ABSTRACT
Sorghum is the second most important cereal crop in Kenya after maize, grown on an area of 117000 ha -1 with about 144000 tonnes being produced annually. Striga hermonthica is among the major causes of sorghum yield loss especially in Western and Nyanza regions of the country. Farmers have traditionally managed Striga using cultural methods but the most effective and practical solution to poor smallholder farmers is to develop Striga resistant varieties. A field trial consisting of Sixty-four sorghum genotypes comprising of wild relatives, landraces, improved varieties and F4 progenies were evaluated in a sickplot (field with Striga inoculum capable of causing up to 100% incidence in susceptible sorghum genotypes) and in a potted trial at KALRO-Kenya Agricultural and Livestock Research Organization Alupe during the 2019 rainy season. The experiment was laid out in a square lattice design with three replications. These accessions were also genotyped using Diversity Array Technology markers to assess their diversity. In another experiment, Marker Assisted Selection (MAS) was used to transfer Striga resistance quantitative trait loci into adapted farmer preferred varieties Gadam and Kari-Mtama-1. Crosses were also made between known Striga resistance namely N13, Framida, SRN39 and Hakika as donor sources and Gadam and Kari- Mtama-1 as the female parents to obtain F1 and BC1F1 generations. Backcross generation crosses were genotyped using DArT markers to trace heterozygous alleles and to confirm successful backcrossing. The (ASNPC) selection criteria was used to identify resistant genotypes in the trials. Wild genotypes GBK045827, GBK044336, GBK047293 and GBK048921, improved varieties F6YQ212, ICSV III_IN and F4 population F6YQ212 × B35, B35 × Lodoka and B35 × ICSVIII_IN had lower ASNPC values than N13, the resistant check under sickplot conditions. Four wild genotypes GBK016109, GBK016085, GBK045827, GBK048152, one improved variety F6YQ212 and three F4 population crosses F6YQ212 × B35, LODOKA × Landiwhite, ICSVIII_IN × E36-1 had lowest ASNPC values in the potted trial. Genotypes SRN39, F6YQ212, GBK045827 and F6YQ212 × B35 were the most resistant to Striga in both field and potted trials. MACIA, B35, E36-1, OKABIR × AKUOR-ACHOT and LODOKA × ICSVIII_IN were the most tolerant to Striga recording superior yield performance in both trials. Negative correlation was observed between yield traits (100 grain weight, dry panicle weight, yield (t/ha) and Striga related traits across both trials while Striga response related traits (ASNPC, NSmax, NSFC) significantly (<0.001) correlated positively with each other in both trials. Days to flowering and plant height were also negatively correlated to yield and Striga resistance.
The overall best performing genotypes in terms of Striga resistance and yield in both trials were Macia, SRN39,GBK 045827 and GBK 016085. SNPs generated from DArT-sequencing grouped the genotypes into three major clusters, with all resistant checks grouping in the same cluster except N13. The results from this analysis revealed successful backcrosses for the crosses Gadam × N13 × Gadam, Gadam × Framida × Gadam and Gadam × SRN39 × Gadam with heterozygous allele percentages ranging from 63% to 77%. High heritability values for yield and ASNPC suggest additive gene action and selection for improvement of these trait will be beneficial. Demonstrated genetic gain for Striga tolerance points the possibility of development of Striga tolerant varieties that give substantial yield under Striga pressure. The study showed that Striga resistance and Striga tolerance alleles are available within the local wild relatives, in local landraces and in improved sorghum genotypes and there is need tap into this potential to improve sorghum production in the crop.
Table of Contents
DECLARATION i
DECLARATION OF ORIGINALITY ii
DEDICATION iii
ACKNOWLEDGEMENT iv
LIST OF ABBREVIATIONS viii
LIST OF TABLES ix
LIST OF FIGURES xi
LIST OF APPENDICES xii
ABSTRACT xiii
CHAPTER ONE: INTRODUCTION 1
1.1 Sorghum production and importance 1
1.2.1 Striga in Sorghum 3
1.3 Wild relatives of Sorghum 4
1.4 Statement of the problem 5
1.5 Justification 7
1.6 General objective 8
1.6.1 Specific objectives 8
1.6.2 Hypotheses 8
CHAPTER TWO: LITERATURE REVIEW 9
2.1 Taxonomy of sorghum 9
2.2 Morphology of sorghum plant 10
2.3 Reproduction in Sorghum 11
2.4 Striga hermonthica 12
2.4.1 Biology 12
2.4.2 Morphology of Striga hermonthica plant 13
2.4.3 Striga etiology 14
2.4.4 Haustorium Development 14
2.5 Effects of Striga weeds on sorghum production in Kenya 15
2.6 Resistance to Striga in sorghum 16
2.7 Physiological and biochemical basis of resistance 17
2.8 Genetic basis of resistance mechanisms to Striga in sorghum 18
2.9 DNA markers and markers assisted selection in Sorghum 19
2.9.1 Hybridization-based molecular markers 20
2.9.2 Markers based on the Polymerase Chain Reaction process 20
2.10 Striga resistance QTLs in sorghum 22
2.11 Marker Assisted Backcrossing for Striga resistance in sorghum 23
2.12 Wild relatives of sorghum 23
2.13 Flowering and fertilization in sorghum 24
2.13.1 Strategies for controlled pollination in sorghum 25
2.13.1.1 Hand emasculation 25
2.13.1.2 Genetic male sterility 26
2.13.1.3 Cytoplasmic male sterility 26
2.13.1.4 Hot water emasculation 26
2.13.1.5 Anther dehiscence control 27
CHAPTER THREE: SCREENING OF SORGHUM WILD RELATIVE, LANDRACES AND IMPROVED GENOTYPES FOR STRIGA RESISTANCE USING MORPHOLOGICAL AND MOLECULAR MARKERS 28
3.0 Abstract 28
3.1 Introduction 29
3.2 Materials and Methods 31
3.2.1 Field trials 31
3.2.2 Planting material and experimental layout 31
3.2.3 Data collection 33
3.2.4 Extraction of DNA for Sequencing 34
3.3 Data analysis 35
3.3.1 ANOVA and Striga data analysis 35
3.3.2 Heritability estimates 36
3.4 Results 37
3.4.1 Evaluation of Striga resistance in a sickplot at KALRO Alupe. 37
3.4.1.1 Agronomic performance of genotypes in Striga sickplot at KALRO Alupe 38
3.4.1.2 Response of sorghum genotypes to Striga at KALRO Alupe 41
3.4.1.3 Genotypic and phenotypic coefficients of variations and trait heritability measured in the sickplot 42
3.4.1.4 Relationship among agronomic, yield and Striga related traits measured in sickplot 43
3.4.2 Evaluation of Striga resistance in a potted trial during the long and short rains of 2019
at KARLO, Alupe 46
3.4.2.1 Agronomic performance of genotypes in potted trial at KALRO Alupe 46
3.4.2.2 Response of sorghum genotypes to Striga in Potted trial at KALRO Alupe 49
3.4.2.3 Heritability estimates for traits in the potted trial 51
3.4.2.4 Correlations among traits in the potted trial at KALRO Alupe 52
3.4.3 Agronomic mean performance of the genotypes between the two trials 54
3.4.4 Rank Summation Index for yield and ASNPC in Potted trial and Sickplot 55
3.5 Genotyping and Diversity Estimation 58
3.5.1 Genetic relatedness among sorghum accession 58
3.5.2 Parental genotype relatedness 60
3.6 Discussion 62
3.10 Conclusion 67
CHAPTER FOUR: TRANSFER OF STRIGA RESISTANCE QTL FROM KNOWN DONORS INTO CULTIVATED FARMER PREFERRED SORGHUM VARIETIES THROUGH MARKER ASSISTED BACKCROSSING 68
4.0 Abstract 68
4.1 Introduction 69
4.2 Materials and Methods 70
4.2.1 Hybridization of the sorghum genotypes 70
4.2.2 Greenhouse Activities 71
4.2.3 DNA extraction for DArT sequencing. 72
4.3 Data Analysis 73
4.3.1 Heterosis and Genetic gain in response to Striga infestation 73
4.4 Results 75
4.4.1 Hybridization Process 75
4.4.2 Chi-square test for the observed genotypic proportions 77
4.5 Genetic gain for striga resistance and yield 79
4.5.1 Confirmation of hybridity for the crosses screened in the Striga filed trials 79
4.5.2 Gain in tolerance and resistance to Striga in the F4 progenies 80
4.6 Discussion 84
4.7 Conclusion 86
CHAPTER FIVE: GENERAL DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS 87
5.1 General discussion 87
5.2 Conclusions 88
5.3 Recommendations 89
REFERENCES 90
APPENDIXES xiv
LIST OF ABBREVIATIONS
ASNPC: Area under Striga Number Progress Curve DTF: Days to Flowering
NSFC: Number of Striga Forming Capsules NSmax: Maximum Striga Count
PNH: Panicles Harvested DPW: Dry Panicle Weight HGW: Hundred Grain Weight YLD: Yield
GBK: Gene Bank of Kenya
KALRO: Kenya Agricultural and Livestock Research Organization ICRISAT: International Crops Research Institute for the Semi-Arid Tropics FAO: Food and Agriculture Organization
DNA: Deoxyribonucleic acid QTL: Quantitative Trait Loci SSR: Simple Sequence Repeat
DArT: Diversity Array Technology GS: Genomic Selection
TASSEL: Trait Analysis by association, Evolution and Linkage
LIST OF TABLES
Table 1. Importance of sorghum in Africa and Kenya (FAO, 2019) 2
Table 2. Planting materials used in the study 32
Table 3. Means for agronomic traits for selected sorghum genotypes sown in a sickplot during the long rains of 2019 at KARLO, Alupe. 40
Table 4. Response of selected sorghum genotypes to Striga in the sick plot sown during long rains and short rains 2019 at KARLO, Alupe. 42
Table 5. Genotypic and phenotypic coefficients of variations and heritability of traits measured in the field trial 43
Table 6. Correlations between agronomic, yield and Striga related traits in the sick plot 45
Table 7. Means for agronomic traits for selected sorghum genotypes sown in a potted trial during the long rains of 2019 at KARLO, Alupe 48
Table 8. Response of selected sorghum genotypes to Striga in the potted trial in 2019 long rain season at KARLO, Alupe 50
Table 9. Heritability estimates for traits in the potted trial at KALRO Alupe 51
Table 10. Correlation coefficients between traits in the Potted trial. 53
Table 11. Rank Summation Index in ASNPC and yield for genotypes in the potted trial 56
Table 12. North Carolina Design II mating scheme between N13, Framida, SRN39 and Hakika as male parents and Gadam and Kari Mtama 1 as female parents. 71
Table 13. Progeny analysis for Gadam × Framida × Gadam in the BC1F1 generation. 76
Table 14. Progeny analysis for Gadam × SRN 39 × Gadam in the BC1F1 generation 76
Table 15. Progeny analysis for Gadam × N13 × Gadam in the BC1F1 generation 77
Table 16. Progeny analysis for Gadam × Hakika × Gadam in the BC1F1 generation 77
Table 17. Progeny analysis for Karimtama-1 × N13 × Karimtama-1 in the BC1F1 generation............................77
Table 18. Chi-Square test for successful backcross progenies for Gadam × Framida × Gadam..............................78
Table 19. Chi-Square test for successful backcross progenies for Gadam × N13 × Gadam 78
Table 20. Chi-Square test for successful backcross progenies for Gadam × SRN39 × Gadam.................................78
Table 21. Confirmation of hybridity among the F4 progenies using SNP markers. 79
Table 22. Proportion of gain in tolerance and resistance to Striga in the sick plot and pot trial with a ranking of the crosses from the highest to the lowest yielding 80
Table 23. Broad sense heritability estimates among parents measured under the Sickplot for yield and Striga related traits. 82
Table 24. Broad sense heritability estimates among progenies measured under the Sickplot for yield and Striga related traits. 82
Table 25. Broad sense heritability estimates among parents measured under the potted trial for yield and Striga related traits. 83
Table 26. Broad sense heritability estimates among progenies measured under the potted trial for yield and Striga related traits. 83
LIST OF FIGURES
Figure 1. Global area harvested and production of cereal food crops in order of importance (FAO, 2020) 2
Figure 2. Subgenera of Sorghum.’ n’ denotes haploid chromosome number 9
Figure 3: Species and subspecies of subgenus Eusorghum.’ n’ represents haploid chromosome number. 10
Figure 4. Response to Striga at the sick plot 37
Figure 5. Tolerance and resistance response to Striga in Sick plot 37
Figure 6. Response to Striga in potted trial. A., Susceptible response B., Resistance response.............................49
Figure 7. Boxplots showing the performance of genotypes in the sickplot and potted trial 54
Figure 8.Dendrogram showing the clustering of 153 accessions used in this study. Red-wild accession, Green-landrace, Blue-improved variety and Black -F4 population. The arrows originate from the root of the cluster. 59
Figure 9. Dendrogram showing the clustering of Striga resistance donors among 39 parental accessions used in this study. Red-wild accession, Green- improved variety ,Blue- landrace...................................61
Figure 10. The hybridization process; (A) Panicle that with ready pollen, (B) Hand emasculation using a needle, (C) Emasculated panicle ready for pollination, (D) Pollinated panicle with successful seed set. 75
LIST OF APPENDICES
Appendix 1.Generation of backcrosses in a greenhouse at the University of Nairobi field Station xiv
Appendix 2. Laying out the Potted trial at KALRO Alupe Station xiv
Appendix 3. Fully established Potted trial at KALRO Alupe Station. xv
Appendix 4. Two weeks old sorghum seedlings in a screenhouse at ICRAF ready for leaf sampling for DNA extraction. xv
Appendix 5. Diversity analysis in TASSEL xvi
Appendix 6. Means for agronomic traits for all sorghum genotypes sown in the field trial during the long rains of 2019 at KARLO, Alupe xvii
Appendix 7.Response of sorghum genotypes to Striga in the field trial sown during long rains of 2019 at KARLO, Alupe xix
Appendix 8. Means for agronomic traits for all sorghum genotypes sown in the potted trial during the long rains of 2019 at KARLO, Alupe. xxi
Appendix 9. Response of sorghum genotypes to Striga in the potted trial during the long rains of 2019 at KARLO, Alupe xxiii
CHAPTER ONE: INTRODUCTION
1.1 Sorghum production and importance
Sorghum is a cereal grass of the Gramineae family commonly found in the tropical regions. Its domestication dates back to around 1000 BC in northern parts of Africa along the Nile river or Ethiopian regions (Kimber, 2000). Sorghum genotypes are widely adapted to ecological and climatic conditions and can tolerate high salinity, drought, water logging as well as poor soil fertility. At a global scale, sorghum is ranked fifth after maize, rice, wheat and barley with respect to its importance as cereal staple(Kiprotich et al., 2015). In Africa, sorghum had an annual production of 27,219,117 tonnes in 2019 ranking it second with maize leading in terms of importance for cereal consumption (FAOSTAT, 2019).
The major sorghum growing regions in Africa include countries in west Africa like Nigeria and Burkina Faso and Eastern African countries like Sudan and Ethiopia and these account for approximately 70% of Africa’s total production (Taylor, 2004). In Kenya, sorghum is placed second after maize in tonnage and the area under sorghum production amounts to 144,000 ha (FAO STAT, 2019). Sorghum is mainly cultivated in the Eastern, Nyanza and Coast Provinces that experience little rainfall annually and are prone to drought. The crop performs best in regions with an altitude of 500 to 1700 meters above sea level and minimum rainfall of 300mm per season (Grain production report in Kenya, 2005).
Sorghum is a versatile in terms of its applicability and has been used for both commercial and subsistence purposes for food and non-food products. It is used as a major ingredient in the baking industry to make bread, cakes and biscuits (CFC and ICRISAT, 2004). Industrial products prepared from sorghum include alcohol (Seetharama et al., 2002), malt (Jaya et al., 2001) starch and by-products glucose, high fructose syrup (Anonymous 2002; 2003), modified starches, maltodextrins, sorbitol (Rainer and Silveira, 2003) and citric acid. Sweet stalk sorghum is a potential raw material for preparation of jaggery, syrup as well as ethanol. Process of making jaggery from sorghum is identical to production from sugarcane and the jaggery obtained is comparable to sugarcane jaggery (CFC and ICRISAT, 2004). However, grain yield of sorghum in farmer fields has remained at low at 954.6 kilograms per hectare (FAO STAT, 2019).
1.2 : Constraints in sorghum production
Sorghum is a C4 plant with high photosynthetic efficiency; however, its physiological growth and production parameters are impaired under drought stress, Striga stress and poor soil fertility limiting grain yield. Approximately one-third of 1.5 billion hectares of the world’s agricultural land are affected by drought which leads to low yield and poverty (James, 2002). In the arid and semi-arid areas drought accounts for approximately 70% yield loss (Ejeta et al., 2007). Striga is the most important biotic factor affecting sorghum production in Sub- Saharan Africa (Rodenburg et al., 2005). Striga problem is associated with degraded environments and highly affects subsistence farming systems that have little resources to address the weed. Sorghum farmers are undoubtedly in need of both short and long term affordable solutions to Striga problems.
1.2.1 Striga in Sorghum
Striga species is an obligate parasitic weed that is a major biotic stress in sorghum cultivation especially in areas with poor soil fertility (Baptiste et al., 2012). The weed germinates upon stimulation by a strigolactone (Bouwmeester et al., 2019; Aliche et al., 2020) induced by the host, or in some cases, non-host plants. The germinated Striga then attaches to the host plant roots, by means of a special invasive organ, haustorium (Yoshida et al., 2016). The haustorium enables water and nutrients uptake from the host plants for growth and development of Striga, as well as the introduction of phytotoxins to the host (Hast et al., 2000). Consequently, the growth and development of the host plants become severely affected resulting in yield losses of up to 100% (Kim et al., 2002; Ejeta, 2007). A fully-grown Striga plant is estimated to produce up to 100,000 tiny seeds that can remain viable in the soil for more than 20 years (Pieterse and Pesch 1983; Gurney et al., 2005), making it extremely difficult to control.
Striga species are believed to have evolved alongside sorghum in the grasslands of the old world and semiarid tropics, mainly in Ethiopia (Kroschel, 1999). It has since spread to other parts of the world with severe infestation occurring in sub-Saharan Africa (Mohammed et al., 2006). The most important Striga species affecting cereals are: S. hermonthica, S. aspera, S. densiflora, S. passargei Engle, S. asiatica, S. angustifolia, S. forbesii Benth, S. laterica Vatke,
S. multiflora Benth, S. parviflora Benth and S. curviflora Benth. Striga hermonthica and S. asiatica are the most notorious (Dafaallah et al., 2019). Over 21 million ha of land under cereal crops have been reported to be affected by Striga hermonthica (Sauerborn, 1991), amounting to 20-80% yield loss, equal to 4.1 million tons of grain per year. These losses affect livelihoods of approximately 100 million people (Kanampiu et al., 2002).
Striga asiatica (L.) Kuntze and Striga hermonthica (Del.) Benth are the main Striga species found in Kenya. Striga hermonthica is widely spread in western Kenya, while Striga asiatica is distributed in the coastal region of the country (Odhiambo, 1998). On average, 76% of land planted to maize (Zea mays L.) and sorghum (Sorghum bicolor (L.) Moench) in western Kenya is Striga hermonthica infested, ensuing annual losses projected at 40.8 million dollars (Kanampiu et al., 2002). Striga asiatica is not widely distributed in Kenya, and its occurrence has only been recorded along the Indian Ocean coast (Frost,1994). Genetic diversity studies within S. hermonthica populations parasitizing crops in west, east and central Africa reported existence of biotypes within the species with diversity levels of up to 6.8% (Olivier et al.,1998). These biotypes are believed to be responsible for the breakdown of Striga resistance in previously resistant crops (Doggett, 1952).
Wild sorghum genotypes have demonstrated resistance to Striga over the years and are believed to harbor novel resistance genes that if exploited they can help in improvement of adapted sorghum varieties for Striga resistance.
1.3 Wild relatives of Sorghum
Domesticated sorghum genotypes are often susceptible to Striga (Ejeta, 2007). This is a result of domestication in which the bottleneck effect has limited the genetic diversity (Papa et al., 2005). Wild sorghums belong to the subsp. Verticilliflorum, comprised of the races arundinaceum, verticilliflorum virgatum and aethiopicum (Harlan and de Wet, 1972). Semien Mountains of Ethiopia and the Nubian Hills of Sudan are believed to be the centers of domestication of Sorghum, the host on which monocot‐parasitizing Striga species have evolved and spread throughout Africa and Asia (Vasudeva‐Rao and Musselman, 1987). Its therefore most likely that wild relatives of sorghum have Striga resistance genes that have enabled them to survive amidst Striga pressure. Wild relatives of sorghum as Striga resistance sources have been reported in the past. Mbuvi et al. (2017) reported three wild sorghum genotypes, (WSE-1,WSA-1 and WSA-2) exhibited a resistance response significantly higher than N13, which is a known Striga resistant landrace. The use of wild sorghum genotypes in future breeding programmes is justified by the reports of successful interspecific hybridization occurring naturally between cultivated and wild sorghum.
Interspecific hybridization between cultivated sorghum and its wild relatives has been reported and the progenies of this process are classified as drummondii (Paterson et al., 2013). The interspecific hybridization between sorghum wild relatives and cultivated sorghum results in disruptive selection which is responsible for the gene-flow into the local landraces (Magomere et al., 2015). Increased genetic diversity and allelic heterozygosity within domesticated and wild sorghum populations, has been reported in the past in Kenya (Mutegi et al., 2007). Given the complex mechanisms of genetic resistance to Striga in sorghum, there is need to widen the genetic base in the wild relatives. This study aimed at screening wild relatives along landraces and adapted sorghum for Striga resistance with the hope to discover new sources of Striga resistance that can be used in future breeding research.
1.4 Statement of the problem
Striga hermonthica has been a major problem in production of sorghum in western regions of Kenya (Khan et al., 2006). Increased population pressure has subsequently increased pressure on land and continuous land use coupled with cereal monoculture has aggravated the Striga problem in these regions (Ogutu et al., 1993). A study by Woomer and Savala (2009) reported about 217,000 ha in Kenya to be infested with Striga leading to losses of US $53 million annually. The study also revealed that out of 83 farms under the study, Striga infestation was at 73%. Striga is responsible for approximately 1.15, 1.10 and 0.99 tons yield loss per hectare for maize, sorghum and millet, respectively (Mac Opiyo et al., 2010). The severity of destruction caused by Striga depends on Striga population size, affected species and genotype, cropping system, amount of nutrients in the soil and rainfall regime in the area of agriculture (Atera et al., 2012). Striga form a complex parasitic relationship with the host by producing haustoria that penetrate the host and extract nutrients from the host plant. This relationship leads to malnutrition of the plant and subsequent death or stunted growth. Poor soil fertility aggravates the Striga problem because the plant is unable to get additional nutrients to compensate the nutrients deficiency caused by uptake by the parasitic Striga weed. Previous studies have reported Striga seed and plant densities in western Kenya at about 1,188 seeds per mature Striga seed capsule (Van Delft et al., 1997) and about 14 plants per m2 (Mac-Opiyo et al., 2010) respectively. In Kenya, the three crops most devastated by Striga hermonthica are maize, finger millet and sorghum.
Conventionally, farmers manage Striga in sorghum using cultural and mechanical methods including hand weeding (Frost, 1994), intercropping and crop rotations with edible legumes such as common bean (Phaseolus vulgaris L.), pigeonpea (Cajanus cajan (L) Millsp.) and mung bean (Vigna radiata (L.) R. Wilczek) (Aasha et al., 2017, Oswald and Ransom 2001). Effective bioherbicide activity of Fusarium oxysporum f. sp. strigae isolates has been reported, particularly when combined with other control practices (Rebeka et al.,2013) but has received low adoption due to cost implications. Push-pull technology has also been used and it involves planting of cereals alongside a trap crop (pull), usually Napier grass (Pennisetum purpureum), and a push forage legume crop, usually desmodium (Desmodium spp.) (Khan et al., 2011) but has resulted in low adoption due to lack of alternative use for desmodium by farmers. “Suicidal death” of Striga, which is achieved by inducing germination of Striga by non-host legumes has been employed in the reduction of Striga seed banks (Rubiales, 2012) but the strategy is not yet ready for direct application.
Chemical control has been tested in maize (Menkir et al., 2010) and sorghum (Dembele et al., 2005: Tuinstra et al., 2009) but are not environmentally friendly besides being unaffordable for the average sorghum farmer in Kenya.
Although genes controlling Striga resistance in sorghum have been identified, advances in incorporating these genes into susceptible sorghum backgrounds have remained minimal. This partly due to the recessive nature of inheritance of some of these genes especially the low germination stimulus production genes (lgs) that makes the breeding process lengthy and tedious as well as lack of adequate understanding of the action of hypersensitive response genes (Rodenburg et al., 2005). The complex interaction between host genotype and Striga populations in different environments lead to differences in Striga virulence levels and specificity due to adaptation to different host plant resistance mechanisms further complicating the process of evaluation for Striga resistance in field trials (Fantaye, 2018). Breakdown of resistance in previously resistant varieties due to the many Striga ecotypes has also been a worrying occurrence (Muchira et al., 2021).
1.5 Justification
The most effective and practical solution to the smallholder sorghum farmers is to develop Striga resistant sorghum varieties. Sorghum germplasm screening against Striga is the first step towards the identification of Striga resistant genotypes. Striga resistance has been reported to be abundant among the sorghum wild and landrace gene pool and evidence of gene transfer from wild to cultivated sorghum genotypes has been documented in Kenya (Maiti et al.,1984; Mutegi et al., 2010; Mutegi et al., 2012), Ethiopia and Niger (Tesso et al., 2008), northern Cameroon (Barnaud et al., 2009), and western Africa (Sagnard et al., 2011). Wild relatives of sorghum as superior sources of Striga resistance have been reported with significantly higher resistance than N13, a known Striga resistant landrace (Mbuvi et al., 2017) and it provides a strong justification for more screening of sorghum wild and landraces towards the identification of additional sources of resistance to Striga.
Further studies have identified the specific genes conferring Striga resistance and these are the Hrs1 and Hrs2 genes for hypersensitive response to Striga (Haussmann et al., 2000) and Lgs gene for low germination stimulus production as a mechanism of Striga resistance (Ramaiah et al., 1990). As a result, several sorghum genotypes with Striga resistance including N13, SRN39, Framida, IS9830 and Hakika have been documented as Striga resistance donor sources and can be used in breeding programmes for Striga resistance improvement.
Five genomic regions (QTLs) associated with stable Striga resistance from resistant variety N13 have been identified based on screening across a series of field trials in Mali and Kenya. The use of molecular markers in breeding for Striga resistance in sorghum is made possible by the availability of identified molecular markers linked to these Striga resistance QTLs. These advances in MAS techniques and use of markers for diversity studies have led to reliable estimation of genetic diversity and relatedness among populations and accelerated the introgression of genes of interest into adapted cultivars.
The use of molecular markers for genetic analysis and manipulation of important agronomic and stress-tolerance traits has gained increased acceptance in sorghum improvement. Transfer of these traits into susceptible sorghum background through marker assisted backcrossing (MABC) will provide a solid foundation to improve Striga resistance in farmers preferred lines. With the use of high throughput marker technology like Diversity Array Technology markers, lines which will be used as parents in next generation are selected.
This is made possible with the aid of molecular markers that are closely linked or flanking already detected and validated QTLs.
The outcrossing nature of Striga that results in different ecotypes with mixed response to different genotypes (Fantaye, 2015) would require the pyramiding of multiple alleles from diverse sources into farmer-preferred varieties if the resistance were to be durable.
1.6 General objective
To enhance sorghum productivity in Striga prone areas by identifying novel sources of Striga resistance genes followed by introgression of the genes in to cultivated farmer preferred sorghum varieties through Marker Assisted Backcrossing.
1.6.1 Specific objectives
1. To screen sorghum wild relatives, landraces and improved genotypes for Striga
resistance using morphological and molecular markers to identify resistance sources.
2. To transfer Striga resistance QTLs from known donor sources to susceptible farmer preferred varieties using marker assisted backcrossing with DArT molecular markers.
1.6.2 Hypotheses
1. Wild, landrace and improved sorghum varieties do not vary in terms of, yield, agronomic and commercially desirable traits.
2. Genetic variability for Striga resistance cannot be transferred from known donors into cultivated farmer preferred sorghum varieties through hybridization and Marker assisted Backcrossing.
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