspace.ug.edu.gh 78 5.2.5.2 Aphid Damage Index (DI) The mean performance based on Aphid damage Index (DI) for genotypes over environmental index was 35.36 and ranged from the lowest entry ICGX – SM 00020/5/9 (17.30) to the highest KWANKWASO X MANIPENTA with DI of 64.97 (Table 5.8). Two entries; ICGX – SM 00020/5/9 (Aphid resistant lines) and ICGV IS 07890 (Rosette resistant line) had the lowest DI of less than 20. Sixteen entries had DI values of between 20 - 30 with ICGX – SM 00020/5/9 X ICGV IS 07890 having the lowest DI value of 20.45. Genotype MANIPENTA, ICGV IS 07899 X MANIPENTA and ICGX – SM 00020/5/9 X ICGV IS 07890 have significantly low DI values of 14.38, 15.18 and 16.98 respectively at Samaru and in Lafia, ICGX – SM 00017/5/P10/P1 X ICGX – SM 00020/5/P4/P1, ICGV IS 07890, ICGX – SM 00020/5/9 and ICGX – SM 00017/5/P10/P1 X ICIAR-19BTwere the genotypes with the lowest DI values of 14.38, 15.28, 16.22 and 16.98 respectively (Table 5.8). A highly significant variation (P < 0.01) was observed among the entries for DI values. University of Ghana http://ugspace.ug.edu.gh 79 Table 5. 8: Performance of parents and their F2 progenies for aphid damage Index (DI over Samaru and Lafia environment in 2012 Genotypes DI Samaru Lafia Mean ICGX – SM 00020/5/9 18.37 16.22 17.30 ICGX – SM 00017/5/P10/P1 34.17 22.66 28.41 ICGX – SM 00020/5/P4/P1 25.52 29.17 27.34 ICGV IS 07890 21.73 15.28 18.51 ICGV IS 07899 46.15 20.65 33.40 ICIAR-19BT 36.67 22.32 29.49 SAMNUT 14 44.27 52.60 48.44 KWANKWASO 44.46 55.58 50.02 MANIPENTA 14.38 65.87 40.12 ICGX – SM 00020/5/9 X ICGX – SM 00017/5/P10/P1 33.85 18.37 26.11 ICGX – SM 00020/5/9 X ICGX – SM 00020/5/P4/P1 21.02 34.17 27.60 ICGX – SM 00020/5/9 X ICGV IS 07890 16.98 25.52 21.25 ICGX – SM 00020/5/9 X ICGV IS 07899 19.18 21.73 20.45 ICGX – SM 00020/5/9 X ICIAR-19BT 17.99 46.15 32.07 ICGX – SM 00020/5/9 X SAMNUT 14 18.18 36.67 27.42 ICGX – SM 00020/5/9 X KWANKWASO 20.54 44.27 32.41 ICGX – SM 00020/5/9 X MANIPENTA 22.92 44.46 33.69 ICGX – SM 00017/5/P10/P1 X ICGX – SM 00020/5/P4/P1 43.75 14.38 29.06 ICGX – SM 00017/5/P10/P1 X ICGV IS 07890 21.54 33.85 27.70 ICGX – SM 00017/5/P10/P1 X ICGV IS 07899 50.52 21.02 35.77 ICGX – SM 00017/5/P10/P1 X ICIAR-19BT 43.99 16.98 30.48 ICGX – SM 00017/5/P10/P1 X SAMNUT 14 51.46 19.18 35.32 ICGX – SM 00017/5/P10/P1 X KWANKWASO 40.42 17.99 29.20 ICGX – SM 00017/5/P10/P1 X MANIPENTA 54.03 18.18 36.10 ICGX – SM 00020/5/P4/P1 X ICGV IS 07890 55.58 20.54 38.06 ICGX – SM 00020/5/P4/P1 X ICGV IS 07899 18.37 16.22 17.30 University of Ghana http://ugspace.ug.edu.gh 80 ICGX – SM 00020/5/P4/P1 X ICIAR-19BT 53.46 22.92 38.19 ICGX – SM 00020/5/P4/P1 X SAMNUT 14 25.48 43.75 34.62 ICGX – SM 00020/5/P4/P1 X KWANKWASO 59.90 21.54 40.72 ICGX – SM 00020/5/P4/P1 X MANIPENTA 35.10 50.52 42.81 ICGV IS 07890 X ICGV IS 07899 38.90 43.99 41.44 ICGV IS 07890 X ICIAR-19BT 24.96 53.22 39.09 ICGV IS 07890 X SAMNUT 14 36.46 46.04 41.25 ICGV IS 07890 X KWANKWASO 39.90 54.03 46.97 ICGV IS 07890 X MANIPENTA 44.45 55.58 50.01 ICGV IS 07899 X ICIAR-19BT 38.64 53.46 46.05 ICGV IS 07899 X SAMNUT 14 21.43 25.48 23.45 ICGV IS 07899 X KWANKWASO 22.66 59.90 41.28 ICGV IS 07899 X MANIPENTA 29.17 35.10 32.13 ICIAR-19BT X SAMNUT 14 15.18 38.90 27.04 ICIAR-19BT X KWANKWASO 20.65 24.96 22.80 ICIAR-19BT X MANIPENTA 18.75 37.50 28.13 SAMNUT 14 X KWANKWASO 45.55 40.87 43.21 SAMNUT 14 X MANIPENTA 52.29 64.16 58.23 KWANKWASO X MANIPENTA 42.86 63.07 52.96 Mean 33.37 35.22 34.30 LSD 34.31 36.41 35.36 DI = aphid damage Index and LSD = least significant difference University of Ghana http://ugspace.ug.edu.gh 81 5.2.5.3 Area Under Disease Progress curve (AUDPC) Area Under Disease Progress curve for genotypes over environmental index has mean and median 31.95 and 34.16 respectively, and ranged from 12.69 for genotype with lowest AUDPC value ICGX – SM 00020/5/9 X MANIPENTA to 49.23 for ICIAR-19BT X KWANKWASO with the highest AUDPC values. The value for AUDPC was smaller (p < 0.01) for the resistant (AUDPC < 30) and moderately resistant parents (AUDPC 31 – 50) compared to the moderately susceptible parents (AUDPC 51 – 60) and susceptible (AUDPC > 60) genotypes. Genotypes classified as moderately resistant and moderately susceptible differ (P < 0.05) statistically for AUDPC (Table 5.9). Based on AUDPC, eighteen genotypes were regarded as being the resistant genotypes with AUDPC values of < 30, five of these genotypes, ICGX – SM 00020/5/9 X MANIPENTA, ICGV IS 07890 X ICIAR-19BT IS 07899, ICGX – SM 00020/5/9 X ICGV IS 07890, ICGX – SM 00020/5/9 and ICGV IS 07890 have AUDPC of < 20 and considered to be resistant/tolerant to rosette disease infection. At both Samaru and Lafia, ICGX – SM 00020/5/9 X MANIPENTA and ICGV IS 07890 X ICIAR-19BThave the lowest AUDPC value each with 10.94 and 14.44, respectively, and ICGX – SM 00020/5/9 X KWANKWASO and ICIAR-19BT X KWANKWASO were with the highest values of 43.14 and 43.96 respectively at Samaru. At Lafia, it was SAMNUT 14 and MANIPENTA that have the highest values of 58.25 and 58.50 respectively. The AUDPC values across the two environments indicated Lafia to have the highest AUDPC of 38.08 while the value at Samaru was 25.84, the differences were statistically significant (P < 0.05), a clear indication of genotype x Location interaction for SKWPPT, DI and AUDPC (Fig. 5.1). University of Ghana http://ugspace.ug.edu.gh 82 Table 5. 9: Performance of parents and their F2 progenies for AUDPC over Samaru and Lafia environment in 2012 Genotypes AUDPC Samaru Lafia Mean ICGX – SM 00020/5/9 14.44 18.75 16.59 ICGX – SM 00017/5/P10/P1 14.44 31.25 22.84 ICGX – SM 00020/5/P4/P1 14.44 25.75 20.09 ICGV IS 07890 14.81 19.75 17.28 ICGV IS 07899 11.31 33.00 22.16 ICIAR-19BT 11.31 57.00 34.16 SAMNUT 14 10.94 58.25 34.59 KWANKWASO 10.94 51.25 31.09 MANIPENTA 34.75 58.50 46.63 ICGX – SM 00020/5/9 X ICGX – SM 00017/5/P10/P1 25.94 19.25 22.59 ICGX – SM 00020/5/9 X ICGX – SM 00020/5/P4/P1 29.69 19.25 24.47 ICGX – SM 00020/5/9 X ICGV IS 07890 11.31 19.25 15.28 ICGX – SM 00020/5/9 X ICGV IS 07899 25.94 19.75 22.84 ICGX – SM 00020/5/9 X ICIAR-19BT 24.13 19.75 21.94 ICGX – SM 00020/5/9 X SAMNUT 14 22.83 19.75 21.29 ICGX – SM 00020/5/9 X KWANKWASO 43.14 19.25 31.19 ICGX – SM 00020/5/9 X MANIPENTA 10.94 14.44 12.69 ICGX – SM 00017/5/P10/P1 X ICGX – SM 00020/5/P4/P1 26.40 51.00 38.70 ICGX – SM 00017/5/P10/P1 X ICGV IS 07890 29.81 39.25 34.53 ICGX – SM 00017/5/P10/P1 X ICGV IS 07899 31.98 44.25 38.12 ICGX – SM 00017/5/P10/P1 X ICIAR-19BT 38.48 19.75 29.11 ICGX – SM 00017/5/P10/P1 X SAMNUT 14 33.60 39.25 36.43 ICGX – SM 00017/5/P10/P1 X KWANKWASO 29.31 39.50 34.41 ICGX – SM 00017/5/P10/P1 X MANIPENTA 29.31 40.50 34.91 ICGX – SM 00020/5/P4/P1 X ICGV IS 07890 29.12 37.38 33.25 ICGX – SM 00020/5/P4/P1 X ICGV IS 07899 14.44 18.75 16.59 ICGX – SM 00020/5/P4/P1 X ICIAR-19BT 38.87 37.38 38.12 University of Ghana http://ugspace.ug.edu.gh 83 ICGX – SM 00020/5/P4/P1 X SAMNUT 14 30.51 46.00 38.25 ICGX – SM 00020/5/P4/P1 X KWANKWASO 30.51 51.25 40.88 ICGX – SM 00020/5/P4/P1 X MANIPENTA 30.51 48.00 39.25 ICGV IS 07890 X ICGV IS 07899 30.51 51.25 40.88 ICGV IS 07890 X ICIAR-19BT 28.95 45.00 36.97 ICGV IS 07890 X SAMNUT 14 10.94 14.44 12.69 ICGV IS 07890 X KWANKWASO 39.18 39.50 39.34 ICGV IS 07890 X MANIPENTA 34.36 39.25 36.81 ICGV IS 07899 X ICIAR-19BT 30.95 51.75 41.35 ICGV IS 07899 X SAMNUT 14 14.06 39.75 26.91 ICGV IS 07899 X KWANKWASO 19.94 39.75 29.84 ICGV IS 07899 X MANIPENTA 13.24 39.75 26.49 ICIAR-19BT X SAMNUT 14 13.91 39.75 26.83 ICIAR-19BT X KWANKWASO 25.24 37.75 31.5 ICIAR-19BT X MANIPENTA 43.96 54.50 49.23 SAMNUT 14 X KWANKWASO 34.36 57.50 45.93 SAMNUT 14 X MANIPENTA 34.94 58.25 46.59 KWANKWASO X MANIPENTA 37.38 53.00 45.19 Mean 40.97 54.50 47.73 LSD 25.84 38.08 31.95 AUDPC = Area under disease progress curve and LSD = least significant difference University of Ghana http://ugspace.ug.edu.gh 84 Figure 5. 1: Cross over Genotype x Location Interaction for sound kernel yield per plant across Batstari and Lafia Locations Figure 5. 2:Performance of F2 groundnut for sound kernel yield per plant and AUDPC University of Ghana http://ugspace.ug.edu.gh 85 5.2.5.4 Association between agronomic traits and rosette disease Negative correlations between AUDPC and most of the yield parameters (Table 5.10). A weak negative correlations between AUDPC and plant height as (r = −0.15, p > 0.05), AUDPC and pod weight per plant (r = −0.18, p > 0.05), AUDPC and sound kernel weight per plant (r = −0.26, p > 0.05) were recorded. However, a highly significant negative correlations was observed for between AUDPC and pod weight ton ha-1 (r = −0.45, p < 0.01) and AUDPC and sound kernel weight ton ha-1 (r = −0.46, p < 0.01). AUDPC was positively correlated (r = 0.63, p < 0.01) with Aphid damage index. Nevertheless, genotypes classified as resistant generally had lower AUDPC (p < 0.01) than the susceptible genotypes (Table 5.10). University of Ghana http://ugspace.ug.edu.gh 86 Table 5. 10: Correlations among area under the disease progress curve and agronomic traits in groundnut 1 2 3 4 5 6 7 8 9 10 Plant Height (cm) 0.49** 0.27 0.15 0.43** 0.36* 0.49** 0.05 -0.20 -0.15 Number of pod per plant 0.45** 0.11** 0.36** 0.44 0.25 -0.16 0.13 -0.13 Pod weight per Plant (g), 0.24 0.59** 0.94** 0.55** -0.58** -0.34* -0.18 100 sound kernel weight (g) 0.35** 0.26 0.36** -0.08 -0.10 0.01 Pod weight per Plant (g) 0.62** 0.92** -0.29 -0.36** -0.46** Sound kernel weight ton ha-1 0.60** -0.33** -0.42** -0.26 Sound kernel weight per plant (g) -0.28 -0.44** -0.46** Shelling percentage (%), 0.09 -0.02 Aphid damage Index 0.62** Area under disease progress curve * and ** significant at P < 0.05 and P < 0.01 levels of probability respectively Data based on measured on 9 parents and 36 F2 groundnut hybrids evaluated across Samaru and Lafia Location in 2012 University of Ghana http://ugspace.ug.edu.gh 87 5.3 General and specific combining ability for traits For all the traits studied, the mean square (MS) values of combining ability (GCA) were significant (P < 0.05). Similarly, the MS for specific combining ability were significant for traits except the MS of SHP. For DI and AUDPC, the SCA MS were higher the GCA MS. (Table 5.9). The magnitude of this interaction for most traits was however, small relative to the GCA main effect. Significant SCA×L interactions (p < 0.05) were observed for all traits. The estimates of 2ˆGCA and 2ˆ SCA were significantly different from zero for all the traits except for pod weight in tons per hectare. To understand the relative importance of general and specific combining abilities for DI and AUDPC, estimates of components of GCA and SCA that approximates variances were estimated according Bakers ratio (Becker, 1978). The ratio was closer to unity (0.73) for aphid damage index and low value was obtained for AUDPC (0.30) (Table 5.11). The estimates of 2ˆ A and 2ˆ D showed that greater proportions of total genetic variance are attributed to non – additive (i.e. dominance and epistasis) for AUDPC (with 2ˆ A = 136.27 and 2ˆ D = 323.08). Partitioning of genotypes into genetic effects indicated significant GCA effects (p < 0.01) and SCA effects (p < 0.01) for all traits. The GCA effects for AUDPC ranged from – 10.61 for ICGX – SM 00020/5/9 to 5.44 in MANIPENTA (Table 5.12). Genotypes with the lowest desirable negative GCA effects were ICGX – SM 00020/5/9 (– 10.61), SAMNUT14 (– 2.04), ICGV IS 07890 (– 1.58) and ICGX – SM 00017/5/P10/P1 (–1.41). The highest GCA effects for this trait were exhibited by KWANKWASO (5.44) and MANIPENTA (3.58) which were the most susceptible genotypes in this study. The GCA effect for sound kernel weight per plant (g) was University of Ghana http://ugspace.ug.edu.gh 88 highest in ICGV IS 07890 (5.74) and lowest in MANIPENTA (- 9.25). The parents, ICIAR- 19BT (4.69), ICGX – SM 00020/5/9 (3.78) and ICGX – SM 00017/5/P10/P1 (3.10) depicted significantly high positive GCA effects. The specific combining ability effects for AUDPC ranged from – 15.65 to 35.96. Most crosses revealed positive SCA effect, 13 out of 36 crosses (36.11 %) had negative SCA effects (Table 5.13). The F2 combinations, ICGX – SM 00017/5/P10/P1 X ICIAR-19BT (–15.65) had the best desirable negative SCA effects. Other crosses with desirable negative and significant SCA effects for this trait includes ICGX – SM 00020/5/P4/P1 X SAMNUT 14 (–9.66), ICGV IS 07899 X SAMNUT 14 (–8.73) and ICGX – SM 00017/5/P10/P1 X ICGV IS 07890 (–8.1). In contrast, ICGV IS 07890 X ICGV IS 07899 (-7.64), ICGX – SM 00020/5/9 X MANIPENTA (-7.22) and ICGX – SM 00020/5/9 X SAMNUT 14 (-5.52) had high negative but not significant SCA effects. The greatest SCA effect (35.96) was recorded for ICGX – SM 00020/5/P4/P1 X MANIPENTA. University of Ghana http://ugspace.ug.edu.gh 89 Table 5. 11: Mean squares of combined ANOVA for half 9 x 9 diallel analysis for general and specific combining abilities and their interactions with location for ten morphological traits of groundnut evaluated at two locations in 2012 Source of variation Df PHT NOPP PWPT SKWPT 100SKWT PWTON SKWTTON SHP DI AUDPC GCA 8 961.23** 777.85* 4578.12** 2132.30** 187.92** 12.80** 9.43** 614.69* 5197.60** 165.90** SCA 36 931.16** 311.86** 1194.09** 282.54** 241.50** 1.86** 1.09* 203.75 578.97* 652.03** GCA X L 8 967.45** 226.85** 478.98** 67.13** 0.68 0.63** 1.44** 187.45** 530.68** 440.04** SCA X L 36 175.90** 98.43** 459.05** 10.50** 0.61 0.22** 0.27** 218.19** 239.59** 67.57** ERROR 88 655.12 301.74 997.02 698.01 2978.89 1.37 4.40 1846.13 2670.79 190.74 *and ** significant at P < 0.05 and P < 0.01 levels of probability respectively PHT=Plant Height (cm), NOPP= Number of pod per plant, PWPT= Pod weight per Plant (g), SKWPT= Sound kernel weight per plant (g), 100SKWT= 100 sound kernel weight (g), PWTON= Pod weight ton ha-1, SKWTTON= Sound kernel weight ton ha-1, SHP= Shelling percentage (%), DI= Aphid damage Index and AUDPC = Area under disease progress curve University of Ghana http://ugspace.ug.edu.gh 90 Table 5. 12: Variance component for GCA, SCA and their interactions with location, Bakers ratio, additive and dominance variances considering random effect model for 9 parents and 36 F2 evaluated across Samaru and Lafia Locations in 2012 Variance components of F2 Bakers ratio Traits 2E 2GCA 2SCA 2GCA x L 2SCA x L  2A  2D Plant Height (cm) 15.66 2.15 457.75 959.62 168.07 0.01 4.30 457.75 Number of Pod per plant 6.69 33.29 152.59 223.505 95.085 0.30 66.57 152.59 Pod weight per Plant (g) 22.41 241.72 585.84 467.775 447.845 0.45 483.43 585.84 Sound kernel weight per plant (g) 11.57 132.13 135.49 61.345 4.715 0.66 264.25 135.49 100 sound kernel weight (g) 2.15 – 119.68 – – – – 119.68 Pod weight ton ha-1 0.03 0.78 0.92 0.615 0.205 0.63 1.56 0.92 Sound kernel weight ton ha-1 0.08 0.60 0.51 1.4 0.23 0.70 1.19 0.51 Shelling percentage (%) 34.11 29.35 84.82 170.395 201.135 0.41 58.71 84.82 Aphid damage Index 55.26 329.90 261.86 503.05 211.96 0.72 659.80 261.86 AUDPC 5.87 68.13 323.08 437.105 64.64 0.30 136.27 323.08 University of Ghana http://ugspace.ug.edu.gh 91 Table 5. 13: Estimates of general combining ability (GCA) effects of 9 parental lines for four important morphological characters of groundnut evaluated across Samaru and Lafia Locations in 2012 PARENTS PWPT SKWPT DI AUDPC ICGX – SM 00020/5/9 1.22 3.78** -4.77** -10.61** ICGX – SM 00017/5/P10/P1 5.74 ** 3.10** -15.33** -1.41 ICGX – SM 00020/5/P4/P1 4.72 ** 2.77** -4.66** 2.53** ICGV IS 07890 12.64** 5.74** 3.50** -1.58 ICGV IS 07899 3.96** 0.39 -2.55* 2.20 ICIAR-19BT -0.45 4.69** -1.69 1.89 SAMNUT 14 -6.69** -4.68** 2.20 -2.04 KWANKWASO -12.62** -6.54** 11.73** 3.58** MANIPENTA -8.50** -9.25** 11.57** 5.44** SE± 1.13 0.59 1.12 1.41 *and ** significant at P < 0.05 and P < 0.01 levels of probability respectively Other crosses depicting significantly positive SCA effects includes ICGX – SM 00020/5/P4/P1 X ICGV IS 07890 (12.56), ICGV IS 07890 X MANIPENTA (15.48), ICGX – SM 00017/5/P10/P1 X KWANKWASO (15.84) and ICGX – SM 00017/5/P10/P1 X SAMNUT 14 (18.61) (Table 5.14). The SCA effects for sound kernel weight per plant (g) was highest in ICGV IS 07890 X SAMNUT 14 (19.03) and lowest in ICGX – SM 00020/5/P4/P1 X ICGV IS 07890 (- 16.07). The other parents that recorded the significantly high SCA effects were ICGV IS 07899 X ICIAR- 19BT (15.17), SAMNUT14 X KWANKWASO (13.94), ICIAR-19BT X SAMNUT14 (11.55), University of Ghana http://ugspace.ug.edu.gh 92 ICGX – SM 00020/5/9 X ICGV IS 07899 (7.88), ICGX – SM 00020/5/9 X ICGX – SM 00020/5/P4/P1 (7.74), ICGX – SM 00020/5/9 X ICGV IS 07890 (7.22) and ICGX – SM 00017/5/P10/P1 X ICIAR-19BT (6.75). Whereas ICGX – SM 00020/5/9 X ICGX – SM 00017/5/P10/P1 (-10.64), ICGV IS 07890 X ICGV IS 07899 (-13.84) and ICGX – SM 00020/5/P4/P1 X ICGV IS 07890 (-16.07) had the lowest SCA effects for sound kernel yield per plant (Table 5.14). However, the F2s that combined significant and desirable SCA effects for SKWPT and AUDPC were ICGX – SM 00017/5/P10/P1 X ICIAR-19BT (6.75, –15.67), ICGX – SM 00020/5/9 X ICGX – SM 00020/5/P4/P1 (7.74, –2.77), ICGX – SM 00020/5/P4/P1 X SAMNUT 14 (6.00, – 9.66) and ICGV IS 07899 X KWANKWASO (3.75, – 2.10). University of Ghana http://ugspace.ug.edu.gh 93 Table 5. 14: Estimates of specific combining ability (SCA) effects measured in the 36 F2 progenies evaluated across Samaru and Lafia Locations in 2012 CROSSES PWPT SKWPT DI AUDPC ICGX – SM 00020/5/9 X ICGX – SM 00017/5/P10/P1 -5.66 -10.64** 2.59 -4.82 ICGX – SM 00020/5/9 X ICGX – SM 00020/5/P4/P1 12.30** 7.74** 2.84 -2.77 ICGX – SM 00020/5/9 X ICGV IS 07890 15.50** 7.22** -8.39** 9.53** ICGX – SM 00020/5/9 X ICGV IS 07899 -6.85** 7.88** -5.86 1.74 ICGX – SM 00020/5/9 X ICIAR-19BT -2.97 4.62** 12.98** 2.79 ICGX – SM 00020/5/9 X SAMNUT 14 0.70 -7.29** 1.09 -5.52 ICGX – SM 00020/5/9 X KWANKWASO -0.93 -4.74** 1.50 -1.57 ICGX – SM 00020/5/9 X MANIPENTA 1.43 2.17 7.62 -7.22 ICGX – SM 00017/5/P10/P1 X ICGX – SM 00020/5/P4/P1 -7.85 3.69 -0.73 2.81 ICGX – SM 00017/5/P10/P1 X ICGV IS 07890 2.94 -4.75** 10.06** -8.10 ICGX – SM 00017/5/P10/P1 X ICGV IS 07899 -0.31 -3.35 1.44 5.87 ICGX – SM 00017/5/P10/P1 X ICIAR-19BT -1.86 6.75** -1.97 -15.65** ICGX – SM 00017/5/P10/P1 X SAMNUT 14 0.44 2.37 -4.02 18.61** ICGX – SM 00017/5/P10/P1 X KWANKWASO -4.17 -2.78 -12.33** 15.84** ICGX – SM 00017/5/P10/P1 X MANIPENTA -10.79** -7.11** -30.59** 7.97 ICGX – SM 00020/5/P4/P1 X ICGV IS 07890 -14.78** -16.07** -10.16** 12.56** ICGX – SM 00020/5/P4/P1 X ICGV IS 07899 -17.51** -1.22 0.97 9.57** ICGX – SM 00020/5/P4/P1 X ICIAR-19BT -20.29** -5.21** 11.17** 0.71 ICGX – SM 00020/5/P4/P1 X SAMNUT 14 2.02 6.00** -11.61** -9.66** *and ** significant at P < 0.05 and P < 0.01 levels of probability respectively University of Ghana http://ugspace.ug.edu.gh 94 ICGX – SM 00020/5/P4/P1 X KWANKWASO -1.05 -8.59** 6.03 -4.18 ICGX – SM 00020/5/P4/P1 X MANIPENTA -0.14 -0.24 2.57 35.96** ICGV IS 07890 X ICGV IS 07899 -3.03 -13.84** -3.86 -7.64 ICGV IS 07890 X ICIAR-19BT 52.39** -1.52 16.45** 3.14 ICGV IS 07890 X SAMNUT 14 -11.99** 19.03** 6.65* 4.33 ICGV IS 07890 X KWANKWASO -10.71** 0.98 4.75 -1.39 ICGV IS 07890 X MANIPENTA -10.31** -3.35 24.53** 15.48* ICGV IS 07899 X ICIAR-19BT 2.07 15.17** -8.96** 3.34 ICGV IS 07899 X SAMNUT 14 -5.79 -6.39** 6.99* -8.73* ICGV IS 07899 X KWANKWASO -4.47 3.75* 6.37** -2.10 ICGV IS 07899 X MANIPENTA -2.79 5.69** -10.45** 29.13** ICIAR-19BT X SAMNUT 14 3.52 11.55** -13.20** 3.43 ICIAR-19BT X KWANKWASO 1.91 -6.17** -10.31** 9.46** ICIAR-19BT X MANIPENTA 13.25** 1.43 9.46 4.05 SAMNUT 14 X KWANKWASO 11.79* 13.94** 7.99** 1.82 SAMNUT 14 X MANIPENTA 6.54* 3.30 8.45 0.81 KWANKWASO X MANIPENTA 8.40 5.10 14.00** 9.61 SE± 3.21 1.68 3.19 4.03 *and ** significant at P < 0.05 and P < 0.01 levels of probability respectively University of Ghana http://ugspace.ug.edu.gh 95 5.4 Selection for superior genotypes for resistance to groundnut rosette disease Based on the estimates predicted for the selection index of Mulamba and Mock (1978) using as economic weights that includes pod and sound kernel weight per plant which are vital for groundnut improvement and with lower DI and AUDPC (measures of diseased parameter) are presented in Table 5.15. Based on the RSI, ICGX – SM 00020/5/9 X ICGV IS 07890 (RSI = 27), ICGX – SM 00020/5/9 X ICGX – SM 00017/5/P10/P1 (RSI = 28) and ICGX – SM 00020/5/9 X ICGX – SM 00020/5/P4/P1 (RSI = 34), the best three F2 segregates. Among the parents ICGV IS 07890 (RSI = 13), ICGX – SM 00020/5/P4/P1 (RSI = 27) and ICGX – SM 00017/5/P10/P1 ((RSI = 36) were the best genotypes to deploy for development GRD-resistant. University of Ghana http://ugspace.ug.edu.gh 96 Table 5. 15: The top 10 and 4 poorest performing F2 genotypes selected based on Rank summation Index of SKWT, PWT, DI and AUDPC Genotypes Response Status RANKS RSI SKWT PWT DI AUDPC Top 10 F2 progenies ICGX – SM 00020/5/9 X ICGV IS 07890 R x R 9 10 4 4 27 ICGX – SM 00020/5/9 X ICGX – SM 00017/5/P10/P1 R x R 6 3 7 12 28 ICGX – SM 00020/5/9 X ICGX – SM 00020/5/P4/P1 R x R 3 5 11 15 34 ICGX – SM 00020/5/9 X ICGV IS 07899 R x R 5 4 14 13 36 ICGX – SM 00017/5/P10/P1 X ICGV IS 07890 R x R 15 13 3 14 45 ICGX – SM 00020/5/9 X ICIAR-19BT R x R 7 7 12 27 53 ICGV IS 07899 X ICIAR-19BT R x R 16 14 19 10 59 ICGX – SM 00017/5/P10/P1 X ICIAR-19BT R x R 21 16 6 18 61 ICGV IS 07890 X ICGV IS 07899 R x R 13 11 18 20 62 ICGV IS 07890 X ICIAR-19BT R x R 1 1 30 31 63 Bottom 4 SAMNUT 14 X MANIPENTA S x S 25 30 43 41 139 ICGV IS 07890 X KWANKWASO R x S 36 34 41 30 141 SAMNUT 14 X KWANKWASO S x S 45 45 42 22 154 KWANKWASO X MANIPENTA S x S 40 40 44 43 167 Parents ICGV IS 07890 R 2 2 2 7 13 ICGX – SM 00020/5/P4/P1 R 4 6 9 8 27 ICGX – SM 00017/5/P10/P1 R 5 4 14 13 36 ICGX – SM 00020/5/9 R 23 38 1 6 68 ICGV IS 07899 R 20 20 22 11 73 ICIAR-19BT R 30 25 17 5 77 SAMNUT 14 S 37 33 40 3 113 MANIPENTA S 44 44 31 19 138 KWANKWASO S 45 45 42 22 154 University of Ghana http://ugspace.ug.edu.gh 97 5.5 Discussion Significant (P < 0.05) genetic variation and few transgressive segregates within the experimental population were identified for sound kernel yield and resistance to rosette disease. These data indicate that selection with significantly (P < 0.05) superior performance is possible within this population. Some resistant progenies from crosses between aphid and rosette resistant lines could be used in integrated management of groundnut rosette disease. These breeding lines are already in good agronomic background and can be used directly for commercial production following multi-locational evaluations and release. Out of the 36 progenies generated from aphid and rosette resistant sources, the best F2 segregates were selected based on RSI of Mulamba and Mock (1978) and were also found to show field resistance across the two locations. The use of the area under disease progress curve (AUDPC) as a measure of disease severity and as a tool for plant resistance evaluation helps to describe disease progress throughout the whole growing season (Campbell and Madden, 1990). In this study, the highest AUDPC values were for breeding line with the highest disease infection. There were differences in the AUDPC values between breeding lines within location and between locations. The differences observed between AUDPC values of the breeding lines within location suggest differences in resistance of individual lines. On the other hand, the difference observed between locations could be explained by the differences in the environmental conditions. Although AUDPC and DI seem to be a better index for the whole disease progress, it would be wise to consider these traits together during the breeding progress. The 10 best crosses were competitive in both pod and sound kernel yield, DI and AUDPC as determined by RSI. This implies that selection based on several traits is necessary. As an index, AUDPC, not only provides adequate information on rate of GRD spread University of Ghana http://ugspace.ug.edu.gh 98 but also allows the comparison among genotypes in an accurate way, especially when compared to methods proposed in the literature. Moreover, this simple screening procedure can be utilized to integrate applied and basic groundnut research to increase knowledge (Yang et al., 2010) while developing cultivars with a trait that has been very difficult to measure, rosette disease of groundnut. This tool can also be applied in both early- and late-maturing groundnut breeding programs for selecting desirable genotypes in target environments. It also provides a good phenotyping tool for genetic studies at the molecular and physiological levels (Simko and Piepho, 2012). With the AUDPC approach, more precise phenotyping can be possible allowing pathologist, geneticists and breeders to work together to accurately identify loci and genotypes with rosette disease properties and measure a low heritable trait that was often thought as difficult to measure. The performance based on the degree of reaction to groundnut rosette disease of 9 parents and 36 F1s differed significantly. Some of the genotypes showed good field resistance to GRD accompanied with reasonable sound kernel yield per plant. For example, among the parents ICGV IS 07890 (43.46 g) and ICGX – SM 00017/5/P10/P1 (38.40 g) were the most tolerant to GRD while among the crosses ICGX – SM 00020/5/9 X ICGX – SM 00017/5/P10/P1 (38.72 g) and ICGX – SM 00020/5/9 X ICGX – SM 00020/5/P4/P1 (38.06 g) were the most GRD tolerant genotypes. Most of the promising genotypes with regards to sound kernel yield and AUDPC were from a cross that involved at least one aphid or rosette resistant parent. The high levels of field resistance to GRD in these genotypes suggest that they could also be selected indirectly for aphid resistance because both Aphid craccivora and the presence of virus are required concurrently in the expression of GRD. This is agreement with the reports by Naidu et al. (1999) University of Ghana http://ugspace.ug.edu.gh 99 that both aphids and the virus responsible for the expression of GRD intricately depend on each other and play a crucial role in the biology and perpetuation of GRD disease. Because of the low frequency of GRD resistant plants with high yield per plant, early generation selection as part of pedigree selection program would be inefficient, hence inter-mating of GRD resistant progenies or retaining high yielding but susceptible progenies in bulk from which resistance forms could be selected in advance generation in GRD improvement program. However, studies have shown that high yield potential and high degree of resistance do not generally go together (Nigam et al., 1990) while the breeding programs target them together. Therefore, in breeding for GRD resistant variety a balance has to be struck between the yield potential and level of resistance to avoid any possible yield drawback. As a consequence, several genotypes with high yield potential and moderate levels of GRD resistance were identified in this study The significant variances due to genotypes x location interactions observed for aphid damage index and AUDPC demonstrate the inconsistent performances of the parents and the F2 populations in the two locations, which was a result of the differences in the weather conditions. Similar findings were reported by Adamu et al. (2008), Bentur et al. (2004), Senapathi et al. (2004) and Heriprasanna et al. (2008), who concluded that identification of suitable genotypes having maximum G x E interaction with moderate levels of field resistance or susceptibility to disease would be needed to improve the production of groundnut. Mothilal et al. (2010) further reported significant linear component of G x E interaction for kernel yield and concluded that genotypes differed for their linear response to fluctuating environments. In a related study, Molken and Stuefer (2011) showed that climatic conditions have a strong influence on the severity of disease. University of Ghana http://ugspace.ug.edu.gh 100 Knowledge of magnitude of association between characters is useful in making simultaneous selection for more than one character. For improvement of groundnut rosette disease resistance and sound kernel yield, it is necessary to know the magnitude and direction of relationships among resistance parameters, sound kernel yield and some other important traits as this aids selection. In this study, yield is drastically reduced by GRD parameter (AUDPC) (r = −0.2624, p > 0.05). The significant reductions in sound kernel yield of groundnut due to GRD incidence which is in agreement Adamu et al. (2001) who reported reduction in pod and haulm yield due to rosette infection. Generally, negative correlations were obtained between sound kernel yield per plant and the AUDPC and DI suggesting that selecting for reduced levels of AUDPC, which is an index of GRD response, will improve sound kernel yield per plant in these genotypes. The significant negative correlation between AUDPC and plant height clearly suggest that excessive AUDPC could reduce groundnut height, which is common symptom of groundnut plant attacked with GRD, i.e. stunted growth, twisting and bushy nature are symptoms depicted by groundnut attacked by GRD virus (Olorunju et al.,2001). In the present study, the values of narrow sense heritability for DI and AUDPC were low and high broad sense heritability. It follows that resistance to GRD was much more heritable in broad sense than in narrow sense and that the greater portion of heritable variation is of non-additive in nature. Therefore, the low Baker‘s ratio value and the relative importance of broad sense heritability relative to narrow sense heritability emphasized the preponderant role of non-additive gene action in controlling GRD. Broad-sense heritability provides information of genetic variation but does not provide indication for the progress expected from selection. Considering low heritability estimates and the presence of non-additive gene effects could hinder the progress University of Ghana http://ugspace.ug.edu.gh 101 of selection indicated by low genetic gain. Therefore, selection of superior genotypes in the early generation will be ineffective. Selection in more advanced generations based on progeny performance when homozygosity is fixed will be more effective. Both general combining and specific combining abilities made a significant and important contribution to progeny variation for both DI and AUDPC. All the parents of the most disease- resistant crosses; ICGX – SM 00017/5/P10/P1, ICIAR-19BT, ICGX – SM 00020/5/9 and ICGX – SM 00020/5/P4/P1 had appreciable per se resistance and also favourable GCA resistance values. This suggests that, although resistance to GRD tends to be at least partly dominant (Clements et al., 2004), optimal resistance in progenies will require crossing parental genotypes that both carry resistance, which support the finding of Loffler et al. (2011) and Hung and Holland (2012). Therefore, selection for resistance should not be confined to a single group but should be performed in parallel in all groups. The study did not observe any cross between two susceptible genotypes that resulted in progenies with good resistance GRD. The study however, observed that heterosis is critical for GRD resistance. In contrast, Loffler et al. (2011) observed that hybrids often had more disease than their parental inbreds, perhaps because they used quite susceptible tester lines and higher inoculum pressure for hybrids than parents. SCA was most important for determining progeny resistance, but GCA was also important for disease resistance. Significant SCA detected in 13 of 36 possible combinations indicate the presence of non-additive gene effect. Significant SCA effect were observed for the combinations ICGX – SM 00017/5/P10/P1 X ICIAR-19BT (aphid resistant//rosette resistant), ICGX – SM 00020/5/9 X ICGX – SM 00020/5/P4/P1 (aphid resistant//aphid resistant), ICGX – SM 00020/5/P4/P1 X SAMNUT 14 (aphid resistant//rosette susceptible) and ICGV IS 07899 X KWANKWASO University of Ghana http://ugspace.ug.edu.gh 102 ((aphid resistant//rosette susceptible). These results indicate resistance of these progenies was higher than would be expected from average of their expected parents based on AUDPC symptom rating. The largest positive SCA effects correspond to ICGX – SM 00020/5/P4/P1 X MANIPENTA. This combination was more susceptible than predicted average parent performance indicating the importance of non-additive gene effect in this particular cross. Kenga et al. (2004) suggest that the difficulty in predicting the resistance level of the hybrid, on the basis of GCA alone should necessitate testing of specific male-female combinations. The SCA values provide important information about the performance of the hybrid relative to its parents. Arunga et al. (2010) found that the SCA effect alone has limited value for parental choice in breeding programs. They, therefore, suggested that the SCA effects should be used in combination with other parameters, such as hybrid means and the GCA of the respective parents such that a hybrid combination with both high mean and favourable SCA estimates and involving at least one of the parents with high GCA, would tend to increase the concentration of favourable alleles; which is desired by any breeder. Furthermore, it was observed that crosses involving one good combiner and one average or poor combiner showed negative SCA effects. For example, MANIPENTA and KWANKWASO had poor GCA values for GRD resistance, while their crosses with ICGV IS 07899 and ICGX – SM 00020/5/9, respectively, and had significant and desirable SCA effects. This is in agreement with Hannan et al. (2007) who observed that some parents exhibited a similar phenomenon in studies on tomato (Lycopersicum esculentum Mill.) while Habarurema et al. (2012) made similar conclusion in study on bacterial blight ((Xanthomonas oryzae pv.oryzae) in rice University of Ghana http://ugspace.ug.edu.gh 103 The combining ability ratio, also known as Baker‘s ratio, for resistance to GRD observed in this study was less than unity. According to Baker (1978), when combining ability ratio approaches unity, GCA alone cannot predict the performance of the parents. Thus, the GCA scores could not be used to predict the performance of the parents in the present study, because the value of Baker‘s ratio is much lower than the theoretical maximum of unity. Low Baker‘s ratio observed for AUDPC in this study highlighted the importance of SCA variance, and hence the importance of dominance and/or epistatic gene effects for increasing resistance to GRD. This implies that selection at latter generations would be done much more based on better hybrid combinations rather than the performance of the parents involved in crossing programs. Partitioning G x L into variance to GCA x L and SCA x L interaction effects indicate significant variances of both GCA x L and SCA x L effects. The significant of GCA x L variance implied that GRD symptom rating (AUDPC) was sensitive to environmental conditions and data from additional environments or seasons would make GCA effect more precise. 5.6 Conclusions and Recommendations The nature of genetic variation for aphid damage index and AUDPC and its relationships with sound kernel yield are important for planning successful breeding program for GRD resistance. The breeding lines identified from this study as being resistant to GRD could complement already existing GRD resistant lines and could serve as new sources of resistance to GRD for IAR groundnut breeding unit. Significant G x E interaction and low narrow sense heritability estimates of GRD resistance and Non-additive gene effects confound the selection of superior genotypes, and there is a need to develop alternative selection strategies. Genetic relationships University of Ghana http://ugspace.ug.edu.gh 104 between GRD resistance traits and agronomic traits, especially sound kernel yield, have been established in this study. There is a high possibility to simultaneously improve pod yield and GRD resistance in groundnut populations evaluated in this study. Agronomic traits such as pod and sound kernel yield were closely related with low GRD infection. Therefore, they could be used as indirect selection tools for resistance to groundnut rosette disease Superior GRD resistant genotypes could only be identified at later generation when homozygosity is achieved. However, some few transgressive F2 segregates with field resistance to GRD and appreciable sound kernel yield based on their performance and RSI across the two locations. ICGX – SM 00020/5/9 X ICGV IS 07890, ICGX – SM 00020/5/9 X ICGX – SM 00017/5/P10/P1 and ICGX – SM 00020/5/9 X ICGX – SM 00020/5/P4/P1 with higher sound kernel yield and most field resistance to GRD pathogen were identified and could be used for commercial production in Northern ecological zones of Nigeria. ICGX – SM 00017/5/P10/P1 , ICIAR-19BT, ICGX – SM 00020/5/9 and ICGX – SM 00020/5/P4/P1 found to have good GCA for reduced GRD resistance should be deploy for groundnut breeding programme to improve the level resistance to the disease. The study recommends about evaluations at four locations that are clearly different in terms of altitude, vegetation, temperatures, soil types, rainfall to select for yield and GRD resistance. The use of GGBiplot tools could be used to select promising GRD resistant genotypes that are stable in a range of environments and also to identify environments that effectively discriminate genotypes based on GRD parameters. University of Ghana http://ugspace.ug.edu.gh 105 CHAPTER SIX 6 MOLECULAR CONFIRMATION OF ROSETTE RESISTANCE IN PROMISING GROUNDNUT GENOTYPES BY ONE-STEP REVERSE TRANSCRIPTASE POLYMERASE CHAIN REACTION (RT – PCR) 6.1 Introduction Efforts in breeding for host–plant resistance with appreciable yield and superior seed quality have contributed to the development of several groundnut genotypes with acceptable levels of field resistance to rosette disease (Subrahmanyam et al., 1998; Olorunju et al., 2001; van der Merwe and Subrahmanyam, 2001). Efficient and accurate diagnosis is a key to mitigating the consequences associated with aphid transmission of viruses in groundnut. Currently, the most common diagnostic method adopted by groundnut breeders focuses largely on the monitoring of viral-like symptoms on plants. Other methods include determining the biological (mechanical or vector transmission, host-range test ⁄ infectivity assay, and in vitro features) and serological (Gillaspie et al., 2007) properties of the viruses. Frequently, several of these methods are used in combination for greater effectiveness, thereby further intensifying existing efforts and resources devoted to this aspect of groundnut production (Naidu and Hughes, 2003). Thus, it is not surprising that despite the importance of GRD viruses and their crop-loss potential if left unchecked and accompanied with favourable conditions may lead to epidemics. Only limited attempts have been made to accurately screen field resistant genotypes with a more powerful diagnostic tool as RT-PCR. In earlier breeding programmes for GRD, resistance was assessed by lack of symptom expression and therefore was largely due to GRV and sat RNA resistance (Bock et al., 1990; University of Ghana http://ugspace.ug.edu.gh 106 Subrahmanyam et al., 1998; Olorunju et al., 2001) and this resistance to GRV resulted in indirect resistance to sat RNA and did not show symptoms. Although such groundnut rosette disease- resistant materials did not develop rosette disease symptoms in the field due to the absence of GRV and sat RNA, yield reduction in such plants was observed under both artificial and natural disease pressure presumably due to their susceptibility to groundnut rosette assistor virus (GRAV) (Subrahmanyam et al., 1998; Olorunju et al., 2001). Several methods have been used for GRD virus detection in groundnut plants. GRAV were detected in plants and aphids by the triple-antibody sandwich form of enzyme linked immunosorbent assay (TAS-ELISA) using monoclonal antibodies (MAbs) raised to potato leaf- roll virus (Naidu et al., 1998; Rajeshwari et al., 1987; Scott et al., 1996). The GRV RNA and sat RNA can be detected in plants by nucleic acid hybridization (Blok et al., 1994). Due to their cross reactions with different luteoviruses, a panel of MAbs have to be used in TAS-ELISA to verify that a luteovirus detected in groundnut or in aphids is indeed GRAV (Scott et al., 1996). Moreover, TAS-ELISA cannot provide information on whether or not the aphids carry particles containing GRAV-RNA, GRV-RNA or sat-RNA. Additionally, low concentrations of the rosette disease agents in vector aphids and host plant make it essential to develop a reliable and sensitive method for their detection. Studies have shown that viruses can be detected in individual groundnut plants and aphid using RT-PCR (Canning et al., 1996; Singh et al., 1996; Olmos et al., 1997; Stevens et al., 1997, Naidu et al., 1998). The use of RT – PCR employed in a study could ensure that, GRD field resistance genotypes are free of contamination from any of GRAV, GRV and sat-RNA viruses. GRD field resistance may be symptomless (latent) and presence of any of the agents of GRD disease in the field resistant sources could prevent its use as new University of Ghana http://ugspace.ug.edu.gh 107 sources of resistance for GRD breeding program. The rapid and accurate confirmation of the new sources of resistance is an essential prerequisite for the development of a breeding strategy towards controlling this disease. The objective of this study was to confirm the resistance status of identified promising GRD-resistant parents and F2 progenies of groundnut genotypes by RT – PCR. 6.2 Materials and methods 6.2.1 Collection of plant samples Two to three seeds of 14 GRD field resistant groundnut genotypes with appreciable sound kernel yield, along with susceptible checks MANIPENTA and KWANKWASO were planted in the greenhouse. They were further challenged with veruliferous aphids collected from heavily infested groundnut plant maintained on SAMNUT14 (susceptible control) and monitored for two weeks at IAR screen house. Leaf samples were collected randomly from each plant for each genotype labelled separately and tested at IAR Biotechnology Laboratory by RT-PCR to being free from any of GRAV, GRV and sat RNA. 6.2.2 RNA Extraction and Purification Extraction was carried out using GeneJET Plant RNA Purification Mini kit #K0801# (Thermo Scientific). For each sample, about 200 mg of young leaves were ground into fine powder under liquid nitrogen with mortar and pestle. It was quickly transferred into RNase DNase free 1.5ml microcentrifuge tubes containing 500ul of plant RNA Lysis solution and vortexed for 20s, incubated for 3 min at 560C and centrifuged for 5 min at 14,000 rpm. The supernatant (550 µl) was transferred into a clean microcentrifuge tube and mixed with 250 µl of 96 % ethanol. The University of Ghana http://ugspace.ug.edu.gh 108 prepared mixture was then transferred into a purification column inserted in a collection tube, and the column was centrifuge for 1min at 11,000 rpm, and the flow through solution was discarded while the column and the collection tube was reassembled. 700 µl Wash buffer (WB1) was added to the column and centrifuged for 1min at 11,000 rpm, the flow through and collection tube was discarded and the purification column was placed into a clean 2ml collection tube. 500µl Wash Buffer (WB2) was added to the purification column and centrifuged for 1min at 11,000 rpm, the flow through was discarded while the column and collection tube was reassembled. Washing with WB2 was repeated but with centrifugation at 14,00rpm. The flow through in a collection tube was discarded. The purification column was inserted into RNase free 1.5ml tube. The RNA was eluted by adding 50µl nucleases free water to the center of purification column and centrifuged for 1min at 11,000rpm. This was repeated once to a total of two washes. The purification column was discarded and the RNA was stored at -200C for further downstream analysis. 6.2.3 Complementary DNA (cDNA) synthesis and Polymerase Chain Reaction (PCR) Primers for specific amplification of nucleic acid sequences for each of the three agents of rosette disease were presented in the Table 6.1. RT-PCR reactions were set up separately for GRAV, GRV and sat RNA. cDNA was obtained by using M-MuLV Reverse Transcriptase #EP0352# ( ThermoScientific). Total RNA as a template used in RT-PCR reaction was set up separately for GRAV, GRV and Sat-RNA in two different stage protocols: First Strand cDNA synthesis and PCR Reaction. 5 µl RNA Template was added into sterile nuclease free tube on Arctic Ice PCR PL* Temp Sensitive. It was followed by adding 20pmol primer (forward and University of Ghana http://ugspace.ug.edu.gh 109 reverse), the volume was made up to 11.5 µl DEPC-treated water. Subsequently, 4 µl 5X- reaction buffer, 0.5 µl nuclease free water, 2 µl (1mM final concentration) dNTP mix 25mM each and 2ul (40 µl) M-MuLV Reverse Transcriptase were added to a total volume of 20ul. It was mixed gently and centrifuged briefly. The reaction was incubated at 370C for 1hr and terminated by heating at 700C for 10min. First Strand cDNA was amplified in PCR machine (PTC 200. MJ Research), using gene specific primers. The PCR reaction mixture 50 µl consists of: 2 µl cDNA, 10 pmol Primer Forward and Reversed each, 25 µl DreamTaq Green PCR Master Mix (2X) and 21 µl nuclease free water. (ThermoScientific). PCR amplification was set to run 30 cycles of 900C for 45s, 550C for 1min, 720C for 1min followed by extension cycle of 720C for 1hr and held at 40C. The PCR product was run on 2% Agarose gel in 0.5X TBE buffer, stained with Ethidium bromide, which was later visualized under UV transilluminator, and the images were captured with the aid of digital camera. University of Ghana http://ugspace.ug.edu.gh 110 Table 6. 1: Primers used in amplification of various regions of causal agents of groundnut rosette disease complex primers in the 100 series represent internal primers for specified regions Primers Sequence Specific to Source reference HRP92 ATGAATACGGTCGTGGTTAGG GRAV-CP Naidu et al., 1998; Deom et al.,2000; Wangai et al.,2001 HRP93 TTTGGGGTTTTGGACTTGGC GRAV-CP Naidu et al., 1998; Deom et al.,2000; Wangai et al.,2001 HRP94 GGAAGCCGGCGAAAGCTACC GRV ORF3P and 4P Taliansky et al., 1996 HRP95 GGCACCCAGTGAGGCTCGCC GRV ORF3P and 4P Taliansky et al., 1996 HRP96 GGTTTCAATAGGAGAGTTGC Sat-RNA Naidu et al., 1998; Deom et al.,2000; Scott et al.,1996 HRP97 AAATGCCTACTTTGGGCGTG Sat-RNA Naidu et al., 1998; Deom et al.,2000; Scott et al.,1996 HRP110 GGAGGGTCTGGCGAAACATT GRAV-CP Naidu et al., 1998; Deom et al.,2000; Wangai et al.,2001 HRP111 CCCTTGTAAAGGAACCGGAAT GRAV-CP Naidu et al., 1998; Deom et al.,2000; Wangai et al.,2001 HRP104 CGAGGAGACCAAAGGGTGGT GRV ORF 3P and 4P Taliansky et al., 1996; Wangai et al.,2001 HRP105 AGCTCCGACACAATAGCGAAG GRV ORF 3P and 4P Taliansky et al., 1996; Wangai et al.,2001 HRP108 GAAAAGGTGAGGGGTGTGT Sat-RNA Naidu et al., 1998; Deom et al.,2000; Wangai et al.,2001 HRP92 ATGAATACGGTCGTGGTTAGG GRAV-CP Naidu et al., 1998; Deom et al.,2000; Wangai et al.,2001 HRP109 TAGCTTGATTTCAAGCTCGC Sat-RNA Naidu et al., 1998; Deom et al.,2000; Scott et al.,1996; Wangai et al.,2001 Source: Wangai et al., 2001 University of Ghana http://ugspace.ug.edu.gh 111 6.3 Results Complementary DNA (cDNA) made from the total RNA preparations was subsequently used in PCR to amplify GRV and sat RNA-specific sequences using primers specific for GRAV and GRV, and sat-RNA specific primers respectively. A total of 16 groundnut genotypes comprising of 14 promising GRD resistance and 2 susceptible farmers‘ varieties (checks) (Table 6.1) were subjected to confirmatory test for the presence of the three agents of GRD; GRAV, GRV and sat- RNA. With primer GRAV-CP 400 nt fragment amplified 12 of the 16 genotype tested (Fig 6.1). For PCR fragment specific to GRV, primer also amplified products in 14 out of 16 genotypes. The sat-RNA on the other hand, amplified fragments in all the 16 genotypes, revealing the presence of sat-RNA in all the genotypes (Fig 6.1). ICIAR-19BT X MANIPENTA was the only genotype that is resistant to GRD as no amplification was observed with the two GRAV and GRV specific primers. University of Ghana http://ugspace.ug.edu.gh 112 Table 6. 2: Field resistance scored by DI and AUDPC and RT – PCR confirmation of GRD-resistance in some groundnut genotypes Genotypes SCORES RT – PCR DI AUDPC Yield Field Status GRAV sat-RNA GRV ICGX – SM 00020/5/9 17.30 16.59 17.98 Resistance + + – ICGX – SM 00017/5/P10/P1 28.41 22.84 38.40 Resistance + + + ICGX – SM 00020/5/P4/P1 27.34 20.09 35.28 Resistance + + + ICGV IS 07890 18.51 17.28 43.46 Resistance + + + ICGV IS 07899 33.40 22.16 25.49 Resistance + + + ICIAR-19BT 29.49 34.16 23.74 Resistance + + + ICGX – SM 00020/5/9 X ICGX – SM 00017/5/P10/P1 26.11 22.59 38.72 Resistance + + + ICGX – SM 00020/5/9 X MANIPENTA 33.69 10.94 11.99 Susceptible + + + ICGX – SM 00020/5/9 X ICGV IS 07899 20.45 22.84 29.09 Resistance + + + ICGX – SM 00017/5/P10/P1 X ICGV IS 07899 35.77 29.84 31.78 Resistance + + + ICGV IS 07899 X SAMNUT 14 41.28 31.50 22.74 Susceptible + + + ICIAR-19BT X MANIPENTA 43.21 26.83 20.39 Susceptible + – – ICIAR-19BT X SAMNUT 14 22.80 45.93 24.25 Susceptible + + + ICGV IS 07890 X MANIPENTA 46.05 41.35 31.36 Susceptible + + + MANIPENTA 40.12 34.59 11.03 Susceptible – + + KWANKWASO 50.02 31.09 9.41 Susceptible + + + University of Ghana http://ugspace.ug.edu.gh 113 P ri m er s p ec if ic to G R A V – C P P ri m er sp ec if ic to G R V – C P P ri m er s p ec if ic t o s at – s at N A Figure 6. 1: Amplification banding pattern of GRAV – CP (HRP92/93), GRV – CP (OFR3P and 4P) and Sat-RNA markers in 16 groundnut genotypes R ib o R u le r L o w R ag e R N A L ad d er ,( 1 0 0 -1 0 0 0 b as es ), IC G X – S M 0 0 0 2 0 /5 /9 IC G X – S M 0 0 0 1 7 /5 /P 1 0 /P 1 IC G X – S M 0 0 0 2 0 /5 /P 4 /P 1 IC G V I S 0 7 8 9 0 IC G V I S 0 7 8 9 9 IC IA R -1 9 B T I S 0 7 8 9 9 S A M N U T 1 4 K W A N K W A S O M A N IP E N T A IC G X – S M 0 0 0 2 0 /5 /9 X IC G X – S M 0 0 0 1 7 /5 /P 1 0 /P 1 IC G X – S M 0 0 0 2 0 /5 /9 X IC G X – S M 0 0 0 2 0 /5 /P 4 /P 1 IC G X – S M 0 0 0 2 0 /5 /9 X IC G V I S 0 7 8 9 0 IC G V I S 0 7 8 9 9 X S A M N U T 1 4 IC IA R -1 9 B T I S 0 7 8 9 9 X S A M N U T 1 4 IC IA R -1 9 B T I S 0 7 8 9 9 X M A N IP E N T A IC G X – S M 0 0 0 2 0 /5 /9 X S A M N U T 1 4 IC G V I S 0 7 8 9 0 X M A N IP E N T A C o n tr o l W /c D N A t em p la te C o n tr o l/ cD N A /P ri m er C o n tr o l o n ly w at er R ib o R u le r L o w R ag e R N A L ad d er ,( 2 0 0 -6 0 0 0 b as es ) L 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 C C C L University of Ghana http://ugspace.ug.edu.gh 114 6.4 Discussion GRD has been a major viral disease of groundnut for decades. Sources of resistance have been previously detected by ELISA. However, as a phloem limited virus, it is present in low concentration hence required highly sensitive methods for its detection. Nucleic-acid based detection methods are recognized as being very powerful, rapid, specific and highly sensitive techniques for plant virus detection (James et al. 2006; Boonham et al. 2008; Olmos et al. 2008; Vincelli and Tisserat 2008). The use of RT-PCR in plant virus detection is well established, although studies on detection of Potyviridae are rare. Even the few studies that have investigated the detection of Potyviridae in plants with low luteovirus titre have relied on the use of species- specific-primers (Naidu et al., 1998; Gibbs et al., 2003; Gibbs et al., 2008). RT-PCR detection of GRAV, GRV and sat-RNA was possible in all plant samples included in this study. The use of RT-PCR used in this study has facilitated the confirmation of resistance status of identified field GRD resistant groundnut genotypes and subsequent identification of different agents of GRD viruses. The molecular approach employed in this study, whilst not new to modern plant virus disease diagnostics, is unique to the investigation of GRD viruses associated with groundnut germplasm. As such, the findings and experimental methods will be substantially informative, from a phytosanitary perspective, to repositories around the groundnut regional bank which collect, maintain and distribute groundnut germplasm. Some of the more conspicuous symptoms such as chlorosis, severe stunting and mosaic are advantageous from a disease- management standpoint because they increase the likelihood that groundnut plants with such symptoms will be identified and rogued out early in the growing season of production. This in turn significantly reduces the chances of subsequent virus University of Ghana http://ugspace.ug.edu.gh 115 dissemination by insect vectors (Hao et al., 2003). However, it should be noted that although plants with symptoms were found to be infected with viruses in most cases, some infected GRD field resistant plants remained symptomless and did not induce any. These plants tested positive for at least one of the viruses (GRV, GRAV and sat-RNA) only after being screened by RT- PCR. Thus, as with other virus–plant interaction systems, some combinations of viruses and groundnut plants can give rise to asymptomatic or possibly latent infection (Odedara et al., 2007; Salem et al., 2010). Because these infected plants are not easily noticeable and can serve as virus reservoirs in the field, they are of serious concern in terms of biosecurity and virus disease control. These observations also confirmed the notion that symptomatology alone cannot be taken as a reliable basis for resistance to plant virus diseases; especially since symptom induction can be complicated by the occurrence of mixed virus infection, as demonstrated by this study. This is in agreement with Gillaspie (2006, 2007) and Salem et al. (2010), who observed that the use of symptom visualization in field plots improved screening for GRD-resistance in groundnut plants, but failed to improve the speed or accuracy of screening for GRD-resistance, because the GRD symptoms were apparently too mild in many groundnut genotypes for accurate diagnosis. It is important to note that GRD-induced symptoms can also occur in plants co-infected with satellite RNAs (Simon et al., 2004; Salem et al.,2010), as observed for GRD-infected plants showing mild or no GRD symptoms observed in the present study. The molecular diagnosis in this study clearly demonstrated that at least one of the three agents of GRD viruses, GRV, GRAV and sat-RNA, were present in all field resistant genotypes identified in this study. This is consistent with other studies reporting the occurrence of these viruses in groundnut (Naidu et al., 1998) and Salem et al. (2010) for CABMV and CMV in cowpea as well University of Ghana http://ugspace.ug.edu.gh 116 as those describing the occurrence of GRV, GRAV and sat-RNA in groundnut germplasm maintained at IAR. Since none of the genotypes tested has demonstrated resistance to all the three components based on RT – PCR assay and because GRAV is the main component involved in aphid transmission, genotypes such as ICGX-SM-000/20/5/P4/P1, ICIAR -19-BT and ICGV 07899 that showed a negative response to GRAV, could be exploited in resistance breeding programmes to restrict the spread of groundnut rosette disease. The GRV resistant genotypes ICG-IS-07899 X SAMNUT14 and ICIAR -19-BT X MANIPENTA could be used as sources of resistance to GRV and for commercial production under favourable conditions. ICG-IS-07899 X SAMNUT14 and ICIAR - 19-BT X MANIPENTA are susceptible to the aphid vector but resistant to GRV and ICGX-SM- 000/20/5/P4/P1, ICIAR -19-BT and ICGV 07899 are susceptible to rosette disease agents but resistant to feeding by the aphid vector, the two groups of genotypes offer a good source for molecular gene pyramiding. 6.5 Conclusions and Recommendations The findings of the present study demonstrated the occurrence of most GRD viruses causing severe damage in groundnut in Northern Nigeria. Based on RT-PCR diagnostic test, it is further concluded that GRD appeared as a major devastating virus in the leading groundnut growing regions of Nigeria. This study has also shown the sensitivity of the use of RT-PCR to facilitate reliable diagnosis of viruses, and is particularly useful in cases where suspected viral symptoms are mild or absent. Although reverse transcription followed by PCR can be used to detect the presence of GRD in groundnut, the procedure provides little information regarding the relationship between the three agents of GRD. The intensity and appearance of GRD symptoms University of Ghana http://ugspace.ug.edu.gh 117 varies among genotypes, as does the efficiency of transmission by aphid vectors. Therefore, information on the relationship of these three agents of GRD is required to establish its distribution and epidemiology. It is evident from this study that the use of RT-PCR has successfully confirmed resistant status groundnut breeding lines with differential levels GRD resistance. Indeed, this study has allowed identification of at least one breeding line with combined resistance to GRAV and GRV. Further studies with multiplex RT-PCR, a method for detecting simultaneous infections by more than one viroid species in the same host is suggested. This could lead to a new era in the understanding of some of the unknowns in the often mysterious nature of this infection, and perhaps to better ways of disease management. The current work is the first attempt at using molecular approaches to confirm resistance status of groundnut genotypes at IAR in Nigeria. University of Ghana http://ugspace.ug.edu.gh 118 CHAPTER SEVEN 7 GENERAL DISCUSSION 7.1 Participatory rural appraisal (PRA) A participatory rural appraisal conducted indicated that farmers recognized the groundnut production constraints that did not differ significantly between the two districts from different ecologies. Farmers in both Batsari and Nasarawa-eggon were aware of groundnut rosette disease which they called by various local names to be an important constraint to groundnut productivity in their locations. Management strategies for this disease ranged from doing nothing by the majority of farmers to the use of resistant varieties by a few farmers. Another constraint pointed out by farmers across locations was unavailability of improved seed. This implies that the breeding emphasis should focus on development of host resistant genotypes as the most important management strategy for GRD as most farmers across the studied areas were resource- poor who cannot afford the use of chemical control measures. The development of resistant varieties should involve a multidisciplinary team of breeders, pathologists, socio-economists, agronomist, soil scientists and farmers as earlier stipulated by several researchers (Witcombe et al., 2005; Gyawali et al., 2007; Muhinyuza et al., 2012). They emphasized the need for active farmer participation in plant breeding as critical for successful adoption of improved varieties and their production. However, the link between research and farmers is still very weak or absent in the studied locations and even in most developing countries (Ortiz et al., 2001). Failure of including farmers could result in low rate of adoption of improved varieties and persistent use of locally available GRD susceptible varieties. This probably has been the cause of low yield and sporadic occurrence of GRD disease in both areas. Efforts should therefore be concentrated on University of Ghana http://ugspace.ug.edu.gh 119 the breeding for high yielding GRD resistant varieties and their promotion. The study found that, community involvement in a breeding program for GRD was completely lacking. Few farmers were involved in participatory variety selection (PVS) i.e. the mother and baby trials of Tropical Legume II project leaving behind bulk of other stakeholders who played a great role in variety selection for sale and supply. Farmers have some special preferences that breeders normally do not select for in their programs. These preferences are part of the reasons farmers do not easily adopt improved cultivars even if they have important traits like resistance to diseases. These preferences are very diverse posing a big challenge to the breeders. However, with farmers‘ participation in the process of varietal development, breeders can appreciate and comprehend those preferences and include them in their selection. Since farmers have indicated their willingness to participate in varietal selection, this can be a means of ensuring early adoption of the cultivars selected by farmers themselves. 7.1.1 Epidemiology of GRD from the studied genotypes The strict dependence on the presence of the agents of GRD for occurrence of the disease in farmers field probably explains why all the 9 parents and the field resistant progenies screened by RT – PCR were found to carry at least one of the three agents of GRD. The ability to detect each of the three agents of rosette disease in GRD field resistance genotypes is crucial in gaining an understanding of the ecology and epidemiology of the disease and to identify sources of resistance to the individual agents of the disease in groundnut. Like other persistently transmitted viruses, GRAV reported to be retained by the vector for periods of up to 14 days and possibly for life (Misari et al., 1988, Waliyar et al.,, 2007). With these transmission characteristics, spatial spread of a persistently-transmitted rosette can be expected to be much more general and University of Ghana http://ugspace.ug.edu.gh 120 widespread than the more localized distribution of diseases caused by non-persistently or semi- persistently transmitted viruses (Duffus, 1973). Rosette symptoms may appear following the establishment of GRV and its sat-RNA in the absence of GRAV, but such plants will not serve as sources of inoculum for subsequent spread by aphids. Thus, long acquisition access feeding is essential to acquire GRAV, but the ability of the aphid vector to inoculate all three agents may depend on the inoculation period. The study indicated that some of the susceptible varieties e.g. KWANKWASO with typical chlorotic rosette symptoms did not contain GRAV, whereas some apparently symptomless field resistant genotypes contained GRAV (assayed by RT-PCR), this observation was also reported by Naidu and Kimmins (2007) with plant inoculated by single viruliferous aphids under laboratory conditions. The reason for this observation was explained based on the transmission efficiency of the three agents of GRD which increases with an increase in inoculation access period (IAP). The above observations show clearly that, from some F2 lines containing both GRV and GRAV inoculated by aphids become infected with GRV and sat-RNA only. In the symptomless F2 groundnut genotypes with particles of GRAV coat protein alone and available for acquisition by aphids may not be transmitted in sufficient quantity to cause infection hence, genotypes infected with GRAV alone are virtually symptomless as observed in this study. A separation of GRAV from GRV infective agents could result in either aphids acquiring or transmitting only one kind of particle from the susceptible plant or from inoculating a sufficient dose of only one kind of particle to the recipient plant. Further, Naidu et al. (2007) reported that GRV and its sat-RNA may not always occur in the same tissue together with GRAV which explain the transmissions of GRAV alone. Aphids normally inoculate phloem-limited viruses immediately after sieve element penetration through a brief period of salivation in the sieve elements (Prado and Tjallingii, 1994), University of Ghana http://ugspace.ug.edu.gh 121 which is followed by phloem ingestion. On a susceptible host, ingestion may be sustained and uninterrupted for several hours. In this situation, a single aphid may not release enough virus particles containing GRAV RNA into the plant to initiate GRAV replication. However, sufficient particles containing GRV RNA and its sat RNA may have been deposited in mesophyll tissue during exploratory probes to establish these agents (Ntare and Olorunju, 2001). In the light of this GRD epidemiology, ICGX – SM 00020/5/9, ICGX – SM 00017/5/P10/P1, ICGX – SM 00020/5/P4/P1, ICGV IS 07890, ICGV IS 07899 and ICIAR-19BT with remarkably higher field resistance to GRD (symptomless) would be recommended for use as breeding sources of resistance to GRD and could be recommended for commercial production. It is, however, suggested that the segregates are advanced to F6 generations to ascertain their true status before multi-location testing and recommendation for release and for commercial production. 7.1.2 Performance of the genotypes across the two contrasting locations The study demonstrated that GRD infections substantially shifted the ranking of genotypes with respect to relative fitness (in terms of pod weight and sound kernel weight) between the two locations. Based on this finding and on general predictions from evolutionary theory, it is suggested that GRD might have played an important yet unrecognized role in the long-term maintenance of genotypic diversity in groundnut through variable selection and G × E interactions. For pathogen-caused G × E interactions to occur, Weijschede´ et al. (2006) opined that infections should significantly affect plant performance and fitness. In this study, GRD compromised sound kernel weight, retarded vegetative propagation and curtailed the spatial expansion capabilities of infected as compared with field resistant groundnut genotypes. These University of Ghana http://ugspace.ug.edu.gh 122 findings are in accordance with other studies reporting negative effects of virus infection on plant performance (Molken and Steufer, 2011). The effects of GRD infection on the genotypes showed conspicuous levels of genotypic variation for most development and growth-related traits recorded in this study. Consequently, the genotypes which performed best in the one location did not occupy high ranks in the other location, and vice versa. This suggests that GRD infections cause significant alterations in genotype frequencies within groundnut breeding lines used. The observed G × E interactions indicated genotypic dissimilarities in sensitivity of groundnut to GRD infection, which may be caused by variation in virulence levels of the three agents of GRD. Mitchell-Olds (1992) postulated three conditions for the maintenance of genotypic variation through G × E interactions as it affect fitness. The first condition demands genotypic variation in fitness which is clearly demonstrated by this study that showed strong genotypic variation in closely fitness-related traits such as per se performance for pod and sound kernel weight, which are the most important economic traits in groundnut. These results are consistent with other studies showing genotypic variation for many fitness-associated traits in T. repens (Turkington, 1989; Weijschede´ et al., 2006). The second condition requires genotype fitness to vary between environments, again in this study, the performance of genotypes differed greatly between the two locations studied, resulting in a marked shift in the ranking of genotypes between these environments. A similar conclusion was reported by Pagan et al. (2008) who showed genetic variations between different accessions for disease reaction across environments. Such negative frequency-dependent selection occurs when common genotypes as compared with less common genotypes suffer from a fitness disadvantage in virus-prone environments (Brunet and Mundt, 2000; Rueffler et al., 2006). The apparent negative effects of GRD on plant performance, significant G × E interaction University of Ghana http://ugspace.ug.edu.gh 123 for GRD and evident repercussions for relative fitness reported in this study clearly stressed the significance of GRD infections for ecological and evolutionary processes and identified virus agents of this disease as possible key factors for driving population dynamics and selection gain. 7.1.3 Inheritance of resistance to groundnut rosette disease The significant and preponderance of SCA for GRD parameters and low narrow sense heritability estimates denotes the importance of non-additive gene action in governing the expression GRD resistance in groundnut. This implies selection of the superior genotypes will be possible only at later generations to allow fixation of maximum homozygosity. Genotypes, ICGX – SM 00020/5/9, ICGX – SM 00017/5/P10/P1and ICGV IS 07890 with significant positive GCA effects for sound kernel yield accompanied desirable general combining ability for reducing the GRD severity in groundnut serve as good sources of resistance for GRD breeding . Segregates; ICGX – SM 00017/5/P10/P1 X ICIAR-19BT, ICGX – SM 00020/5/P4/P1 X SAMNUT, ICGV IS 07899 X SAMNUT14 and ICGX – SM 00017/5/P10/P1 X ICGV IS 07890 could be recommended for utilization as sources of resistance and for commercial groundnut production, because they exhibit favourable SCA estimates for yield and GRD resistance. Accurate identification and detection of the GRD free groundnut genotype is the first steps in successful management of the GRD disease. The complete sequence, similarity and phylogenetic relationships among the three agents of GRD had been completed. This information has helped in the confirmatory test of GRD resistant lines. This study has shown that GRD virus infection of groundnut genotypes is common and RT-PCR has facilitated reliable diagnosis of each agent of GRD, and was particularly useful in this study, where suspected viral symptoms were mild or absent. The confirmatory test showed none of the field resistance genotypes were free of all the University of Ghana http://ugspace.ug.edu.gh 124 three agents of GRD, confirming the earlier reports. The current work represents a first attempt at using these molecular approaches to confirm resistance status of field resistance GRD groundnut genotypes at the Institute for Agricultural Research, Samaru, Nigeria. This will pave way for quick and reliable identification of resistance sources for management of groundnut rosette disease in Nigeria. 7.2 Challenges Conventional selection has been the method of selection over the years, and has produced tangible results and appreciable genetic gains. Many high-yielding GRD resistant groundnut cultivars have been developed and released to farmers in Nigeria. The success of conventional selection for GRD resistance depends on environmental factors due to unpredictable and sporadic occurrence of GRD. To enhance natural virus infection, additional resources are required for rearing veruliferous aphids that act as vector for GRD, and inoculating each plant or establishing infector rows in the nurseries. Besides, the disease might not be uniformly distributed; for natural virus enhancement through spreader rows, disease might not develop early in the season to allow effective screening. Moreover, such techniques are laborious and increase costs of field screening. It is acknowledged that the number of loci screened in the current study is low, particularly for a tetraploid species with 20 linkage groups and hence a close linkage between markers/alleles and loci controlling disease resistance cannot not be expected. Moreover, trait data are limited and are from 2 locations in 1 year only and may introduce bias Accurate selection is also complicated because development of symptoms depended on time of infection. In such a case, MAS would be ideal to facilitate selection as it does not rely on University of Ghana http://ugspace.ug.edu.gh 125 symptoms or field conditions. It is suggested that, when the laboratory facilities are established and the appropriate molecular markers are available, use of MAS in breeding for GRD resistance would be recommended. 7.3 Conclusions and recommendations The genetic diversity analysis undertaken has presented a valuable opportunity to additionally identify loci putatively linked to GRD resistance. The SSR results of this study provide critical information to breeders for planning future breeding strategies. It will also enable plant breeders to make informed decisions about parental selection for developing mapping populations; This type of analysis also offers a mechanism for breeders to counteract further genetic impoverishment of the cultivated groundnut gene pool. The two groups of new sources of resistance could offer a good source for molecular gene pyramiding for GRV and vector resistance genes into a single genotype to achieve broad-based genetic resistance for developing sustainable crop management strategies against groundnut rosette. From a practical plant breeding perspective, utilization of more than one form of resistance should be expected to reduce the frequency of appearance of virus strains able to infect groundnut varieties that are released to farmers for production over large acreages. It is hoped that in the near future teams of breeders at IAR will have a much more sophisticated understanding of the mechanisms of virus replication and movement and of factors controlling host range. It is anticipated that the knowledge from this study will be utilized in developing viable strategies that can expand our repertoire of methods available to protect groundnut plants against GRD disease for the benefit of groundnut farmers in Africa and Nigeria in particular. University of Ghana http://ugspace.ug.edu.gh 126 Bibliography Adamu, A. K., Olorunju, P. E., Ado, S. G., & Alabi, S. O. (2008). General and Specific Combining Ability Estimates for Rosette Resistance, Early Maturity and other Agronomic Traits in Groundnut (Arachis hypogaea L.) International Journal of Pure and Applied Sciences. 2(1):33–41, 2008 www.ijpas.com Adu – Dapaah, H. K., Asibou, J. Y., Danquah, O – A., Asumadu, H., Haleegoah, J., & Asafo – Adjei, B. (2004). Farmers participation in groundnut rosette resistant varietal selection in Ghana In: Fisher et al (eds). New direction for a diverse planet: Proceeding for the 4th International Crop Science congress Brisbane, Australia 26th September – 1st October, 2004. Ali, N., Nawaz, M. S., Bashir, K., and Mirza, M. Y. (2001). Combining Ability estimates in F2 and F3 Generations for Early Maturity and Agronomic Traits in Peanut (Archis yhpogaea L.). Pakistant Journal of Botany 33(1):93 – 99 Alegbejo, M. D. (2000). A checklist of host plants of Aphis craccivora Koch (Homoptera: Aphididae) in Samaru, Nigeria. Nigerian Journal of Entomology 17: 39-40. Alhassan, G. A., & Egbe M. O. (2013). Participatory Rural Appraisal of Bambara Groundnut (Vigna subterranea (L.) Verdc.) Production in Southern Guinea Savanna of Nigeria. Agricultural Science (1)18-31 ISSN 2291-4471 E-ISSN 2291-448X Amin, P. W. (1985). Resistance of wildd species of groundnut to insect and mite pests. In Cytogenetics of Arachis. Proceedings of the International Workshop, 31 Oct – 2 November 1983, ICRISATT Center, India. Patancheru, Andhra. Pradesh 502 324, India: International Crops Research Institute f or Semi-Arid Tropics Amini, F., Majidi, M. M., & Mirlohi, A. (2013). Interaction Analysis for Agronomical and Some Morphological Traits in Half-Sib Families of Tall Fescue. Crop Science. 53:411– 421. Anderson, W. F ; Patanothai, A; Wynne J. C & Gibbons R. W. (1990) Assessment of a diallel crosses for multiple foliar pest resistance in peanut Olaugineux. , 45, N"8/9, p. 373-378 Anderson, W. F., Holbrook, C. C., & Culbreath, A. K. (1996). Screening the core collection for resistance to tomato spotted wilt virus Peanut Science 23, 57–61. Andima, D., Kidula, N., & Makini. F. (2006). Research Priorities for KARI Kisii Mandate area . Ansa, O. A., Kuhn, C. W. Misari, S. M. Demski, J. W. Casper, R. & Breyel, E. (1991). Single and mixed infections of groundnut (Peanut) with groundnut rosette virus and groundnut University of Ghana http://ugspace.ug.edu.gh 127 rosette assistor virus (Abstract.). 1990. Proceedings of Peanut Research Education Society, 22, 41.doi: 10.2135/cropsci2012.05.0277 Arunga, E. E., Van Rheenen, H. A, & Owuoche, J. O. (2010). Diallel analysis of Snap bean (Phaseolus vulgaris L.) varieties for important traits. African Journal of Agricultural Research 5(15):1951-1957. Assefa, T., Abebe, G., Fininsa, C., Tesso, B., Al-Tawaha, A. R. M. (2005). Participatory bean breeding with women and small holder farmers in eastern Ethiopia. World Journal of Agricultural Science. 1: 28-35. Baker, R. J. (1978). Issues in diallel analysis. Crop Science 18 (4):533-536. Belaj, A., Satovic Z., Cipriani G., Baldoni, L., Testolin R., Rallo L., & Trujillo, I. (2003) Comparative study of the discriminating capacity of RAPD, AFLP and SSR markers and of their effectiveness in establishing genetic relationships in olive. Theoretical and Applied Genetics 107: 736–744 Bentur, M. G., Parameshwaroppa, K. G, & Malligawad, L. H. (2004). Stability in large seeded groundnut genotypes for pod yield and its component traits. Journal of oil seed research 21 (1) 17 – 20. Berchoux de, C. D., (1960). La rosette de l.arachide en Haute-Volta. Comportement de lignées résistantes. Oléagineux 15:229-223. Blok, V. C., Ziegler, A., Robinson, D. J., & Murant, A. F., (1994). Sequences of 10 variants of the satellite-like RNA-3 of groundnut rosette virus. Virology 202, 25–32. Bock, K., Murant, A., & Rajeshwari. R. (1990). The nature of the resistance in groundnut to rosette disease Annals of Applied Biology 117:379–384. Bock, K. R., & Nigam, S. N. (1988). Methodology of groundnut rosette screening and vector- ecology studies in Malawi. In Coordinated research on groundnut rosette virus disease, pp. 6-10. Patancheru, Andhra Pradesh 502 324, India: International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) Boonham, N., Glover, R., Tomlinson, J., & Mumford, R. (2008). Exploiting generic platform technologies for the detection and identification of plant pathogens. European Journal of Plant Pathology, 121, 355-363 Botstein, D., White, R. L., Skolnick, M., & Davis, R. W. (1980). Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American Journal of Human Genetics. 32:314–331. Brunet, J., & Mundt, C. C. (2000). Disease frequency – dependent selection, and genetic polymorphism: experiments with stripe rust and wheat. Evolution 54: 406 – 415. University of Ghana http://ugspace.ug.edu.gh 128 Bucheyeki, T. L., Shennkalwa, E., Kadadi, D., & Lobulu, J. (2011). Assessment of Rice Production Constraints and Farmers Preferences in Nzega and Igunga Districts. Journal of Advances in Developmental Research 2 (1): 30-37. ISSN: 0976-4704 (Print), e-ISSN: 0976-4844 (Online) Buddenhagen, I. W., & Ponti, O. M. B. (1984). Crop improvement to minimize future losses to disease and pest in the Tropics. In: Breeding for durable disease and pest resistance. Pp. 23 – 47. FAO Plant production and protection paper 55. Rome Italy. Burow M. D., Simpson CE, Faries M. W., Starr J. L., Paterson, A. H., (2009) Molecular biogeographic study of recently described B- and A genome Arachis species, also providing new insights into the origins of cultivated peanut. Genome 52, 107–119 Buiel, A. A. M., Dwivedi, S. L., Prasad, M.V.R., Singh, A. B., Dharmaraj, P. S., & Parlevliet, J. E.(1995). Multi-environment testing for reduced incidence of peanut bud necrosis disease in India. Pages 47-54 in Recent studies on peanut bud necrosis disease: Proceedings of a Meeting, 20 Mar 1995, ICRISAT Asia Center, India (Buiel, A.A.M., Parlevliet, J.E., and Lenne, J.M. , eds.). Patancheru 502 324, Andhra Pradesh, India: International Crops Research Institute for the Semi- Arid Tropics; and P O Box 386, 6700 AJ Wageningen, The Netherlands: Department of Plant Breeding, Agricultural University of Wageningen. 80 pp. Campbell, C. L., & Madden, L. V. (1990). Introduction to plant disease epidemiology. John Wiley & Sons, NY., USA. Canning, E. S. G., Penrose, M. J., Barker, I., & Coates. D. (1996). Improved detection of barley yellow dwarf virus in single aphids using RT-PCR. Journal of Virology and Methods 56, 191–197. Casper, R., S. Meyer, D. E., Lesemann, Reddy, D.V.R., Rajeshwari, R., Misari, S.M., & Subbarayudu, S.S (1983). Detection of a luteovirus in groundnut rosette diseased groundnuts (Arachis hypogaea) by enzyme-linked immunosorbent assay and immunoelectron microscopy. Phytopathology Journal. 108, 12–17. Ceccarelli, S., & Grando, S. (2007), "Decentralized-participatory plant breeding: an example of demand driven research", Euphytica, Vol. 155, pp. 349-360. Census, 2006. The 2006 Population census of Nigeria Clements, M. J., Maragos, C. M., Pataky, J. K., & White, D. G. (2004). Sources of resistance to fumonisin accumulation in grain and Fusarium ear and kernel rot of corn. Phytopathology 94:251–260. doi:10.1094/PHYTO.2004.94.3.251 Cooper, M., & DeLacy, I. H. (1994). "Relationships Among Analytical Methods Used To Study Genotypic Variation And Genotype-By-Environment Interaction In Plant-Breeding University of Ghana http://ugspace.ug.edu.gh 129 Multi Environment Experiments." Theoretical and Applied Genetics 88(5): 561-572. Citations WOS 102 GS 142. Cuc, L. M., Mace, E. S., Crouch, J. H., Quang, V. D., Long, T. D., & Varshney, R. K. (2008). Isolation and characterization of novel microsatellite markers and their application for diversity assessment in cultivated groundnut (Arachis hypogaea). BMC Plant Biology. 8, 55. Danial, D. L. (2003). Aprendiendo de la investigacion participativa con agricultores: Caso Preduza, In: Danial, D.L. (ed.) Agrobiodiversidad y produccion de Semilla con el Sector Informal a traves del Mejoramiento Participativo an la Zona Andina, Lima, Peru, 22-26 de pp 86-96 (English summary). David, J. B., Bruna, V., Stephan, N., Milind, B. R., Tae-Ho, L., Soraya, C. M. L., Changsoo, K., Patricia, M. G., Guillermo, S., Trude, S., Andrew, H. P., Pat, H. H., & Ana. C. G. A. (2012). The repetitive component of the A genome of peanut (Arachis hypogaea) and its role in remodeling intergenic sequence space since its evolutionary divergence from the B genome. Annals of Botany . 1 – 15 doi:10.1093/aob/mct128 Deom, C. M., Naidu, R. A., Chiyembekeza, A. J., Ntare, B. R., & Subrahmanyam, P. (2000). Sequence diversity within the three agents of groundnut rosette disease. Phytopathology 90:214-219. Department for International Development. (2002). Marketing and processing of Bambara groundnuts in West Africa. DFID Programme. R. No. R7581. Derera, J., Tongoona, P., Langyintuo, A., Laing, M.D., & Vivek, B. (2006). Farmer perceptions on maize cultivars in the marginal eastern belt of Zimbabwe and their implications for breeding. African Crop Science Journal 14, 1-15 Dhillon, S. S., Rake, A. V., & Miksche, J. P. (1980).Reassociation kinetics and cytophotometric characterization of peanut (Arachishypogaea L.) DNA. Plant Physiology 65: 1121–1127. Duan,Y. P., Chen, W. G., Li, M. S., Li, X. H., Liu, X., Tian, Q. Z., Bai, L., & Zhang, S. H. (2006). The genetic diversity among 27 maize populations based on SSR data. Science Agriculture Sinica 39: 1102–1113. Duffus, J. E (1973). The yellowing virus of beet. Annal Review of Entomology 30: 155 – 174. Dwivedi, S. I, Crouch, J. H., Nigam, S. N., & M. Ferguson, E. (2003). Molecular breeding of groundnut for enhanced productivity and food security in the semi-arid tropics: Opportunities and challenges. Advances in Agronomy. 80: 153-221. University of Ghana http://ugspace.ug.edu.gh 130 Dwivedi, S. L., Bertioli, D. J., Crouch, J. H., Valls, J. F., Upadhyaya, H. D., Favero, A., Moretzsohn, M., & Paterson, A. H. (2007) Peanut. In: C. Kole (Ed.), Genome Mapping and Molecular Breeding in Plants, 2, Oilseeds. Springer, Berlin, Germany, pp. 115–151 Dwivedi, S. L., Gurtu, S., Chandra, S., Yuejin, W., & Nigam, S. N. (2001). Assessment of genetic diversity among selected groundnut germplasm - I: RAPD analysis. Plant Breeding. 120, 345 – 349. Dwivedi, S. L., Nagabhusanam, G. V. S., Nigam, S. N., Raghunata, K., & Jambunathan, R. (1994). Germplasm enhancement for seed quality traits in groundnut. International Arachis Newsletter No. 14: 14-20. Dwivedi, S. L., Thendapani, K., & Nigam, S. N. (1998). Heterosis and combining ability studies and relationship among fruits and seed characters in peanut. Peanut Science 68: 71 – 73. Fahima, T., Roder, M. S., Grama, A., & Nevo, E. (1998). Microsatellite DNA polymorphism divergence in Triticum dicoccoides accessions highly resistant to yellow rust. Theoretical and Applied Genetics 96: 187–195. Falconer, D. S., & Mackay, T. F. C. (1996). Introduction to quantitative genetics. Longman, London, UK. Franke, M. D., Brenneman, T. B., & Holbrook, C. C. (1999). Identification of resistance to Rhizoctonia limb rot in a core collection of peanut germplasm. Plant Disease. 83:944- 948. Galgaro, L., Lopes, C. R., Gimenes, M., Valls, J. F. M., & Kochert, G. (1998). Genetic variation between several species of sections Extranervosae, Caulorrhizae, Heteranthae and Triseminatae (genus Arachis) estimated by DNA polymorphism. Genome 41, 445—454. Gibbon, D., & Pain, A. (1985). Crops of the Drier Regions of the Tropics (1st ed. p.155). Longman Publishing Company, Singapore. Gibbons, R. W., (1977). Groundnut rosette virus. In: Kranz, J., Schutter, J., Koch, W. (Eds.), Diseases of Tropical Crops. Verlag Paul Parey, Berlin, pp. 19–21. Gibbs, A. J., Mackenzie, A. M., & Gibbs, M. J. (2003). The ―potyviridae primers‖ will probably provide phelogenetically informative DNA fragments from species of potyviridae. Journal of Virology Methods 112: 41 – 4 Gibbs, A. J., Ohshima, K., Phillips, M. J., & Gibbs, M. J. (2008). The prehistory of potyviruses: their initial radiation was during the dawn of agriculture. PLoS ONE 3, e2523 University of Ghana http://ugspace.ug.edu.gh 131 Gillaspie Jr, A. G. (2007). Attempts to improve the method for screening cowpea germplasm for resistance to cucumber mosaic virus and blackeye cowpea mosaic virus. Plant Pathology Journal 6, 202–5. Gillaspie Jr, A. G. (2006). New method for screening cowpea for resistance to Cucumber mosaic virus. Plant Disease 90, 611–4. Gregory, W. C., Krapovickas, A. & Gregory, M. P. (1980) Structure, variation, evolution and classification in Arachis. In: R.J. Summerfield and A.H. Bunting (Eds.), Advances in Legume Sciences. Royal Botanic Gardens: Kew, United Kingdom, pp. 469–481 Griffing, B. (1956). Concept of general and specific combining ability in relation to diallel crossing system. Australian Journal of Biological Science 9:463–493. Grilhuber, J. (2005). Intraspecific Variation in Genome Size in Angiosperms: Identifying its Existence. Annals of Botany 95: 91–98. Guo, Y., Khanal, S., Tang, S., Bowers, J. E., Heesacker, A. F., Khalilian, N., Nagy,E. D., Zhang, D., Taylor, C. A Stalker, H. T., Ozias-Akin, P., & Knapp, S. J. (2012). Comparative mapping in intraspecific populations uncovers high degree of macrosynteny between A – and B – genome diploid species of groundnut. BioMed Central Genomics 13: 608 Gupta, S. C. and Lagoke, S. T. O.(1999). Transfer of Striga resistance into elite sorghum breeding lines in Nigeria. pp 95-101. In: Haussmann, B.I.G., Hess, D.E., Koyama,M.L., Grivert, L., Rattunde, F.H.W. & Geiger, H.H. (eds.), Breeding for Striga Resistance in Cereals, Proceedings of a Workshop held at IITA, Ibadan, Nigeria, from 18- 20 August 1999. Weikersheim: Margraf, 2000. Gyawali, S., Sunwar, S., Subedi, M., Tripathi, M., Joshi, K.D., & Witcombe, J. R. (2007). "Collaborative breeding with farmers can be effective", Field Crops Research, 101 88-95. Habarurema, I., Asea, G., Lamo J., Gibson P., Edema, R., Séré Y., & Onasanya, R. O. (2012). Genetic analysis of resistance to rice bacterial blight In Uganda. African Crop Science Journal : 105 – 112 Hallauer, A. R., Miranda, J. B., & Carena, M. J. (2008). Quantitative genetics in maize breeding. 3rd ed. Iowa State University Press, Ames, IA. Halward, T. M., Stalker, H. T., LaRue, E., & Kochert, G., 1991. Genetic variation detectable with molecular markers among unadapted germplasm resources of cultivated peanut and related wild species. Genome. 34, 1013–1020 University of Ghana http://ugspace.ug.edu.gh 132 Hannan, M. M., Biswas, M. K., Ahmed, M. B., Hossain, M., & Islam, R. (2007). Combining ability analysis of yield and yield components in tomato (Lycopersicum esculentum Mill.). Turkish Journal of Botany 31:559-563. Hao, N. B., Albrechtsen, S. E., & Nicolaisen, M. (2003). Detection and identification of the blackeye cowpea mosaic strain of Bean common mosaic virus in seeds of Vigna unguiculata species from North Vietnam. Australasian Plant Pathology 32, 505–9 Hariprasanna, K., Chuni, L., & Radhakrishnan, T. (2008). Genotype x environment interaction and stability analysis in large seeded genotypes of groundnut (Arachis hypogaea L.). Journal of Oilseed Research. 25, 125-131. Harkness, C. (1977). The breeding and selection of groundnut varieties for resistance to rosette virus disease in Nigeria. Report submitted to African Groundnut Council. 45 pp. He G., Meng R., Newman M., Gao G., Pittman R. N., & Prakash C. S. (2003). Microsatellites as DNA markers in cultivated peanut (Arachis hypogaea L.) BioMed Central Plant Biology, 3:3. He, G., & Prakash, C. S. (1997) Identification of polymorphic DNA markers in cultivated peanut (Arachis hypogaea L.). Euphytica 97, 143–149. He, G., & Prakash, C. S. (2001) Evaluation of genetic relationships among botanical varieties of cultivated peanut (Arachis hypogaea L.) using AFLP markers. Genetic Resources and Crop Evolution 48, 347–352. He, G., Meng R., Gao, H., Guo, B., Gao, G., Newman, M., Pittman, R. N.,& Prakash C.S. (2005) Simple sequence repeat markers for botanical varieties of cultivated peanut (Arachis hypogaea L.) Euphytica 142: 131–136 DOI: 10.1007/s10681-005-1043-3 Henderson, C. R. (1952). Specific and general combining ability. In Heterosis, J. W. Gowen, (ed.), pp. 350–70. Iowa State University Press, Ames, IA. Herselman, L. (2003). Genetic variation among Southern African cultivated peanut (A. hypogaea L.) genotypes as revealed by AFLP analysis. Euphytica 133, 319 – 327. Herselman, L., Thwaites, R., Kimmins, F. M., Courtois, B., van der Merwe P. J. A., & Seal, S. E. (2004). Identification and mapping of AFLP markers linked to peanut (Arachis hypogaea L.) resistance to the aphid vector of groundnut rosette disease. Theoretical and Applied Genetics 109: 1426–1433DOI 10.1007/s00122-004-1756-z Hung, H-Y., & Holland, J. B. (2012). Diallel Analysis of Resistance to Fusarium Ear Rot and Fumonisin Contamination in Maize. Crop Science 52:2173–2181. doi: 10.2135/cropsci2012.03.0154 University of Ghana http://ugspace.ug.edu.gh 133 Janila, P., Nigam, S. N., Pandey, M. K., Nagesh, P., & Varshney, R. K. (2013). Groundnut improvement: use of genetic and genomic tools. Front. Plant Science. 4: 1-16. James, D., A. Varga., V. Pallas., & Candresse, T. (2006). Strategies for simultaneous detection of multiple plant viruses. Canadian Journal of Plant Pathology. 28:16–29. Jiang, H; Boshou Liao, B; Ren, X; Lei, Y; Mace, E; Tingdong Fu, T & Crouch, J. H. (2007) Comparative Assessment of Genetic Diversity of Peanut (Arachis hypogaea L.) Genotypes with Various Levels of Resistance to Bacterial Wilt through SSR and AFLP Analyses. Journal of Genetics and Genomics 34(6): 544-554 Kamara, Y. A., Kureh, I., Mekir, A., Kartung, P., Tarfa, B. & Amaza, P. (2006). Participatory on-farm evaluation of the performance of drought-tolerant maize varieties in the Guinea savannas of Nigeria. Journal of Food, Agriculture and Environment 4, 192-196 KARI. (1996). Report of participatory Research Workshop held at Egerton University, Njoro, Kenya. June 10-29 Kearsey, M. J., & Pooni, H.S. J. (1996). The genetical Analysis of quantitative traits. Chapman and Hall, London Kenga, R., Alabi, S.O., & Gupta, S.C. (2004). Combining ability studies in tropical sorghum (Sorghum bicolor (L.) Moench) Field Crops Research 88:251-260. Khera, H. D. U., Manish, K. P., Roorkiwal, M., Sriswathi, M., Janila, P., Guo, Y., McKain, M. R., Nagy, E. D., Knapp S. J., Leebens-Mack, J., Conner, J. A., Ozias-Akins, P., & Varshney, R. K. (2013). Single Nucleotide Polymorphism–based Genetic Diversity in the Reference Set of Peanut (Arachis spp.) by Developing and Applying Cost- Effective Kompetitive Allele Specific Polymerase Chain Reaction Genotyping Assays. The Plant Genome 6, 1 – 11 doi: 10.3835/plantgenome2013.06.0019 Kochert, G., Stalker, H. T., Ginenes, M., Galgaro, L., & Moore, K. (1996). RFLP and cytogenetic evidence for the progenitor species of allotetraploid cultivated peanut (Arachis hypogaea L.). American Journal of Botany. 83:1282–1291. Kolodny, G. M. (1984). Method and kit for silver staining, developing an image and visualizing biological materials Annals of Biochemistry 126: 374 – 380. Koppolu. R., Hari, U., Sangam, D., David, H., & Varshney, R. K. (2010). Genetic relationships among seven sections of genus Arachis studied by using SSR markers. BioMed Central Plant Biology .10:15. Kottapalli, K. R., Burow, M. D., Burow, G., Burke, J., & Uppala, N. (2007). Molecular characterization of the US peanut mini core collection using microsatellite markers. Crop Science. 47:1718-1727. University of Ghana http://ugspace.ug.edu.gh 134 Krapovickas, A. (1969). The origin, variability and spread of the groundnut (Arachis hypogaea). In: P.J. Ucko and G.W. Dimbleby (Eds.). The Domestication and Exploration of Plants and Animals. Duckworth, London, pp. 427–441. Krapovickas, A., & Gregory, W. C. (1994) Taxonomı´a del ge´nero Arachis (Leguminosae). Bonplandia 8, 1–186. Krishna, G. K., Zhang, J., Burow, M., Pittman, R.N., Delikostadinov, S.G., Lu, Y. & Puppala, N. (2004). Genetic diversity analysis in Valencia peanut (Arachis hypogaea l.) using microsatellite markers. Cellular and Molecular Biology Letters, 9 (4A) 685-697. Legrève, A and Duveiller, E (2010). Preventing Potential Disease and Pest Epidemics under a Changing Climate. © CAB International. Climate Change and Crop Production (ed. M.P. Reynolds) 50 – 70 Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R.D., & Schabenberger, O. (2006). SAS® for mixed models. Second edition. SAS Institute Inc., Cary, NC, USA. Liu, Z; Feng, S; Pandey, M. K; Chen, X; Culbreath, A. K; Varshney, R. K & Guo B. (2013) Identification of Expressed Resistance Gene Analogs from Peanut (Arachis hypogaea L.) Expressed Sequence Tags. Journal of Integrative Plant Biology 00 (00): 1–9 Loffler, M., Kessel, B., Ouzunova, M., & T. Miedaner, T. (2011). Covariation between line and testcross performance for reduced mycotoxin concentrations in European maize after silk channel inoculation of two Fusarium species. Theoretical and Applied Genetics. 122:925–934. doi:10.1007/s00122-010-1499-y Mace, E. S., Buhariwalla, H. K., & Crouch, J. H. (2003). A high throughput DNA extraction protocol for tropical molecular breeding programs. Plant Molecular Biology Reporter 21: 459– 459 Mace, E. S., Varshney, R. K., Mahalakshmi, V., Seetha, K., Gafoor, A., Leeladevi, Y., and. Crouch, J. H. (2007): In silico development of simple sequence repeat markers within the aeschynomenoid/ dalbergoid and genistoid clades of the Leguminosae family and their transferability to Arachis hypogaea, groundnut. Plant Science. 174, 51—60. Makanda, I., Tongoona, P and Derera J (2009) Appraisal of factors impacting on crop productivity in the semi – arid environments in Zimbabwe and their implication on crop improvement goals and policy interventions. African Crop Science Conference Proceeding, 9: 705 – 718 Makne, V. G. (1992). Diallel analysis for studying the inheritance of branches, developed pods and harvest index in groundnut. Journal of Maharastra Agricultural. University. 17: 153- 154 University of Ghana http://ugspace.ug.edu.gh 135 Marame, F., Dessalegne, L., Finisa, C., & Sigvald, R. (2009). Heterosis and heritability in crosses among Asian and Ethiopian parents of hot pepper genotypes. Euphytica 168, 235- 247 Martins, O., Hugo, D. G., Omari., O., & Patrick, O. (2002). Participatory Rural Appraisal of Farmers‘ Criteria for Selection of Maize Varieties and Constraints to Maize Production in Moist-Midaltitude Zone of Western Kenya: A case study of Butere-Mumias, Busia and Homa Bay Districts Mensah, C., Difonzo, C., & Wang, D. (2008). Inheritance of soybean aphid resistance in PI 567541B and PI 567598B. Crop Science 48:1759–1763 Mensah, C., Difonzo, C., Nelson, R. L. & Wang, D. (2005). Resistance to soybean aphid in early maturing soybean germplasm. Crop Science 45:2228–2233 Milla-Lewis, S. R., Zuleta, M. C., & Isleib, T. G. (2010). Assessment of Genetic Diversity Changes in U.S. Runner-type peanut cultivars Released between 1943 and 2009 Using Simple Sequence Repeat (SSR) Markers. Department of Crop Science, North Carolina State University, Raleigh, NC 27695-7629 Misari, S. M., Abraham, J. M., Demski, J. W., Ansa, O. A., Kuhn, C. W., Casper, R & Breyel E. (1988). Aphid transmission of the viruses causing chlorotic rosette and green rosette diseases of peanut in Nigeria. Plant Disease.72:250-253. Mitchell-Olds, T., (1992). Does environmental variation maintain genetic variation – a question of scale. Trend in Ecology and Evolution 7: 397-398. Mkandawire, F. I., & Sibuga, K. P. (2002). Yield response of bambara groundnut to plant population and seedbed type. Africa Crop Science Journal, 10(1), 39-49. Moldovan, V., Moldovan, M., & Kadar, R. (2005). Assessment of winter wheat cultivars for resistance to Fusarium head blight. Annual Wheat Newsletter. 51: 97-98. Molken, T. V and Stuefer, J. F (2011). The potential of plant viruses to promote genotypic diversity via genotype 3 environment interactions. Annals of Botany 107: 1391–1397 Mondal, S., & Badigannavar, A. M. (2010). Molecular diversity and association of SSR markers to rust and late leaf spot resistance in cultivated groundnut (Arachis hypogaea L.) Plant Breeding 129, 68 – 71 Moon, H. S., Nifong, J. M., Nicholson, J. S., Heineman, A., Lion, K., Van der Hoeven, R., & Lewis, R. S. (2009). Microsatellite-based Analysis of Tobacco (L.) Genetic Resources. Crop science, 49 (6), 2149-2159. University of Ghana http://ugspace.ug.edu.gh 136 Moretzsohn, M. C., Leoi, L., Proite, K., Guimara˜ es, P. M., Leal-Bertioli, S. C., Gimenes, M. A., Martins, W. S., Valls, J. F. M., Grattapaglia, D., & Bertioli, D. J., (2005). A microsatellite- based, gene-rich linkage map for the AA genome of Arachis (Fabaceae). Theoretical and Applied Genetics. 111, 1060—1071. Moretzsohn, M. C., Gouves, E. G., Inglis, P. W., Leal-Bertioli, S. C. M., Valls, J. F. M., & Bertioli, D. J. (2013). A study of the relationships of cultivated peanut (Arachis hypogaea L.) and its most closely related wild species using intron sequences and microsatellite markers. Annals of Botany. (London) 111:113–126. doi:10.1093/aob/mcs237 Mothilal, A., Vindhiya, V. P., & Manivannan, N. (2010). Phenotypic stability for kernel yield in groundnut (Arachis hypogaea L.). Electronic Journal of Plant Breeding. 1, 173-176. Muhinyuza, J. B., Shimelis, H., Melis, R., Sibiya, J., & Nzaramba, M. N. (2012). Participatory assessment of potato production constraints and trait preference in potato development in Rwanda. International Journal of Development and Sustainability (1) 2: 358 – 380. IJDSI2080201 Mulamba, N. N., & Mock, J. J. (1978). Improvement of yield potential of the Eto Blanco maize (Zea mays L.) population by breeding for plant traits. Egypt. Journal of Genetic and Cytology. 7:40–51. Murant, A. F., Rajeshwari R., Robinson, D. J., & Raschke, J. H. (1988) A satellite RNA of groundnut rosette virus that is largely responsible for symptoms of groundnut rosette disease. Journal of General Virology. 69:1479-1486. Murant, A. F. (1989). Groundnut rosette assistor virus. In: AAB Descriptions of Plant Viruses, No. 345. Murant, A. F., & Kumar, I. K. (1990). Different variants of the satellite RNA of groundnut rosette virus are responsible for the chlorotic and green forms of groundnut rosette disease. Annals Applied Biology 117, 85–92. Naidu, R. A., & Kimmins, F. M. (2007). The Effect of Groundnut rosette assistor virus on the agronomic performance of four groundnut (Arachis hypogaea L.) genotypes. Journal of Phytopathology 155, 350–356 doi: 10.1111/j.1439-0434.2007.01243.x Naidu, R. A., & Hughes J.d‘A. (2003). Methods for the detection of plant virus diseases. p 233– 260. In: ―Plant Virology in Sub-Saharan Africa‘‘ (J.d‘A. Hughes, B.O. Odu, eds.). Proceedings of a conference organized by IITA, 4–8 June 2001, Ibadan, Ngeria. University of Ghana http://ugspace.ug.edu.gh 137 Naidu, R. A., Bottenberg, H., Subrahmanyam, P., F. Kimmins., Robinson, D. J., & Thresh, J. M. (1998). Epidemiology of groundnut rosette disease: current status and future research needs. Annals Applied Biology. 132, 525–548. Naidu, R.A., Kimmins, F. M., Deom, C. M., Subrahmanyam, P., Chiyembekeza, A. J., & van der Merwe, P. J. A. (1999). Groundnut rosette: a virus disease affecting groundnut production in Sub- Saharan Africa. Plant Disease 83:700–709. Ndjeunga, J., Ntare, B. R., Abdoulaye, A., Ibro, A. Zarafi, M. A, Cisse,Y., Moutari A., Kodio, O. Echekwu, C. A., Mohammed, S. G., & Micko, I. (2010). Farmer preferences for groundnut traits and varieties in West Africa: Cases of Mali, Niger and Nigeria. International Crops Research Institute for the Semi-Arid Tropics. Working paper series no. 2 Ndjeunga, J., Ntare, B. R., Waliyar F., Ondio K. J., & Traore, A. (2003). Assessing the diffusion of groundnut varieties in Mali. In International Arachis Newsletter No. 23. Patancheru 502 324, Andhar Pradesh, India ICRISAT. Nei, M., Tajima, F., & Tateno, Y. (1983). Accuracy of estimated phylogenetic trees from molecular data. Journal of Molecular Evolution, 19, 153 – 170. Nigam, K.B., Kandalkar, V.S., & Dhumale, D. B. (1990). Induced mutants in opium poppy. Indian Journal of Agricultural Science. 60, 267–268. Nkonya, E. M., & Featherstone, A. M. (2001). Cross-pollinated crop variety adoption studies and seed recycling: the case of maize in Tanzania. Eastern African Journal of Rural Development 17(1): 25-34. Nsabiyera, V., Ochwo-Ssemakula, M., Sseruwagi, P., Ojiewo, C., & Gibson, P. (2013) Combining ability for field resistance to disease, fruit yield and yield factor among hot pepper (Capsicum annum L.) genotypes in Uganda. International Journal of Plant Breeding. Global Science Books Ntare, B. R and Olorunju, P. E. (2001). Variation in yield and resistance to groundnut rosette disease in early and medium maturing groundnut genotypes in Nigeria. African Crop Science Journal. 9:451-461. Ntare, B. R., Olorunju, P. E., & Hildebrand, G. L. (2002). Progress in breeding early maturing peanut cultivars with resistance to groundnut rosette diseases in West Africa. Peanut Science 29:17-23. University of Ghana http://ugspace.ug.edu.gh 138 Ntare, B R., Diallo, AT., Ndjeunga, J., & Waliyar, F (2007). Groundnut Seed Production Manual pp 24 Odedara, O. O., Hughes, J. d‘A., & Odu, B. O. (2007). Occurrence of latent virus infection in visually-rated cowpea (Vigna unguiculata L. Walp) seedlings. Archives of Phytopathology and Plant Protection 42, 882–90. Okoko, N., Kidu. N., Wasilwa, L., Makini, F., Murithi, F., & Graham, K. (2010). Participatory evaluation and dissemination of improved groundnut varieties and technologies for processing and utilization Okoko, N., Kwach, J., Nyang‘or, J., & Odera, P. (1998). PRA report on groundnut in Rangenya village west Kenyamwa Ndhiwa division. Pages 52-59. In Rees, Njue, Makini and mbugua (1998 eds.). Participatory rural appraisals of the farming systems of southwest Kenya, 1995 and 1996 Kitale. Kenya Olmos, A., Bertolini, E., Capote, C. & Cambra, M. (2008). An evidence-based approach to plum pox virus detection by DASI-ELISA and RT-PCR in dormant period. Virology: Research and Treatment, 1, 1-8 Olmos, A., Cambra, M., & Dasi, M. A. (1997). Simultaneous detection and typing of plum pox potyvirus (PPV) isolates by heminested-PCR and PCR-ELISA. Journal of Virology Methods 68, 127–137. Olorunju, P. E., Kuhn, C. W., Demski, J .W., Misari, S. M., & Ansa, O. A. (1992). Inheritance of resistance in peanut to mixed infections of groundnut rosette virus (GRV) and groundnut rosette assistor virus and a single infection of GRV. Plant Disease. 76:95-100. Olorunju, P. E., Ntare, B. R., Pande, S., & Reddy, S.V. (2001). Additional sources of resistance to groundnut rosette disease in groundnut germplasm and breeding lines. Annals of applied Biology 139:259-268. Ortiz, O., Frias, G., Ho, R., Cisneros, H., Nelson, R., Castillo, R., Orrego, R., Pradel, W., Alcazar, J., & Bazán, M. (2008). "Organizational learning through participatory research: CIP and Care in Peru", Agriculture and Human Values 25, 419-431. Padgham, D. E., Kimmins, F. M., & Ranga Rao, G. V. (1990). Resistance in groundnut (Arachis hypogaea L.) to Aphis craccivora Koch. Annals of Applied Biology 117: 353–358. Pensuk, V., Wongkaew, S., Jogloy, S., & Patanothai, A. (2002). Combining ability for resistance in peanut bud necrosis tospovirus (PBNV). Annals of Applied Biology 141 (2): 141 – 146. Pittman, R.N. (1995) United States Peanut Descriptors. ARS-132,USDA-ARS. University of Ghana http://ugspace.ug.edu.gh 139 Prado, E., and Tjallingii, W. F. (1994). Aphid activities during sieve element punctures. Entomologia Experimentalis et Applicata 72:157-165 In: Spatiotemporal Separation of Groundnut Rosette Disease Agents. Naidu, R. A., Kimmins, F. M., Holt, J., Robinson, D. J., Deom, C. M., and Subrahmanyam,. Phytopathology 89:934-941 Puttha, R., Jogloy, S., Wongkaew, S., Sanitchon, J., Kesmala, T., & Patanothai. (2008). Heritability, phenotypic and genotypic correlation of peanut bud necrosis virus resistance and agronomic traits in peanut. Asian Journal of plant science 7 (3): 276 – 283. Rachier, G. O., K‘Oloo O., & Nyakundi B. N. S. (2006). Identification and on-farm evaluation of Groundnut lines tolerant to Rosette virus and Leaf spot Diseases in West Kenya. Paper presented in 10th Biennial KARI Conference 2006. Raina, S. N., Rani, V., Kojima, T., Ogihara, O. Y., Singh K. P., & Devarumath, R. M. (2001). RAPD and ISSR fingerprints as useful genetic marker for analysis, genetic diversity, varietal identification and phylogenetic relationship in peanut (Arachis hypogaea L.) cultivars and wild species. Genome 44: 763 – 772. Rajeshwari, R., Murant, A. F., & Massalski, P. R., (1987). Use of monoclonal antibody to potato leaf roll virus for detecting groundnut rosette assistor virus by ELISA. Annals of Applied Biology. 111, 353–358. Ramburan, S., & Van den Berg, M. (2011). Review and analysis of post-release variety evaluation of sugarcane: A South African perspective. International Sugar Journal 113, 444-452. Reddy, D. V. R., Murant, A. F., Duncan, G. H., Ansa, O.A., Demski, J. W., & Kuhn, C. W. (1985). Viruses associated with chlorotic rosette and green rosette diseases of groundnut in Nigeria. Annals of Applied Biology. 107, 57–64. Reddy, T. Y., Reddy, V. R., & Anbumozhi, V. (2003). Physiological responses of peanut (Arachis hypogaea L.) to drought stress and its amelioration: A critical review. Plant Growth Regulators. 41:75–88. Reddy., D. V. R. (1991). Groundnut viruses and virus diseases: distribution, identification and control. Review of Plant Pathology. 70:665-678. Rego, E. R., Rego, M. M., Finger, F. L., Cruz, C. D., & Casali, V. W. D. (2009). A diallel study of yield components and fruit quality in chilli pepper (Capsicum baccatum). Euphytica 168 (2): 275–287. Robinson, D. J., Ryabov, E. V., Raj, S. K., Roberts, I. M., & Taliansky, M. E. (1999). Satellite RNA is essential for encapsidation of groundnut rosette umbravirus RNA by groundnut rosette assistor luteovirus coat protein. Virology. 254:104-114. University of Ghana http://ugspace.ug.edu.gh 140 Robledo, G., Lavia, G. I., & Seijo, G. (2010). Genome re-assignment of Arachis trinitensis (Sect.Arachis, Leguminosae) and its implications for the genetic origin of cultivated peanut. Rueffler, C., Van Dooren, T. J. M., Leimar, O., & Abrams, P. A. (2006). Destructive selection and then what? Trend in Ecology and Evolution 20: 481- 486 Ryabov, E. V., Robinson, D. J., & Taliansky, M. E. (1999). A plant virus encoded protein facilitates long-distance movement of heterologous viral RNA. Proceedings of the National Academy of Sciences USA 96:1212-1217. Salem, N. M., Ehlersb, J. D., Robertsb P. A & Ng, J. C. K. (2010). Biological and molecular diagnosis of seedborne viruses in cowpea germplasm of geographically diverse sub- Saharan origins. Plant Pathology. 59, 773–784. Doi: 10.1111/j.1365-3059.2010.02285.x Sajib, A. M; Hossain, M. M; Mosnaz, A.T.M.J; Hossain, H; Islam, M. M; Ali, M. S & Prodhan; S. H (2012). SSR marker-based molecular characterization and genetic diversity analysis of aromatic landreces of rice (Oryza sativa L.) Journal of Biological Science and Biotechnology. 1(2): 107-116. ISSN: 1314-6246 SAS Institute. (2013). The SAS system for Windows. Release 9.3.1. SAS Inst., Cary, NC. Scott, K. P., Farmer, M-J., Robinson, D. J., Torrance, L., & Murant, A. F. (1996). Comparison of the coat protein of groundnut rosette assistor virus with those of other luteoviruses. Annals of Applied Biology 128, 77–83. Seijo, G. J., Lavia, G. I., Fernández, A., & Krapovickas, A. (2007). Genomic relationships between the cultivated peanut (Arachis hypogaea, Leguminosae) and its close relatives revealed by double GISH. American. Journal Botany. 94: 1963-1971. Seijo, J. G., Lavia, G. I., Fernandez, A., Krapovickas, A., Ducasse, D., & Moscone, E. A. (2004) Physical mapping of the 5S and 18S-25S rRNA genes by FISH as evidence that Arachis duranensis and A. ipaensis are the wild diploid progenitors of A. hypogaea (Leguminosae). AmericanJournal of Botany. 9, 1294–1303. Senapathi, B. K., Maily, D., & Sarkar, G. (2004). Stability evaluation of summer groundnut (Arachis hypogaea L.) under coastal saline zone of West Bengal. Legume Research, 27: 103 – 106 Shoba, D; Manivannan N & Vindhiyavarman, P (2010) Genetic diversity analysis of groundnut genotypes using SSR markers. Electronic Journal of Plant Breeding, 1(6): 1420-1425. ISSN 0975-928X Sikinarum, J., Jaisil, P., Jagloy, S., Toomson, B., Kesmala, T., & Patanothai, A. (2007). Heritability and correlation for nitrogen (N2) fixation and related traits in peanut (Aracchis hypogaea L.) Pakistan Journal of Biological Science 10 (12): 1956 – 1962 University of Ghana http://ugspace.ug.edu.gh 141 Simko, I., & Piepho, H. P. (2012). Area under disease progress stairs: Calculation, advantage and application. Phytopathology 102 (4): 381 – 389 doi: 10.1094/PHYTO-07-11-0216. Simon, A. E (2004) Plant Virus Satellite and Defective Interfering RNAS: New Paradigms for a New Century. Annual Review of Phytopathology 42:415–37 doi: 10.1146/annurev.phyto.42.040803.140402 Singh, R. P., Kurz, J., & Boiteau, G. (1996). Detection of styletborne and circulative potato viruses in aphids by duplex reverse polymerase chain reaction. Journal of Virology Methods 59, 189–196 Sitaresmi, T., Sujiprihati, S., & Syukur, M. (2010). Combining ability of several introduced and local chilli pepper (Capsicum annum L.) genotypes and heterosis of the offspring. Journal Agronomy Indonesia 38 (3) 212 – 217 Smartt, J., & Stalker, H. T. (1982). Speciation and cytogenetics in Arachis. In: H.E. Pattee and C.E. Young (Eds.), Peanut Science and Technology. American Peanut Research and Education Society, Yoakum, TX, pp. 21–49 Snapp, S., (2002). Quantifying farmer evaluation of Technologies: The mother and Baby Trial Design. In Quantitative analysis of Data from Participatory Methods in Plant Breeding (Bellon MR and Reeves J, eds.). Mexico, DF: CIMMYT Stalker, H. T., Phillips, T. G., Murphy, J. P., & Jones, T.M. (1994). Diversity of isozyme patterns in Arachis species. Theoretical and Applied. Genetics. 87, 746–755. Stalker, H.T., & Moss, J. P. (1987). Speciation, Cytogenetic and Utilization of Arachis species. Advanced agronomy 41: 1 – 40 Stalker, H.T., & Simpson, C. E. (1995). Genetic resources in Arachis. In: H.E. Pattee and H.T. Stalker (Eds.), Advances in Peanut Science. American Peanut Research and Education Society, Stillwater, Oklahoma, 14–53. Stalker, H.T., Dhesi J. S., Parry, D. C., & Hahn, J. H. (1991). Cytological and inter fertility relationships of Arachis section Arachis. American Journal of Botany. 78, 238–246. Stalker, H.T., Ferguson, M. E., Valls, J. F. M., Pittman, R. N., Simpson, C. E., & Bramel- Cox, P. (2002). Catalog of Arachis Germplasm Collection. Web version. http://www.icrisat.org/ GroundNut/Arachis/Start.htm Stansfield, W. D. (1986). Theory and Problems of genetics McGraw Hill Book Co, New York, USA University of Ghana http://ugspace.ug.edu.gh 142 Stevens, M., R., & Smith H. G. (1997). Comparison of ELISA and RT-PCR for the detection of beet yellows closterovirus in plants and aphids. Journal of Virology Methods 68, 9–16. Subrahmanyam, P., Hilderbrand, G. L., Naidu, R. A., Reddy, L. J., & Singh, A. K., (1998) Sources of resistance to groundnut rosette disease in global groundnut germplasm. Annals of Applied Biology. 132:473-485. Subramanian, V. S., Gurtu, R. C., Nageswara, R., & Nigam. S. N. (2000). Identification of DNA polymorphism in cultivated groundnut using random amplified polymorphic DNA (RAPD) assay. Genome 43:656–660. doi:10.1139/g00-034 Taliansky M. E., Robinson, D. J., & Murant, A. F. (1996). Complete nucleotide sequence and organization of the RNA genome of groundnut rosette umbravirus. Journal of General Virology 77:2335–2345. Taliansky M. E., Robinson, D. J., & Murant, A. F. (2000). Groundnut rosette disease virus complex: biology and molecular biology. Advance Virus Research 55:357–400. Taliansky, M. E., Robinson, D. J., & Murant, A. F. (1997). Complete nucleotide sequence and organization of the RNA genome of groundnut rosette umbravirus. Journal General Virology. 77:2335-2345 Tang, H., Bowers, J., Wang, X., Ming, R., Alam, M., & Paterson, A. (2008). Synteny and collinearity in plant genomes. Science 320:486–488. Tang, J. B., Xia, H. A., Cao, M. L., Zhang, X. Q., & Zeng, W. Y. (2004). A comparison of rice chloroplast genome. Plant physiology 135: 412 – 420. Tang, R., Gao, G., He, L., Han, Z., Shan, S., Zhong, R., Zhou, C., Jiang, J., Li, Y., & Zhuang, W. (2007). Genetic Diversity in Cultivated Groundnut Based on SSR Markers. Journal of Genetics and Genomics 34(5): 449 – 459. Tanimu, B., & Aliyu, L. (1995). Country report on Bambara groundnut production in Nigeria. In Heller J., F. Begeman, & J. Mushonga (Eds.), bambara groundnut Vigna subterranea (L.) Verdc. Promoting the conservation and use of underutilized and neglected crops. The FAO. (2012). Statistical Database (FAOSTAT): http://faostat.fao.org/site/567/default.aspx. Tillman, B. L., & Stalker, H. T. (2009). Peanut: In Johann Vollmann and Istvan Rajcan Handbook of Plant Breeding, Oil Crops 4: 287 – 316. Torres, A. L., Arias, A. S., Arahana, V., & Torres, M. L. (2008). Preliminary Assessment of Genetic Diversity and Phenetic Relations for Section Lasiocarpa by Means of Heterologous SSR Markers. Crop Science. 48:2289–2297 University of Ghana http://ugspace.ug.edu.gh 143 Turkington, R., (1998). The growth, distribution and neighbour relationships of Trifolium repens in a permanent pasture. VI. Conditioning effects by neighbours. Journal of ecology 77: 734-311 Upadhyaya, H. A., Mukri, G., Nadaf, H. L., & Singh, S. (2012). Variability and Stability Analysis for Nutritional Traits in the Mini Core Collection of Peanut. Crop Science. 52:168–178 doi: 10.2135/cropsci2011.05.0248 Upadhyaya, H. D., &. Ortiz, R. (2001). A mini core subset for capturing diversity and promoting utilization of chickpea genetic resources in crop improvement. Theoretical and Applied Genetics 102:1292–1298. Upadhyaya, H. D., Mallikarjuna, B. P., Swamy, P. V., Goudar, K., Kullaiswamy, B. Y., & Singh, S. (2005). Identification of diverse groundnut germplasm through multi- environment evaluation of a core collection for Asia. Field Crops Research. 93:293–299. Upadhyaya, H. D., Ortiz, R., Bramel, P. J., & Singh, S. (2003). Development of a groundnut core collection using taxonomical, geographical and morphological descriptors. Genetic Resource and Crop Evolution. 50:139–148. doi:10.1023/A:1022945715628 Upadhyaya, H. D., Reddy, L. J., Gowda, C. L. L., & Singh S. (2006). Identification of diverse groundnut germplasm sources of early maturity in a core collection. Field Crops Research. 97:261–267. Upadhyaya, H.D., Bramel, P. J., Ortiz, R., & Singh, S. (2002). Developing a mini core of peanut for utilization of genetic resources. Crop Science. 42:2150 – 2156. Upton, M. (1987). African Farm Management. New York : Cambridge University Press. Valls, J. F. M., & Simpson, C. E. 2005. New species of Arachis (Leguminosae) from Brazil, Paraguay, and Bolivia. Bonplandia 14, 35–64. Van der Merwe, P. J. A., Subrahmanyam, P., Hildebrand, G. L., Reddy, L. J., Nigam, S. N., Chiyembekeza, A. J., Busolo-Bulafu, C. M., & Kapewa, T. (2001). Registration of groundnut Cultivar ICGV-SM 90704 with Resistance to Groundnut Rosette International Arachis Newsletter [Publication type: JOURNAL] Varshney, R. K., Mahendar T., Aruna R., Nigam, S. N., Neelima, K., Vadez, V., & Hoisington, D. A. (2009). High level of natural variation in a groundnut (Arachis hypogaea L.) germplasm collection assayed by selected informative SSR markers. Plant Breeding. Blackwell Verlag GmbH doi:10.1111/j.1439-0523.2009.01638.x Vincelli, P., & Tisserat, N. (2008). Nucleic acid-based pathogen detection in applied plant pathology. Plant Disease. 92:660-669. University of Ghana http://ugspace.ug.edu.gh 144 Vishnuvardhan, K. M., Vasanthi, R. P., & Reddy, K. H. (2011). Combining ability of yield, yield traits and resistance to late leaf spot and rust in groundnut. Journal of SAT Agricultural Research 9 Waliyar, F., Kumar, P. L ., Ntare, B .R., Monyo, E., Nigam, S. N., Reddy, A. S Osiru, M., & Diallo, A. T. (2007). A Century of Research on Groundnut Rosette Disease and its Management. Information Bulletin no. 75. Technical Report. International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Andhra Pradesh, India. Wang S. H and Du, X. M., (2005) SSR fingerprinting analysis on distinct mutants of fiber development in Gossypium hisutum Scientia Agricultura Sinica, 38 (2005), pp. 2139– 2146 (in Chinese Wang, H., Penmetsa. R. V., Yuan M., Gong, L., Zhao, Y., Guo, B., Farmer, A .D, Rosen, B.D, Gao, J., Isobe, S., Bertioli, D. J., Varshney, R. K., Cook & He, G. (2012). Development and characterization of BAC-end sequence derived SSRs, and their incorporation into a new higher density genetic map for cultivated peanut (Arachis hypogaea L.). BioMed Central Plant Biology. 12, 10. Wangai, A. W., Pappu, S. S., Pappu, H. R., Deom, C. M., & Naidu, R. A. (2001). Distribution and characteristics of groundnut rosette disease in Kenya. Plant Disease. 85:470-474. Weijschede, J., Martinkova, J., De Kroon, H., & Huber, H. (2006). Shade avoidance in Trifolium repens: Cost and benefits of plasticity in petiole length and leaf size. New phytologist 172: 655-666. Witcombe, J. R., Joshi, K. D., Gyawali, S., Musa, A. M., Johansen, C., Virk, D. S., & Sthapit. B. R. (2005). "Participatory plant breeding is better described as highly client-oriented plant breeding I. Four indicators of client-orientation in plant breeding", Experimental Agriculture, 41: 299-319. Wricke, G., & Weber, W. E. (1986). Quantitative genetics and selection in plant breeding. Walter de Gruyter and Co., Berlin, Germany. Yan, W., & Kang, M.S. (2003). GGE Biplot Analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, FL. Yang, J., Carena, M. J., & Uphaus, J. (2010). Area Under the Dry Down Curve (AUDDC): A Method to Evaluate Rate of Dry Down in Maize. Crop Science. 50:2347–2354 (2010). Yayock, J. Y., Rossel, H. W., & Harkness, C. (1976). A review of the 1975 groundnut rosette epidemic in Nigeria. Samaru Conference Paper 9. Zaria, Nigeria: Institute for Agricultural Research (Samaru), Ahmadu Bello University, 12 University of Ghana http://ugspace.ug.edu.gh 145 Yeh, F.C., & Yang, R. C. (2000). POPGENE (population Genetic Analysis) University of Alberta. V.1.32. Molecular Biology and Biotechnology Centre and Centre for International Forestry Research. Alberta, Canada. Yi, C. X., Guo, W Z., Zhu, X. F., Min, L. F & Zhang, T. Z. (2004). Pyramiding breeding by marker-assisted recurrent selection in upland cotton: selected effects on resistance to Helicoverpa armigera. Agricultural Sciences in China, 3,330-339. Yin, X. G., Shang, X. W., Pang, B. S., Song, J. R., Cao, S. Q., Li, C., & Zhang, X. W. (2006). Molecular mapping of two novel stripe rust resistant genes YrTpl and YrTp2 derived from Triticum aestivum x Thinopyrum ponticum. Agricultural Sciences in China, 5,483-490. Young, N. D., Weeded N. P., & Kochert, G. (1996). Genome mapping in legumes (Family Fabaceae). In Genome mapping in plants. Edited by; Paterson A.H. Austin TX: RG Lands. Zecevic, B., Dordevic, R. R., Balkaya, A., Damnjanovic, J., Dordevic, M., & Vujoševic, A. (2011) Influence of parental germplasm for fruit characters in F1, F2 and F3 generations of pepper (Capsicum annuum L.). Genetika, 43, (2) 209-216 Zhang, Y., Khang, M. S., & Lamkey K. R. (2005). DIALLEL-SAS05: A comprehensive program for Griffing‘s and Gardner – Eberhart analyses. Agronomy Journal. 97:1097 – 1106. Dio:2134/agronj2004.0260 Zhebentyayaeva, T. N., Reighard, G. L., Gorina, V., & Abbott, A. G. (2003). Simple sequence repeat (SSR) analysis for assessment of genetic variability in apricot germplasm. Theoretical and Applied Genetics 106: 435–444. University of Ghana http://ugspace.ug.edu.gh 146 APPENDIX 1: Participatory Rural Appraisals questionnaire A. Household and Socio – Economic Characteristics Name of respondent ……………………………………………… Age of Household head ……………………………………………… Sex of household head 1 = Male 2 = Female District ……………………………………………… Local government Area ……………………………………………… State ……………………………………………… 1. Formal Education (highest level attain) i. Illiterate ii. Primary school iii. Secondary school – SS / JSS iv. Technical school v. Poly/Mono-technic/College of Education/University 2. Marital status i. Single ii. Married iii. Others (specify) 3. Size of production unit i. Total acreage…………………….. acres ii. Cultivated area……………….….. acres iii. Grazing area……………………... acres iv. Area under fallow……………….. acres 4. Tenure of land (Give acreage) i. Communal…………………………………….. acres ii. Private……………………………….…..…….. acres iii. Government / Institutional…………………….. acres iv. Family …………………………………..…….. acres 5. How long have you been farming…………………….. (Years) University of Ghana http://ugspace.ug.edu.gh 147 6. Do you farm part – time or full time i. Part – time ii. Full – time 7. If you are not full time farmer, how much of your time do you devote to farming operation (Tick the appropriate) i. Less than half ii. Half iii. More than half 8. Is any member of your household involved in any off – farm activities? (i) Yes (ii) No. if yes please specify the activity (ies) i. Formally employed ii. Making basket, winnowers iii. Trading / business iv. Hiring out oxen / farm implements / labour v. Others (specify) 9. Is anyone else in the household who does not live there involved in any off – farm activities Livestock Type Ownership (Male / Female) Cattle …………………………………… Goats …………………………………… Sheep …………………………………… Donkeys …………………………………… Chickens …………………………………… Pigs …………………………………… Turkeys …………………………………… Others (specify) …………………………………… 10. Give 5 of the major crops that are usually grown in first (rainy) season, who grows it and the purpose of production Crop Acreage Gender Purpose of production 1 University of Ghana http://ugspace.ug.edu.gh 148 2 3 4 5 11. How much of the crop listed above did you sell last year? Crop Quantity produced (bags/tins/basins/kg Amount sold (bags/tins/basins/kg Price (Bushy/kg Rainy season Dry season Rainy season Dry season Rainy season Dry season B. LABOUR 1. What family labour is available for production activities Age group Full time participating in farming activities Part time participating in farming activities Male Female Male Female 2. Do you use hired labour? (i) Yes (ii) No 3. If yes, specify for which crops…………………………………………………………….. 4. What kind of hired labour do you use per season (on average) Rainy season Type of hired labour Number of males Number of females Casual Permanent Village labour exchange Dry season Type of hired labour Number of males Number of females Casual Permanent University of Ghana http://ugspace.ug.edu.gh 149 Village labour exchange 5. For which activities do you hired labour? Activity Casual Permanent village 1 Land preparation 2 Planting 3 Weeding 4 Harvesting 5 On – farm transport 6 Others (specify) C. Groundnut Production 1. Give constraints / problems affecting your groundnut production (Tick whichever is appropriate and mention the control strategy) Constraints Tick as appropriate Control strategy Diseases (Specify) Field pest (Specify) Shortage of land Shortage of labour Poor quality seed Storage pest Low output price Low/reduced soil fertility Others (Specify) 2. Give acreage under improved and local groundnut variety Groundnut varieties Acreage Improved varieties Local variety 3. Which variety of groundnut do you grow? Variety Year 1st planted Initial seed source Current seed source 1 SAMNUT21 2 SAMNUT22 3 SAMNUT23 4 SAMNUT10 5 Maiyado 6 Others (specify) University of Ghana http://ugspace.ug.edu.gh 150 7 4. Why do you prefer these varieties? Variety Reasons (use code below) 1 SAMNUT21 2 SAMNUT22 3 SAMNUT23 4 SAMNUT10 5 Maiyado 6 Others (specify) 7 Codes: 1 = high yield; 2 = disease resistance; 3 = early maturity; 4 = good taste; 5 = colour 6 = drought tolerance; 7 = easy to pound; 8 = good storability; 9 = weed suppression; 10 = uniform maturity; 11 = field pest resistance; 12 = big seeded; 13 = ready market; 14 = fetches higher price 5. What don‘t you like about these varieties? (see code) Variety Reasons (use code below) 1 SAMNUT21 2 SAMNUT22 3 SAMNUT23 4 SAMNUT10 5 Maiyado 6 Others (specify) 7 Codes: 1 = poor yield; 2 = susceptible to groundnut rosette disease; 3 = late maturity; 4 = bitter taste; 5 = colour 6 = drought susceptibility; 7 = hard to pound; 8 = easily affected by storage pest; 9 = difficult to harvest; 10 = non- uniform maturity; 11 = small seeded; 12 = restricted market 6. Desirable characteristics of a good groundnut variety in order of preference. List characteristics in order of importance 1 2 3 4 5 University of Ghana http://ugspace.ug.edu.gh 151 7. Which variety would demand more labour, explain why? Variety Reasons 1 SAMNUT21 2 SAMNUT22 3 SAMNUT23 4 SAMNUT10 5 Maiyado 6 Others (specify) 7 8. Which colour of groundnut do you prefer, give reason (s) for your preference. i. Red ii. Light tan iii. No preference iv. Others (specify) 9. Groundnut production systems Activity Months Who performs? Men Women children Field selection Bush clearing Ploughing Planting Weeding Harvesting Transportation Drying Shelling Sorting Marketing Others (specify) D. Perception of groundnut rosette disease (use photo) 1. Do you know groundnut rosette disease? (i) Yes (ii) No i. What name do you call this disease? ii. What do you think causes this disease iii. In your own view how is this disease transmitted? iv. How do you try to control the disease? University of Ghana http://ugspace.ug.edu.gh 152 2. What is the loss in yield do to groundnut rosette disease? i. Low (less than 20 %) ii. Moderate (21 – 40 %) iii. High (over 50 %) iv. Total loss (100 %) 3. Do you know any variety (s), which is not affected by groundnut rosette disease? (i) Yes (ii) No i. ------------------------------------------------------------------------------ ii. ------------------------------------------------------------------------------ iii. ------------------------------------------------------------------------------ iv. ------------------------------------------------------------------------------ 4. In you view what is the trend of occurrence of this disease over the years? i. Increasing ii. Same iii. Decreasing 5. Do you plant groundnut in lines/rows? (i) Yes / (ii) No (specify spacing) 6. Do you grow groundnut in pure stand? i. Pure stand / sole ii. Mixed / intercropped 7. How many times do you weed groundnut? i. Once ii. Twice iii. Thrice 8. At what stage do you weed the groundnut? University of Ghana http://ugspace.ug.edu.gh 153 9. What purchased input do you use in production of groundnut? Input Purchased, borrow or hired Approximate cost 10. How easy is it for you to obtain the relevant inputs for production? (use codes below) Type of input Input availability Seed Hoes Fertilizers Herbicide Insecticide Fungicides Others (specify) Code: 1 = very easy; 2 = easy; 3 = not easy and 4 = others (specify) E. Use, Marketing and Decision Making 1 What are your uses of groundnut? 2 What proportion do you sell? 3 What proportion do you retain for seed? 4 If sold where you do sell? 5 When do you sell? 6 Sold sell or unshelled? 7 Do you sell at once? 8 Do you store any of these groundnut Who make the following decisions? Decision Who makes? 1 How much to plant 2 How much seed to retain 3 How much to eat 4 How much to sell 5 When to sell 6 Where to sell University of Ghana http://ugspace.ug.edu.gh 154 F. Institutions 1. Is any member of a household a member of any group / association? Yes/No. if yes, specify the kind of group (Name the group Extension contact group Farmer association Others (specify) 2. What are the major functions of the group/association? i. When did you become a member of the group /association? (give year) ii. Why do you become a member of the group (any benefit)? iii. Does the group/association address agricultural issues? Yes / No iv. If yes, enumerate the agricultural issues address 3. What are the major sources of information about agricultural activities? i. Government extension staff ii. NGO (specify) iii. Radio iv. Neigbour v. School vi. Parents vii. Training workshop viii. On – farm research / demonstration ix. Exchange visit/ field tours x. Visiting researcher xi. Newspaper/Newsletter/pamphlet xii. Others (specify) 4. Do you have a radio in your house? Yes / No 5. Do you listen to agricultural educational programs? Yes / No if yes, (Name the program) 6. If yes, is the coverage of the programs satisfactory? Yes / No 7. Did extension agents visit you last year? Yes / No 8. If yes, what time of the year or during which operation? University of Ghana http://ugspace.ug.edu.gh 155 i. Ploughing-------------------------- 1, Number of visit --------------------------- ii. Planting----------------------------- 2, Number of visit --------------------------- iii. Weeding --------------------------- 3, Number of visit --------------------------- iv. Harvesting-------------------------- 4, Number of visit --------------------------- 9. Have you ever attended field day or demonstration trials? Yes / No 10. Have you ever attended a farmers training course? Yes / No 11. Please give any comment/suggestions relating to agriculture and groundnut production in particular. University of Ghana http://ugspace.ug.edu.gh 156 University of Ghana http://ugspace.ug.edu.gh