University of Ghana http://ugspace.ug.edu.gh COMBINING ABILITY AND HETEROTIC GROUPS OF EARLY- MATURING WHITE MAIZE (ZEA MAYS L.) INBREDS UNDER STRIGA- INFESTED AND STRIGA-FREE ENVIRONMENTS. SUBMITTED BY: AWITY MAWULAWOE ADJOA INDEX NUMBER: 10249086 A DISSERTATION SUBMITTED TO THE COLLEGE OF AGRICULTURE AND CONSUMER SCIENCES, UNIVERSITY OF GHANA, IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF PHILOSOPHY PLANT BREEDING AND GENETICS DEGREE DEPARTMENT OF CROP SCIENCE UNIVERSITY OF GHANA LEGON MAY, 2016 i University of Ghana http://ugspace.ug.edu.gh DECLARATION I hereby declare that apart from the references to the works of others which have been duly cited, this work is the result of my own research and has not been submitted for any degree course in Ghana or elsewhere. ............................................................... AWITY MAWULAWOE ADJOA (STUDENT) ............................................................... PROF. S. K. OFFEI (SUPERVISOR) ............................................................. DR. BAFFOUR BADU-APRAKU (SUPERVISOR) i University of Ghana http://ugspace.ug.edu.gh ABSTRACT Striga hermonthica constitutes a major constraint to maize production and productivity. It is the largest single biological constraint to food production in Sub-Saharan Africa. Hence there is the need to develop hybrids that are tolerant to this constraint. Knowledge and understanding of Striga resistance in early maturing maize inbreds are crucial for the developments of hybrids adapted to Striga-infested environments in the sub-region as well as for enhancing breeding strategies. This study examined the performance of 15 early maturing inbreds and their F1 progenies derived from a 15 x 15 diallel mating design under Striga-infested and Striga-free environments; estimated general and specific combining ability effects for grain yield and other related traits and classified the inbred lines into heterotic groups. The inbred lines were evaluated using the randomized complete block design while a 10 x 11 lattice was used in the evaluation of the hybrids. The evaluations were conducted at three locations in 2015 in Nigeria. Grain yield, plant height, days to silking and anthesis, ear height, root lodging, ear aspect, plant aspect, Striga damage at 8 and 10 weeks after planting and number of emerged Striga plants at 8 and 10 weeks after planting were the traits measured. Data were subjected to analysis of variance and correlation analysis using PROC GLM in SAS while the grain yield was subjected to GGE biplot analysis to determine the stability of the genotypes. General combining ability (GCA) and specific combining ability (SCA) mean squares were significant for grain yield and other agronomic traits under Striga-infestation and across research environments indicating that both additive and non-additive gene effects were important in the inheritance of grain yield under the two contrasting environments. The larger proportion of GCA sum of squares over SCA for grain yield under Striga-infested, Striga-free and across research environments indicated the predominance of additive over non-additive genetic effects for grain ii University of Ghana http://ugspace.ug.edu.gh yield and that GCA was the major component accounting for the differences in grain yield among the hybrids. The inbreds TZEI 56 and TZEI 383 were the highest yielding inbreds across the research environments, whilst TZEI 7 and TZEI 326 were the most stable. The hybrid TZdEI 352 x TZEI 355 was the highest yielding across research environments based on the IITA selection index. The GGE biplot analysis identified the hybrids TZdEI 352 x TZEI 355, TZEI 296 x TZdEI 352 and TZEI 7 x TZdEI 352 as high yielding and stable across research environments. These should be extensively tested and promoted for commercialization to contribute to improved maize production and productivity in SSA. Inbreds TZEI 7 and TZdEI 352 were identified as the best testers based on display of highly significant and positive GCA effects, classification into specific heterotic groups and grain yield performance. These inbred lines may be used to classify other early maturing white maize inbred lines into heterotic groups and also to identify superior hybrid combinations. Key words: Striga hermonthica, combining ability, testers, heterotic groups. iii University of Ghana http://ugspace.ug.edu.gh DEDICATION This work is dedicated firstly, to God Almighty and then to Apostle Alex Nkrumah. This work is also dedicated to my family and friends for being my source of strength and encouragement. I am grateful to have people like you; who have taught me never to give up and also, that no matter the circumstances that beset my path, I can still make it to the top. To my husband Jireh Anato- Dumelo, thank you for everything. iv University of Ghana http://ugspace.ug.edu.gh ACKNOWLEDGEMENT I express my sincerest gratitude to the Most High God, YHWH, for His abundant grace and favour. I also wish to express my sincere gratitude to the Support to Agricultural Research for Development of Strategic Crops in Africa (SARD-SC) for providing the scholarship throughout the period of my research work. I wish to acknowledge the support of my supervisors, Prof. Offei and Dr. Badu-Apraku, who taught me how to be an independent thinker and to work hard. I sincerely appreciate Dr. Badu-Apraku for the quality training I received while under his supervision at IITA. Special thanks to the staff of the Maize Improvement Program, IITA, Ibadan, for their support especially Benjamin Annor for his significant contributions to the success of my research. I wish to express my deepest gratitude to Rodney Owusu-Darko; words cannot express how grateful I am to you. v University of Ghana http://ugspace.ug.edu.gh TABLE OF CONTENTS DECLARATION.............................................................................................................i ABSTRACT…………………………………………………………………………….ii DEDICATION..................................................................................................................iv ACKNOWLEDGEMENT...............................................................................................v TABLE OF CONTENTS..................................................................................................vi LIST OF FIGURES...........................................................................................................x LIST OF TABLES............................................................................................................xi LIST OF ABBREVIATIONS.........................................................................................xiii CHAPTER 1 1.0 INTRODUCTION......................................................................................................1 CHAPTER 2 2.0 LITERATURE REVIEW..........................................................................................5 2.1 The maize crop: Production, distribution and importance..........................................5 2.2 Maturity groups in maize…………………………………………………………....8 2.3 Striga Parasitism.........................................................................................................8 2.3.1 Striga life cycle and host resistance………………………………………10 vi University of Ghana http://ugspace.ug.edu.gh 2.3.2 Striga Control………………………………………………………..…14 2.4 Striga hermonthica parasitism of maize in WCA………………………………..16 2.5 Mechanism and genetics of Striga resistance in maize…………………………..17 2.6 Combining ability, heterosis and heterotic group studies………………………..19 2.6.1 Combining Ability……………………………………………………...20 2.6.2 Heterosis and Heterotic group studies………………………………….22 2.7 Biplot analysis…………………………………………………………………….26 CHAPTER 3 3.0 MATERIALS AND METHODS.........................................................................28 3.1 Germplasm and generation of crosses....................................................................28 3.2 Field studies............................................................................................................28 3.3 Data collection........................................................................................................31 3.4 Data analysis...........................................................................................................32 CHAPTER 4 4.0 RESULTS...............................................................................................................38 4.1 Performance of inbreds and hybrids under Striga-infested and Striga-free environments…………………………………………………………………………..38 4.2 Relationship between performance of inbred lines and hybrids…………………………………………………………………………………49 vii University of Ghana http://ugspace.ug.edu.gh 4.3 Stability of performance of inbreds and single-cross hybrids under Striga-infested and Striga- free environments…………………………………………………………………….54 4.4 Heterotic grouping of inbred lines………………………………………………..61 4.5 Specific combining ability (SCA) effects for grain yield…………………………64 4.6 Comparison of the breeding efficiencies of SCA, HSGCA and HGCAMT heterotic grouping methods………………………………………………………………………………..68 4.7 IDENTIFICATION OF TESTERS………………………………………………..73 CHAPTER 5 5.0 DISCUSSION..........................................................................................................75 CHAPTER 6 6.0 CONCLUSIONS AND RECOMMENDATIONS………………………………83 6.1 Conclusions…………………………………………………………………………83 6.2 Recommendations…………………………………………………………………..84 REFRERENCES............................................................................................................85 APPENDICES................................................................................................................112 Appendix 1: SCA estimates of grain yield of best 10 and worst 10 performing hybrids under Striga infestation, optimal and across research environments, 2015…………………………..112 Appendix 2: Estimates of heterosis for grain yield and other agronomic traits of 105 hybrids under Striga infestation………………………………………………………………………..113 Appendix 3: Estimates of heterosis for grain yield and other agronomic traits of 105 hybrids under optimum conditions………………………………………………………………….….116 Appendix 4: Estimates of heterosis for grain yield and other agronomic traits of 105 hybrids across environments…………………………………………………………………………….119 viii University of Ghana http://ugspace.ug.edu.gh Appendix 5: Dendrogram of the 15 parental early-maturing white maize inbred lines constructed from SCA effects of grain yield (SCA) using Ward’s minimum variance cluster analysis under Striga-infested environment………………………………………………………………122 Appendix 6: Dendrogram of the 15 parental early-maturing white maize inbred lines constructed from SCA and GCA effects of grain yield (HSGCA) using Ward’s minimum variance cluster analysis under Striga-free environments………………………………………………….123 Appendix 7. Dendrogram of the 15 parental early-maturing white maize inbred lines constructed from SCA and GCA effects of grain yield (HSGCA) using Ward’s minimum variance cluster analysis across research environments…………………………………………………….124 ix University of Ghana http://ugspace.ug.edu.gh LIST OF FIGURES Fig. 2.1. The Striga plant life cycle…………………………………………………………14 Plate 4.1. Striga-susceptible and Striga-resistant hybrid evaluated at Abuja, 2015………...53 Fig. 4.1. A ‘‘which won where” genotype plus genotype x environment interaction biplot of grain yield of 20 early maturing white maize inbreds evaluated under Striga-infested and Striga-free conditions at Abuja, Ikenne and Mokwa in 2015 . IK_OPT=Ikenne Striga-free; AB_STR= Abuja Striga; AB_OPT= Abuja Striga-free; MO_STR= Mokwa Striga; MO_OPT= Mokwa Striga-free conditions……………………………………………………………………………………56 Fig. 4.2. A ‘‘which won where” genotype plus genotype x environment interaction biplot of grain yield of 30 early maturing white maize hybrids evaluated under Striga-infested and Striga-free conditions at Abuja, Ikenne and Mokwa in 2015 . IK_OPT=Ikenne Striga-free; AB_STR= Abuja Striga-infested; AB_OPT= Abuja Striga-free; MO_STR= Mokwa Striga-infested; MO_OPT= Mokwa Striga-free conditions……………………………………………………………….57 Fig. 4.3. The entry/tester genotype plus genotype x environment biplot based on grain yield of 20 early maturing white maize inbreds including 5 checks evaluated under Striga-infested and Striga- free conditions at Abuja, Ikenne and Mokwa in 2015. IK_OPT=Ikenne Striga-free; AB_TR= Abuja Striga-infested; AB_OPT= Abuja Striga-free; MO_STR= Mokwa Striga-infested; MO_OPT= Mokwa Striga-free………………………………………………………………59 Fig. 4.4. The entry/tester genotype plus genotype x environment biplot based on grain yield of 30 early maturing white maize hybrids evaluated under Striga-infested and Striga-free conditions at Abuja, Ikenne and Mokwa in 2015. IK_OPT=Ikenne Striga-free; AB_STR= Abuja Striga- infested; AB_OPT= Abuja Striga-free; MO_STR= Mokwa Striga-infested; MO_OPT= Mokwa Striga-free……………………………………………………………………………………..60 x University of Ghana http://ugspace.ug.edu.gh LIST OF TABLES Table 3.1: List of inbred lines used for diallel crosses…………………………………………30 Table 3.2. The Gardener-Eberhart Analysis II model for analysis of variance of Griffing’s diallel method II which was used to evaluate the 105 hybrids as well as the 15 early white inbreds used as parents under Striga-infested and Striga-free environments…………………………………37 Table 4.1. Mean squares from analysis of variance for grain yield and other agronomic traits of 20 early white maize inbred lines evaluated under Striga –infested and Striga-free environments in Nigeria, 2015…………………………………………………………………………………….40 Table 4.2. Mean squares from analysis of variance for grain yield and other agronomic traits of 105 early-maturing white maize single cross hybrids evaluated under Striga-infested and Striga- free environments in Nigeria, 2015………………………………………………………………41 Table 4.3. Mean squares from analysis of variance for grain yield and other agronomic traits of 105 early-maturing white maize single cross hybrids across 5 environments at Abuja, Mokwa and Ikenne in 2015……………………………………………………………………………………42 Table 4.4. General combining ability (GCA) effects for grain yield and other agronomic traits of the 15 early maturing white-endosperm inbred lines evaluated under Striga infested, Striga-free and across test environments in Nigeria, 2015…………………………………………………..43 Table 4.5. Grain yield and other agronomic traits of 15 early maturing white-endosperm inbred lines evaluated in diallel crosses under Striga infested, Striga-free and across test environments in Nigeria, 2015…………………………………………………………………………………….46 Table 4.6.Grain yield and other traits of hybrids (best 10 and worst 10 based on base index) and checks evaluated under Striga-infestation……………………………………………………….47 Table 4.7.Grain yield and other traits of hybrids (best 10 and worst 10 based on base index) and checks evaluated under Striga-free environments……………………………………………….48 Table 4.8. Correlations between maize parental lines and their hybrids for grain yield and other agronomic traits under Striga-infested, Striga-free and across research environments in Nigeria, 2015………………………………………………………………………………………………50 Table 4.9. Correlation between grain yield and other agronomic traits of maize inbred lines and grain yield of their hybrids under Striga-infested, Striga-free and across research environments in Nigeria, 2015……………………………………………………………………………………..51 Table 4.10. Average mid-parent heterosis for grain yield and other agronomic traits of hybrids and maize parental lines under Striga-infested, Striga-free and across research environments in Nigeria, 2015……………………………………………………………………………………..52 Table 4.11. Summary of the hetrotic groups of the 15 early-maturing white inbred lines identified by different heterotic grouping methods under Striga infestation, Striga-free and across research environments……………………………………………………………………………………..63 xi University of Ghana http://ugspace.ug.edu.gh Table 4.12 Estimates of specific combining ability effects of 15 early maturing white-endosperm inbred lines evaluated under Striga-infested environments in Nigeria, 2015…………………...65 Table 4.13 Estimates of specific combining ability effects of 15 early maturing white-endosperm inbred lines under Striga-free environments in Nigeria, 2015…………………………………..66 Table 4.14 Estimates of specific combining ability effects of 15 early maturing white-endosperm inbred lines evaluated across research environments……………………………………………67 Table 4.15.The number of hybrids within the first 35 arranged from descending order of yield (group 1), from 36th to 70th (group 2) and from 71st to 105th (group3)…………………………..70 Table 4.16. Breeding efficiency (%) of SCA, HSGCA and HGCAMT heterotic grouping methods under Striga-infested, Striga-free and across research environments……………………………72 xii University of Ghana http://ugspace.ug.edu.gh LIST OF ABBREVIATIONS SSA Sub-Saharan Africa FAO Food and Agriculture Organization WCA West and Central Africa CIMMYT International Maize and Wheat Improvement Centre IITA International Institute of Tropical Agriculture GCA General Combining Ability SCA Specific Combining Ability HGCAMT Heterotic grouping based on the GCA of multiple traits GEI Genotype x Environment Interaction DAP Days after planting HSGCA Heterotic groups’ specific and general combining ability of grain yield ASI Anthesis-silking interval STRR Striga syndrome damage STRCO Emerged Striga plants WAP Weeks after planting xiii University of Ghana http://ugspace.ug.edu.gh CHAPTER ONE INTRODUCTION Maize (Zea mays) is one of the most important staple food crops in sub-Saharan Africa (SSA) and is produced in different parts of the sub-region under diverse climatic and agro- ecological conditions. It accounts for 15-20% of the total daily calories in the diets of more than 20 countries in Latin America and Africa (Adetiminrin et al., 2008). It is estimated that by 2050, demand for maize in developing countries will double, and will become the crop with the greatest production globally and in developing countries by 2025 (CIMMYT and IITA, 2010). Global maize production is 736 million tons, with Africa accounting for only about 7% (49 million tons) (FARA, 2009). Maize is high yielding, easy to process into a variety of local food products, readily digested, and cheaper than other cereals in the sub-region. It is therefore, a popular food crop amongst the rural poor. These qualities have increased the potential of the crop to mitigate challenges of food security posed by population increase in West and Central Africa (WCA) (Badu-Apraku et al., 2010). Early-maturing maize cultivars (90-95 days to maturity) are crucial to the fight against hunger in the savannas of WCA because they mature quickly, are more responsive to fertilizer application and can be harvested much earlier than the traditional sorghum and millet crops. Such cultivars provide farmers in the various agro-ecological zones with flexibility in planting dates. Early-maturing maize can be used for early plantings during normal rainfall conditions and also when rains are delayed. Another important advantage of these cultivars is that, they can be used to fill the hunger gap after the long dry period when all food reserves are depleted. In the forest zone, there is also a high demand for early-maturing maize because they allow farmers to market the crop at a premium price in addition to being compatible for intercropping with cowpea, soybean and cassava. (Badu-Apraku et al., 2013a) 1 University of Ghana http://ugspace.ug.edu.gh The savannas of WCA have the greatest potential to contribute to the achievement of food security in the sub-region due to the high incident solar radiation and low pests and diseases. However, Striga hermonthica constitutes a major constraint to maize production and productivity (Badu-Apraku et al., 2008). It is the largest single biological constraint to food production in SSA (Ejeta, 2007). Its prevalence in the lowland savanna and mid-altitude agro-ecologies, where yield potentials are greatest, imposes severe limitations on maize productivity (Yallou et al., 2009). Striga is an obligate parasite which has a deleterious effect on its host because it robs it of water and nutrients (Amusan et al., 2008). This root-attaching parasite affects the livelihoods of over 100 million people (Kanampiu and Friesen, 2003; Berner et al., 1995). Among the three species of Striga, namely S. hermonthica, S. asiatica and S. aspera, that parasitize maize in the savannas of WCA, Striga hermonthica is the most economically important (Aggarwal, 1991; Lagoke et al., 1991). Maize grain yield losses due to Striga occurs in three ways. Firstly, there is a direct loss in yield because of the attack by the weed. Secondly, farmers incur yield losses by switching to other crops, such as millet, which are less susceptible but have lower yields. Thirdly, farmers abandon their land as a result of heavy Striga infestation. Grain yield losses in maize from S. hermonthica infestation in Africa range from 20 to 80% (Berner et al., 1995), but can reach 100% in susceptible maize cultivars under severe field infestation and may compel farmers to abandon their fields (Ransom et al., 1990; Hausmann et al., 2000; Badu-Apraku et al., 2011b). In West Africa, 64% of the land devoted to cereal production is severely infested with Striga (Gressel et al., 2004). The increased infestation is a consequence of crop intensification resulting in declining soil fertility thereby increasing the accumulation of Striga seeds in the soil (Vogt et al., 1991; Badu-Apraku, 2010). Striga infestation usually impairs the photosynthetic efficiency of the host plant (Stewart et al., 1991) and reduce yield due to their 2 University of Ghana http://ugspace.ug.edu.gh phytotoxic effect (Ransom et al., 1996). Grain yield losses due to Striga infestation are estimated to range from 30-90% (Van Ast et al., 2005). This however depends on the Striga seed bank in the soil. Badu-Apraku et al. (2004) in a study to compare the effects of drought stress and Striga hermonthica on maize yield under field conditions reported yield reduction of 53% due to drought stress and 42% under Striga infestation. Conventional control methods such as crop rotation, manual weeding, fertilizer application, fallow, trap and catch crops, seed treatment, and the use of common pre- and post-emergence herbicides have not been effective in controlling Striga (Kim, 1991, 1994; Parkinson et al., 1989; Odhiambo and Ransom, 1994, 2000; Shaxson and Riches, 1998). Host plant resistance or tolerance is considered the most feasible and effective Striga control strategy, and is the most sustainable and practical option for reducing yield loss from S. hermonthica for farmers who lack the financial means to use high-input management practices and other options to control Striga in maize fields (Doggett, 1988; Ramaiah et al., 1991; Badu-Apraku et al., 2004, 2009). The Maize Improvement Programme at the International Institute of Tropical Agriculture (IITA) has developed several early-maturing source populations, cultivars and inbred lines with improved field tolerance and resistance to Striga (Kim, 1994; Badu-Apraku et al., 2007a). However, information is lacking on the combining ability and heterotic patterns of the numerous maize inbred lines developed recently at IITA for commercialization in WCA. Hence, accurate assessment of the performance of the new inbred lines in hybrid combinations is crucial to the success of the IITA maize program to develop outstanding hybrids, synthetic varieties and to introgress Striga resistance genes into source populations. Combining ability is defined as the ability of cultivars to combine during hybridization such that favourable genes/characters are transmitted to their progenies (Panhwer et al., 2008). Combining ability studies are important in 3 University of Ghana http://ugspace.ug.edu.gh designing plant breeding programs when studying and comparing the performance of inbred lines in hybrid combinations. The objectives of this study were to: (i) Determine the combining ability of selected early-maturing white-endosperm inbreds, (ii) Classify the inbreds into heterotic groups and identify the best testers across environments, (iii) Determine the inbred-hybrid relationships, and (iv) Examine the performance and stability of inbreds and their hybrids under Striga infested, Striga-free and across environments. 4 University of Ghana http://ugspace.ug.edu.gh CHAPTER TWO LITEREATURE REVIEW 2. 1 The maize crop: Production, distribution and importance Zea mays L., commonly referred to as maize or corn, is an annual grass belonging to the tribe Andropogoneae of the family Gramineae (Poaceae). It covers a wide range of varieties, from white and yellow to brown, red and almost black. The white and yellow are the most preferred varieties. Maize is predominantly an outcrossing species making it highly adaptable and responsive to selection pressure. It is a versatile crop and can be cultivated across a wide range of agro- ecological zones (Akinbode, 2010). It is also high yielding, readily digested, easy to process and cheaper than most cereals. Maize is the third most important cereal in the world after wheat and rice in respect of acreage, total production and consumption (Bello et al., 2012). It is the preferred food for one- third of all malnourished children and 900 million poor people worldwide (CIMMYT- http://maize.org/why-maize/). Almost every part of the maize plant has economic importance. The leaves, stalks, and tassels can be fed to livestock, either in the form of fodder, silage or stover. They can also be incorporated into the soil as mulch to improve the physical structure of the soil, or dried and burnt as fuel. The grains can be consumed as human food, fed to livestock and can also be fermented to produce a wide range of foods and beverages. It also serves as a basic raw material in the production of starch, oil, sugar and ethanol. In SSA, maize is the most important cereal crop and the principal staple food consumed by 50% of the population. It is grown predominantly by smallholder farmers under rain-fed conditions. It can be eaten fresh on the cob, cooked or simply roasted. The grain can also be ground and cooked into a paste (cornmeal) and eaten while still warm. It can also be boiled into 5 University of Ghana http://ugspace.ug.edu.gh porridge or fermented into beer. Each country has one or more maize dishes that are unique to its culture. Examples are ogi (Nigeria), kenkey (Ghana), koga (Cameroon), injera (Ethiopia), tô (Mali), and ugali (Kenya). In industrialized countries, a large proportion of the grain is used as livestock feed and as industrial raw material for food and non-food uses. However, in developing countries, the bulk of maize produced is used as human food, although its use as animal feed is increasing. Maize is an important source of carbohydrates, protein, vitamin B, iron and minerals (IITA, 2009). It compares favourably with root and tuber crops as an energy source, and it is similar in energy value to dried legumes. Africans consume maize in a wide variety of ways and it plays an important role in filling the hunger gap in July in the savanna zones after the long dry season when all food reserves are depleted (Badu-Apraku and Fakorede, 2006). World maize production has increased steadily over the past few decades. In 1997, United States of America topped the list of the world’s maize producers with about 238 million tonnes, followed by China (105 million tonnes), Brazil (34 million tonnes), and Mexico (18 million tonnes), all from the world’s total production of over 589 million tonnes (FAOSTAT, 1997). A decade after, the world’s maize production reached a record high of 597 million tonnes in 2007 (USDA, 2007). In 2010, United States and China accounted for approximately 60% of maize production, whilst Brazil, Mexico, Argentina, India and France accounted for 12% of the maize production (FAOSTAT, 2012). The global maize production in 2011 was estimated at 883 million tonnes (FAO, 2011). Average maize yield of 1.2 tonnes per hectare recorded in Africa in 1989-1991 was twice that recorded in the 1950s when improved cultivars were unavailable (Byerlee and Heisey, 1997). However, in recent years there has been a phenomenal increase in maize production. This is mostly 6 University of Ghana http://ugspace.ug.edu.gh due to the widespread adoption of improved maize varieties in the savannas which has led to the transformation of maize from a backyard food crop into a cereal crop of high commercial value (Eckebil, 1994; Smith et al., 1997; Babatunde et al., 2008). Land area devoted to maize production has also increased significantly. In Southern, Central, Eastern and Western Africa, maize accounts for approximately 60% of total harvested area of annual food crops and 30 - 70% of total calorific consumption (FAOSTAT, 2007). Maize production in Nigeria has increased nearly ten-fold between 1961 and 2014, thus Nigeria has become the largest maize producer in SSA and 11th largest producer in the world accounting for over 0.9% of the world production (FAOSTAT, 2014). In recent years, maize has moved up on the scale of importance of cereal crops in Africa to displace staples like sorghum and pearl millet (Smith et al., 1997). It has been reported that over 85% of the rural population of West and Central Africa (WCA) are cultivating maize because it fits into the different farming systems and has high potential for increasing yield under improved management practices as compared with other cereal crops (Badu-Apraku et al., 2013a). An improvement in maize performance compared with pearl millet and sorghum in WCA from 1980 to 2001 has also been observed (Fakorede et al., 2003). Some of the factors contributing to the displacement of sorghum, the traditional staple crop in the savanna of WCA, by maize include high yield, better taste, easy digestibility, early maturity, and availability during the hunger period (Enyong et al., 1999). 7 University of Ghana http://ugspace.ug.edu.gh 2.2 Maturity groups in maize Maize production in the maize-growing belt of Africa is constrained by recurrent drought resulting in losses close to 90 percent in severe cases. Measures being adopted to prevent the negative effects of drought by researchers include the development of early and extra-early maturing varieties that complete their life cycles before the onset of drought or possess genes for drought tolerance at the flowering and grain-filling periods. Maize is categorized into four major maturity groups based on time required to attain physiological or harvest maturity, viz. late (120 days), intermediate (105-110 days), early (90-95 days) and extra-early (80-85 days) (Oluwaranti et al., 2008; Badu-Apraku et al., 2012). The importance of early maturing maize in SSA cannot be overemphasized due to its ability to tolerate drought which makes it easily adaptable to regions with low rainfall and the offer of planting date flexibility for farmers especially in areas where there is bimodal rainfall (Badu-Apraku and Fakorede, 1999). Early maturing varieties are also used to fill the hunger gap in the savanna regions and for intercropping because they are less competitive with the component crops such as cowpeas and soybeans. IITA in collaboration with the national maize programs has made available early and extra-early maturing maize varieties and hybrids to farmers in WCA. 2.3 Striga parasitism Striga species fall into two main groups based on host preference or specificity (Mohammed et al., 2001; Parker and Riches, 1993). The first group, include the most devastating species found throughout Africa (i.e., S. hermonthica, S. aspera, and S. asiatica) which parasitize members of the Poaceae (grass family) including the agronomically important food and forage grain crops such as maize, rice, sorghum, and millet. The second group includes S. gesnerioides, 8 University of Ghana http://ugspace.ug.edu.gh the most morphologically variable and widely distributed of the witchweeds. These species preferentially attack dicotyledonous plants including wild and cultivated legumes (Parker and Riches, 1993). Among the agronomically important hosts for S. gesnerioides is cowpea, the most important grain legume grown on the African continent (Timko and Singh, 2008). Parasitism as a life strategy and a way of survival has evolved multiple times within Angiospermae, and today parasitic angiosperms can be found in a variety of ecological habitats around the world (Westwood et al., 2010). The most damaging parasitic flowering plants belong to the genus Striga (Scrophulariceae). The name Striga (Latin for “witch”), and its common names, both in English (witch weed) and in its various African local names such as Kayongo (West Kenya) and Kiduha (Tanzania), refer to those host symptoms which appear before the parasite emerges, as if a hex has befallen the crop. Host plants usually suffer from a characteristic infection resembling the symptoms of severe drought, including leaf scorching, folded leaves and wilting, even in the presence of enough moisture and nutrients in the soil. Striga is predominantly found in semi-arid areas of Africa, but they also endanger regions of similar temperature and humidity all over the world. It is a hemi-parasite, but host damage up to total crop failure is possible if fields are heavily infested. Hemi-parasitism is a nutritional life- style where a parasitic plant contains chlorophyll, and can photosynthesize, but depends on its host for water and nutrients (Press and Graves, 1995). This parasitic plant can spread epidemically on cultivated land, destroying crops of legumes or grains, including maize (Zea mays), sorghum (Sorghum bicolor), pearl millet (Pennisetum glaucum), upland rice (Oryza sativa) and cowpea (Vigna unguiculata), leading to severe economic loss (Sallé and Raynal-Roques, 1989; Sauerborn, 1991). Increasing population density leading to an increase in food demand, causing more frequent land use has resulted in an expanding Striga infestation (Sauerborn, 1991). 9 University of Ghana http://ugspace.ug.edu.gh Presently about two-thirds of the farmland under crop production in SSA is infested with one or more Striga species, directly affecting the livelihoods of more than 300 million farmers in over 25 countries. The estimated losses of yield due to Striga infestation exceed 7 billion United States Dollars (USD) annually (Ejeta, 2007; Parker, 2009). A major reason for the threat of Striga to agriculture in SSA is its extreme fecundity. A single Striga plant can produce from 50,000 to over 200,000 seeds depending on the species and these could remain viable in the soil for up to 14 years (Berner et al., 1995; Yoder and Musselman, 2006). 2.3.1 Striga life-cycle and host resistance Generally Striga seeds cannot germinate in the season in which they are produced (Kust, 1963) hence; they need to pass through a period of dormancy. They are released from dormancy through a process known as conditioning during which species specific temperature and moisture requirements must be met. Once the seeds are conditioned, germination proceeds in response to signals from host plants. The important steps in the life cycle of Striga species are germination, radicle growth to the host root, haustorium formation and attachment to the host root, the successful establishment of a xylem connection and compatible interaction (penetration), and seed production. Germination depends on stimulants that are exuded from the roots of the host plant i.e. Striga seeds will only germinate after induction by a chemical signal exuded from the roots of their host. These exudates are collectively described as Strigolactones (Radoslava et al., 2005). Strigol was the first Striga germination stimulant to be identified in the false host cotton (Gossypium hirsutum; Cook et al., 1972) and later in maize (Siame et al., 1993), sorghum (Hauck et al., 1992; Siame et al., 1993), and millet (Siame et al., 1993). 10 University of Ghana http://ugspace.ug.edu.gh Strigolactones are plant signaling molecules that are secreted by the roots of many mono- and dicotyledonous plants into the rhizosphere. They have two main functions: first, as endogenous hormones to control plant growth and development, and secondly as components of root exudates to promote symbiotic interactions between plants and soil microbes. However, some parasitic plants have established a third function, which is to stimulate germination of their seeds when in close proximity to the roots of a suitable host plant. After germination, the Striga seedling must attach itself to the host plant in order to survive. Successful attachment is a critical stage in its life cycle. Root function in Striga is supplemented by a structure known as the haustorium. Chemical stimulants in the host rhizosphere called Haustorial Initiation Factors (HIFs) trigger this developmental transition. It is very important that this transition occurs very close to the host root, as further radicle elongation stops with haustorial formation. It must be noted that the haustorial initiation factors are different from compounds that stimulate Striga seed germination. Kinetin, simple phenolic compounds and quinones such as 2, 6-dimethoxy-1, 4-benzoquinone (DMBQ) are quite active haustorial initiation factors (Riopel and Timko, 1995), though their presence in exudates is only detectable when host roots are mechanically damaged (Riopel et al., 1990). The haustorium develops and attaches itself to the roots of the host plant. Host plant resistance can be linked to low production of HIFs (Yoder and Scholes, 2010). In these cases the resistance is known as ‘pre-attachment’ resistance (Jamil et al., 2011); that is, the resistance operates before the attachment of the haustorium to the host root. The main function of this structure is to procure nutrients from the host plant. By this stage, the seedling is anchored to its host and ready for penetration. Following Striga attachment, the parasite penetrates the host root’s vascular system and makes a xylem-to-xylem contact with the host through the haustorium. As an obligate parasite, this step is essential for the survival of Striga, and allows the transfer of host- 11 University of Ghana http://ugspace.ug.edu.gh derived nutrients and water (Press and Graves, 1995). Established Striga plants have no direct phloem connections with their host (Dörr, 1997) and hence obtain their carbon needs from the host xylem sap or through other apoplastic pathways (Press and Whittaker, 1993). Much less is known about the molecular basis of host resistance acting at this stage, which is normally known as ‘post- attachment’ resistance (Cissoko et al, 2011). However, through microscopic examination of incompatible interactions various host post-attachment resistance phenotypes have been described; for example, the blocking of parasite growth in the host root cortex, at the endodermis, and before, or after, connection to the host vasculature have all been reported, either for Striga, or for the closely related holoparasitic genus Orabanche (Yoshida and Shirasu, 2009; Yoder and Scholes, 2010). These observations suggest that a variety of molecular responses may underlie post- attachment resistance, even within a single host species; for example, for various sorghum cultivars, endodermal thickening, pericycle lignification and silica crystal deposition have all been observed in post-attachment resistance to S. hermonthica (El Hiweris, 1987). The most recent advancement in understanding the molecular basis of host post-attachment resistance to Striga has been for the S. gesnerioides-cowpea interaction. Research on the existence of ‘races’ of S. gesnerioides, with particular patterns of virulence against sets of cowpea cultivars (Lane et al., 1993; Botanga and Timko, 2006), recently led to the identification of the first sequenced resistance gene against a parasitic plant, and confirmation that gene-for-gene resistance can occur in these interactions (Li and Timko, 2009). After successful attachment and penetration, the witchweed develops underground. It is important to note that these plants lack chlorophyll at this stage hence they depend entirely on their host plants for nutrients. However, once they emerge, Striga plants produce chlorophyll and begin to produce their own assimilates, although water and mineral nutrients are drawn from the host 12 University of Ghana http://ugspace.ug.edu.gh plant. Growth and photosynthesis measurements collected from S. hermonthica on graminous hosts suggest that the parasite cannot sustain growth without host-supplied carbon. As much as 85% of the carbon in S. hermonthica leaves is host derived (Graves et al., 1990). Evidence of infestation is visible even before the Striga plant emerges above the ground, as the host plants are visibly stunted. Although the parasite inhibits the development of the above-ground parts of the crops, the roots remain relatively unaffected. This strategy has two consequences. First, the parasite keeps that part of the crop which provides it with mineral nutrients and water from the soil. Secondly, by reducing the biomass of the shoot, the parasite reduces the host’s need for the same resources. Four to seven weeks after planting, the Striga plants emerge above ground and turn green. The rapidly growing plant competes very effectively with the host plant shoot system for water. Flowering usually occurs when the plant is about six to eight weeks old. The two main factors favouring the spread of the witch weed causing problems for small-scale subsistence farmers include low soil fertility and low rainfall. Such farmers are usually confined to these agronomically poor areas and often lack the resources to control this weed and are least able to afford measures that will check the devastating effects of the weed. 13 University of Ghana http://ugspace.ug.edu.gh Fig. 2.1: The Striga plant life cycle. Adapted from Rich and Ejeta (2007). 2.3.2 Striga control Striga is difficult to control as it produces thousands of small and light seeds per plant that are easily and widely dispersed by water, wind, animals, and agricultural implements. Seeds of this parasitic plant have a long dormancy period but are still potentially active for many years. Striga control falls into two broad categories; cultural and seed-based. However the key to successfully managing this parasitic plant is to combine methods from both categories. Each of these categories has its own strengths and weaknesses, hence there is the need for integrated management control practices. 14 University of Ghana http://ugspace.ug.edu.gh The cultural methods of Striga control include crop rotation, intercropping, use of different planting techniques and management of soil fertility. Crop rotation is a low-cost technology that has a wide adoption depending on whether the crop is a high-value crop that fits into the cropping system and its seeds are readily available. However, this method is limited by the availability of land. Cotton and some leguminous crops such as soybean and cowpea, can be intercropped with maize to reduce the Striga seed bank in the soil by causing the Striga seeds to germinate but since the seeds cannot attach to these crops they die. Intercropping is quite similar to crop protection. The use of different planting techniques such as late or deep planting has a couple of challenges which include increasing labour requirements as well the high probability that the cropping practice may not fit in well with practices and tools used. Soil fertilization is also limited by increasing labour costs for fertilizer application, land availability and affordability of the fertilizers. Seed based technologies include biological control, use of imazypyl herbicide coated seeds and germplasm based-Striga resistance. Biological control using natural enemies of the weed is environmentally friendly and economically viable. An example is Fusarium oxysporium which attacks the weed during all its growth stages thus preventing its germination. Herbicide dressing is also very efficient and has been successfully commercialized in Kenya. Seeds with resistance to herbicides are coated with the herbicide and planted, the herbicide is then absorbed by the maize plant and also moves through the soil killing the weed and also forming a localized protective zone around the growing maize roots. Breeding for resistance or tolerance to the weed is the most feasible method for farmers to control Striga. The International Institute of Tropical Agriculture (IITA) and International Maize and Wheat Improvement Centre (CIMMYT) have successfully 15 University of Ghana http://ugspace.ug.edu.gh developed open-pollinated maize varieties, hybrids, and inbred lines that are resistant to Striga hermonthica (Kim, 1994; Badu-Apraku et al., 2007b). 2.4 Striga hermonthica parasitism of maize in WCA The Guinea savannas of WCA are known as the “maize belt” of the sub-region and have the potential to contribute towards the achievement of food security in the sub-region due to the high incident solar radiation, low night temperature and low incidence of pests and diseases. However, one of the major constraints to increased productivity and production of maize in WCA is Striga parasitism (Badu-Apraku et al., 2008). Among the three species of Striga, namely S. hermonthica, S. asiatica and S. aspera, that parasitize maize in the savannas of WCA, Striga hermonthica is the most widespread and destructive (Aggarwal, 1991; Lagoke et al., 1991). Average grain yield reduction caused by Striga on susceptible varieties under controlled studies was 67% with a range of 41 to 91% (Kim, 1991). Grain yield losses in maize from S. hermonthica infestation in WCA average 68% (Kim et al., 2002), but under severe field infestation in marginal production areas, it may lead to 100% yield loss in susceptible maize cultivars compelling farmers to abandon their fields (Badu-Apraku et al., 2011b). In West Africa, approximately 64% of the land devoted to cereal production is severely infested with Striga (Gressel et al., 2004). The increased infestation is a consequence of crop intensification resulting in declining soil fertility thereby increasing the accumulation of Striga seeds in the soil (Vogt et al., 1991; Badu-Apraku, 2010). 16 University of Ghana http://ugspace.ug.edu.gh Since 1982, one of the focal issues for the Maize Improvement Program (MIP) of IITA has been to combat Striga parasitism and have developed an artificial Striga infestation technique for screening several genotypes for tolerance or resistance (Kim, 1991; Adetimirin et al., 2000). Major achievements have been made in breeding for resistance and tolerance of the maize plant to Striga. 2.5 Mechanism and genetics of Striga resistance in Maize Striga damage symptom rating is an index for tolerance while Striga emergence count and yield performance are used as indices for resistance (Kim, 1994; Badu-Apraku et al., 2010). While several studies have been conducted to characterize mechanisms of resistance to Striga in other crops, including cowpea (Vigna unguiculata; Riopel and Timko, 1995), sorghum (Sorghum bicolor; Hess et al., 1992; Arnaud et al., 1999; Mohamed et al., 2003; Rich et al., 2004), and rice (Oryza sativa; Gurney et al., 2006), information on mechanisms of resistance to Striga in wild relatives of maize is lacking. However, over the last decade, encouraging reports on the mechanism of Striga resistance in maize or its wild relatives have emerged. In a pot study conducted using Zea diploperennis, a perennial teosintes, about 10% of the entries showed resistance relative to the other teosinte accessions and to maize. Resistant genotypes had fewer attached S. hermonthica capable of establishing vascular connections. Among parasitic seedlings capable of reaching the vascular tissues of these resistant genotypes, many died within a few days of penetration and those few that eventually emerged in the resistant Z. diploperennis pots were smaller than those on the susceptible genotypes and on the Zea mays check (Lane et al., 1997). In another study, Tripsacum dactyloides, another wild relative of maize, expressed resistance such that S. hermonthica plants attached at a frequency of 25% less than that on Z. mays. Parasites that were able to tap the xylem of the T. dactyloides hosts had a diminutive haustorial development subsequently as compared to 17 University of Ghana http://ugspace.ug.edu.gh the acquisition organ developed after vascular connection on the maize hosts. Average total dry weight of supported Striga shoots on the roots per T. dactyloides at the end of six weeks of laboratory culture was 1000x less than that of Striga on maize (Gurney et al., 2003) Tropical maize genotypes occasionally show resistance reactions, but those are often associated with avoidance or escape mechanisms. Hybrid maize genotypes from resistant x resistant inbreds supported fewer emerged parasites and these emerged Striga plants on the resistant hybrids were less likely to flower and set seed (Adetimirin et al., 2000). Early maturing maize varieties tested in Kenya generally were more resistant to Striga than late maturing varieties (Oswald and Ransom, 2004). Maize resistance can be expressed through low stimulation of Striga seed germination (Gurney et al., 2003; Pierce et al., 2003; Kiruki et al., 2006), low haustorial induction (Gurney et al., 2003), avoidance through root architecture (fewer thin branches) (Amusan et al., 2008), escape due to early maturity (Oswald and Ransom, 2004), resistance to attachment (as demonstrated by ZD05, an inbred line developed through a long term breeding effort at IITA- seemingly not the result of low haustorial initiation) (Amusan et al., 2008) and failure to support attached parasites (incompatibility) (Lane et al., 1997; Gurney et al., 2003; Amusan et al., 2008). Reports of genetic resistance to Striga have been documented in rice (Oryza sativa; Bennetzen et al., 2000; Gurney et al., 2006), sorghum (Sorghum bicolor; Maiti et al., 1984; Hess et al., 1992; Vogler et al., 1996; Haussmann et al., 2004) and maize (Adetimirin et al., 2000; Gethi and Smith, 2004; Menkir, 2006). Studies have shown that Striga resistance in maize is quantitatively inherited (Kim, 1994). Kim (1994), reported high heritabilities for host damage scores, ear rating, and yield under Striga infestation, and six to nine genes were estimated to be involved in the inheritance of these traits. Badu-Apraku (2007) also reported a moderately low to 18 University of Ghana http://ugspace.ug.edu.gh high narrow sense heritability estimates in an early tropical white maize population after three cycles of recurrent selection for Striga resistance. Research conducted has shown low genetic correlations between Striga emergence and host plant damage, suggesting that different genes control the two traits in maize (Kim, 1994; Akanvou et al., 1997). Both additive and non-additive gene actions are important for Striga resistance/tolerance in maize. Several reports have indicated that additive gene action plays a major role in the inheritance of Striga damage ratings (tolerance) while Striga emergence (resistance) is largely controlled by non-additive gene action (Kim, 1994; Akanvou et al., 1997; Badu-Apraku, 2007; Badu-Apraku and Oyekunle, 2012). However, other studies have reported that non-additive gene action was more important in Striga tolerance while additive gene action was of major importance in the inheritance of Striga resistance (Gethi and Smith, 2004; Badu-Apraku, 2007, Yallou et al., 2009). The differences observed in the studies have been attributed to the use of germplasm possessing genes with different modes of action. 2.6 Combining ability, heterosis and heterotic grouping in maize Combining ability studies are important since it is impossible to predict the performance of hybrids from the visual assessment or measurement of their per se performance or of the component inbred lines or genotypes. The concept of combining ability introduced by Sprague and Tatum (1942) is of prime importance in plant breeding as it provides information for the selection of parents as well as information regarding the nature and magnitude of gene action involved. Information on combining ability of germplasm under evaluation can also help in the exploitation of heterosis. 19 University of Ghana http://ugspace.ug.edu.gh 2.6.1 Combining Ability Allard (1960) defined combining ability as the measure of the value of a genotype based on the performance of its offspring produced in a definite mating system. The genotypes used could be inbred lines, populations or varieties. Combining ability studies are important in designing plant breeding programmes and also help breeders to select appropriate breeding methodologies for the improvement of traits (Kiani et al., 2007; Panhwar et al., 2008). The combining ability of an inbred line is determined by the potential usefulness of its hybrid combinations and the worth of an inbred line can best be determined by its hybrid performance. Sprague and Tatum (1942) refined the concept of combining ability to obtain the two expressions of general combining ability (GCA) and specific combining ability (SCA). GCA is the average performance of a genotype in hybrid combinations expressed as a deviation from the overall mean of all crosses with other parental lines and is largely due to genes which are additive in their effects (Rojas and Sprague, 1952; Falconer, 1981). SCA is defined as those instances in which certain hybrids are either better or poorer than would be expected based on the average performance of the parental inbred lines included in the crosses (Sprague and Tatum, 1942). SCA is due to genes with dominance or epistatic effects. Combining ability analyses are widely used in maize breeding programmes to determine GCA and SCA effects of maize germplasm for identification of the nature of gene action involved in the expression of quantitative traits, genetic diversity evaluation, suitable parental line selection for hybridization, heterotic groups and pattern classification, heterosis estimation and development of hybrids (Fan et al., 2002; Melani and Carena, 2005; Barata and Carena, 2006; Bello and Olaoye, 2009). Combining ability study via diallel crosses is an important tool used by many plant breeders 20 University of Ghana http://ugspace.ug.edu.gh in the development of hybrid maize varieties and offers an opportunity in identification and selection of potential inbred lines and parental combinations (Halleur, 1990). Several techniques are used in the estimation of combining ability. These include top cross suggested by Davis (Davis, 1927), diallel cross analysis by Griffing (Griffing, 1956), line x tester analysis by Kempthorne (Kempthorne, 1957) and North Carolina design by Comstock and Robinson (Cromstock and Robinson, 1948). The line x tester analysis is a powerful tool used to estimate GCA and SCA effects and to select desirable parents and crosses (Kamara et al., 2014). Several researchers have used this design extensively in maize (Sharma et al., 2004). In this design, full-sib progenies are generated through crossing a number of lines to a number of testers. A tester is a genotype that is used to identify superior germplasm in accordance with the breeding objectives of a hybrid-oriented programme. The developed progenies, as well as parents, are evaluated in field trials. Kamara et al. (2014) used this method to estimate GCA and SCA effects of some maize inbred lines for grain yield and other agronomic traits under two nitrogen fertilizer levels. Rovaris et al. (2014) estimated the combining ability of the parents and identified promising white maize genotypes and their hybrids for agronomic traits, yield and grits quality using line x tester method. The North Carolina design is also useful for studying combining ability in fixed model experiments and gene action when random models are applied. The larger the size or number of lines, the greater the accuracy of genetic estimates achieved from the data in North Carolina designs (Fasahat et al., 2016). The diallel mating design originally proposed by Griffing (1956) allows parents to be crossed in all possible combinations, including selfs and reciprocals, and is useful for estimating GCA and SCA effects for a set of genotypes and their implications in plant breeding. This mating design is the most used and abused of all mating designs in obtaining various genetic information 21 University of Ghana http://ugspace.ug.edu.gh (Halleur et al., 2010). This abuse could probably be due to the presence of two models for diallel analysis; random and fixed models (Griffing, 1956). A random model is useful for estimating GCA and SCA variances; it involves parents that are random members of a random mating population. In contrast, when parents are considered as fixed effects, the purpose is to measure the GCA effects of each parent and the SCA effect of each pair of parents. 2.6.2 Heterosis and Heterotic grouping When inbred lines are crossed, usually the progeny shows an increase in value for characters/traits that suffered a reduction from inbreeding. This increase is usually termed as hybrid vigour or heterosis. Hybrid vigour is undoubtedly one of the greatest practical contributions of genetics to the agricultural world. Shull (1908) coined the term heterosis to express the unusual vigour of the first filial (F1) generation resulting from the hybridization of two inbred lines of maize (Duvick and Brown, 1981). It can also be defined as the measure of superiority of F1 compared to the average value of its parents (Falconer and Mackay, 1996; Mengesha, 2013). Heterosis restores reduced vigour associated with inbreeding and leads to higher performance of progenies over the parents. Generally, heterosis is manifested as an increase in vigour, size, growth rate, yield or some other characteristic and this superiority is estimated over the average of the two parents (mid-parent value). The major heterotic trait of interest for breeding purposes is seed yield. Heterosis has been found to be controlled by dominance complementarity (Shull, 1908) and epistasis effects (Lippman and Zamir, 2006). In maize crops, it was on the basis of heterosis developed in such a unique way that hybridization was recommended as a valuable breeding method. It is unlikely that any other crop species has so significantly benefited from scientific research and achieved such a large response to selection. Heterosis is not only dependent on parent combinations but also on the effect of 22 University of Ghana http://ugspace.ug.edu.gh environmental conditions as well as the trait under consideration (Chapman et al., 2000). However, maximum heterosis is only attainable when the genetic distance between the parental lines used in the cross is high. Two parental genotypes which manifest a relatively high magnitude of heterosis from their crosses can be said to be genetically more different than two varieties that exhibit little or no heterosis in their F1 hybrids. It is estimated that in maize production, heterosis contributes at least 15 – 50% of the total crop yields (Lippman and Zamir, 2006). In tropical maize, Betran et al., (2003) reported an extremely high expression of heterosis under stress, especially under severe drought stress. Generally, it is believed that inbred lines with superior yields under stress conditions will result in superior hybrids under these conditions, even though correlations between inbred parent performance and hybrid performance are relatively weak (Vasal et al., 1997). Saleh et al. (2002) also reported high estimates of heterosis for grain yield, ear weight, grain weight per ear, moderate estimates for plant and ear height, shelling percentage, ear circumference, number of kernel rows per ear, number of kernels per ear row and grain weight. In order to maximize the exploitation of heterosis, inbred lines need to be classified into different heterotic groups before crosses are made. Studies have shown that the combination of genotypes from different heterotic groups results in hybrids with higher chances of genetically expressing a target trait (Austin et al., 2000; Birchler et al., 2003; Tollenaar et al., 2004; Ricci et al., 2007). A heterotic group can be defined as a group of related or unrelated genotypes from the same or different populations which display similar combining ability effects when crossed with genotypes from other germplasm groups (Warburton et al., 2002). A heterotic pattern is a specific pair of heterotic groups which express high heterosis in hybrid combinations. Assigning inbred lines to heterotic groups would prevent the development and evaluation of crosses that should be 23 University of Ghana http://ugspace.ug.edu.gh discarded, allowing maximum heterosis to be exploited by crossing inbred lines belonging to different heterotic groups (Terron et al., 1997). Methods that can be used in identifying heterotic groups include making crosses in a diallel fashion (Hayman, 1954; Badu-Apraku et al., 2015 a, b), the line x tester mating design (Agbaje et al., 2008), the North Carolina Design II (Robinson et al., 1958; Badu-Apraku et al,. 2015a) mating design as well as using DNA markers in germplasm classification (Melchinger, 1999; Badu- Apraku et al., 2015 a, b). The technology of using DNA/molecular markers is to refine existing heterotic groups and patterns or for expediting the establishment of new heterotic groups, through the determination of genetic distances. The choice of which method to use is determined by the source germplasm under study as well as the available resources. Heterotic groups that are complimentary to each other comprise a heterotic pattern, that is, specific crosses between genotypes which show high levels of heterosis. Classifying inbred lines into heterotic groups is critical to determine the potential usefulness of the lines for the development of high yielding hybrids and synthetic varieties. Although heterotic patterns are very essential for maximizing the potential expression of heterosis in hybrids, they have not been well established and improved in a systematic manner by the majority of maize improvement programmes in the tropics. The high level of diversity in tropical maize has made it relatively difficult to find uniform heterotic groups (Warburton et al., 2002). However in recent years, heterotic groups have become more distinct and refined due to the emphasis on selection within elite line crosses for developing parents of single crosses and with the assistance of molecular markers (Melchinger, 1999). Several researchers have used the SCA effects of grain yield to classify maize inbred lines into heterotic groups (Menkir et al., 2004; Melani and Carena, 2005; Agbaje et al., 2008; Akaogu 24 University of Ghana http://ugspace.ug.edu.gh et al. 2012). However, SCA effects of grain yield of inbred lines have often been found to be influenced by the interaction between two inbred lines and the environment. This has often led to the classification of the same inbred lines to different heterotic groups in different studies (Fan et al., 2008; Badu-Apraku et al., 2013b). Fan et al., (2008) proposed the use of specific and general combining ability (HSGCA) method by combining both SCA and GCA effects of grain yield as a more appropriate method for assigning inbred lines into heterotic groups. However heterotic grouping of inbred lines based on one trait, that is, grain yield, poses a serious challenge due to the complexity of this trait. Grain yield is controlled by several genes (polygenic), influenced by other traits and has low heritability under stress conditions. Bolaños and Edmeades (1993) reported that selection for grain yield under drought conditions is inefficient due to the decline in the estimate of heritability of grain yield under environmental stress. Hence, heterotic grouping based on the GCA of multiple traits (HGCAMT) was proposed (Badu-Apraku et al., 2013b). This method is based on measured multiple traits associated with grain yield of inbreds with significant GCA effects across contrasting environments. Hence, classification based on the GCA effects of multiple traits should be a better, more predictable and realistic method for heterotic grouping of inbreds since GCA measures the additive gene effects of each trait. Badu-Apraku et al., (2013b, 2015 a, b,) have successfully used this method to classify early and extra-early inbred lines into heterotic groups. 25 University of Ghana http://ugspace.ug.edu.gh 2.7 Biplot Analysis Multiple environment trials (MET) are usually carried out by plant breeders to select and recommend stable and high-yielding genotypes across a set of environments. The ultimate aim of plant breeders is to develop high-yielding cultivars with wide adaptability. However, attaining this goal is made more complicated by (GEI) genotype x environment interactions (Gauch and Zobel, 1996). The considerable variation in crop performance due to climatic conditions and different soils, causes large annual variations in yield performance of crops. This is mainly due to the low heritability of yield as a typical quantitative trait. Thus, grain yield could be affected by not only genotype, but also by the environment as well as the genotype x environment interactions. Therefore, the analysis of MET data often results in genotype x environment interactions which often makes the interpretation of results difficult and reduces efficiency in selecting superior genotypes. AMMI and GGE biplot analysis are two recent statistical tools that are widely used to overcome these difficulties in MET data analysis (Agyeman et al., 2015). The AMMI biplot analysis has proved to be a powerful and highly efficient multivariate analytical tool used by researchers to assess the performance of genotypes evaluated in a number of environments, identify stable and high yielding genotypes and to determine the magnitude of GEI (Crossa, 1990; Gruneberg et al., 2005). The most well-known and appealing component of AMMI analysis is the graphical display of the results in a very informative biplot that shows both main and interaction effects for both genotype and environment (Zobel et al., 1988). However, despite these features the AMMI biplot does not have the most important property of a true biplot, namely the inner-product property, and it also does not display the discriminating ability and representativeness view of a biplot which is effective in evaluating test environments (Agyeman et al., 2015). The GGE biplot, however, has the inner-product property of a biplot; it shows not 26 University of Ghana http://ugspace.ug.edu.gh only the mean performance and stability of each genotype, but also the relative performance of each genotype in each environment. This graphical tool displays, interprets and explores two important sources of variation, namely genotype main effect and GE interaction of MET data (Fan et al., 2007; Yan et al., 2000). The GGE biplot has been used in crop variety trials to effectively identify the best-performing genotype across environments, identify the best genotypes for mega- environment delineation, whereby specific genotypes can be recommended to specific mega- environments and to evaluate the yield and stability of genotypes (Yan and Kang, 2003; Yan and Tinker, 2006). This relative versatility of the GGE biplot, especially in mega-environment analysis and genotype selection, is worthy of being exploited for selection of genotypes for specific environments (Badu-Apraku et al. 2012; Agyeman et al., 2015). A number of researchers have compared the AMMI and GGE biplot using MET to determine which is most effective in analysing GEI. However, these came out with differing results. Gruneberg et al., (2005) showed that AMMI was highly effective for the analysis of MET. Kandus et al., (2010) also found the AMMI model as the best model to describe the GEI in maize. Stojaković et al., (2010) and Mitrovic et al., (2012) found that both models provided similar results. However, contrary to these results, Yan et al., (2007) and Badu-Apraku et al., (2012) concluded in their comparison of both models that the GGE biplot was superior to the AMMI biplot in mega-environment analysis and genotype evaluation. 27 University of Ghana http://ugspace.ug.edu.gh CHAPTER THREE MATERIALS AND METHODS 3.1 Germplasm and Generation of Crosses Diallel crosses were made among fifteen selected inbreds developed in the IITA Maize Improvement Programme (Table 3.1) excluding reciprocals (assuming no reciprocal effects of Striga resistance/tolerance in maize) to obtain a set of 105 F1 single-cross hybrids. The inbred lines were selected based on the reactions to stresses under which they were evaluated, namely drought and Striga infestation. Each inbred selected was planted in twenty row plots measuring 4 m long in the IITA-Ibadan breeding nursery in 2014. Plant spacing between rows was 0.75 m and 0.25 m between plants in the row. Two seeds were sown per hill and the seedlings thinned to one per hill two weeks after planting. Bulked pollen was collected from each of the lines to pollinate the other lines to obtain all possible combinations amongst all the inbred lines. 3.2 Field studies The field studies involved the evaluation of 105 early maturing white hybrids derived from the 15 x 15 diallel crosses plus five drought and Striga resistant normal endosperm hybrid checks. In addition, the 15 early maturing white parental inbred lines were separately evaluated in an adjacent block in the same field. The single cross hybrids and parental inbreds were evaluated under Striga infested and Striga-free (optimal) environments at Abuja (9о16’N, 7о20’E, 300 m altitude, and 1500 mm annual rainfall) and Mokwa (9о18’N, 5о 4’E, 457 m altitude, 1100 mm annual rainfall) during the growing season of 2015. The inbreds and hybrids were also evaluated at Ikenne (lat. 37’E, long. 687’N, elevation 30 m, 1200 mm annual rainfall) under optimal environments during the growing season of 2015. A randomized complete block design with two replications was used in the evaluation of the inbred lines while a 10 x 11 rectangular design with 28 University of Ghana http://ugspace.ug.edu.gh two replications was used for evaluating the hybrids. The Striga infestation method developed by IITA Maize Program that ensures uniform Striga infestation with no escapes (Kim, 1991; Kim and Winslow, 1991) was used. The Striga seeds used were collected from fields of sorghum [Sorghum bicolor (L.) Moench] at the end of the growing season, stored for about six months to allow for seed conditioning and breakage of dormancy. A germination test as described by Menkir (2006) was conducted six months later. The seeds were then thoroughly mixed with finely sieved sand at the ratio 1:99 by weight. The sand served as the carrier and provided adequate volume for rapid and uniform infestation. Two weeks before planting, ethylene gas was injected into the soil to induce suicidal germination of existing Striga seeds in the plots at both testing sites. This was done to ensure uniform infestation. The ethylene gas injector was plunged into the soil at a depth of 12 cm before the gas was injected into the soil. This was repeated at intervals of I m. At planting on the Striga- infested fields, 8.5 g sand/Striga mixture (about 5,000 germinable seeds) was placed in each planting hole. Except for Striga infestation, similar management practices were applied to both Striga-infested and non-infested plots. Fertilizer application was delayed until 21 days after planting (DAP) and 30 kg ha-1 each of N, P and K was applied as 15-15-15 NPK to the artificially Striga-infested fields. The reduced rate and delay in application of fertilizer were necessary to subject the maize plants to stress, a condition that favours the production of strigolactones, which enhances good germination of Striga seeds and attachment of Striga plants to the roots of host plants in Striga infested plots (Kim, 1991). However, in the Striga-free plots, fertilizer was applied at the rate of 60 kg ha–1 each of nitrogen (N), phosphorus (P) and potassium (K). An additional 60 kg ha–1 N was top-dressed at five weeks after planting (WAP). 29 University of Ghana http://ugspace.ug.edu.gh Single-row plots, each 4 m long, with a spacing of 0.75 m between rows and 0.40 m between plants within rows were used. Three seeds were planted per hill and emerged seedlings were thinned to two plants per stand at 2 WAP to give a final plant population density of 66,666 ha-1. Weeds other than Striga were controlled manually on the Striga-infested plots while the optimal environment plots were kept weed-free with the application of atrazine and gramoxone as pre- and post-emergence herbicides at 5 l/ha each of primextra and paraquat and subsequently, by hand weeding. Table 3.1. List of early maturing white endosperm maize inbred lines used for the diallel crosses and their reactions to drought and Striga hermonthica infestation. Inbred Pedigree Reaction to stresses Drought Striga TZEI 326 (TZEI 1 x TZEI 2) S6 inbred 45-1/3-4/4-1/1 T T TZEI 352 (TZEI 1 x TZEI 2) S6 inbred 109-8/10-3/3-1/1 T T TZEI 355 (TZEI 1 x TZEI 2) S6 inbred 125-2/5-1/2-1/1 T T TZEI 383 (TZEI 1 x TZEI 2) S6 inbred 190-5/9-2/2-1/1 T T TZEI 410 (TZEI 1 x TZEI 2) S6 inbred 241-3/4-2/4-1/1 T T TZdEI 120 TZEE-W POP STR 104 S5 18/208-2/2-3/4-1/2-2/2 T S TZdEI 352 TZE-W Pop STR 107 S5 24/254-1/2-1/1-1/1-2/2 T T TZdEI 425 TZE-W Pop STR 107 S5 223/254-1/2-2/2-2/3-1/2 T T TZEI 296 (TZEI 1 x TZEI 2) S6 inbred 13-2/2-1/3-1/1 S T TZEI 7 WEC STR S7 Inbred 12 S T TZEI 18 TZE-W Pop STR Co S6 Inbred 136-3-3 T T TZEI 31 TZE-W Pop x LD S6 Inbred 4 T T TZEI 56 TZE-W Pop STR Co S6 Inbred 75-1-3 T T TZEI 5 TZE-W Pop x 1368 STR S7 Inbred 9 T S TZEI 80 TZE-W Pop x 1368 STR S7 Inbred 3 S T S= susceptible; T= tolerant; R= resistant 30 University of Ghana http://ugspace.ug.edu.gh 3.3 Data collection Data were recorded on Striga infested and Striga-free plots for 50% silking (DYS) and 50% anthesis (DYA) i.e. when 50% of plants had silked and shed pollen, respectively. The anthesis-silking (ASI) interval was determined as the difference between DYS and DYA. Plant height (PLHT) was measured as the distance from the base of the plant to the height of the first tassel branch and ear height (EHT), the distance from the base of the plant to the node bearing the upper ear (the mean of ten random plants). Root lodging (RL) was recorded as the percentage of plants that fell down from the root and stalk lodging (SL) as the percentage of plants broken at/below the highest ear node. Plant aspect (PASP) was scored on a scale of 1 to 5 based on overall plant architecture, where 1 = excellent and 5 = poor. Ear aspect (EASP) which is the assessment of the general appeal of the ears without the husks was also scored on a scale of 1 to 5 based , where 1 = clean, uniform, large and well-filled ears and 5 = ears with undesirable features. The factors considered included size of the ear, uniformity of size, grain colour and texture; extent of grain filling, insect and disease damage. Husk cover (HUSK) was rated on a scale of 1 to 5, with 1 = husks tightly wrapped and extended beyond ear tip and 5 = ear tips exposed. The number of ears per plant (EPP) was computed as the total number of ears with at least one fully developed grain and divided by the number of harvested plants. Husk was removed and field weight of the ears per plot was measured using a measuring balance. A moisture tester was used to determine the amount of moisture in the grain. Grain yield was calculated in kilograms per hectare and was estimated based on 80% shelling percentage and adjusted to 15% moisture. Grain yield under Striga-infestation was calculated as follows: (100 − 𝑚) 1000 𝐺𝑌 = 𝑓𝑤𝑡 × × × 0.8 85 (Ȣ × ɸ) GY = grain yield (kg ha-1), 31 University of Ghana http://ugspace.ug.edu.gh fwt = field weight of harvested ears per plot (kg), m = grain moisture content at harvest 10,000= land area per hectare (m2), Ȣ= land area per plot (0.75 m x 0.4 m), ɸ = number of hills/plot (11) and 0.80 = 80% shelling percentage. Striga damage syndrome rating (Kim, 1991) and number of emerged Striga plants were recorded at 8 and 10 weeks after planting (WAP) (56 and 70 DAP) in the Striga-infested plots. Striga damage syndrome rating was scored per plot on a scale of 1-9 where 1 = no damage, indicating normal plant growth and high resistance, and 9 = complete collapse or death of maize plants, that is, highly susceptible (Kim, 1991). 3.4 Data Analysis Data on number of emerged Striga plants was transformed using [log (counts+1)] to reduce the heterogeneity of variances for Striga counts. Root lodging data was converted to percentages and subsequently transformed using the square root transformation. Analysis of variance was done separately for data collected under Striga infested as well as Striga-free environments, followed by combined ANOVA across Striga infested and Striga-free environments. The ANOVA was done on plot means for grain yield, EPP, PLHT, EHT, DYA, DYS, ASI, EASP, PASP, HUSK, RL, SL, Striga damage and number of emerged Striga plants at 8 and 10 WAP with PROC GLM in Statistical Analysis Systems (SAS) version 9.3 using a RANDOM statement with the TEST option (SAS Institute, 2011). Excluding the checks, GCA effects of the parents and SCA of the crosses, as well as their mean squares across Striga infested and Striga-free environments for grain yield and other measured traits were computed for the 15 x 15 diallel crosses following Griffing’s 32 University of Ghana http://ugspace.ug.edu.gh method 2 model 1 (fixed model) (Griffing, 1956) using DIALLEL-SAS program developed by Zhang et al. (2005) adapted to SAS software version 9.3 (SAS Institute, 2011). The GCA and SCA effects were tested for significance using the standard error. The standard errors of the GCA and SCA effects were estimated as the square root of the GCA and SCA variances (Griffing, 1956). The statistical model for the diallel analysis is as follows: Yijk = µ + Ee + gi + gj + sij + gEeg + sEes +εijk where Yijk is the observed measurement for the ijth cross grown in the kth environment; μ is the grand mean; Ee is the main effect of Environment; gi and gj are the GCA effects; sij is the SCA effect; gEeg is the interaction effect between GCA and Environment; sEes is the interaction effect between SCA and the Environment, and εijk is the error term associated with the ijth cross evaluated in the kth replication or environment. The restrictions imposed on the combining ability effects are: ∑gi = 0, _∑sij = 0 for each j (Griffing, 1956). Effects of GCA and SCA for the traits were computed from the mean values adjusted for the block effects for each environment and across environments. The grain yield data for the single cross hybrids and inbred lines were separately subjected to genotype main effect plus genotype × environment interaction (GGE) biplot analysis to decompose the G x E interactions into its components (Yan, 2001). The GGE biplot was used to obtain information on the most promising inbreds and hybrids across stress and non-stress environments and to investigate the stability of hybrids in the Striga-infested and Striga-free environments using GGE biplot software, a windows application that fully automates biplot analysis (Yan, 2001). The GGE biplot model equation used is as follows: Ŷij – Yj = 1i1j1 + 2i2j2 + ij 33 University of Ghana http://ugspace.ug.edu.gh where Ŷij is the genetic value of the combination (pure line or hybrid) between Entry i and Tester j for the trait of interest; Yj is the mean of all combinations involving Tester j; 1 and 2 are the singular values for PC1 and PC2 respectively; i1 and i2 are the PC1 and PC2 eigenvectors, respectively, for Entry i; j1 and j2 are the PC1 and PC2 eigenvectors, respectively, for Tester j; ij is the residual of the model associated with the combination of Entry i and Tester j. In diallel cross data, each genotype is an entry and a tester, i and j refer to the same or different genotypes. The GGE biplot analysis data were not transformed (‘Transform=0’), not standardized (‘Scale=0’) and were environmentally-centred (‘Centring=2’). Heterotic grouping based on GCA in respect of multiple traits (HGCAMT) proposed by Badu-Apraku et al. (2013b, 2015b), was used to group the 15 inbred lines. Grouping by the HGCAMT method was achieved by standardizing the GCA effects (mean of zero and standard deviation of 1) of the traits that had significant mean squares for G under each study conditions using the following statistical model: where Y is HGCAMT, which is the genetic value measuring relationship among genotypes based on the GCA of multiple traits i to n; Yi is the individual GCA effect of genotypes for trait i, Ȳi is the mean of GCA effects across genotypes for trait i, s is the standard deviation of the GCA effects of trait i and εij is the residual of the model associated with the combination of inbred i and trait j. 34 University of Ghana http://ugspace.ug.edu.gh The traits included days to silking and anthesis, ASI, plant and ear heights, ear aspect, plant aspect, and ears per plant. The standardised GCA effects were subsequently subjected to Ward’s minimum variance cluster analysis to construct the groupings using SAS software version 9.3 (SAS Institute, 2011). Heterotic groupings were also performed based on the SCA effects of grain yield and heterotic groups’ specific and general combining ability (HSGCA) of grain yield as described by Fan et al. (2009) and Akinwale et al. (2014). Classification of an inbred line into a heterotic group was based on the significant (P <0.05) SCA effects with another inbred line or significant (P <0.05) negative SCA effects with the other with a mean yield equal to or greater than one standard error (S.E) above the grand mean of all single crosses. To assign an inbred line into heterotic groups under Striga-infested, optimal growing environments and across research environments the heterotic group’s specific and general combining ability (HSGCA) method proposed by Fan et al. (2008) was used as follows: HSGCA = Cross mean X ij – Tester mean (Xi.) = GCA + SCA. where X is the mean yield of the cross between ith tester and jthij line, Xi. is the mean yield of the i th tester across of jth line. To identify the inbred lines as well as single-cross hybrids for tolerance to Striga, a selection index (I) known as the base index for selection was computed for Striga infestation using standardized data for selected variables. The Striga base index combines the standardized means of grain yield, number of emerged Striga plants, Striga damage syndrome rating, and number of ears per plant. The best and worst performing genotypes under stress conditions were identified based on the index values estimated as follows: I= [(2*YLI) + EPP – (SDR8 + SDR10) – 0.5(ESP8 + ESP10)], 35 University of Ghana http://ugspace.ug.edu.gh where YLI was the grain yield of Striga-infested plots, EPP was the number of ears at harvest in the Striga-infested plots, SDR8 and SDR10 were Striga damage ratings at 8 and 10 WAP, and ESP8 and ESP10 were number of emerged Striga plants at 8 and 10 WAP. Each trait was standardized with a mean of zero and a standard deviation of 1 to minimize the effects of different scales. A positive I value was an indicator of tolerance of the genotypes to Striga parasitism while a negative value was an indicator of susceptibility. The mid-parent values for grain yield, days to silking and anthesis, anthesis-silking interval (ASI), plant and ear heights, ear aspect, plant aspect, ears per plant, Striga damage at 8 and 10 WAP, and number of emerged Striga plants at 8 and 10 WAP were computed as the mean of the two parental lines averaged across environments for each treatment separately and across. The relationship between the performance of the parental lines and their hybrids under Striga infestation was estimated using Spearman’s rank correlation analysis. The correlations of the traits of parental lines with grain yield of their hybrids was performed using mean grain yields of hybrids and the corresponding mid-parent values (Meseka et al., 2006) Mid-parent heterosis (MPH) was calculated as: MPH = (F1 – MP) x 100 MP where, F1 is the mean of the F1 hybrid performance and MP = (P1 + P2)/2 in which P1 and P2 are the respective means of the inbred parents. 36 University of Ghana http://ugspace.ug.edu.gh Table 3.2. The analysis of variance table of Griffing’s diallel method II which was used in the data analysis. Source of variation Df Mean squares Expected mean (MS) squares Environments (E) e-1 M5 Replication/E e(r-1) M4 --Genotypes [n(n+1)/2]-1 M3 σ 2 + r σ2 2en + er σ n Parents n-1 Parents vs. Crosses 1 Crosses [n(n-1)/2-1] GCA n-1 SCA n(n-3)/2 Genotypes x environments (e-1){[n(n+1)/2]-1} M σ2 2 + r σ 2 en E x parents (e-1)(n-1) E x parents vs. Crosses e-1 E x crosses (e-1){[n(n-1)/2]-1} GCA x environments (e-1)(n-1) SCA x environments (e-1)[n(n-3)/2] Pooled Error e(r-1){[n(n+1)/2]-1} M1 σ 2 Total [ern(n+1)/2]-1 GCA = general combining ability, SCA = specific combining ability, DF = degrees of freedom, n = number of inbred lines used for crosses, e = environments and r = number of replications. 37 University of Ghana http://ugspace.ug.edu.gh CHAPTER FOUR RESULTS 4.1 Performance of inbreds and hybrids under Striga-infested and Striga-free environments The analysis of variance of the inbred lines evaluated under Striga infestation showed that the variations among inbreds (G) and environments (E) were highly significant (P < 0.01) for all measured traits except anthesis-silking interval (ASI). However, variations among the inbred lines were not significant for plant height (PLHT).Similarly, the variations among the environments were not significant for Striga damage rating at 8 WAP (STRR1). However, the inbred x environment interactions (GEI) mean squares were significant for all the measured traits except for ASI, PLHT, EPP and number of emerged Striga plants at 8 WAP (STRCO1). Under optimal environments, variation among the inbred lines was significant for all measured traits. However, GEI was not significant for grain yield and other measured traits except ASI and PLHT (Table 4.1). The analysis of variance of the diallel crosses evaluated under Striga-infested environments revealed highly significant variations among the hybrids and environments for all measured traits except PLHT of hybrids and Striga damage at 10 WAP (STRR2) for environments (Table 4.2). GEI under Striga-infested environments was significant for Striga damage and number of emerged Striga plants at 8 and 10 weeks after planting (WAP). Under optimal environments, highly significant differences among the hybrids and environments was observed for all measured traits except for EPP. Hybrid x environment interaction was significant for only PLHT and plant aspect (PASP). The GCA effects under Striga infestation were highly significant for measured traits except for PLHT. Similarly, SCA effects were significant for the measured traits except for ASI, 38 University of Ghana http://ugspace.ug.edu.gh PLHT, EPP and STRCO2. The GCA effects x environment interaction was highly significant for all measured traits with the exception of ASI, PLHT and EPP. However, SCA effects x environment interaction was significant for only STRCO1. Under Striga-free environments, the GCA and SCA effects were significant for all measured traits except for GCA and SCA effects for EPP and ASI for SCA effects. GCA effects x environment interaction was highly significant for YIELD, PLHT AND PASP. The SCA effects x environment interaction showed no significant differences among the measured traits. The combined analysis of variance of the single cross hybrids across environments (two Striga-infested and three optimal environments) revealed highly significant variations among the genotypes and the environments for all measured traits. Hybrid x Environment interaction mean squares were significant for all measured traits except for ASI and PLHT (Table 4.3). The GCA and SCA effects across environments were significant for all measured traits with the exception of SCA effects for PLHT and EPP. Furthermore, the GCA effects were larger than the SCA effects for all measured traits under Striga-infested, Striga-free and across research environments with the exception of plant height under Striga-infested environments (Tables 4.2 and 4.3). Under Striga-infested environments, inbreds TZEI 7, TZEI 296, TZdEI 352, TZEI 355 and TZEI 383 recorded highly significant and positive GCA effects for grain yield. Under Striga-free conditions, TZEI 5, TZEI 7, TZdEI 120, TZdEI 352 and TZdEI 425 also recorded positive and highly significant GCA effects for grain yield. However across environments, inbreds TZEI 7 and TZdEI 352 were the only inbreds that recorded positive and highly significant GCA effects (Table4.4). 39 University of Ghana http://ugspace.ug.edu.gh Table 4.1. Mean squares from analysis of variance for grain yield and other agronomic traits of 20 early white maize inbred lines evaluated under Striga –infested and Striga-free environments in Nigeria, 2015. Source of variation Df YIELD ASI PLHT EASP EPP STRR1 STRR2 STRCO1 STRCO2 PASP Striga-infested Inbred 19 278397.3** 3.2 178.2 1.7* 0.09** 1.97** 2.67** 44.03* 84.55 - Env 1 530518.4* 0.05 3634.2** 2.8 0.29** 0.2 2.11* 561.8** 2952.45** - Inbred x Env 19 117974.7* 1.5 217.1 1.6* 0.02 0.67** 0.85* 33.48 101.63* - Error 39 55530.7 1.7 136.6 0.8 0.02 0.19 0.40 20.03 54.04 - Optimum Inbred 19 338631.2** 1.5** 223.5* 1.1* 0.1* - - - - 2.1** Env 2 1745387.7** 9.2** 18302.9** 7.8** 0.1* - - - - 15.1** Inbred x Env 38 117150.2 1.4** 218.6** 0.8 0.03 - - - - 0.9 Error 59 103614.1 0.6 108.8 0.5 0.03 - - - - 0.8 *, ** = Significant at 0.05 and 0.01 probability levels, respectively; Env = environment; ASI = Anthesis-silking interval; PLHT=Plant height; EASP= Ear aspect; EPP= Ears per plant; STRR1 and STRR2= Striga damage at 8 and 10 weeks after planting (WAP); STRCO1 and STRCO2=Number of emerged Striga plants at 8 and 10 WAP; PASP=Plant aspect. 40 University of Ghana http://ugspace.ug.edu.gh Table 4.2. Mean squares from analysis of variance for grain yield and other agronomic traits of 105 early-maturing white maize single cross hybrids evaluated under Striga-infested and Striga-free environments in Nigeria, 2015. Source of variation Df YIELD ASI PLHT EASP EPP STRR1 STRR2 STRCO1 STRCO2 PASP Striga-infested Env. 1 24550516** 10.69* 4530.34** 7.74** 0.19** 13.04** 0.15 20650.06** 36363.01** - Hybrid 104 1035778** 4.59** 216.09 1.52** 0.04** 13.04** 1.77** 126.46** 228.61** - GCA 14 4371977** 17.21** 162.71 6.22** 0.14** 5.68** 7.89** 445.48** 845.13** - SCA 90 516814** 2.63 224.39 0.79* 0.02 0.76* 0.82** 76.83* 132.71 - Hybrid x Env. 104 411532 1.87 258.36 0.66 0.02 0.80** 0.79* 98.47** 171.74** - GCA x Env. 14 815401** 3.83 437.75 1.60** 0.02 2.43** 1.82** 254.08** 429.79** - SCA x Env 90 348708 1.57 230.45 0.51 0.03 0.55 0.63 74.26* 131.59 - Rep x Env 2 1547301 4.97 192.53 11.37** 0.03 2.12 0.7 111.73 686.79** - Block(Rep x Env) 40 51732** 3.01 427.37 3.16** 0.02 0.98** 1.14** 110.07** 226.21** GCA/SCA ratio 8.5 6.5 0.7 7.9 7 7.5 9.6 5.8 6.4 Error 208 334306 2.33 284.28 0.53 0.02 0.53 0.54 54.04 111.87 - Optimum Env 2 30480997** 8.41** 13151.99** 9.98** 0.02 - - - - 9.86** Hybrid 104 1799785** 2.44* 360.63** 1.55** 0.01 - - - - 1.46** GCA 14 8471603** 3.55* 1342.55** 6.44** 0.01 - - - - 6.92** SCA 90 761947** 2.26 207.88* 0.79** 0.01 - - - - 0.61** Hybrid x Env. 208 318453 1.78 200.64** 0.41 0.01 - - - - 0.48* GCA x Env 28 805662** 2.15 473.96** 0.49 0.01 - - - - 1.28** SCA x Env 180 242664 1.72 158.12 0.39 0.01 - - - - 0.35 Rep x Env 3 2295657** 2.56 462.49 3.16** 0.02 - - - - 1.83** Block(Rep x Env) 30 454725** 1.89 314.73** 0.53** 0.01 - - - - 0.69** GCA/SCA ratio 11.1 1.5 6.5 8.2 1 11.3 Error 312 290895 1.76 144.00 0.36 0.01 - - - - 0.38 *, ** = Significant at 0.05 and 0.01 probability levels, respectively; Env = environment; ASI = Anthesis-silking interval; PLHT=Plant height; EASP= Ear aspect; EPP= Ears per plant; STRR1 and STRR2= Striga damage at 8 and 10weeks after planting (WAP); STRCO1 and STRCO2= Number of emerged Striga plants at 8 and 10 WAP; PASP = Plant aspect; GCA= General combining ability; SCA= Specific combining ability. 41 University of Ghana http://ugspace.ug.edu.gh Table 4.3. Mean squares from analysis of variance for grain yield and other agronomic traits of 105 early-maturing white maize single cross hybrids across 5 environments at Abuja, Mokwa and Ikenne in 2015. Source of variation Df YIELD ASI PLHT EASP EPP Df PASP Env 4 183223832** 65.27** 66515.81** 31.50** 0.34** 2 9.86** Hybrids 104 1865351** 3.66** 334.72** 1.95** 0.02** 104 1.46** GCA 14 8386978** 9.58** 1072** 8.08** 0.06** 14 6.92** SCA 90 850875** 2.75* 220.03 1.00** 0.01 90 0.61** Hybrids x Env 416 504663** 2.19 225.41 0.65** 0.02** 208 0.48* GCA x Env 56 1720832** 4.83** 454.73** 1.79** 0.03** 28 1.28** SCA x Env 360 315481 1.79 189.74 0.47 0.01 180 0.35 Rep x Env 5 1996315** 3.52 354.51 6.45** 0.01 3 1.83** Block (Rep x Env) 100 613521** 2.34 359.79** 0.69** 0.02 60 0.69** GCA/SCA ratio 9.8 3.5 4.9 8.1 6 11.3 Error 520 308259 1.99 200.11 0.43 0.01 312 0.38 *, ** = Significant at 0.05 and 0.01 probability levels, respectively; Env = environment; ASI = Anthesis-silking interval; PLHT=Plant height; EASP= Ear aspect; EPP= Ears per plant; PASP=Plant aspect; GCA= General combining ability; SCA= Specific combining ability. 42 University of Ghana http://ugspace.ug.edu.gh Table 4.4. General combining ability (GCA) effects for grain yield and other agronomic traits of the 15 early maturing white-endosperm inbred lines evaluated under Striga infested, Striga-free and across test environments in Nigeria, 2015. Inbred Grain yield Days to silking ASI PLHT STR OPT ACR STR OPT ACR STR OPT ACR STR OPT ACR TZEI 5 -511.83** 367.84** 15.97 2.36** 0.45** 1.21** 1.19** -0.10 0.42** -2.08 2.13 0.45 TZEI 7 268.59** 544.36** 434.06** 1.44** 0.67** 0.97** 0.03 -0.25 -0.14 1.52 0.39 0.84 TZEI 18 -75.47 80.55 18.14 0.70 0.60** 0.64* 0.47* 0.22 0.32* -2.24 -2.88 -2.62 TZEI 31 -272.39** -567.85** -449.67** 1.36** 0.71** 0.97** 0.33 -0.17 0.03 0.32 -0.73 -0.31 TZEI 56 -13.73 -567.85 -79.256 -2.12** -1.72** -1.88** -0.32 0.08 -0.08 -0.03 -1.58 -0.96 TZEI 80 -94.31 -434.18** -298.23** -1.28** -0.94** -1.07** -0.53** -0.16 -0.31* -2.19 -1.56 -1.81 TZdEI 120 61.12 235.34** 165.65 -1.79** -1.69** -1.73** 0.35 0.13 0.22 3.50 4.16* 3.89 TZEI 296 235.8** -70.93 51.76 -0.2 0.44* 0.18 -0.24 0.08 -0.05 -0.12 -2.58 -1.59 TZEI 326 -83.73 69.08 7.96 0.76 1.19** 1.02** -0.03 0.06 0.03 2.05 8.52** 5.93** TZEI 352 -137.028 -332.47** -254.29** -0.37 0.08 -0.10 -0.11 0.52** 0.27* -1.72 -9.06** -6.12** TZdEI 352 578.89** 456.14** 505.24** 1.78** 1.01** 1.32** 0.66** 0.11 0.33* -1.88 3.17* 1.15 TZEI 355 354.79** -157.76** 47.26 -3.47** -1.86** -2.50** -1.13** 0.08 -0.41** 0.92 -4.98** -2.62 TZEI 383 231.47* -182.99** -17.21 -0.24 0.31 0.089 -0.63** -0.32 -0.45** 1.69 0.92 1.23 TZEI 410 -331.49** -173.9** -236.93* 0.34 0.55** 0.47 -0.32 -0.11 -0.19 -0.38 2.17 1.15 TZdEI 425 -210.71* 289.72** 89.55 0.74 0.21 0.42 0.28 -0.16 0.017 0.65 1.89 1.39 Std. error 89.31 71.59 91.96 0.39 0.17 0.31 0.19 0.15 0.13 2.28 1.61 1.88 Inbred PASP EASP EPP Striga Damage rating Emerged Striga plants OPT ACR STR OPT ACR STR OPT ACR 8 WAP 10 WAP 8 WAP 10 WAP TZEI 5 -0.26** -0.26** 0.72** -0.23** 0.15* -0.13** 0.00 -0.05** 0.59** 0.77** 0.12 1.42 TZEI 7 -0.25** -0.25** -0.19 -0.25** -0.23** -0.03 -0.00 -0.02 -0.21 -0.1 -1.04 -0.56 TZEI 18 -0.23** -0.23** -0.09 -0.16* -0.13 0.00 0.01 0.01 -0.09 -0.08 -1.40 -2.12 TZI 31 0.32** 0.32** 0.37** 0.39** 0.38** -0.03 -0.02 -0.02* 0.27* 0.17 3.81 5.71** TZEI 56 0.04 0.04 0.22* 0.21** 0.21** -0.00 -0.01 -0.01 -0.06 0.07 0.45 1.55 TZEI 80 0.32** 0.32** 0.05 0.31** 0.21** 0.00 0.01 0.00 -0.02 -0.04 2.41 4.30 TZdEI 120 -0.45** -0.45** -0.26* -0.47** -0.39** -0.02 0.01 0.00 -0.08 -0.14 -3.46* -5.68** TZEI 296 0.20** 0.20** -0.28* 0.08 -0.06 0.04* -0.02* 0.01 -0.12 -0.18 -0.92 1.38 TZEI 326 -0.19** -0.19** -0.05 -0.19** -0.13 -0.02 0.01 -0.00 0.08 0.03 -0.19 -0.68 TZEI 352 0.33** 0.33** 0.26* 0.31** 0.29** 0.02 -0.01 0.00 0.06 0.19 2.04 1.22 TZdEI 352 -0.54** -0.54** -0.61** -0.46** -0.52** 0.05** 0.01 0.03* -0.71** -0.91** -2.38 -4.47* TZEI 355 0.36** 0.36** -0.32** 0.33** 0.07 0.06** 0.00 0.02* -0.39** -0.33** -0.06 -0.37 TZEI 383 0.13 0.13 -0.32** 0.17* -0.03 0.09** -0.00 0.04** -0.15 -0.31** -6.02** -9.06** TZEI 410 0.13 0.13 0.22* 0.06 0.12 -0.02 0.00 -0.01 0.42** 0.4** 0.54 2.76 TZdEI 425 0.11 0.11 0.29** -0.12 0.05 -0.01 0.01 0.00 0.40** 0.46** 6.09** 4.61* Std. error 0.07 0.07 0.11 0.07 0.07 0.02 0.01 0.01 0.11 0.11 1.55 2.12 *, ** Significance at 0.05 and 0.01 probability levels, respectively; STR = Striga- infested environment; OPT =Striga-free environments; ACR = across research environments. 43 University of Ghana http://ugspace.ug.edu.gh Inbreds TZdEI 120 and TZEI 383 had negative and significant GCA effects for number of emerged Striga plants at 8 and 10 WAP, whereas TZdEI 352 had negative and significant GCA effects for number of emerged Striga plants at 10 WAP. TZdEI 352 and TZEI 355 also recorded negative significant GCA effects for Striga damage rating at 8 and 10 WAP, whilst TZEI 383 recorded negative and significant GCA effects for number of emerged Striga plants at 8 and 10 WAP. The significant positive GCA effect of TZdEI 425 for Striga damage and number of emerged Striga plants, both at 8 and 10 WAP, indicates that it is susceptible to Striga. The mean grain yield of the inbred lines ranged between 175 kg ha-1 for TZEI 5 to 1045 kg ha-1 for TZEI 383 under Striga-infested conditions. Under Striga-free conditions, the mean grain yield ranged between 662 kg ha-1 for TZEI 18 to 1485 kg ha-1 for TZdEI 352 (Table 4.5.). Using the IITA base index, TZEI 383, TZEI 56, TZdEI 352 and TZEI 410 were identified as best performing inbreds in terms of yield and tolerance to Striga. There were no significant differences among the top eight performing inbred lines. TZEI 5 and TZEI 80 were the worst inbreds in terms of grain yield under Striga-infested conditions. Under Striga-free conditions, TZdEI 352 was identified as the highest yielding inbred with a mean grain yield of 1485 kg ha-1 whilst TZEI 383 was identified as the highest yielding inbred with a mean grain yield of 1045 kg ha-1 under Striga- infested conditions. However, TZEI 5 was identified as the lowest yielding inbred under Striga- infested conditions with a mean grain yield of 175 kg ha-1 whilst TZEI 18 was identified as the lowest yielding under Striga-free conditions with a mean grain yield of 662 kg ha-1 (Table 4.5.). Under Striga-infested environment, the grain yield of the hybrids ranged from 563 kg/ha for TZEI 5 x TZEI 326 to 3081 kg ha-1 for TZEI 7 x TZdEI 352 (Table 4.6.). However under Striga-free conditions, the grain yield ranged from 1870 kg ha-1 for TZEI 31 x TZEI 80 to 4206 44 University of Ghana http://ugspace.ug.edu.gh kg/ha for TZEI 7 x TZEI 326 (Table 4.7). Under Striga-infested environments, the hybrids TZdEI 352 x TZEI 355, TZdEI 352 x TZEI 383, TZdEI 120 x TZEI 383, TZEI 80 x TZdEI 352 and TZEI 7 x TZdEI 352 (with mean grain yields of 2536, 2757, 1856, 2025 and 3081 kg ha-1) were superior in terms of grain yield and tolerance to Striga compared to the five early-maturing hybrid checks(TZE-W Pop DT STR x TZEI 7, TZEI 60 x TZEI 83, TZEI 188 x TZEI 86, TXEI 60 x TZEI 86 and TZEI 60 x TZEI 5) (Table 4.6). However, there were no significant differences among the top ten best hybrids. TZEI 296 x TZdEI 352 and TZEI 352 x TZEI 383 were identified to be high yielding though they supported high number of emerged Striga plants but had reduced Striga damage under Striga infestation. 45 University of Ghana http://ugspace.ug.edu.gh Table 4.5. Grain yield and other agronomic traits of 15 early maturing white-endosperm inbred lines evaluated in diallel crosses under Striga infested, Striga-free and across test environments in Nigeria, 2015. Inbred Grain yield ASI EPP EASP PASP STRR STRCO BASE (kg/ha) (1-9) (1-9) (1-9) INDEX I NI I NI I NI I NI NI 8 10 8 10 TZEI 383 1045 1255 1.0 0.7 1.0 0.8 4.3 4.5 4.7 4.3 4.8 2.3 2.3 6.7 TZEI 56 1032 1239 0.8 1.2 0.9 0.9 4.3 4.2 3.8 3.3 4.0 6.3 9.8 6.3 TZdEI 352 979 1458 3.5 1.2 1.0 1.0 4.0 4.3 2.7 3.8 4.5 5.8 8.5 6.2 TZEI 410 895 1128 0.8 0.5 0.9 1.0 4.5 4.7 4.5 4.3 5.3 2.0 14.0 4.2 TZEI 355 786 1172 0.8 0.8 0.9 1.0 4.3 4.2 4.3 4.0 5.0 1.5 7.0 3.7 TZEI 7 809 948 1.5 0.5 0.8 0.8 4.0 4.7 4.7 4.0 5.0 2.8 17.0 2.4 TZEI 352 568 1150 2.0 0.8 0.9 0.8 5.0 4.3 3.8 4.0 5.0 4.5 8.8 1.8 TZEI 326 822 1081 0.8 1.3 0.9 0.9 4.5 4.8 4.7 5.0 5.8 3.0 12.0 1.0 TZEI 296 602 1116 0.8 0.7 0.8 0.9 4.8 4.8 4.5 4.8 5.5 3.5 10.5 0.0 TZEI 18 470 662 2.8 1.7 0.6 0.7 4.8 5.2 5.2 5.0 5.3 7.8 12.3 -3.1 TZEI 31 466 1061 2.5 1.5 0.8 0.8 5.3 4.8 4.2 5.0 6.0 10.3 20.3 -4.1 TZdEI 425 423 873 2.0 0.2 0.8 0.8 5.5 5.2 5.2 5.0 6.3 14.5 15.5 -4.5 TZdEI 120 450 1323 2.3 0.8 0.6 0.8 4.8 4.2 3.8 5.8 6.3 4.5 6.3 -4.7 TZEI 80 229 727 1.3 0.3 0.7 0.6 6.0 5.2 4.2 5.5 7.3 5.3 12.0 -6.8 TZEI 5 175 1485 3.3 0.8 0.4 0.8 6.3 4.2 4.3 6.0 6.8 5.3 6.8 -9.1 Grand Mean 650 1112 1.8 0.9 0.8 0.8 4.8 4.6 4.3 4.7 5.1 5.3 10.9 SE 72.8 61.6 0.3 0.1 0.04 0.03 0.2 0.1 0.2 0.2 0.2 0.2 0.1 ASI=Anthesis-silking interval; PLHT=plant height; EASP=ear aspect; EPP=ear per plant; STRR=Striga damage rating at 8 and 10 WAP; STRCO=Number of emerged Striga plants at 8 1nd 10 WAP; PASP= Plant aspect; I= Striga-infested plot, NI= Striga-free plots. 46 University of Ghana http://ugspace.ug.edu.gh Table 4.6.Grain yield and other traits of hybrids (best 10 and worst 10 based on base index) and checks evaluated under Striga-infestation. Hybrid Yield ASI PLHT EASP EPP STRR STRCO (kg/ha) (1-9) 8 WAP 10 WAP 8 WAP 10 WAP Base Index TZEI 7 x TZdEI 352 3081 2.4 138 3.2 0.9 3.4 3.7 0.5 2.5 9.1 TZdEI 352 x TZEI 383 2757 3.1 145 3.8 0.9 3.7 4.0 1.2 2.8 9.5 TZdEI 352 x TZEI355 2536 1.0 140 4.2 1.0 4.4 4.3 4.1 5.1 11.3 TZEI 352 x TZdEI 352 2465 1.5 140 4.1 1.0 3.9 4.3 2.1 0.4 8.0 TZEI 18 x TZdEI 352 2393 1.9 141 4.6 1.0 3.6 4.2 0.4 0.4 8.4 TZEI 296 x TZdEI 352 2391 3.2 145 4.2 1.1 3.9 4.4 17.7 23.3 8.7 TZEI 7 x TZEI 383 2337 0.8 131 4.1 1.0 4.3 4.9 8.3 13.4 8.1 TZEI 80 x TZdEI 352 2025 2.1 143 4.7 0.9 3.9 4.9 1.7 13.3 9.1 TZdEI 120 x TZEI 383 1856 -0.8 139 4.7 0.9 4.3 4.1 1.9 1.6 9.3 TZEI 352 x TZEI 383 1764 2.2 146 4.9 1.3 4.8 5.4 10.4 23.3 7.7 *TZE-W Pop DT DTR x TZEI 7 1374 0.2 153 5.8 0.9 4.3 5.0 5.8 17.0 -0.1 *TZEI 60 x TZEI 83 1340 0.3 144 5.4 0.7 4.2 5.1 15.5 29.9 -1.0 TZEI 31 x TZdEI 425 1180 3.9 140 5.1 0.8 5.7 6.6 31.3 35.1 -6.8 *TZEI 188 x TZEI 98 1179 -0.2 154 5.9 0.9 5.2 7.0 12.5 19.1 -3.1 TZE1 352 x TZdEI 425 1057 2.4 142 5.3 0.7 5.7 6.6 20.8 21.9 -7.4 *TZEI 60 x TXEI 86 1049 1.9 161 4.9 0.8 5.1 5.5 9.4 14.9 -5.9 TZEI 31 x TZEI 80 977 1.0 142 5.6 0.8 5.3 5.9 11.4 19.7 -8.0 TZEI 5 x TZdEI 425 866 3.5 136 5.9 0.9 5.5 6.4 19.0 21.3 -7.8 TZEI 18 x TZdEI 425 842 4.1 145 6.5 0.9 5.2 6.2 19.9 21.1 -7.1 TZEI 5 x TZEI 80 685 1.2 122 5.9 0.7 5.5 6.4 18.9 33.0 -9.5 *TZEI 60 x TZEI 5 657 3.0 155 6.2 0.7 5.1 5.5 6.3 10.3 -6.5 TZEI 326 x TZdEI 425 653 1.9 156 6.0 0.8 5.4 6.1 17.1 25.1 -6.7 TZEI 5x TZEI 18 641 4.4 146 6.0 0.7 6.1 6.3 6.1 8.0 -7.5 TZEI 5 x TZdEI 120 633 5.9 151 6.6 0.7 6.1 6.3 5.3 6.1 -7.6 TZEI 5 x TZEI 326 563 1.2 126 6.4 0.7 6.4 6.9 10.4 17.1 -8.1 Grand mean 1668 1.8 144 5.1 0.9 4.7 5.3 10.0 16.1 - Standard Error 144.1 0.3 1.5 0.2 0.03 0.2 0.2 1.4 1.8 - ASI=Anthesis-silking interval; PLHT=plant height; EASP=ear aspect; EPP=ear per plant; STRR=Striga damage rating at 8 and 10 WAP; STRCO=Number of emerged Striga plants at 8 and 10 WAP; *=Checks. 47 University of Ghana http://ugspace.ug.edu.gh Table 4.7.Grain yield and other traits of hybrids (best 10 and worst 10 based on base index) and checks evaluated under Striga-free environments. Hybrid Grain ASI PLHT EASP (1-9) EPP PASP yield (cm) (kg/ha) TZEI 18 x TZdEI 352 4206 0.6 171 3.6 0.9 4.0 TZEI 296 x TZdEI 352 4178 0.5 157 4.1 1.0 4.3 TZEI 5 x TZEI 326 4010 0.4 192 4.0 1.0 4.7 TZdEI 352 x TZEI 355 3925 0.3 178 4.3 1.0 4.9 TZEI 7 x TZdEI 352 3839 0.3 176 4.3 1.0 4.8 TZEI 352 x TZdEI 352 3820 1.4 172 3.9 1.0 4.7 *TZEI 60 x TZEI 5 3804 0.0 193 3.7 0.9 4.4 TZdEI 352 x TZEI 383 3713 0.4 178 3.7 1.0 5.1 *TZEI 60 x TZEI 83 3662 0.3 191 4.5 1.0 4.3 TZEI 18 x TZdEI 425 3624 0.4 168 4.4 1.0 4.6 *TZEI 188 x TZEI 98 3507 -0.1 174 4.7 0.9 5.3 TZEI 326 x TZdEI 425 3493 0.6 180 3.7 0.9 4.3 *TZEI 60 x TZEI 86 3471 0.3 182 4.7 0.9 5.1 TZEI 5 X TZEI 18 3433 0.5 146 4.1 1.1 5.1 TZEI 5 x TZdEI 425 3432 0.6 181 5.2 1.0 5.4 *TZE-W Pop DT STR x TZEI 7 3410 0.3 165 4.6 0.9 4.1 TZEI 5 x TZdEI 120 3349 1.0 176 3.8 0.9 4.7 TZEI 7 x TZEI 383 3274 0.3 173 4.7 1.0 5.1 TZdEI 120 x TZEI 383 3217 -0.3 180 3.5 0.9 4.7 TZEI 352 x TZdEI 425 3187 0.5 170 4.5 1.0 5.3 TZEI 80 x TZdEI 352 3104 0.8 180 4.8 1.0 5.4 TZEI 31 x TZdEI 425 2923 0.7 171 5.3 1.0 5.7 TZEI 5 x TZEI 80 2573 0.5 157 4.9 0.9 5.3 TZEI 352 x TZEI 383 2257 -0.4 158 6.1 1.0 5.9 TZEI 31 x TZEI 80 1870 0.4 177 5.1 0.9 5.9 GRAND MEAN 3341 0.4 175 4.5 1.0 5.0 STANDARD ERROR 102.8 0.1 1.7 0.1 0.01 0.1 ASI=Anthesis-silking interval; PLHT=plant height; EASP=ear aspect; EPP=ear per plant; PASP=plant aspect; *=Checks. 48 University of Ghana http://ugspace.ug.edu.gh 4.2 Relationship between performance of inbred lines and hybrids Under Striga-infestation, phenotypic correlation between mid-parent values and their corresponding hybrid means were positive and highly significant for all measured traits except for plant height. Highly significant and positive correlations were also obtained for all measured traits except for EPP under Striga-free conditions. Across environments, highly significant and positive phenotypic correlations between mid-parent values and their corresponding hybrid means for all measured agronomic traits were observed (Table 4.8.). Positive and highly significant correlations between grain yield and EPP of the inbred lines and the grain yield of their hybrids were observed under Striga-infested conditions. However, negative and highly significant correlations were obtained between ear aspect, Striga damage at 8 and 10 WAP and number of emerged Striga plants at 8 and 10 WAP of the inbred lines and the grain yield of their hybrids under Striga-infested conditions. Across environments, both positive and negative highly significant correlations were observed between all measured traits of the inbred lines and the grain yield of their hybrids with the exception of the number of ears per plant (Table 4.9.). Under Striga infestation, mid-parent heterosis for grain yield was 56.49%, 65.6% under Striga-free environments and 63.34% across research environments. Negative heterosis values were recorded for ASI under both research and across research environments. Negative heterosis values were also observed for Striga damage rating at 10 WAP under Striga infestation and ear aspect under Striga-free environments. However, positive heterosis values were obtained for all other traits under Striga-infested and Striga-free environments and across research environments (Table4.10.). 49 University of Ghana http://ugspace.ug.edu.gh Table 4.8. Correlations between maize parental lines and their hybrids for grain yield and other agronomic traits under Striga-infested, Striga-free and across research environments in Nigeria, 2015. Correlation coefficient (r) Striga- Trait infested Striga-free Across Grain yield 0.47** 0.51** 0.44** Anthesis-silking interval 0.56** 0.19** 0.46** Plant height 0.11 0.49** 0.29** Plant aspect - 0.59** - Ear aspect 0.51** 0.39** 0.40** Number of ears plant-1 0.49** 0.09 0.32** Striga damage rating at 8WAP 0.37** - - Striga damage rating at 10WAP 0.37** - - Emerged Striga plants at 8WAP 0.48** - - Emerged Striga plants at 10WAP 0.49** - - 50 University of Ghana http://ugspace.ug.edu.gh Table 4.9. Correlation between grain yield and other agronomic traits of maize inbred lines and grain yield of their hybrids under Striga-infested, Striga-free and across research environments in Nigeria, 2015. Correlation coefficient (r) Striga- Trait infested Striga-free Across Grain yield 0.47** 0.51** 0.44** Anthesis-silking interval -0.07 0.19 0.30** Plant height 0.17 0.19* 0.35** Plant aspect - -0.49** - Ear aspect -0.58** -0.49** -0.49** Number of ears plant-1 0.36** 0.24* 0.11 Striga damage rating at 8WAP -0.40** - - Striga damage rating at 10WAP -0.40** - - Emerged Striga plants at 8WAP -0.33** - - Emerged Striga plants at 10WAP -0.25** - - 51 University of Ghana http://ugspace.ug.edu.gh Table 4.10. Mid-parent heterosis for grain yield and other agronomic traits of hybrids and maize parental lines under Striga-infested, Striga-free and across research environments in Nigeria, 2015. Average heterosis (%) Striga- Trait infested Striga-free Across Grain yield 56.49 65.6 63.34 Anthesis-silking interval -19.76 -86.5 -41.52 Plant height 28.53 29.53 29.03 Plant aspect - 16.6 - Ear aspect 8.09 -0.3 3.34 Number of ears plant-1 10.63 12.2 11.59 Striga damage rating at 8WAP 5.03 - - Striga damage rating at 10WAP -0.17 - - Emerged Striga plants at 8WAP 50.04 - - Emerged Striga plants at 10WAP 36.14 - - 52 University of Ghana http://ugspace.ug.edu.gh Plate 4.1. Striga-susceptible and Striga-resistant hybrid evaluated at Abuja, 8 weeks after planting, 2015. 53 University of Ghana http://ugspace.ug.edu.gh 4.3. Stability of performance of inbreds and single-cross hybrids under Striga-infested and Striga-free environments The significant genotype and genotype x environment interactions for grain yield and most agronomic traits under Striga-infested, Striga-free and across research environments prompted the use of the GGE biplot to decompose the genotype x environment interactions and to examine the yield performance and stability of the early maturing inbred lines and hybrids across test environments. The GGE biplot of grain yield of the fifteen early maturing white inbreds and five checks evaluated under Striga-infested and Striga-free conditions are presented in Figs. 4.1 and 4.3 while that of the 10 best and 10 worst performing single-cross hybrids and five checks are presented in Fig. 4.2 and 4.4. The GGE biplot principal component (PC) axis 1 and 2 for model 3 explained 70.7% of grain yield variation for the inbred lines and 80.2% for the hybrids. Therefore, the biplot of PC1 and PC2 adequately approximated the environment-centered data. The “which-won-where” view (Fig. 4.1 and 4.2) of the GGE biplot (Yan et al., 2000) is an effective visual tool in mega-environment analysis. In the polygon view (Fig. 4.1 and 4.2), the vertex genotype (inbred or hybrid) of each sector represents the highest yielding genotype in the location that falls within the particular sector. The distance between the genotype and the biplot origin (centre-point of the biplot) is a measure of the genotype’s peculiarity (i.e. how it differs from the mean of all genotypes), which is a hypothetical genotype that has an average grain yield represented by the biplot origin. The genotypes within the polygon, particularly those located close to the biplot origin are less responsive than the vertex genotypes. The GGE biplot analysis showed TZEI 56 (Entry 5) as the highest yielding inbred under Striga-infestation at Mokwa and Abuja. TZEI 5 (Entry 1) was the best inbred under Striga-free environments at Abuja, Mokwa and Ikenne. Although, inbreds TZEI 296 (Entry 8) and TZEI 80 (Entry 6) were vertex inbreds in some sectors, the sectors were not matched with any of the locations used (Fig. 4.1). Furthermore, the polygon 54 University of Ghana http://ugspace.ug.edu.gh view in Fig. 4.2 revealed hybrid TZdEI 352 x TZEI 355 (Entry 23) as the highest yielding under Striga-infested and Striga-free environments at Mokwa and Abuja while TZEI 5 x TZEI 326 (Entry 4) was the best under Striga-free environment at Ikenne. 55 University of Ghana http://ugspace.ug.edu.gh ENTRY PEDIGREE 1 TZEI 5 2 TZEI 7 3 TZEI 18 4 TZEI 31 5 TZEI 56 6 TZEI 80 7 TZdEI 120 8 TZEI 296 9 TZEI 326 10 TZEI 352 11 TZdEI 352 12 TZEI 355 13 TZEI 383 14 TZEI 410 15 TZdEI 425 16 TZEI 2 17 TZEI 3B 18 TZEI 68 19 TZEI 314 20 TZEI 408 Fig. 4.1. A ‘‘which won where” genotype plus genotype x environment interaction biplot of grain yield of 20 early maturing white maize inbreds evaluated under Striga-infested and Striga-free conditions at Abuja, Ikenne and Mokwa in 2015 . IK_OPT=Ikenne Striga- free; AB_STR= Abuja Striga; AB_OPT= Abuja Striga-free; MO_STR= Mokwa Striga; MO_OPT= Mokwa Striga-free conditions. 56 University of Ghana http://ugspace.ug.edu.gh ENTRY PEDIGREE 1 TZEI 5 x TZEI 18 2 TZEI 5 x TZEI 80 3 TZEI 5 x TZdEI 120 4 TZEI 5 x TZEI 326 5 TZEI 5 x TZdEI 425 6 TZEI 7 x TZEI 296 7 TZEI 7 x TZdEI 352 8 TZEI 7 x TZEI 383 9 TZEI 18 x TZdEI 352 10 TZEI 18 x TZdEI 425 11 TZEI 31 x TZEI 80 12 TZEI 31 x TZdEI 425 13 TZEI 56 x TZEI 296 14 TZEI 80 x TZdEI 352 15 TZdEI 120 x TZEI 383 16 TZEI 296 x TZEI 326 17 TZEI 296 x TZdEI 352 18 TZEI 326 x TZdEI 352 19 TZEI 326 x TZdEI 425 20 TZEI 352 x TZdEI 352 21 TZEI 352 x TZEI 383 22 TZEI 352 x TZdEI 425 23 TZdEI 352 x TZEI 355 24 TZdEI 352 x TZEI 383 25 TZEI 355 x TZdEI 425 26 Check 1 - TZEI 60 x TZEI 86 27 Check 2 - TZEI 60 x TZEI 83 28 Check 3 - TZEI 60 x TZEI 5 29 Check 4 - TZEI 188 x TZEI 98 30 Check 5 - TZE-W Pop DT STR x TZEI 7 Fig. 4.2. A ‘‘which won where” genotype plus genotype x environment interaction biplot of grain yield of 30 early maturing white maize hybrids evaluated under Striga-infested and Striga-free conditions at Abuja, Ikenne and Mokwa in 2015 . IK_OPT=Ikenne Striga-free; AB_STR= Abuja Striga-infested; AB_OPT= Abuja Striga-free; MO_STR= Mokwa Striga-infested; MO_OPT= Mokwa Striga-free conditions. 57 University of Ghana http://ugspace.ug.edu.gh The GGE biplot presented in Figures 4.3 and 4.4 shows the “mean performance vs. stability” of the inbreds and hybrids across Striga-infested and Striga-free environments, respectively. The single-arrow line that passes through the biplot origin and the average environment is referred to as the average-tester axis; this line points to the average environment from the biplot origin. Genotypes are ranked along the average-tester axis, with the arrow pointing to a greater value based on their mean performance across environments. The double-arrow lines separate the entries below-average means from those with above-average means. Mean yield of the genotypes is measured by the projections of their markers on the average- tester axis while stability of the inbred or hybrid is measured by the projections onto the average- tester co-ordinate y-axis single-arrow line. The shorter the absolute length of the projection of an inbred or hybrid, the more stable it is and vice versa. Therefore, inbreds TZEI 56 (Entry 5) and TZEI 383 (Entry 13) were identified as the highest yielding across the environments. Inbreds, TZEI 7 (Entry 2), TZEI 314 (Entry 19) and TZEI 326 (Entry 9) were the most stable with relatively high grain yield (above the grand mean) while TZdEI 120 (Entry 7) was identified to be the least stable across the two research environments (Fig 4.3). TZE-W Pop DT STR x TZEI 7 (Entry 30), a hybrid check, and TZEI 7 x TZdEI 352 (Entry 7) were identified to be the most stable hybrids across research environments while TZdEI 352 x TZEI 355 (Entry 23) was the highest yielding hybrid across the two research environments (Fig 4.4). TZEI 5 x TZEI 326 (Entry 4) produced grain yield greater than the mean grain yield but was the least stable. 58 University of Ghana http://ugspace.ug.edu.gh ENTRY PEDIGREE 1 TZEI 5 2 TZEI 7 3 TZEI 18 4 TZEI 31 5 TZEI 56 6 TZEI 80 7 TZdEI 120 8 TZEI 296 9 TZEI 326 10 TZEI 352 11 TZdEI 352 12 TZEI 355 13 TZEI 383 14 TZEI 410 15 TZdEI 425 16 TZEI 2 17 TZEI 3B 18 TZEI 68 19 TZEI 314 20 TZEI 408 Fig. 4.3. The entry/tester genotype plus genotype x environment biplot based on grain yield of 20 early maturing white maize inbreds including 5 checks evaluated under Striga-infested and Striga-free conditions at Abuja, Ikenne and Mokwa in 2015. IK_OPT=Ikenne Striga-free; AB_TR= Abuja Striga-infested; AB_OPT= Abuja Striga-free; MO_STR= Mokwa Striga-infested; MO_OPT= Mokwa Striga-free. 59 University of Ghana http://ugspace.ug.edu.gh ENTRY PEDIGREE 1 TZEI 5 x TZEI 18 2 TZEI 5 x TZEI 80 3 TZEI 5 x TZdEI 120 4 TZEI 5 x TZEI 326 5 TZEI 5 x TZdEI 425 6 TZEI 7 x TZEI 296 7 TZEI 7 x TZdEI 352 8 TZEI 7 x TZEI 383 9 TZEI 18 x TZdEI 352 10 TZEI 18 x TZdEI 425 11 TZEI 31 x TZEI 80 12 TZEI 31 x TZdEI 425 13 TZEI 56 x TZEI 296 14 TZEI 80 x TZdEI 352 15 TZdEI 120 x TZEI 383 16 TZEI 296 x TZEI 326 17 TZEI 296 x TZdEI 352 18 TZEI 326 x TZdEI 352 19 TZEI 326 x TZdEI 425 20 TZEI 352 x TZdEI 352 21 TZEI 352 x TZEI 383 22 TZEI 352 x TZdEI 425 23 TZdEI 352 x TZEI 355 24 TZdEI 352 x TZEI 383 25 TZEI 355 x TZdEI 425 26 Check 1 - TZEI 60 x TZEI 86 27 Check 2 - TZEI 60 x TZEI 83 28 Check 3 - TZEI 60 x TZEI 5 29 Check 4 - TZEI 188 x TZEI 98 30 Check 5 - TZE-W Pop DT STR x TZEI 7 Fig. 4.4. The entry/tester genotype plus genotype x environment biplot based on grain yield of 30 early maturing white maize hybrids evaluated under Striga-infested and Striga-free conditions at Abuja, Ikenne and Mokwa in 2015. IK_OPT=Ikenne Striga-free; AB_STR= Abuja Striga-infested; AB_OPT= Abuja Striga-free; MO_STR= Mokwa Striga-infested; MO_OPT= Mokwa Striga-free. 60 University of Ghana http://ugspace.ug.edu.gh 4.4 Heterotic grouping of inbred lines A summary of the heterotic groups of the early maturing inbred lines identified by different grouping methods under Striga-infested, Striga-free and across research environments is presented in Table 4.11. The results of the dendrogram constructed based on the SCA effects of grain yield under Striga-infested environments revealed four different groups. Group 1 consisted of TZEI 5, TZEI 31, TZEI 410, TZdEI 425 and TZEI 56; Group 2 was made up of TZEI 18, TZdEI 352 and TZdEI 120; while Group 3 comprised TZEI 7 and TZEI 296. Group 4 consisted of TZEI 80, TZEI 383, TZEI 355, TZEI 326 and TZEI 352. The HSGCA effects of grain yield identified five different groups: Group 1 consisted of TZEI 5 and TZdEI 425; Group 2 comprised TZEI 18, TZEI 80, TZdEI 352 and TZdEI 120; Group 3 comprised TZEI 7, TZEI 296 and TZEI 56; Group 4 was made up of TZEI 31, TZEI 352, TZEI 383 and TZEI 326; while Group 5 consisted of TZEI 355 and TZEI 410. Using the HGCAMT method, three different groups were identified under Striga-infested environments. Inbreds TZEI 5 and TZdEI 425 constituted Group 1; Group 2 was made up of TZEI 7, TZEI 18, TZEI 326, TZEI 31, TZEI 410, TZEI 56, TZEI 80 and TZEI 352; while Group 3 comprised TZdEI 120, TZEI 296, TZEI 383, TZEI 355 and TZdEI 352. Under the Striga-free environments, the SCA effects of grain yield identified 4 groups: Group 1 was made up of TZEI 5, TZEI 56, TZEI 18, TZEI 80, TZEI 296, TZEI 7, TZEI 31 and TZEI 352; Group 2 comprised TZdEI 120, TZdEI 425and TZdEI 352; Group 3 had Inbred 326; while Group 4 consisted of TZEI 355, TZEI 383 and TZEI 410. Four groups were also identified by the HSGCA effects of grain yield: Group 1 consisted of TZEI 5, TZEI 56, TZEI 18, TZEI 80, TZEI 296, TZEI 31, TZdEI 120 and TZdEI 425; Inbred TZdEI 352 was in Group 2; Group 3 comprised TZEI 7, TZEI 352 and TZEI 326; Group 4 was made up of TZEI 355, TZEI 383 and TZEI 410. The HGCAMT method identified three different groups under Striga-free environments. Inbreds TZEI 61 University of Ghana http://ugspace.ug.edu.gh 5, TZEI 7, TZdEI 352, TZdEI 120, TZEI 326, TZEI 383 and TZEI 410 constituted Group 1, Group 2 comprised TZEI 18, TZdEI 425 and TZEI 31; while Group 3 was made up of TZEI 56, TZEI 80, TZEI 355, TZEI 296 and TZEI 352. Across research environments, the SCA effects of grain yield revealed three different groups. Group 1 comprised TZEI 5, TZEI 31, TZEI 56, TZEI 18, TZEI 80 and TZEI 296; Group 2 was made up of TZdEI 120, TZdEI 425 and TZdEI 352; Inbreds TZEI 7, TZE 410, TZEI 326, TZEI 352, TZEI 383 and TZEI 355 constituted Group 3. The HSGCA effects of grain yield also identified three different groups: Group 1 consisted of TZEI 5, TZEI 31, TZEI 56, TZEI 18, TZEI 80, TZEI 296, TZdEI 120 and TZdEI 425; Group 2 was made up of TZEI 7 and TZdEI 352; while Group 3 comprised TZEI 326, TZEI 352, TZEI 383, TZEI 355 and TZEI 410. Using the HSGCA method, three different groups were identified across the research environments. Inbreds TZEI 5, TZEI 31, TZEI 56, TZEI 18, TZEI 80, TZEI 296, TZdEI 120 and TZdEI 425 constituted Group 1; Group 2 comprised TZEI 7 and TZdEI 352 while Group 3 consisted of TZEI 326, TZEI 352, TZEI 383, TZEI 355 and TZEI 410. 62 University of Ghana http://ugspace.ug.edu.gh Table 4.11. Summary of the hetrotic groups of the 15 early-maturing white inbred lines identified by different heterotic grouping methods under Striga infestation, Striga-free and across research environments. METHOD GROUP 1 2 3 4 5 Striga-infested environment SCA TZEI 5,TZEI 31,TZEI 410,TZdEI TZEI 18,TZdEI TZEI 7,TZEI 296 TZEI 80,TZEI 355,TZEI 425,TZEI 56 352,TZdEI 120 326,TZEI 352,TZEI 383 HSGCA TZEI 5,TZdEI 425 TZEI 18,TZEI 80, TZEI 7,TZEI TZEI 31,TZEI 352,TZEI TZEI 355,TZEI 410 TZdEI 352,TZdEI 120 296,TZEI 56 383,TZEI 326 HGCAMT TZEI 5,TZdEI 425 TZEI 7,TZEI 18,TZEI TZdEI 120,TZEI 326,TZEI 31,TZEI 296,TZEI 383,TZEI 410,TZEI 56,TZEI 355, TZdEI 352 80,TZEI 352 Striga-free environment SCA TZEI 5,TZEI 56,TZEI 18,TZEI TZdEI 120,TZdEI TZEI 326 TZEI 355,TZEI 383,TZEI 80,TZEI 296,TZEI 7,TZEI 31,TZEI 425,TZdEI 352 410 352 HSGCA TZEI 5,TZEI 56,TZEI 18,TZEI TZdEI 352 TZEI 7,TZEI TZEI 355,TZEI 383,TZEI 80,TZEI 296,TZEI 31,TZdEI 352,TZEI 326 410 120,TZdEI 425 HGCAMT TZEI 5,TZEI 7,TZdEI 352,TZdEI TZEI 18,TZdEI TZEI 56,TZEI 120,TZEI 326,TZEI 383,TZEI410 425,TZEI 31 80,TZEI 355,TZEI 296,TZEI352 Across environments SCA TZEI 5,TZEI 31,TZEI 56,TZEI TZdEI 120,TZdEI TZEI 7,TZEI 18,TZEI 80,TZEI 296 425,TZdEI 352 410,TZEI 326,TZEI 352,TZEI 383,TZEI 355 HSGCA TZEI 5,TZEI 31,TZEI 56,TZEI TZEI 7,TZdEI 352 TZEI 326,TZEI 18,TZEI 80,TZEI 296,TZdEI 352,TZEI 383,TZEI 120,TZdEI 425 355,TZEI 410 HGCAMT TZEI 5,TZEI 31,TZEI 410,TZdEI TZEI 56,TZEI TZEI 7,TZEI 425 296,TZEI 355,TZEI 326,TZEI 18,TZEI 80,TZEI 352 383,TZdEI 120,TZdEI 352 63 University of Ghana http://ugspace.ug.edu.gh 4.5 Specific Combining Ability (SCA) effects for grain yield The magnitude and direction of SCA effects varied considerably among the crosses with 47 crosses having a positive SCA estimates and 58 having negative SCA estimates under Striga- infested conditions (Table 4.12). Fifty-two crosses had positive SCA estimates while 53 crosses had negative SCA estimates under Striga-free conditions (Table 4.13). Across research environments, 56 crosses had positive SCA estimates while 49 crosses had negative SCA estimates (Table 4.14). The hybrids TZEI 7 x TZEI 296, TZEI 56 x TZEI 296, TZEI 355 x TZdEI 425 and TZEI 410 x TZdEI 425 had positive and significant SCA estimates under Striga-infestation. Under Striga-free conditions, TZEI 5 x TZEI 326, TZEI 56 x TZEI 326, TZdEI 120 x TZdEI 352, TZEI 326 x TZEI 410, TZEI 352 x TZEI 410, TZdEI 352 x TZEI 355 and TZdEI 352 x TZdEI 425 also recorded positive significant SCA effects while TZdEI 352 x TZEI 355 had a positive significant SCA effects across research environments. TZdEI 352 x TZdEI 425 had a highly significant negative SCA effects across research environments (Table 4.13). The SCA values ranged from - 772. 2 (TZdEI 352 x TZdEI 425) to 713.5 (TZEI 7 x TZEI 296) under Striga-infested environments (Table 4.12). Under Striga-free conditions, SCA values ranged from -1275.3 (TZdEI 352 x TZdEI 425) to 895.9 (TZdEI 352 x TZEI 355) (Table 4.13). Furthermore, across research environments SCA values ranged from -1074.0 (TZdEI 352 x TZdEI 425) to 758.4 (TZdEI 352 x TZEI 355) (Table 4.14). 64 University of Ghana http://ugspace.ug.edu.gh Table 4.12. Estimates of specific combining ability effects of 15 early maturing white-endosperm inbred lines evaluated under Striga- infested environments in Nigeria, 2015. TZEI 5 TZEI 7 TZEI TZEI TZEI TZEI TZdEI TZEI TZEI TZEI TZdEI TZEI TZEI TZEI 18 31 56 80 120 296 326 352 352 355 383 410 TZEI 5 TZEI 7 -235.8 TZEI 18 -39.4 366.7 TZEI 31 245.7 -74.5 -92.6 TZEI 56 456.6 492.0 -463.9 283.0 TZEI 80 -137.1 219.5 71.9 -269.6 -468.7 TZdEI 120 -393.5 -439.3 -188.6 96.0 -244.8 242.1 TZEI 296 -86.9 713.5* -103.4 -283.1 622.7* -193.4 -99.8 TZEI 326 -318.3 -407.7 504.4 257.0 -250.1 -380.5 476.3 437.4 TZEI 352 -31.1 -428.1 386.9 -34.5 112.4 39.1 153.5 -223.6 364.7 TZdEI 352 -377.2 123.4 -188.4 -139.4 32.4 581.7 -546.5 246.6 283.2 429.0 TZEI 355 396.8 95.3 135.4 -314.7 -553.9 270.3 222.8 -62.2 -675.2* -508.0 552.1 TZEI 383 -40.4 63.1 3.8 -12.5 114.1 -16.0 598.4 -298.7 -75.5 -94.2 140.7 -375.3 TZEI 410 463.3 -708.3 -53.8 159.2 46.2 283.7 -72.2 -153.4 -60.9 -154.2 -365.4 -17.3 -375.3 TZdEI 425 97.1 220.1 -338.9 180.0 -177.9 -242.8 195.6 -515.8 -154.9 -12.0 -772.2* 833.8** -17.3 664.2* 65 University of Ghana http://ugspace.ug.edu.gh Table 4.13 Estimates of specific combining ability effects of 15 early maturing white-endosperm inbred lines under Striga-free environments in Nigeria, 2015. TZEI 5 TZEI 7 TZEI TZEI TZEI TZEI TZdEI TZEI TZEI TZEI TZdEI TZEI TZEI TZEI 18 31 56 80 120 296 326 352 352 355 383 410 TZEI 5 TZEI 7 -92.2 TZEI 18 -225.0 66.8 TZEI 31 -107.0 115.5 -269.1 TZEI 56 169.7 -59.7 -284.9 319.2 TZEI 80 -158.4 75.3 -368.0 -136.2 -52.2 TZdEI 120 -375.1 -257.5 30.9 51.6 -363.8 -24.0 TZEI 296 -317.1 86.5 24.2 -465.2 -426.1 -44.0 -9.2 TZEI 326 553.9* 484.6 213.2 157.4 600.7* -73.8 410.5 -391.2 TZEI 352 -295.3 334.4 50.4 367.1 16.5 277.2 134.2 -123.1 -291.5 TZdEI 352 229.2 -62.0 98.0 337.2 -120.8 -314.3 -620.8* 413.0 -59.9 352.9 TZEI 355 455.1 -50.0 -64.4 -163.3 -192.5 240.2 428.6 293.1 -430.9 -812.9 895.9** TZEI 383 70.6 -223.9 340.0 -47.2 322.7 267.5 101.3 -102.8 -496.1 -72.9 145.4 -359.4 TZEI 410 224.2 -120.8 492.8 -294.9 60.3 68.1 207.4 712.0** -893.2** -195.5 -18.5 -438.1 -69.7 TZdEI 425 -132.5 -296.9 -104.9 134.9 11.0 242.5 286.0 349.9 216.3 258.5 -1275.3** 198.6 -14.7 126.6 66 University of Ghana http://ugspace.ug.edu.gh Table 4.14 Estimate of specific combining ability effects of 15 early maturing white-endosperm inbred lines evaluated across research environments. TZEI 5 TZEI 7 TZEI TZEI TZEI TZEI 80 TZdEI TZEI TZEI TZEI TZdEI TZEI TZEI TZEI 18 31 56 120 296 326 352 352 355 383 410 TZEI 5 TZEI 7 -149.7 TZEI 18 -150.8 186.8 TZEI 31 34.1 39.5 -198.5 TZEI 56 284.5 161.0 -356.5 304.7 TZEI 80 -149.9 133.0 -192.1 -189.5 -218.8 TZdEI 120 -382.4 -330.2 -56.9 69.4 -316.2 82.4 TZEI 296 -225.0 337.3 -26.8 -392.4 -6.6 -103.8 -45.4 TZEI 326 205.0 127.7 329.7 197.2 260.3 -196.4 436.8 -59.7 TZEI 352 -189.6 29.4 185.0 206.5 54.8 182.0 141.9 -163.3 -29.0 TZdEI 352 -13.4 12.2 -16.6 146.6 -59.5 44.1 -591.1 346.4 77.4 383.4 TZEI 355 431.8 8.1 15.5 -223.9 -337.1 252.2 346.3 151.0 -528.6 -690.9* 758.4* TZEI 383 26.2 -109.1 205.5 -33.3 239.3 154.1 300.1 -181.2 -327.9 -81.4 143.5 -365.8 TZEI 410 319.9 -355.8 274.1 -113.3 54.7 154.3 95.6 365.8 -560.3 -179.0 -157.3 -269.8 -29.4 TZdEI 425 -40.6 -90.1 -198.5 152.9 -64.6 48.4 249.8 3.6 67.9 150.3 -1074.0** 452.7 0.7 341.6 67 University of Ghana http://ugspace.ug.edu.gh For most crosses, SCA effects were important in predicting F1 hybrid performance and the effects were directly related to high yields. Most crosses with consistent positive SCA effects ranked amongst the best 10 performing hybrids based on the index values and also had higher yields in comparison with the grand mean yield of 1668 kg ha-1 (Striga-infested conditions) and 3341 kg ha-1 (Striga-free conditions). In contrast, those with consistent negative SCA effects were ranked amongst the 10 worst performing hybrids (based on the index values) and had lower yields than those with positive SCA values. 4.6 Comparison of the breeding efficiencies of SCA, HSGCA and HGCAMT heterotic grouping methods To compare the efficiency of the three heterotic grouping methods, the 105 hybrids were arranged from descending order based on grain yield under Striga-infested, Striga-free and across research environments. The total number of hybrids were divided into inter-group and intra-group crosses for each grouping method. These two groups were subsequently divided into high-yielding (yield of group 1 with a mean grain yield ranking among the first 35); intermediate hybrids (yield of group 2 with a mean grain yield between 36th and 70th) and low yielding hybrids (yield of group 3 with a mean grain yield between 71st and 105th) (Table 4.15). According to Fan et al., (2009), breeding efficiency may be defined as the proportion of superior high yielding hybrids obtained across the total number of inter-heterotic crosses, i.e. the best heterotic grouping method is the one that allows inter-heterotic group crosses to produce more of the superior hybrids than the intra- group crosses. Based on this criterion, the SCA method identified 27, HSGCA 28 and HGCAMT 22 high-yielding hybrids out of the total inter-group crosses under Striga-free environments. Under Striga-infested environments, SCA method had 29, HSGCA 27 and HGCAMT 22 high-yielding hybrids of the total number of inter-group crosses identified by the grouping methods (Table 4.15). 68 University of Ghana http://ugspace.ug.edu.gh Furthermore, across research environments, 29 high-yielding inter-group crosses were identified by the SCA method, while the HSGCA and HGCAMT methods identified 31 and 22 high-yielding inter-group crosses respectively (Table 4.15). Classification by the three grouping methods under Striga-infested, Striga-free and across the research environments showed similar but not identical trends. Under Striga-infested environments, inbred lines such as TZEI 5, TZdEI 425, TZEI 18 and TZEI 296 were categorized into the same group by the three methods while some others, such as TZdEI 352, TZdEI 120, TZEI 7 and TZEI 383 were classified into the same group by two of the three grouping methods. TZEI 5 was the only inbred classified into one group by the three grouping methods under Striga-free environments, however inbred lines such as TZEI 56, TZEI 18, TZEI 80, TZEI 7 and TZEI 383 were classified into the same group by two of the three grouping methods. Across research environments, TZEI 5, TZEI 31 and TZEI 326 were placed into the same group by the three methods while TZEI 56, TZEI 18, TZEI 80, TZEI 296 and TZEI 7 were placed in the same group by two of the three methods. In terms of placement of inbred lines into the same heterotic groups, the SCA and HSGCA methods appeared to be more similar than the other comparisons under and across all research environments. Groups 2 and 4 of the two methods had three inbred lines in common under Striga- infested environments. Under Striga-free environments, group 4 of the two methods were the same, however group 1 of both methods had five inbred lines in common. Groups 1 and 3 of the two grouping methods had six and five inbred lines in common respectively across the research environments. 69 University of Ghana http://ugspace.ug.edu.gh Table 4.15.The number of hybrids within the first 35 arranged from descending order of yield (group 1), from 36th to 70th (group 2) and from 71st to 105th (group 3). Yield group Cross type SCA HSGCA HGCAMT Striga-infested environments 1 Intergroup 29 27 22 1 Intragroup 6 8 13 2 Intergroup 26 29 25 2 Intragroup 9 6 10 3 Intergroup 26 32 19 3 Intragroup 9 3 16 Striga-free environments 1 Intergroup 27 28 22 1 Intragroup 8 7 13 2 Intergroup 27 22 26 2 Intragroup 8 13 9 3 Intergroup 19 21 24 3 Intragroup 16 14 11 Across environments 1 Intergroup 29 31 22 1 Intragroup 6 4 13 2 Intergroup 28 22 29 2 Intragroup 7 13 6 3 Intergroup 17 15 24 3 Intragroup 18 20 11 70 University of Ghana http://ugspace.ug.edu.gh The breeding efficiency of the HSGCA method was the highest under Striga-free environments (39.44%) and 45.59% across research environments (Table 4.16). However, under Striga-infested conditions, the SCA method had the highest breeding efficiency of 35.80%. The HSGCA method was generally the most efficient in the classification of the inbreds into heterotic groups in the present study. It was therefore adopted for the classification of the inbreds into five heterotic groups under Striga-infestation four heterotic groups under Striga-free environments and three heterotic groups across research environments. All the inbred lines were classified into heterotic groups by each grouping method. 71 University of Ghana http://ugspace.ug.edu.gh Table 4.16. Breeding efficiency (%) of SCA, HSGCA and HGCAMT heterotic grouping methods under Striga-infested, Striga-free and across research environments. Environment SCA HSGCA HGCAMT Striga-infested 35.80 30.68 33.33 Striga-free 36.99 39.44 30.56 Across 39.19 45.59 29.33 72 University of Ghana http://ugspace.ug.edu.gh 4.7 Identification of testers Rawlings and Thompson (1962) stated that a good tester should be able to provide precision in “discriminating” among genotypes and should provide information that classifies the merit of inbred lines and maximizes genetic gain. According to Pswarayi and Vivek (2008), the choice of lines as potential testers for classifying other lines into heterotic groups should be based on (a) significant and positive GCA effects for grain yield of the inbred, (b) classification into heterotic groups, and (c) per se grain yield. Testers were also selected based on the most efficient grouping method under each and across research environments. HSGCA heterotic grouping method was generally the most efficient grouping method and hence was used in the selection of testers. Based on these criteria, no inbred tester was identified for heterotic group 1 under Striga- infestation. However, TZdEI 352 a high yielding inbred under Striga-infestation, was placed in the second heterotic group, had the highest significant and positive GCA effects for grain yield and hence, was identified as the best tester for heterotic group 2. TZEI 7 also recorded high grain yield, had a highly significant and positive GCA effects for grain yield, was classified into the third heterotic group and was therefore identified as the best tester for heterotic group 3. Furthermore, TZEI 383 was the highest yielding inbred under Striga-infestation, was classified into the fourth heterotic group, had a high significant and positive GCA effects for grain yield, and therefore was identified as the best tester for heterotic group 4. TZEI 355 was identified as the inbred tester for heterotic group 5. Under Striga-free environments, TZEI 5 was identified to be high yielding and had a positive and significant GCA effect for grain yield, hence it was identified as the best tester for heterotic group 1 using HSGCA grouping method. TZEI 7 was also high yielding, had a significant and positive GCA effect for grain yield, was classified into the third heterotic group, and hence was identified as the best tester for heterotic group 3. However, none 73 University of Ghana http://ugspace.ug.edu.gh of the inbreds in heterotic group 4 could satisfy the criteria for a tester and therefore no inbred tester was identified for this group. Although, no inbred tester was identified for heterotic groups 1 and 3 across research environments due to their inability to satisfy the criteria in selecting inbred testers, TZdEI 352, which was placed in the second heterotic group, had the highest positive and significant GCA effect for grain yield, was identified as the highest yielding inbred across research environments, and was therefore the best tester for heterotic group 2. 74 University of Ghana http://ugspace.ug.edu.gh CHAPTER 5 DISCUSSION The significant mean squares detected for the genotypes under Striga-infested and Striga- free conditions for most of the measured traits indicated that there was genetic variability among the inbred lines and hybrids to allow for the selection of Striga-resistance adaptive traits. This result is consistent with the findings of Akinwale et al., (2014). The significant mean squares for environments observed for most measured traits under Striga-infested and Striga-free conditions indicated that the test sites were unique in discriminating among the genotypes. This result emphasized the need for testing in more than one environment over years in the contrasting environments (Badu-Apraku et al., 2007 b, 2011b). This result also confirmed the important role the environment played in the identification of resistant genotypes under Striga-infestation. The presence of significant mean squares for the genotypes across the research environments indicated differential responses of both the inbreds and hybrids to Striga-infested and Striga-free environments. The significant genotype x environment interactions for grain yield, Striga damage and number of emerged Striga plants of the inbreds under Striga-infestation indicated that the lines responded differently to Striga infestation at the different locations suggesting that there may be different strains of S. hermonthica at the different locations. Similar results have been reported by several authors (Badu-Apraku and Lum, 2007; Menkir et al., 2010; Ifie, 2013). In contrast, the genotype x environment interaction for grain yield, ASI, plant height, ear aspect and EPP of the hybrids were not significant under Striga infested environments, an indication that the expression of the hybrids in the varying Striga-infested environments for these traits was not influenced by the environment. This result is consistent with the findings of Akinwale et al., (2013). Striga infestation reduced grain yield of the hybrids by 54% relative to their performance under Striga-free conditions. This result is in agreement with the findings of Adetimirin et al., 75 University of Ghana http://ugspace.ug.edu.gh (2000), who reported a yield reduction of 53.7%. However, it is lower than the 68% reported by Kim et al. (2002) and 65% by Badu-Apraku et al. (2010) but higher than the 23% reported by Badu-Apraku et al. (2011b). The yield reduction of 54% indicated that the level of Striga infestation in the present study was high enough to allow for the identification of superior hybrids with genes for Striga resistance/tolerance. The reduction in the grain yield of the hybrids under Striga-infested conditions was accompanied by increased ASI, poor ear aspect, and increased Striga damage as well as high number of emerged Striga plants. The significant GCA and SCA mean squares obtained for grain yield and other agronomic traits under Striga infestation and across research environments suggested that additive and non- additive gene actions were both important in the inheritance of these traits under the two contrasting environments. Similar results were reported by Gethi and Smith (2004), Badu-Apraku (2007) and Badu-Apraku et al. (2013c). These results also indicated that there was scope for the improvement of these traits and hence a chance to identify a potentially discriminating tester. Furthermore, these results indicated that the inbreds could be classified into unique heterotic groups and that superior hybrids with good combining abilities could be identified under the contrasting research environments. The GCA/SCA ratio with a value greater than one indicated the predominance of additive genetic effect, whereas, GCA/SCA ratio with a value less than one indicated the predominance of non-additive genetic effects (Baker, 1978). The larger proportion of the GCA sum of squares over SCA for grain yield under Striga-infested, Striga-free and across research environments indicated the predominance of additive over non-additive genetic effects for grain yield and that GCA was the major component accounting for the differences in grain yield among the hybrids. The larger proportion of GCA sum of squares over SCA for Striga damage and number of emerged Striga plants at 8 and 10 WAP also indicated that additive gene 76 University of Ghana http://ugspace.ug.edu.gh action played a major role in the inheritance of the Striga damage and number of emerged Striga plants. The implication is that the response of hybrids to the two Striga traits could be predicted based on the GCA of the parents (Munthali et al., 2003). The results of this study are consistent with those of Yallou et al. (2009) and Badu-Apraku et al. (2011b) who reported additive genetic effects to be more important than non-additive genetic effects in the inheritance of Striga damage and the number of emerged Striga plants under artificial Striga infestation. However, these results are contrary to the findings of Badu-Apraku (2007), who reported non-additive gene action to be more important for number of emerged Striga plants at 8 and 10 WAP and Badu-Apraku et al. (2013c), who found non-additive gene action to be more important for number of emerged Striga plants and Striga damage at 8 WAP. The differences in the results of this study and those of other researchers could be attributed to the differences in the germplasm used and the differences in the testing sites. The significant GCA x environment interaction mean squares observed in this study for most measured traits under both research environments and for all measured traits across research environments implied that the parental lines exhibited different performance in hybrid combinations under the contrasting environments (Striga-infested and Striga-free). This suggested that the selection of Striga resistant/tolerant hybrids would be more effective when based on performance across a range of Striga environments. This finding is in support of the earlier view of Kang (1996) that the environment plays an important role in the phenotypic expression of agronomic characters, and that ignoring the environmental component in the field would reduce progress and advances from selection. Under Striga-infested, Striga-free and across research environments, a non-significant SCA x environment mean squares was observed for all measured traits, except for number of emerged Striga plants at 8 WAP under Striga infestation. This 77 University of Ghana http://ugspace.ug.edu.gh indicated that the environmental factors did not influence the expression of most of the measured traits. Furthermore, it indicated that yield performance of the hybrids was consistent in the contrasting research environments. This result is consistent with findings of Badu-Apraku et al. (2013c), who reported a non-significant SCA x environment interactions for all the measured traits except for ear aspect and Striga damage at 10 WAP. However, this is contrary to the findings of Akinwale et al. (2014), who reported a significant SCA x environment interaction mean squares for most measured traits except for grain yield, days to silking and anthesis, ear aspect and plant height under Striga infestation. The GCA effect of an inbred line is a reflection of its relative importance as a tester for improving a target agronomic trait in a population and as a candidate parent for the development of synthetic varieties and hybrids. Outstanding inbreds/hybrids with respect to GCA and SCA for grain yield and other agronomic traits could be used in developing heterotic populations for further improvement and high yielding varieties for Striga-endemic regions of WCA. Significant and positive GCA effects for grain yield are essential for maize genotypes to be productive under Striga-infested and Striga-free conditions. Hence inbred lines TZEI 7, TZEI 296, TZdEI 352, TZEI 355 and TZEI 383 with positive and significant GCA effects for grain yield would contribute high grain yield to their progenies under Striga-infested conditions whilst TZEI 5, TZEI 7, TZdEI 120, TZdEI 352 and TZdEI 425 are expected to contribute favorable alleles for high grain yield in hybrid combinations under optimal environments. Inbreds TZdEI 120, TZEI 383 and TZdEI 352 due to their negative and significant GCA effects for emerged Striga plants at 8 and 10 WAP possess genes for resistance to Striga and could be excellent sources of genes for Striga resistance in hybrid combinations. Inbreds TZdEI 352, TZEI 383 and TZEI 355 which also showed 78 University of Ghana http://ugspace.ug.edu.gh significant negative GCA effects for Striga damage at 8 and 10 WAP are likely to be excellent sources of Striga tolerance genes. One important objective of the study was to identify the highest yielding and stable hybrids under the contrasting environments for commercialization in SSA. The ten most promising hybrids identified using the base index were TZdEI 352 X TZEI 355, TZdEI 352 x TZEI 383, TZdEI 120 x TZEI 383, TZEI 80 x TZdEI 352, TZEI 7 x TZdEI 352, TZEI 296 x TZdEI 352, TZEI 18 x TZdEI 352, TZEI 7 x TZEI 383, TZEI 352 x TZdEI 352 and TZEI 352 x TZEI 383. It is striking that several inbreds with genes from Zea diploperennis were among the high yielding hybrids indicating that Striga-resistant genes from the Zea diploperennis derived lines could be introgressed into the progenies in hybrid combinations and should reduce yield losses under Striga infestation. Of the best ten hybrids identified as Striga resistant/tolerant using the base index under Striga infestation, seven involved resistant x resistant inbred lines while the remaining three involved susceptible x resistant inbred lines. This result is consistent with that of Menkir et al. (2010) and Badu-Apraku et al. (2011), who reported that the highest level of Striga tolerance/resistance was achieved in crosses involving two resistant inbred parents. Of the ten worst single-cross hybrids, three involved resistant x susceptible while seven involved susceptible x susceptible inbreds. The results of this study suggested that the genes controlling Striga resistance in the early maturing maize inbred lines used were recessive, because Striga tolerance/resistance appeared to be more common in resistant x resistant hybrids than in resistant x susceptible hybrids. This result is in agreement with the earlier findings of Gethi and Smith (2004) and Badu-Apraku et al. (2011). Results of the study showed significant correlations between hybrid grain yield and mid- parent values of grain yield and other traits. There was positive and significant correlations 79 University of Ghana http://ugspace.ug.edu.gh between mid-parent values and the hybrid means for grain yield and most of the measured traits under Striga infestation indicating that preliminary screening of parental lines selected for Striga resistance/tolerance would be useful in the development of superior hybrids for the Striga-infested environments. However, the correlation between yields of parental lines and their hybrids under Striga infestation was low indicating that the performance of a hybrid could not be effectively predicted based on the performance of the parental lines. This suggested that crosses between high yielding inbred lines under Striga-infested environments may not result in high yielding hybrids. However, significant positive correlation was observed between mid-parent values and the hybrid means for grain yield and most of the measured traits under Striga-free and across research environments. This result corroborated the findings of other researchers (Betrán et al, 2003a; Gethi and Smith, 2004; Badu-Apraku et al., 2011b) who found a strong correlation between mean grain yield of parents and their hybrids under stress than under Striga-free conditions. Under Striga infestation, the negative and significant correlation between grain yield and ear aspect, Striga damage and number of emerged Striga plants at 8 and 10 WAP implied that these traits were not reliable for indirect selection for grain yield under Striga infestation. Kim and Adetimirin, (1997) and Amusan et al., (2008) reported a negative and significant correlation between grain yield and Striga damage. The significant positive correlation between grain yields of the hybrids and ASI, plant height of the inbred lines across research conditions implied that improvement of the inbred lines for these traits will invariably have an effect on the grain yield of their hybrids. Overall, grain yield of the hybrids was significantly correlated with all the traits used in the computation of the base index for selection of Striga resistant varieties. This justified the use of these traits in the base index for selection of Striga resistant genotypes in maize. 80 University of Ghana http://ugspace.ug.edu.gh The inbreds TZEI 56 and TZEI 383 were the highest yielding across the research environments, whilst TZEI 7 and TZEI 326 were the most stable. The hybrid TZdEI 352 x TZEI 355 was the highest yielding across research environments based on the IITA selection index. The GGE biplot analysis identified the hybrids TZdEI 352 x TZEI 355, TZEI 296 x TZdEI 352 and TZEI 7 x TZdEI 352 as high yielding and stable across research environments. Classification of the inbred lines into heterotic groups is of paramount importance for determining the potential usefulness of parental lines for the development of high yielding hybrids and synthetics. Under Striga-infested conditions, the SCA and HSGCA method placed the inbred lines into four and five heterotic groups respectively with groups 1 and 2 of both methods comprising mainly of Striga susceptible inbreds while groups 3 and 4 comprised mainly Striga tolerant or resistant inbred lines. These inbred lines had diverse genetic backgrounds with the exception of those of group 4 of both methods which had most inbreds from the same genetic background (TZEI 1 x TZEI 2) S6 inbred. Group five of the HSGCA method also had inbred lines from the same genetic background. The fifth heterotic group of the HSGCA method contained only Striga tolerant or resistant inbreds. The HGCAMT method classified the parental lines into three heterotic groups with group one comprising only Striga susceptible lines from different genetic background while groups 2 and 3 comprised mainly Striga resistant or tolerant inbreds. Group 2 consisted of inbreds from four different genetic backgrounds while group 3 consisted of inbred lines from 2 different genetic backgrounds. The indications are that the grouping of the inbred lines was based largely on the reaction of the inbreds to the stress environments. These results are in disagreement with the findings of Badu-Apraku et al. (2013b) and Akinwale (2012). The differences in the results could be due to the fact that the inbred lines used in this study came from seven diverse genetic sources while in other studies, the inbred lines came from two or three 81 University of Ghana http://ugspace.ug.edu.gh different genetic sources. The differences could also be attributed to the differences of testing sites used in the study The significant differences observed in GCA mean squares for grain yield suggested that identification of testers based on GCA for grain yield was possible. Inbred testers were therefore selected on the basis of GCA effects of grain yield while hybrids were selected on the basis of SCA effects (Halleur and Miranda, 1988). The inbreds TZEI 7 and TZdEI 352 were identified as the best testers based on display of highly significant and positive GCA effects, classification into specific heterotic groups and grain yield performance. These inbred testers should be used to classify other tropical early maturing white maize inbred lines into heterotic groups and also to identify superior hybrid combinations. 82 University of Ghana http://ugspace.ug.edu.gh CHAPTER SIX CONCLUSIONS AND RECOMMENDATIONS 6.1 Conclusions Significant environmental and genotypic variations under Striga-infested and Striga-free conditions was observed for most of the measured traits indicating that the research sites were unique in discriminating among the genotypes and that there was adequate genetic variability among the inbred lines to allow for the selection of Striga-resistance traits. The average grain yield reduction of 54% obtained in the single-cross hybrids suggested that the level of Striga infestation of the inbreds was high enough to allow for the identification of superior hybrids with genes for Striga resistance/tolerance. The reduction in the grain yield of the hybrids under Striga-infested environments was accompanied by increased ASI, poor ear aspect, and increased Striga damage as well as high number of emerged Striga plants. The preponderance of the GCA effects over SCA effects of grain yield and most agronomic traits under Striga infested, Striga-free and across research environments indicated the importance of additive genetic effects over non-additive genetic effects for these traits and that GCA was the major component accounting for the differences among the inbreds and hybrids evaluated. Using the base index, inbreds TZEI 383, TZEI 56 and TZdEI 352 were identified as outstanding inbred lines in terms of grain yield under Striga resistance: while TZEI 7 and TZEI 326 were the most stable inbreds across the research environments. The hybrids TZdEI 352 x TZEI 355, TZdEI 352 x TZEI 383 and TZdEI 120 x TZEI 383 were identified as the outstanding hybrids in terms of grain yield under Striga-infested environments using the base index. High yielding and stable hybrids across research environments based on the GGE biplot analysis were TZdEI 352 x TZEI 355 and TZEI 296 x TZdEI 352 and TZEI 7 x TZdEI 352. However, TZE-W Pop DT STR x TZEI 7 (a check) was the most stable hybrid across the research environments. 83 University of Ghana http://ugspace.ug.edu.gh Inbreds TZEI 7 and TZdEI 352 were also identified as the best testers. These inbred lines may be used to classify other early maturing white maize inbred lines into heterotic groups and also to identify superior hybrid combinations. The HSGCA method classified the inbred lines into 5, 4 and 3 heterotic groups under Striga-infested, Striga-free and across research environments, respectively. The inbred lines in each heterotic group may be recombined to form heterotic populations which could be improved through reciprocal recurrent selection to synthesize more genetically diverse and superior inbred lines. Subsequently, these inbred lines could be extracted for the production of superior hybrids and synthetics. The inbreds TZdEI 120, TZdEI 352, TZEI 355 and TZEI 383 can be utilized as sources of Striga resistant genes for introgression into tropical early maturing maize breeding populations for the improvement of target agronomic traits. 6.2 Recommendations  It is recommended that the superior inbreds and hybrids identified be evaluated in on-farm multi-locational trials under contrasting environments to confirm their consistency in performance and subsequently commercialized to combat food insecurity in the sub-region.  Inbred lines identified as sources of Striga resistant genes should be evaluated under drought and low N in order to identify those with combined tolerance to these stresses. 84 University of Ghana http://ugspace.ug.edu.gh REFERENCES Adetimirin, V. O., S. K. Kim, M. E. Aken'Ova. 2000. Expression of mature plant resistance to Striga hermonthica in maize. Euphytica. 115:149–158. Adetimirin, V.O., I. Vroh-Bi, A. Menkir, S.E. Mitchell, and S. Kresovich. 2008. Diversity analysis of elite maize inbred lines adapted to West and Central Africa using SSR markers. Maydica 53: 199-207. Agarwal, V. D. 1991. Research on cowpea-Striga resistance at IITA. P. 90-95. In S. K. Kim (ed.) Combating Striga in Africa: Proc. of an Int. Workshop by IITA, ICRISAT, and IDRC, Ibadan. 22-24 Aug. 1998. IITA, Ibadan, Nigeria. Agbaje, S.A., B. Badu-Apraku and M. A. B. Fakorede. 2008. Heterotic patterns of early maturing maize inbred lines in Striga-free and Striga-infested environments Maydica: 53: 87–96. Agyeman, A., E. Parkes and B. B Peprah. 2015. AMMI and GGE biplot analysis of root yield performance of cassava genotypes in the forest and coastal ecologies. International Journal of Agricultural Policy and Research Vol.3 (3), pp. 222-232. Akaogu, I.C., B. Badu-Apraku, V. O. Adetimirin, I. Vroh B. I., M. Oyekunle, and R.O. Akinwale. 2012. Genetic diversity assessment of extra-early maturing yellow maize inbreds and hybrid performance in Striga-infested and Striga-free environments. Journal of Agricultural. Science 151: 519-537. Akanvou, L., E. V. Doku, and J. G. Kling. 1997. Estimates of genetic variances and interrelationships of traits associated with Striga resistance in maize. African Crop Science Journal 5:1-8. 85 University of Ghana http://ugspace.ug.edu.gh Akinbode, O.A. 2010. Evaluation of antifungal efficacy of some plant extracts on Curvularia lunata, the causal organism of maize leaf spot. African Journal Environmental Science and Technology 4(11): 797-800. Akinwale, R. O. 2012. Evaluation of the heterotic groups of Striga-resistant early maturing maize (Zea mays L.) inbred lines using diallel analysis and molecular markers. Ph. D. Thesis, Department of Crop Production and Protection, Obafemi Awolowo University, Ile-Ife. Nigeria, p 125. Akinwale, R. O., B. Badu-Apraku and M. A. B. Fakorede. 2013. Evaluation of Striga-resistant early maize hybrids and test locations under Striga-infested and Striga-free environments. African Crop Science Journal, Vol. 21, No. 1, pp. 1-19. Akinwale, R. O., B. Badu-Apraku, M. A. B. Fakorede and I. Vroh-Bi. 2014. Heterotic grouping of tropical early-maturing maize inbred lines based on combining ability in Striga-infested and Striga-free environments and the use of SSR markers for genotyping. Field Crops Research. Elsevier. Volume 156. Allard, R. W. 1960. Principles of plant breeding. Wiley, New York, Inc. pp 263-279. Amusan, I. O., J. R. Patrick, A. Menkir, H. Thomas and G. Ejeta. 2008. Resistance to Striga hermonthica in a maize inbred line derived from Zea diploperennis. New Phytologist, 178: 157–166. Arnaud, M. C., C. Veronesi, and P. Thalouarn. 1999. Physiology and histology of resistance to Striga hermonthica in Sorghum bicolor var. Framida. Australian Journal of Plant Physiology 26(1): 63-70. 86 University of Ghana http://ugspace.ug.edu.gh Austin, D.F, M. Lee, L. R. Veldboom and A. R. Hallauer. 2000. Genetic mapping in maize with hybrid progeny across testers and generations: grain yield and grain moisture. Crop Science 40: 30-39. Babatunde, R. O., S. B. Fakayode and A. A. Obafemi. 2008. Fadama maize production in Nigeria: Case study from Kwara state. Research Journal of Agricultural and Biological Science 4:340-345. Badu-Apraku, B. and M. A. B. Fakorede. 1999. Progress in breeding for Striga hermonthica resistant early and extra - early maize varieties. In: Badu-Apraku B., Fakorede M.A.B., Ouedraogo M., and Carsky R.J. (ur.) Impact, challenges and prospects of maize research and development in West and Central Africa: Proceedings of a Regional Maize Workshop, 4-7 May, IITA-Cotonou, Benin Republic, WECAMAN/IITA. 147-162. Badu-Apraku, B., M. A. B. Fakorede. A. Menkir, A. Y. Kamara, L. Akanvou, and Y. Chabi. 2004. Response of early maturing maize to multiple stresses in the Guinea Savanna of West and Central Africa. Journal of Genetics and Breeding 58: 119-130. Badu-Apraku, B. and M. A. B. Fakorede. 2006. Zea mays L. In: Brink, M. and Belay, G. (Editors). PROTA 1: Cereals and pulses/Céréales et légumes secs. [CD-Rom]. Badu-Apraku, B.PROTA, Wageningen, Netherlands. Badu-Apraku, B. 2006. Estimates of genetic variances in Striga resistant extra-early maturing maize populations. Journal New Seeds 8:23-43. Badu-Apraku, B. 2007. Genetic variances and correlations in early tropical white maize population after three cycles of recurrent selection for Striga resistance. Maydica 52:205-217. 87 University of Ghana http://ugspace.ug.edu.gh Badu-Apraku, B., A. Menkir, and A. F. Lum. 2007a. Genetic variability for grain yield and components in an early tropical yellow maize population under Striga hermonthica infestation. Journal of Crop Improvement 20:107-122. Badu-Apraku, B., M. A. B. Fakorede and A. F. Lum. 2007b. Evaluation of experimental varieties from recurrent selection for Striga resistance in two extra early maize populations in the savannas of West and Central Africa. Experimental Agriculture, 43: 183-200. Badu-Apraku, B., and A. F. Lum. 2007. Agronomic Performance of Striga Resistant Early- Maturing Maize Varieties and Inbred Lines in the Savannas of West and Central Africa. Crop Science, 47:737-750. Badu-Apraku, B., M. A. B. Fakorede, and A. Fontem Lum. 2008. S1 family selection in early- maturing maize populations in Striga-infested and Striga-free environments. Crop Science 48: 1984-1994. Badu-Apraku, B., M. A. B. Fakorede, A. F. Lum, and R. Akinwale. 2009. Improvement of yield and other traits of extra-early maize under stress and non-stress environments. Agronomy Journal 101:381-389. Badu-Apraku, B. 2010. Effects of recurrent selection for grain yield and Striga resistance in an extra-early maize population. Crop Sci. 50:1735-1743.Badu-Apraku, B., A. Menkir, S. O. Ajala, R. O. Akinwale, M. Oyekunle, and K. Obeng-Antwi. 2010. Performance of Tropical early maturing maize cultivars in multiple stress environments. Canadian Journal of Plant Science. 90:831–852. 88 University of Ghana http://ugspace.ug.edu.gh Badu-Apraku, B., M. Oyekunle, K. Obeng-Antwi, A.S. Osuman, S. G. Ado, N. Coulibaly, C. G. Yallou, M. Abdulai, G. A. Boakyewaa and A. Didjeira 2011a. Performance of Extra-Early Maize Cultivars based on GGE biplot and AMMI Analysis. Journal of Agricultural Science (Cambridge). Published online: 05 October 2011. Badu-Apraku, B., M. Oyekunle, R. O. Akinwale, and A. F. Lum. 2011b. Combining ability of early-maturing white maize inbreds under stress and non-stress environments. Agronomy Journal. 103: 544-545. Badu-Apraku, B. and M. Oyekunle. 2012. Genetic analysis of grain yield and other traits of extra- early yellow maize inbreds and hybrid performance under contrasting environments. Field Crops Research 129:99–110. Badu-Apraku, B., M. Oyekunle, K. Obeng-Antwi, A. S. Osuman, S. G. Ado, N. Coulibaly, C. G. Yallou, M. Abdulai, G. A. Boakyewaa and A. Didjeira. 2012. Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis. The Journal of Agricultural Science, 150, pp 473-483. Badu-Apraku, B., M. Oyekunle, A. Menkir, K. Obeng-Antwi, C. G. Yallou, I. S. Usman and H. Alidu. 2013a. Comparative performance of early-maturing maize cultivars developed in three eras under drought stress and well-watered environments in West Africa. Crop Science 53:1298-1311. Badu-Apraku, B., M. A. B. Fakorede, I. Vroh, R. O. Akinwale, and M. Aderounmu. 2013b. Combining ability, heterotic patterns and genetic diversity of extra-early yellow inbreds under contrasting environments. Euphytica 192:413-433. 89 University of Ghana http://ugspace.ug.edu.gh Badu-Apraku, B., M. Oyekunle, R. O. Akinwale, and M. Aderounmu. 2013c. Combining ability and genetic diversity of extra-early white maize inbreds under stress and non-stress environments. Crop Science 53:9–26. Badu-Apraku, B. and M. A. B. Fakorede. 2013. Breeding early and extra-early maize for resistance to biotic and abiotic stresses in sub-Saharan Africa. Plant Breeding Reviews. Volume 37. Badu-Apraku, B., M. Oyekunle, M. A. B. Fakorede, I. Vroh, R. O. Akinwale, and M. Aderounmu. 2013d. Euphytica. Badu-Apraku, B., M.A.B. Fakorede, M. Gedil, A.O. Talabi, B. Annor, M. Oyekunle, R.O. Akinwale, T. Y. Fasanmade, M. Aderounmu.. 2015a. Heterotic responses among crosses of IITA and CIMMYT early white maize inbred lines under multiple stress environments. Euphytica. Badu-Apraku, B., B. Annor, M. Oyekunle, R.O. Akinwale, M.A.B. Fakorede, A.O. Talabi, I.C. Akaogu, G. Melaku, and T.Y. Fasanmade. 2015b. Grouping of early maturing quality protein maize inbreds based on SNP markers and combining ability under multiple environments Field Crops Research 183: 169–183. Baker, R. J. 1978. Issues in diallel analysis. Crop Science 18: 533-536. Barata, C., and M. Carena. 2006. Classification of North Dakota maize inbred lines into heterotic groups based on molecular and testcross data. Euphytica 151:339–349. Beck, D. I., S. K. Vasal, and J. Crossa. 1991. Heterosis and combining ability among subtropical and temperate intermediate maturity maize germplasm. Crop Science 31:68-73. 90 University of Ghana http://ugspace.ug.edu.gh Bello, O. B., and G. Olaoye. 2009. “Combining ability for maize grain yield and other agronomic characters in a typical southern guinea savanna ecology of Nigeria,” African Journal of Biotechnology, Vol. 8, pp. 2518-2522. Bello, O. B., J. Mahamood, M. S. Afolabi, M. A. Azeez, S. A. Ige, S. Y. Abdulmaliq, F. Oluleye. 2012. Biochemical analysis and grain yield characteristics of quality protein maize (Zea mays L) in the southern Guinea savanna of Nigeria. World Research Journal of Biochemistry 1: 11-19. Bennetzen, J. L., F. Gong, J. Xu, C. Newton, and A. C. de Oliveira. 2000. The study and engineering of resistance to the parasitic weed Striga in rice, sorghum and maize. In: Haussmann B. I. G., Hess D. E., Koyama M. L., Grivet L., Rattunde H. F. W., Geiger H. H., eds. Breeding for striga resistance in cereals. Ibadan, Nigeria: Margraf Verlag, 197– 205. Berner, D. K., J. G. Kling, and B. B. Singh. 1995. Striga research and control: A perspective from Africa. Plant Diseases 79:652-660. Betrán, F.J., J. M. Ribaut, D. Beck and D. Gonzalez de Leon. 2003. Genetic diversity, specific combining ability, and heterosis in tropical maize under stress and non-stress environments. Crop Science 43:797–806. Bidhendi, M.Z., R. Choukan, F. Darvish, K. Mostafavi, and E. Majidi, E. 2012. Classifying of maize inbred lines into heterotic groups using diallel analysis. World Academy of Science, Engineering and Technology, 6, 1161-1164. 91 University of Ghana http://ugspace.ug.edu.gh Birchler, J.A., Auger, D.L. and Riddle, N.C. 2003. In search of the molecular basis of heterosis. Plant Cell 15: 2236-2239. Bolaños, J. and G. O. Edmeades. 1993. Eight cycles of selection for drought tolerance in tropical maize. II. Responses in reproductive behaviour. Field Crops Research 31:253-268. Botanga, C. J., and M. P. Timko. 2006. Phenotypic relationships among different races of Striga gesnerioides (Willd.) Vatke from West Africa. Genome, 49:1351–1365. Brewer, P.B, H. Koltai, and C. A. Beveridge. 2013. Diverse roles of strigolactones in plant development. Molecular Plant, 6:18–28. Byerlee, D. and P. W. Heisey. 1997. Evolution of the African maize economy. Chapter 2 in Africa’s emerging maize evolution, edited by D. Byerlee and C. K. Eicher. Lynne Rienner Publishers, London, UK. Chapman, S. C., G. L Hammer, D. G. Butler and M. Cooper. 2000. Genotype by environment interactions affecting grain sorghum. III. Temporal sequences and spatial patterns in the target population of environments. Australian Journal of Agricultural Research. 51: 223- 233. CIMMYT and IITA, 2010. MAIZE – Global alliance for improving food security and the livelihoods of the resource-poor in the developing world. Draft proposal submitted by CIMMYT and IITA to the CGIAR Consortium Board. El Batan, Mexico. 91 pp. Cissoko, M., A. Boisnard, J. Rodenburg, M. C. Press, J. D. Scholes. 2011. New Rice for Africa (NERICA) cultivars exhibit different levels of post-attachment resistance against the parasitic weeds Striga hermonthica and Striga asiatica. New Phytologist, 192, 952–963. 92 University of Ghana http://ugspace.ug.edu.gh Comstock, R. E., and H. F. Robinson. 1948. The components of genetic variances in populations of biparental progenies and their uses in estimating the averagedegree of dominance. Biometrics 4 (4): 254-266. Cook, C. E., L. P. Whichard, M. E. Wall, G. H Egley, P. Coggon, P.A. Luhan, and A. T. McPhail. 1972. Germination stimulants. 2. The structure of strigol—a potent seed germination stimulant for witchweed (Striga lutea Lour.). Journal of American Chemistry Society 94: 6198–6199 Crossa, J. 1990. Statistical analysis of multilocation trials. Advanced Agronomy 44: 55-85. Davis, R. L. 1927. Report of Plant Breeder. Annual report of the Pueto Rico Agriculture Experiment Station 14-15. DeVries, J. D. 2000. The inheritance of Striga reactions in maize. p. 73-81. In B. I. G. Haussmann, D. E. Hess, M. L. Koyama, L. Grivet, H. F. W. Rattunde, and H. H. Geiger (ed.) Breeding for Striga resistance in cereals. Proc. of a Workshop held at IITA, Ibadan, Nigeria. 18-20 Aug. 1999, Margraf Verlag, Weiker-sheim, Germany. Doggett, H., 1988. Sorghum, 2nd ed. Longman, London, UK. Dörr, I. 1997. How Striga parasitizes its host: a TEM and SEM study. Annuals of Botany 79: 463– 472. Duvick, D.N. and Brown W.L. 1981. Current breeding methods in maize. Eds: Sneep, J. and Hendricksen, .T. Wageningen, Holand: Pudoc. Plant Breeding Perspectives. pp. 190-203. 93 University of Ghana http://ugspace.ug.edu.gh Eckebil, J. P. 1994. New frontiers for food grain research for the 1990s. Pages 3- 19 in Progress in food grain research and production in semi-arid Africa, edited by J. M. Menyonga, Taye Bezuneh, J. Y. Yayock, and Idrissa Soumana. OAU/STRC-SAFGRAD, Ouagadougou, Burkina Faso. Ejeta, G. 2007. Breeding for Striga resistance in sorghum: Exploitation of an intricate host–parasite biology. Crop Science 47:S216–S227. El Hiweris, S. O. 1987. Nature of resistance to Striga hermonthica (Del.) Benth. parasitism in some Sorghum vulgare (Pers.) cultivars. Weed Research, 27, 305–311. Enyong, L., O. Coulibaly, A. Adesina, A. Youri, and A. Raman. 1999. Dynamics of maize production, adoption of improved varieties and food security in the northern region of Cameroon. In: Strategy for sustainable maize production in West and Central Africa, Badu-Apraku, B., M. A. B. Fakorede, M. Ouedraogo and F. M. Quin (eds). Proceedings of a Regional Maize Workshop, 21-25 April, 1997, WECAMEN/ITA- Cotonou, Benin republic, pp 365-376. Fakorede, M. A. B., B. Badu-Apraku, A. Y. Kamara, A. Menkir, and S. O. Ajala. 2003. Maize revolution in West and Central Africa: an overview. In: Maize revolution in West and Central Africa, Badu-Apraku, B., M. A. B. Fakorede, M. Ouedraogo, R. J. Carsky, and A. Menkir (eds), Proceedings for a Regional Maize Workshop, IITA-Cotonou, Benin Republic, 14-18 May, 2001. WECAMEN/IITA. Fakorede, M.A.B., A. Menkir, and D. Sanogo. 2012. “Conduct and management of maize field trials”. IITA, Ibadan, Nigeria. p 1. 94 University of Ghana http://ugspace.ug.edu.gh Fan XM, Kang MS, Chen H, Zhang YT, Xu JC (2007). Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agronomy Journal 99:220-228. Fan, X. M. J. Tan, J. Y. Yang, F. Liu, B. H. Huang and Y. X. Huang. 2002. “Study on combining ability for yield and genetic relationship between exotic tropical, subtropical maize inbreeds and domestic temperate maize inbreeds,” Scientica Agriculture Sinica, Vol. 35, pp. 743-749. Fan, X. M., H. M. Chen, J. Tan, C. X. Xu, Y. M. Zhang, Y. X. Huang, and M. S. Kang. 2008. Agronomy Journal 100:917-918. Fan, X.M., Y. M. Zhang, W. H. Yao, H. M. Chen, J. Tan, C. X. Xu, X. L. Han, L. M. Luo and M. S. Kang. 2009. Classifying maize inbred lines into heterotic groups using a factorial mating design. Agronomy Journal 101:106–112. Falconer, D. S. 1981. Introduction to quantitative genetics. Longman, New York. pp 340. Falconer, D.S., and T. F. C. Mackey. 1996. Introduction to quantitative genetics. 4th Edition. Longman, Essex, England. FAOSTAT. 1997. Food and Agriculture of the United Nations Statistics, Rome, Italy. http://faostat.fao.org/ FAOSTAT. 2007. Food and Agriculture Organization of the United Nations, Food Security Statistics, 2007. FAOSTAT. 2012. Food and Agriculture of the United Nations Statistics, Rome, Italy. http://faostat.fao.org/ FAO. 2011. FAO Statistical Year., http://faostat.fao.org. 95 University of Ghana http://ugspace.ug.edu.gh FAOSTAT, (2014): FAOSTAT online database (http://faostat3.fao.org/faostat- gateway/go/to/browse/G1/*/E). Fasahat, P., A. Rajab, J. M. Rad and J. Derera. 2016. Principles and Utilization of Combining ability in Plant breeding. Biometrics & Biostatistics International Journal 3 (6). Forum for Agricultural Research in Africa (FARA). 2009. Patterns of Change in Maize Production in Africa: Implications for Maize Policy Development. Networking Support Function 3: Regional Policies and Markets. Ministerial Policy Brief Series. December 3, 2009. Gauch, H. G. and R. W. Zobel. 1996. AMMI analysis of yield trials. In Genotype-by-environment Interaction (Kang, M.S. and H.G. Gauch, eds.), CRC Press, Boca Raton, FL: 85-122. Gethi, J.G., M. E. Smith, S. E. Mitchell, and S. Kresovich. 2005. Genetic diversity of Striga hermonthica and Striga asiatica populations in Kenya. Weed Research 45:64–73. Gethi, J. G., and M. E. Smith. 2004. Genetic responses of single crosses of maize to Striga hermonthica (Del.) Benth. and Striga asiatica (L.) Kuntze. Crop Science 44:2068-2077. Graves, J. D., A. Wylde, M. C. Press and G. R. Stewart. 1990. Plant Cell Environment 13, 367. Gressel. J., A. Hanafi, G. Head, W. Masaras, A. B. Obilana, J. Ochada, T. Souissi, and G. Tzotzos. 2004. Major heretofore intractable biotic constraints to African food security that may be amenable to novel biotechnological solutions. Crop Protection 23:661-680. Griffing, B. 1956. Concept of general and specific combining ability in relation to diallel crossing systems. Australian Journal of Biological Science 9:463-493. 96 University of Ghana http://ugspace.ug.edu.gh Gruneberg, W. J., K. Manrique, D. Zhang and M. Hermann. 2005. Genotype x environment interactions for a diverse set of sweet potato clones evaluated across varying ecographic conditions in Peru. Crop Science 45: 2160-2171. Gurney, A. L., D. Grimanelli, F. Kanampiu, D. Hoisington, J. D. Scholes and M. C. Press. 2003. Novel sources of resistance to Striga hermonthica in Tripsacum dactyloides, a wild relative of maize. New Phytologist 160:557–568. Gurney, A. L., J. Slate, M. C. Press, and J. D. Scholes. 2006. A novel form of resistance in rice to the angiosperm parasite Striga hermonthica. New Phytologist, 169, 199–208. Hallauer, A. R., and J. B. Miranda. 1988. Quantitative genetics in maize breeding. 2nd edition Iowa State University Press Ames, USA. Halleur, A. R. 1990. Methods used in developing maize inbreds. Maydica 35:1-16. Hallauer A. R. and Miranda Filho. 1981. Quantitative genetics in maize breeding. Iowa State University Press, Ames, 468p. Hallauer, A.R., M.J. Carena and J.B.M. Filho. 2010. Quantitative genetics in maize breeding. 6th ed. Springer, Iowa, USA. Haussmann, B. I. G., D. E. Hess, H. G. Welz and H. H. Geiger. 2000. Improved methodologies for breeding Striga resistant sorghums. Field Crops Research, 66: 195-211. Haussmann, B. I. G., D. E. Hess, G. O. Omanya, R. T. Folkertsma, B. V. S. Reddy, M. Kayentao, H. G. Welz, and H. H. Geiger. 2004. Genomic regions influencing resistance to the parasitic weed Striga hermonthica in two recombinant inbred populations of Sorghum. Theoretical and Applied Genetics, 109: 1005-1016. 97 University of Ghana http://ugspace.ug.edu.gh Hauck, C., S. Muller, and H. Schildknecht. 1992. A germination stimulant for parasitic flowering plants from Sorghum bicolor, a genuine host plant. Journal of Plant Physiology 139: 474– 478. Hayman, B. I. 1954. The theory and analysis of the diallel crosses. Genetics 39: 789-809. Hess, D. E., G. Ejeta, and L. G. Butler. 1992. Selecting sorghum genotypes expressing a quantitative biosynthetic trait that confers resistance to Striga. Phytochemistry 31: 493– 497. Ifie Elohor, B. 2013. Genetic Analysis of Striga resistance and low soil nitrogen tolerance in early maturing maize (Zea mays L.) inbred lines. IITA. 2009. Cereals and Legumes Systems, International Institute of Tropical Agriculture, Ibadan, Nigeria. Jamil, M., J. Rodenburg, T. Charnikhova, H. Bouwmeester. 2011. Pre-attachment Striga hermonthica resistance of New Rice for Africa (NERICA) cultivars based on low strigolactone production. New Phytology. Kamara, M. M., I. S. El-Degwy and H. Koyoma. 2014. Estimating combining ability of some maize inbred lines using line x tester mating design under two nitrogen levels. Aust. J. Crop Science 8: 1336-1342. Kanampiu, F. and D. Friesen. 2003. A New Approach to Striga Control. The Royal society of Chemistry, pp: 51-53. Kempthorne, O. 1957. An introduction of genetics statistics. John Wiley and Sons, New York, USA 458-471. 98 University of Ghana http://ugspace.ug.edu.gh Knampiu, F.K., V. Kabambe, C. Massawe, L. Jasi, D. Friesen, J. K. Ransom, J. Gressel. 2003. Multi-state, multi-season field tests demonstrate that herbicide seed-coating herbicide- resistance maize controls Striga spp. and increases yields in several African countries. Crop Protection 22:697-706. Kandus, M., D. Almorza, R. Boggio Ronceros and J. C. Salerno. 2010. Statistical methods for evaluating the genotype- environment interaction in maize (Zea mays L.). Phy.Yld. 39-46. Kang, M.S., 1996. Using genotype-by-environment interaction for crop cultivar development. Advanced Agronomy 62:199–252. Khan, Z.R., C. A. O. Midega, A. Hassanali, J. A. Pickett, L. J Wadhams, A. Wanjoya. 2006. Management of witchweed, Striga hermonthica, and stemborers in Sorghum, Sorghum bicolor, through intercropping with greenleaf desmodium, Desmodium intortum. Int. J. Pest Management 52:297–302. Kiani, G., Nematzadeh, G.A., Kazemitabar, S.K. and Alishah, O. 2007. Combining ability in cotton cultivars for agronomic traits. International Journal of Agriculture and Biology 9: 521–532. Kim, S. K. 1991. Breeding maize for Striga tolerance and the development of a field infestation technique. p. 96-110. In S. K. Kim (ed.) Combating Striga in Africa. Proc. Int. Workshop organized by IITA, ICRISAT, and IDRC. 22-24 Aug. 1988, Ibadan, Nigeria. Kim, S. K., and M. D. Winslow. 1991. Progress in breeding maize for Striga tolerance/resistance at IITA. p. 494-499. In Ransom et al., (ed.) Proc. 5th Int. Symp. On Parasitic Weeds. 24- 30 June 1991. Nairobi, Kenya. CIMMYT, Kenya, 99 University of Ghana http://ugspace.ug.edu.gh Kim, S. K. 1994. Genetics of maize tolerance of Striga hermonthica. Crop Science 34:900-907. Kim, S. K. and V. O. Adetimirin. 1997. Responses of tolerant and susceptible maize hybrids to timing and rate of nitrogen under Striga hermonthica infestation. Agronomy Journal, 89: 38-44. Kim, S. K., V. O. Adetimirin, C. The and R. Dossou. 2002. Yield losses in maize due to Striga hermonthica in West and Central Africa. International Journal of Pest Management, 48:211-217. Kiruki, S., L. A. Onek and M. Limo. 2006. Azide-based mutagenesis suppresses Striga hermonthica seed germination and parasitism on maize varieties. African Journal of Biotechnology 5:866–870. Kust, C. A. 1963. Weeds 11, 247. Lane, J. A., J. A. Bailey, R. C. Butler, P. J. Terry. 1993. Resistance of Cowpea [Vigna unguiculata (L.) Walp.] to Striga gesnerioides (Willd.) Vatke, a parasitic angiosperm. New Phytologist, 125, 405–412. Lane, J. A., D. V. Child, T. H. M. Moore, G. M. Arnold and J. A. Bailey. 1997. Phenotypic characterization of resistance in Zea diploperennis to Striga hermonthica. Maydica. 42:45– 51. Lippman, Z. B. and D. Zamir. 2006. Heterosis: Revisiting the magic. Trends in Genetics Vol.23 No.2. 100 University of Ghana http://ugspace.ug.edu.gh Lagoke, S. T. O., V. Parkinson, and R. M. Agunbiade. 1991. Parasitic weeds and control methods in Africa. p. 3-14. In S. K. Kim (ed.) Combating Striga in Africa: Proc. of an Int. Workshop by IITA, ICRISAT, and IDRC, Ibadan. 22-24 Aug. 1998. IITA, Ibadan, Nigeria. Li, J., and M. P. Timko. 2009. Gene-for-gene resistance in Striga-cowpea associations. Science, 325, 1094–1094. Maiti, R. K., K. V. Ramaiah, S. S. Bisen, and V. L. Chidley. 1984. A comparative study of the haustorial development of Striga asiatica (L.) Kuntze on sorghum cultivars. Annuals of Botany 54: 447–457. M’Boob. S. S. 1986. A regional programme for Striga in West and Central Africa. p. 190-194. In Proc, of the FAO/OAU All-African Government Consultation on Striga Control. 20-24 Oct. 1986. Maroua, Cameroon. Melani, M.D., and Carena, M.J. 2005. Alternative maize heterotic pattern for the northern corn belt. Crop Science 45:2186–2194. Melchinger, A. E. 1999. Genetic diversity and heterosis. In Coors, J. G., and Pandey, S. (ed.) The genetics and exploitation of heterosis in Crops. American Society of Agronomy/Crop Science Society of America Inc., Madison, Wisconsin. p. 99-118. Menkir, A., B. Badu-Apraku, C. The, and A. Adepoju. 2003. Evaluation of heterotic patterns of IITA lowland white maize inbred lines. Maydica 48:161-170. 101 University of Ghana http://ugspace.ug.edu.gh Menkir, A., A. Melake-Berhan, I. Ingelbrecht and A. Adepoju. 2004. Grouping of tropical mid- altitude maize inbred line on the basis of yield data and molecular markers. Theoretical and Applied Genetics 108:1582–1590. Menkir, A. 2006. Assessment of reactions of diverse maize inbred lines to Striga hermonthica (Del.) Benth. Plant Breeding, 125:131-139. Menkir, A., V. O. Adetimirin, C. G. Yallou and M. Gedil. 2010. Relationship of genetic diversity of inbred lines with different reactions to Striga hermonthica (Del.) Benth and the performance of their crosses. Crop Science, 50:602-611. Mengesha, W.A. 2013. Genetic diversity, stability, and combining ability of maize genotypes for grain yield and resistance to NCLB in the mid-altitude sub-humid agro-ecologies of Ethiopia MSc (Plant Breeding) thesis University of the Free State , Republic of South Africa. 1–182 pp. Meseka, S.K., A. Menkir, A. E. S. Ibrahim, and S. O. Ajala. 2006. Genetic analysis of performance of maize inbred lines selected for tolerance to drought under low nitrogen. Maydica 51: 487-495. Mitrovic, B., D. Stanisavljevi, S. Treski, M. Stojakovic, M. Ivanovic, G. Bekavac and M. Rajkovic. 2012. Evaluation of experimental maize hybrids tested in multi-location trials using AMMI and GGE biplot analyses. Turkish Journal of Field Crops, 17: 35-40. Mohamed, K. I., L. J. Musselman, and C. R. Riches. 2001. The genus Striga (Scrophulariaceae) in Africa. Ann. Mo. Bot. Gard. 88:60–103. 102 University of Ghana http://ugspace.ug.edu.gh Mohamed, A., A. Ellicott, T. L. Housley, and G. Ejeta. 2003. Hypersensitive response to Striga infection in Sorghum. Crop Science, 43, 1320. Munthali, W. M., V. W. Saka, J. M. Bokosi, and W. G. Nhlane. 2003. Inheritance of gray leaf spot Cercospora zeae maydis resistance and performance of single cross hybrids in a selected maize Zea mays inbred lines. African Crop Science Conference Proceeding 6: 371-375. New Scientist. 1986. Published by Reed Business Information. Vol. 109: 44-48, No. 1490. Obilana, A. B. 1984. Inheritance of resistance to Striga (Striga hermonthica Benth) in Sorghum. Protection Ecology 7: 305-311. Odhiambo, G. D., and J. K. Ransom. 1994. Long term strategies for Striga control. p. 263-266. In D. C. Jewell et al., (ed.) Maize research for stress environments. Proc. 4th Eastern and Southern Africa Regional Maize Conf., Harare, Zimbabwe. 28 Mar. – 1 Apr. 1994. CIMMYT, Mexico. Odhiambo, G. D., and J. K. Ransom. 2000. Effect of organic and inorganic sources of nitrogen on control of Striga hermonthica and on soil fertility for higher maize productivity in western Kenya. Phytoparasitica 28:175. Oluwaranti, A., M. A. B. Fakorede and B. Badu-Apraku. 2008. Grain yield of maize varieties of different maturity groups under marginal rainfall conditions. Journal of Agricultural Science 53 (3):183-191. Oswald, A., and J. K. Ransom. 2004. Response of maize varieties to Striga infestation. Crop Protection 23:89–94. 103 University of Ghana http://ugspace.ug.edu.gh Panhwar, S. A., M. J Baloch, W. A. Jatoi, N. F. Veesar, and M. S. Majeedano. 2008. Combining ability estimates from line x tester mating design in upland cotton. Proceedings of Pakistan Academy of Science 45(2):69-74. Parker, C. and C. R. Riches. 1993. Parasitic Weeds of the World. Biology and Control. Wallingford, UK: CAB International. p. 332. Parker, C. J. 2009. Observations on the current status of Orobanche and Striga problems Worldwide. Pest Management Science 65:453–459. Parkinson, V., S. K. Kim, Y. Effron, L. Bello, and K. Dashiell. 1989. Potential trap crops as a cultural measure in Striga control for Africa. p. 136-140. In T. O. Robson and H. R. Broad (ed.) Striga improved management in Africa. Proceedings of the FAO/OAU All African Government Consultation on Striga control. Maroua, Cameroon. 20-24 Oct. 1986. FAO, Rome, Italy. Pierce, S., A. M. Mbwaga, M. C. Press and J. D. Scholes. 2003. Xenognosin production and tolerance to Striga asiatica infection of high-yielding maize cultivars. Weed Research 43:139–145. Press, M. C. and J. Graves (Eds.). 1995. Parasitic Plants. Chapman & Hall, London. Press, M. C., and J. B. Whittaker. 1993. Phil. Trans. R. Soc. Lond. B (13), 367. Pswarayi, A. and Vivek, B. S. 2008. Combining ability amongst CIMMYT’s early maturing maize (Zea mays L.) germplasm under stress and non-stress conditions and identification of testers. Euphytica 162:353–362. 104 University of Ghana http://ugspace.ug.edu.gh Radoslava, M., K. Rani, F. W. A. Verstappen, M. C. R. Franssen, M. H. Beale and H. J. Boumeester. 2005. The Strigolactone germination stimulants of the plant-parasitic Striga and Orabanche spp. are derived from the carotenoid pathway. Plant Physiology 139(2): 920-934. Ramaiah, K. V., V. L. Childley, and L. R. House. 1991. A time course study of early establishment stages of the parasitic angiosperm Striga asiatica on susceptible sorghum roots. Annals of Applied Biology, 118: 403-410. Ransom, J. K., G. D. Odhiambo, R. E. Eplee, and A. O. Diallo. 1996. Estimates from field studies of the phytotoxic effects of Striga species on maize. p. 795-800. In M. T. Moreno, J. J. Cubero, D. Berner, D. Joel, L. J. Musselman, and C. Parker (ed.) Advances in parasitic plant research: Proc. of the 6th Parasitic Weed Symp., Cordaba, Spain. 16-18 Apr. 1996. Ransom, J. K., Eplee, R. E., Langston, M. A. 1990. Genetic variability for resistance to Striga asiatica in maize. Cereal Research Community. 18: 329-333. Rawlings, J. O., and D. L. Thompson. 1962. Performance level as criterion for the choice of maize tester. Crop Science. 2:217-220 Ricci, G.C., N. Silva, M. S. Pagliarini and C. A. Scapim. 2007. Microsporogenesis in inbred line of popcorn (Zea mays L.). Genetics and Molecular Research 6: 1013-1018. Rich, P. J., C. Grenier, and G. Ejeta. 2004. Striga resistance in the wild relatives of sorghum. Crop Science 44:2221-9. 105 University of Ghana http://ugspace.ug.edu.gh Rich, P. J., and G. Ejeta. 2007. Biology of host-parasite interactions in Striga species. In: Integrating New Technologies for Striga Control: Towards Ending the Witch-223 Hunt (eds Ejeta G, Gressel J), pp. 3–16. World Scientific. Riopel, J. L.., and M. P. Timko. 1995. Parasitic Plants, Ed. M. C. Press and J. D. Graves (Chapman and Hall, London) p. 39. Riopel, J. L., W. V. Baird, M. Chang, and D. G. Lynn. 1990. Witchweed Research and Control in the United States, Ed. P. F. Sand, R. E. Eplee and R. G. Westbrooks (Weed Science Society of America, Lawrence) p. 27. Rodenburg, J., B. Lammart and M. J. Kropff. 2006. Characterization of host tolerance to Striga hermonthica. Euphytica, Volume 147, Issue 3, pp 353-365. Robinson, H. F., C. C. Cockerham, and R. H. Moll. 1958. Studies on the estimation of dominance variance and effects of linkages bias. In O. Kempthorne (Ed.) Biometrical genetics. Pergamon Press, New York. p. 171-177. Rojas, B.A. and G. F. Sprague. 1952. Comparison of variance components in corn yield trials: III. General hybrids of maize. Indian Journal of Genetics 62: 312-315. Rovaris, S. R. S., M. E. A. G. Zagatto and E. Sawazaki. 2014. Combining ability of white corn genotypes with two commercial hybrids. Maydica, 59: 96-103. SAS Institute. 2011. Statistical analysis software (SAS) user’s guide. SAS Institute, Inc., Cary, NC. Sallé G. and A. Raynal-Roques. 1989. Le Striga. La Recherche No. 206, 20: 44–52. 106 University of Ghana http://ugspace.ug.edu.gh Saleh, G.B., Abdullah, D. and Anuar, A.R. 2002. Performance of heterosis and heritability in selected, tropical maize single, double and three way cross hybrids. Journal of Agricultural Science 138: 21-28. Sauerborn J. 1991. Parasitic flowering plants: ecology and management. Weikersheim, Germany: Josef Margraf. Sharma, S., M. S. Narwal, R. Kumar and S. Dass. 2004. Line x tester analysis in maize (Zea mays L.). Forage Research, 30: 28-30. Shaxson, L., and C. Riches. 1998. Where once there was grain to burn: A farming system crises in eastern Malawi. Outlook Agric. 27:101-105. Shull, G. H. 1908. The composition of a field of maize. Reports of the American Breeders Association: 296-301. Siame, B. A., Y. Weerasuriya, K. Wood, G. Ejeta, and L. G. Butler. 1993. Isolation of strigol, a germination stimulant for Striga asiatica, from host plants. Journal Agric Food Chemistry 41: 1486–1491. Smith, J., G. Weber, V. M. Manyong, and M. A. B. Fakorede. 1997. Fostering sustainable increases in maize productivity in Nigeria. Chapter 8 in Africa’s emerging maize revolution, edited by D. Byerlee, and C. K. Eicher, Lynne Rienner Publishers, London, UK. Sprague, G.F. and L. A. Tatum. 1942. General versus specific combining ability in single crosses of maize. Journal of the American Society of Agronomy 34: 923-932. 107 University of Ghana http://ugspace.ug.edu.gh Stewart, G. R., M. C. Press, J. D. Graver, J. J. Nour, and A. Wylde. 1991. A physiological characterization of the host-parasite association between Sorghum bicolor and S. hermonthica and its implications for Striga control. p. 48-54. In S. K. Kim (ed.) Combating Striga in Africa: Proc. of an Int. Workshop IITA, ICRISAT, and IDRC. 22-24 Aug. 1988. IITA, Ibadan, Nigeria. Stojaković, M., M. Ivanović, Đ. Jocković, G. Bekavac, B. Purar, A. Nastasić, D. Stanisavljević, B. Mitrović, S. Treskić and R. Laišić. 2010. NS maize hybrids in production regions of Serbia. Field and Vegetable Crops Research, 47: 93-02. Timko, M. P. and B. B. Singh. 2008. Cowpea, a multifunctional legume. Pages 227–258 in P. H. Moore and R. Ming, eds. Genomics of Tropical Crop Plants. New York: Springer Science + Business Media. Terron, A., E. Preciado, H. Cordova, H. Mickelson and R. Lopez. 1997. Determinacion del patron heterotico de 30 lineas de maiz derivadas de la poblacion SR del CIMMYT. Agronomy of Mesoamericana 43(8): 26-34. Tollenaar, M., Ahmadzadeh, A. and Lee, E.A. 2004. Physiological basis of heterosis for grain yield in maize. Crop Science 44: 2086-2094. USDA. 2007. United States Department of Agriculture. 1400 Independence Ave. S.W. Washington, DC 20250. Vanlauwe, B., J. J. Ramisch, and N. Sanginga. 2006. Integrated soil fertility management in Africa: from knowledge to implementation. Bioligical Approaches Sustainable Soil Systems 113:257–272. 108 University of Ghana http://ugspace.ug.edu.gh Van Ast, A., L. Bastiaans, and S. Katile. 2005. Cultural control measures to diminish sorghum yield loss and parasite success under Striga hermonthica infestations. Crop Protection, 24, 1023- 1034. Vasal, S. K., H. S. Cordova, D L. Beck, and G. O. Edmeades. 1997. Choices among breeding procedures and strategies for developing stress tolerant maize germplasm. In. G. O. Edmeades, M. Banziger, and C. R. Pena-Valdivia (eds.) Developing Drought and Low N- Tolerant Maize. Proceedings of a Symposium, March 25-29, 1996. CIMMYT, El Batan, Mexico. Mexico D. F., CIMMYT. Vogler, R. K., G. Ejeta, and L. G. Butler. 1996. Inheritance of low production of Striga germination stimulant in sorghum. Crop Science 36:1185-91. Vogt, W., J. Sauerborn, and M. Honisch. 1991. Striga hermonthica distribution and infestation in Ghana and Togo on grain crops. p. 372-377. In J. K. Ransom et al., (ed.) 5th Int. Symposium on parasitic weeds. CIMMYT, Nairobi, Kenya. Warburton M. L., X. Xianchin, J. Crossa, J. Franco, A. E. Melchinger, M. Frich, M. Bohn, and D. Hoisington. 2002. Genetic characterization of CIMMYT Inbred Maize lines and Open Pollinated Populations Using Large Scale Fingerprinting Methods. Crop Science 42: 1832- 1840. Westwood, J. H., J. I. Yoder, M. P. Timko, and C. W. dePamphilis. 2010. The evolution of parasitism in plants. Trends Plant Science 15:227–235. 109 University of Ghana http://ugspace.ug.edu.gh Yallou, C. G., A. Menkir, V. O. Adetimirin, and J. G. Kling. 2009. Combining ability of maize inbred lines containing genes from Zea diploperennis for resistance to Striga hermonthica (Del.) Benth. Plant Breeding 128:143-148. Yan, W., L. A. Hunt, Q. Sheng and Z. Szlavnics. 2000. Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Science, 40: 596–605. Yan, W. 2001. GGE Biplot—A Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal 93:1111–1118. Yan, W., M. S. Kang. 2003. ‘GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists.’ (CRC Press: Boca Raton, FL). Yan, W. and Tinker. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86: 623–645 Yan, W., M. S. Kang, B. Ma, S. Woods and P. L. Cornelius. 2007. GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data. Crop Science, 47: 641–653. Yoder, J.I. and L. J. Musselman. 2006. Striga: a subterranean parasitic angiosperm (witchweed), In: Goodman, R. ed. Encyclopedia of Plant and Crop Science, Taylor and Francis Group, New York. Yoder, J. I. and J. D. Scholes. 2010. Host plant resistance to parasitic weeds; recent progress and bottlenecks. Curr. Opin. Plant Biology 13(4): 478-484 . Zhang, Y., M.S. Kang, and K.R. Lamkey. 2005. DIALLEL-SAS: A comprehensive program for Griffing’s and Gardner-Eberthart analyses. Agronomy Journal 97:1097–1106. 110 University of Ghana http://ugspace.ug.edu.gh Zobel, R. W., M. J. Wright and H. G. Gauch. 1988. Statistical analysis of a yield trial. Agronomy Journal 80: 388–393. 111 University of Ghana http://ugspace.ug.edu.gh APPENDICES Appendix 1 SCA estimates of grain yield of best 10 and worst 10 performing hybrids under Striga infestation, optimal and across research environments, 2015. HYBRID SCA method (Striga-infested environment) HSGCA method (Optimum environment) HSGCA method (Across research environments) CROSS TYPE SCA ESTIMATE CROSS TYPE SCA ESTIMATE Group cross SCA ESTIMATE TZdEI 352 x TZEI 355 INTER 552.1 INTER 895.9 INTER 758.4 TZdEI 352 x TZEI 383 INTER 140.7 INTER 145.4 INTER 143.5 TZdEI 120 x TZEI 383 INTER 598.4 INTER 101.3 INTER 300.1 TZEI 80 x TZdEI 352 INTER 581.7 INTER -314.3 INTER 44.1 TZEI 7 x TZdEI 352 INTER 123.4 INTER -62.0 INTRA 12.2 TZEI 296 x TZdEI 352 INTER 246.6 INTER 413.0 INTER 346.4 TZEI 18 x TZdEI 352 INTRA -188.6 INTER 98.0 INTER -16.6 TZEI 7 x TZEI 383 INTER 63.1 INTER -223.9 INTER -109.1 TZEI 352 x TZdEI 352 INTER 429.0 INTER 352.9 INTER 383.4 TZEI 352 x TZEI 383 INTRA -94.2 INTER -72.9 INTRA -81.4 TZEI 326 x TZdEI 425 INTER -154.9 INTER 216.3 INTER 67.9 TZEI 31 x TZdEI 425 INTRA 180.0 INTRA 134.9 INTRA 152.9 TZEI 18 x TZdEI 425 INTER -338.9 INTRA -104.9 INTRA -198.5 TZEI 352 x TZdEI 425 INTER -12.0 INTER 258.5 INTER 150.3 TZEI 5 x TZEI 18 INTER -39.4 INTRA -225.0 INTRA -150.8 TZEI 5 x TZdEI 120 INTER -393.5 INTRA -375.1 INTRA -382.4 TZEI 5 x TZdEI 425 INTRA 97.1 INTRA 132.5 INTRA -40.6 TZEI 31 x TZEI 80 INTER -269.6 INTRA -136.2 INTRA -189.5 TZEI 5 x TZEI 326 INTER -318.3 INTER 553.9 INTER 205.0 TZEI 5 x TZEI 80 INTER -137.1 INTRA -158.4 INTRA -149.9 112 University of Ghana http://ugspace.ug.edu.gh Appendix 2 Estimates of heterosis for grain yield and other agronomic traits of 105 hybrids under Striga infestation. PEDIGREE YLD ASI PLHT EASP EPP STRR1 STRR2 STRCO1 STRCO2 MP MP MP MP MP MP MP MP MP TZEI 5 x TZEI 7 86.2 35.8 45.5 14.8 13.0 4.8 4.2 102.8 39.2 TZEI 5 x TZEI 18 155.0 22.9 52.4 5.3 47.6 6.4 3.0 83.9 66.9 TZEI 5 x TZEI 31 189.6 -29.8 18.8 6.2 30.2 -5.8 -4.1 79.5 77.2 TZEI 5 x TZEI 56 149.3 21.4 21.8 1.5 26.5 2.4 -1.1 68.1 276.9 TZEI 5 x TZEI 80 229.7 -9.5 26.2 -1.8 33.3 -4.9 -6.3 192.0 196.3 TZEI 5 x TZdEI 120 109.9 108.3 40.1 19.3 27.1 -0.2 -5.7 2.5 44.7 TZEI 5 x TZEI 296 206.0 -34.8 43.8 6.8 40.3 -9.7 -3.3 99.0 182.2 TZEI 5 x TZEI 326 91.8 10.4 19.5 4.0 -0.3 8.0 3.3 112.5 65.8 TZEI 5 x TZEI 352 200.9 -7.0 47.5 6.5 33.5 0.3 -1.8 5.4 -9.5 TZEI 5 x TZdEI 352 193.8 3.5 39.4 -3.2 29.0 0.4 -10.5 -40.5 -11.1 TZEI 5 x TZEI 355 219.4 -47.2 41.0 4.6 52.5 -3.0 2.7 305.2 156.9 TZEI 5 x TZEI 383 86.7 -62.7 37.4 4.5 25.2 8.0 4.0 136.2 122.2 TZEI 5 x TZEI 410 151.3 -35.8 44.3 2.5 23.8 2.2 10.8 183.6 115.2 TZEI 5 x TZdEI 425 115.4 15.2 43.5 4.1 29.9 9.2 -1.4 26.7 31.7 TZEI 7 x TZEI 18 171.4 18.7 53.2 3.0 27.8 -6.3 -4.9 13.5 -42.3 TZEI 7 x TZEI 31 95.4 -18.7 37.9 32.1 -15.7 18.4 11.4 221.9 23.5 TZEI 7 x TZEI 56 130.2 60.9 41.4 26.4 20.0 10.4 15.8 117.5 67.0 TZEI 7 x TZEI 80 250.4 -68.7 41.8 -7.6 10.1 -9.0 -11.4 72.4 47.0 TZEI 7 x TZdEI 120 110.1 4.0 47.9 25.9 18.9 -1.8 -1.8 186.7 -15.1 TZEI 7 x TZEI 296 235.2 -60.2 53.7 2.4 18.1 -1.1 -1.2 72.5 -3.5 TZEI 7 x TZEI 326 33.7 -4.4 50.9 31.0 -6.1 -2.0 2.6 470.0 49.9 TZEI 7 x TZEI 352 114.3 -108.5 40.7 26.2 4.0 21.9 23.8 272.6 137.6 TZEI 7 x TZdEI 352 179.1 -43.7 47.0 4.7 6.2 4.5 -9.5 70.4 -37.8 TZEI 7 x TZEI 355 206.3 -151.4 34.4 5.3 3.0 8.0 -6.7 410.6 29.6 TZEI 7 x TZEI 383 147.9 -50.7 39.2 3.9 13.6 6.4 3.8 -70.0 -49.9 TZEI 7 x TZEI 410 35.4 185.3 50.1 40.6 -12.1 44.2 24.3 535.0 49.8 TZEI 7 x TZdEI 425 134.8 74.2 48.4 11.3 -5.5 22.0 5.4 23.2 19.9 TZEI 18 x TZEI 31 146.8 -38.9 25.2 13.3 15.3 -7.2 -0.3 26.5 38.8 TZEI 18 x TZEI 56 18.2 -23.1 36.8 30.6 8.3 36.0 29.6 18.7 35.6 TZEI 18 x TZEI 80 314.3 -94.8 44.5 -7.1 51.8 -8.9 -15.6 79.6 30.1 TZEI 18 x TZdEI 120 231.0 -12.6 34.6 2.1 38.2 -17.8 -3.9 18.4 -17.3 TZEI 18 x TZEI 296 184.6 18.1 39.5 14.0 30.2 1.6 0.0 24.1 57.9 TZEI 18 x TZEI 326 164.9 24.0 41.7 -0.9 14.9 -14.2 -11.0 27.6 -0.8 TZEI 18 x TZEI 352 157.0 -69.0 53.5 9.3 10.6 3.9 2.0 4.1 39.8 TZEI 18 x TZdEI 352 185.6 -22.3 41.9 4.9 30.7 -11.9 -9.1 -39.7 -46.5 TZEI 18 x TZEI 355 185.4 -41.3 33.1 12.9 27.7 10.8 8.9 254.2 133.5 TZEI 18 x TZEI 383 146.8 -43.6 29.6 2.2 19.7 -5.5 -7.1 103.4 105.6 TZEI 18 x TZEI 410 47.0 -30.9 35.8 17.8 16.4 15.3 9.9 160.0 44.8 TZEI 18 x TZdEI 425 113.4 89.6 53.6 21.1 10.7 10.2 11.8 80.8 54.9 113 University of Ghana http://ugspace.ug.edu.gh Appendix 2 (cont’d) Estimates of heterosis for grain yield and other agronomic traits of 105 hybrids under Striga infestation. PEDIGREE YLD ASI PLHT EASP EPP STRR1 STRR2 STRCO1 STRCO2 MP MP MP MP MP MP MP MP MP TZEI 31 x TZEI 56 92.7 -38.0 37.6 9.9 17.6 4.8 -8.3 47.0 15.2 TZEI 31 x TZEI 80 90.7 -71.0 46.1 11.8 12.0 7.6 -6.3 145.7 80.1 TZEI 31 x TZdEI 120 175.3 -39.1 46.3 6.5 18.6 -3.0 -9.4 48.1 25.9 TZEI 31 x TZEI 296 75.3 -2.1 50.9 19.0 17.3 15.3 0.5 167.7 74.2 TZEI 31 x TZEI 326 85.3 -15.3 37.8 8.9 0.4 8.5 0.8 74.7 65.0 TZEI 31 x TZEI 352 120.9 36.4 32.9 10.4 0.7 26.2 12.9 74.9 -2.0 TZEI 31 x TZdEI 352 168.2 -9.9 36.9 5.1 3.6 0.6 -6.7 -48.7 -36.9 TZEI 31 x TZEI 355 53.7 -53.1 39.8 27.5 -0.9 19.1 0.0 109.9 101.2 TZEI 31 x TZEI 383 108.3 -56.6 35.4 5.6 3.7 7.2 -4.0 -16.6 -45.1 TZEI 31 x TZEI 410 77.6 10.5 48.5 13.9 -5.1 10.2 4.2 120.7 64.5 TZEI 31 x TZdEI 425 138.8 57.1 53.4 1.9 -0.7 11.2 4.1 125.1 98.1 TZEI 56 x TZEI 80 24.8 -166.6 32.9 9.0 -0.5 19.1 5.7 132.0 95.1 TZEI 56 x TZdEI 120 83.1 42.6 33.9 17.8 9.9 6.1 5.6 56.6 55.8 TZEI 56 x TZEI 296 186.7 -58.9 33.4 -1.9 18.2 7.1 1.8 1.0 5.0 TZEI 56 x TZEI 326 44.6 -69.6 34.7 28.4 -1.6 28.5 26.4 180.2 113.3 TZEI 56 x TZEI 352 44.6 5.6 42.4 29.1 -5.4 50.4 48.9 341.4 203.8 TZEI 56 x TZdEI 352 80.4 -7.2 49.5 18.9 -5.0 3.8 5.6 84.2 88.6 TZEI 56 x TZEI 355 76.4 -100.6 35.3 23.5 15.4 21.5 26.8 29.6 43.0 TZEI 56 x TZEI 383 58.5 -38.5 35.5 23.1 1.5 39.2 28.8 75.5 113.6 TZEI 56 x TZEI 410 43.5 -23.2 18.6 29.1 -1.4 35.3 29.6 243.4 51.0 TZEI 56 x TZdEI 425 33.0 53.9 52.3 24.6 8.8 35.1 24.7 41.8 92.9 TZEI 80 x TZdEI 120 416.2 -53.1 34.5 -7.7 53.7 -25.2 -30.9 40.1 -8.5 TZEI 80 x TZEI 296 259.7 -59.7 36.9 -10.6 20.6 -4.6 -15.1 70.3 68.6 TZEI 80 x TZEI 326 132.9 23.3 38.4 -0.6 9.5 -6.9 -17.5 217.0 68.9 TZEI 80 x TZEI 352 207.8 102.2 53.1 10.8 16.1 -0.9 -11.2 145.6 126.3 TZEI 80 x TZdEI 352 289.3 -32.6 44.7 -5.0 25.4 -23.8 -27.4 -8.1 56.2 TZEI 80 x TZEI 355 280.6 -59.4 49.5 -8.3 25.3 4.9 -16.6 433.6 129.3 TZEI 80 x TZEI 383 145.4 -47.5 41.6 -3.6 9.5 8.6 -7.8 143.4 84.0 TZEI 80 x TZEI 410 157.5 54.2 39.4 -4.1 4.9 2.9 -14.0 374.7 142.0 TZEI 80 x TZdEI 425 265.3 -61.3 57.7 -7.7 14.7 6.3 -9.4 197.2 168.2 TZdEI 120 x TZEI 296 232.3 -2.2 30.2 1.6 39.2 -11.9 -9.9 126.7 276.8 TZdEI 120 x TZEI 326 201.1 -9.3 36.7 2.6 22.7 -12.3 -11.4 13.3 -1.6 TZdEI 120 x TZEI 352 175.1 31.8 48.1 -7.1 26.8 -5.2 -9.0 90.2 69.7 TZdEI 120 x TZdEI 352 166.9 -21.7 42.2 3.6 22.8 -4.9 -12.1 23.8 23.8 TZdEI 120 x TZEI 355 199.0 -48.8 20.8 -9.0 23.5 -7.7 -11.8 218.4 156.0 TZdEI 120 x TZEI 383 184.9 -40.9 24.8 0.7 25.4 -19.7 -22.5 -83.9 -94.2 TZdEI 120 x TZEI 410 113.0 -108.7 43.5 -4.8 16.9 1.5 -1.2 13.4 12.8 TZdEI 120 x TZdEI 425 275.0 -60.0 51.7 -1.1 21.5 4.2 12.9 -7.0 1.8 114 University of Ghana http://ugspace.ug.edu.gh Appendix 2 (cont’d) Estimates of heterosis for grain yield and other agronomic traits of 105 hybrids under Striga infestation. PEDIGREE YLD ASI PLHT EASP EPP STRR1 STRR2 STRCO1 STRCO2 MP MP MP MP MP MP MP MP MP TZEI 296 x TZEI 326 226.5 -46.9 34.5 8.7 16.2 -2.1 -11.5 77.2 -19.7 TZEI 296 x TZEI 352 121.2 -13.2 41.8 14.7 9.5 11.6 1.0 158.4 45.3 TZEI 296 x TZdEI 352 199.6 9.4 47.4 -3.2 16.6 -3.1 -13.9 90.2 47.3 TZEI 296 x TZEI 355 201.1 -88.7 30.7 3.2 15.2 -1.7 -2.9 262.2 89.5 TZEI 296 x TZEI 383 120.2 -91.8 28.2 -5.3 12.6 8.5 9.1 183.5 93.5 TZEI 296 x TZEI 410 80.4 -34.9 36.7 7.8 3.0 9.0 -0.4 501.2 186.5 TZEI 296 x TZdEI 425 148.0 39.9 49.1 9.1 1.4 8.1 3.7 145.5 84.4 TZEI 326 x TZEI 352 79.8 -49.5 29.8 6.6 2.2 14.4 8.6 340.1 100.4 TZEI 326 x TZdEI 352 144.7 -23.7 40.0 -3.1 3.6 3.9 -6.2 80.7 67.4 TZEI 326 x TZEI 355 37.7 94.2 26.4 19.1 -14.4 -8.7 -10.9 423.2 57.8 TZEI 326 x TZEI 383 61.6 50.9 38.4 12.8 2.5 1.6 1.5 6.9 -31.5 TZEI 326 x TZEI 410 49.1 225.6 31.3 20.3 -0.5 15.2 -0.1 120.4 -50.7 TZEI 326 x TZdEI 425 32.6 68.7 52.6 15.2 -11.6 8.1 1.9 105.2 83.7 TZEI 352 x TZdEI 352 196.9 -62.7 37.7 -2.0 4.8 0.5 -3.9 64.6 41.2 TZEI 352 x TZEI 355 55.4 -2.5 42.3 29.8 -5.2 25.0 21.7 488.4 228.1 TZEI 352 x TZEI 383 106.0 -13.1 41.0 13.0 49.0 16.8 9.8 -46.6 55.0 TZEI 352 x TZEI 410 59.6 -55.0 36.7 16.0 -1.5 18.9 8.1 -56.7 -37.3 TZEI 352 x TZdEI 425 111.5 -27.7 54.8 6.3 -12.7 25.5 13.5 109.1 81.0 TZdEI 352 x TZEI 355 208.3 -60.1 37.9 3.9 5.3 1.3 -13.8 41.0 -26.8 TZdEI 352 x TZEI 383 136.0 20.7 45.6 2.9 -0.2 -5.0 -11.5 24.1 74.0 TZdEI 352 x TZEI 410 81.2 -94.8 34.3 19.8 1.2 21.6 8.9 321.8 49.7 TZdEI 352 x TZdEI 425 77.3 -57.5 45.2 17.3 9.8 6.2 -3.1 92.0 85.2 TZEI 355 x TZEI 383 28.1 30.7 26.2 25.4 -7.9 28.3 9.2 76.3 -43.5 TZEI 355 x TZEI 410 95.1 -84.9 36.4 22.7 3.9 17.0 12.3 347.9 37.0 TZEI 355 x TZdEI 425 282.7 -51.3 48.3 -8.8 24.3 -18.4 -16.5 59.6 131.2 TZEI 383 x TZEI 410 36.3 -29.9 33.8 25.4 3.1 20.1 7.0 -78.8 -19.5 TZEI 383 x TZdEI 425 95.6 -59.3 45.1 10.4 1.8 6.1 -4.8 20.2 54.4 TZEI 410 x TZdEI 425 152.2 -116.2 57.5 4.1 14.6 9.4 -2.3 25.8 -2.0 115 University of Ghana http://ugspace.ug.edu.gh Appendix 3 Estimates of heterosis for grain yield and other agronomic traits of 105 hybrids under optimum conditions. PEDIGREE YLD ASI PLHT PASP EASP EPP MP MP MP MP MP MP TZEI 5 x TZEI 7 224.9 18.4 46.8 25.7 -4.4 18.0 TZEI 5 x TZEI 18 177.4 -35.5 38.0 17.6 -6.6 24.5 TZEI 5 x TZEI 31 159.4 -43.6 40.8 14.8 8.7 21.8 TZEI 5 x TZEI 56 169.0 -93.0 43.7 21.3 5.1 11.7 TZEI 5 x TZEI 80 156.8 -6.1 30.4 29.6 11.0 35.8 TZEI 5 x TZdEI 120 136.4 -15.3 39.4 5.1 -3.5 10.7 TZEI 5 x TZEI 296 206.9 -78.7 41.3 8.1 -6.9 30.3 TZEI 5 x TZEI 326 230.0 -13.3 50.8 -1.7 -13.2 16.8 TZEI 5 x TZEI 352 125.6 -79.2 41.4 24.7 5.6 13.4 TZEI 5 x TZdEI 352 194.6 -69.6 43.7 19.5 -11.2 13.7 TZEI 5 x TZEI 355 227.1 -89.3 43.0 6.8 -4.8 21.4 TZEI 5 x TZEI 383 154.6 -66.5 40.7 11.8 8.7 17.6 TZEI 5 x TZEI 410 170.5 -15.3 36.5 15.6 3.5 6.3 TZEI 5 x TZdEI 425 178.4 -102.5 43.3 18.7 8.5 5.0 TZEI 7 x TZEI 18 224.9 -73.4 47.1 12.9 -17.4 17.9 TZEI 7 x TZEI 31 230.7 -70.0 39.6 19.9 1.6 22.0 TZEI 7 x TZEI 56 195.9 -102.6 49.6 22.0 1.7 4.8 TZEI 7 x TZEI 80 237.3 -122.4 44.1 27.6 -3.0 33.7 TZEI 7 x TZdEI 120 229.7 -72.0 46.8 10.3 0.8 15.2 TZEI 7 x TZEI 296 324.1 -95.4 47.1 10.9 -1.8 21.6 TZEI 7 x TZEI 326 298.6 11.8 47.0 9.0 -3.9 18.5 TZEI 7 x TZEI 352 250.1 -62.9 54.5 13.3 -6.3 14.7 TZEI 7 x TZdEI 352 217.1 -93.8 42.1 47.6 -8.0 10.9 TZEI 7 x TZEI 355 218.9 -94.7 40.3 21.2 5.2 20.8 TZEI 7 x TZEI 383 194.8 -157.6 44.8 15.7 4.7 13.9 TZEI 7 x TZEI 410 238.9 -52.3 47.1 16.5 -9.2 13.0 TZEI 7 x TZdEI 425 226.0 -48.0 51.2 18.5 -10.8 6.3 TZEI 18 x TZEI 31 139.9 -30.4 37.3 10.0 -8.7 19.2 TZEI 18 x TZEI 56 142.1 -35.1 43.0 38.5 12.5 11.3 TZEI 18 x TZEI 80 152.9 -51.0 41.1 36.5 4.1 37.6 TZEI 18 x TZdEI 120 188.9 -13.0 39.1 16.7 -2.0 16.1 TZEI 18 x TZEI 296 266.0 -27.2 36.2 11.1 3.9 29.7 TZEI 18 x TZEI 326 237.4 -23.1 52.8 -0.9 -11.1 16.3 TZEI 18 x TZEI 352 176.5 -44.1 42.4 22.1 0.1 13.3 TZEI 18 x TZdEI 352 220.4 -47.4 35.4 26.3 -15.4 13.1 TZEI 18 x TZEI 355 192.1 -44.1 36.6 17.8 0.3 19.7 TZEI 18 x TZEI 383 186.2 -63.9 39.4 17.2 -15.0 25.2 TZEI 18 x TZEI 410 247.3 -97.6 44.4 3.8 -9.2 17.5 TZEI 18 x TZdEI 425 215.1 -37.3 35.7 22.3 -0.5 10.7 116 University of Ghana http://ugspace.ug.edu.gh Appendix 3 cont’d Estimates of heterosis for grain yield and other agronomic traits of 105 hybrids under optimum conditions. PEDIGREE YLD ASI PLHT PASP EASP EPP MP MP MP MP MP MP TZEI 31 x TZEI 56 145.9 74.2 40.8 12.8 7.2 6.7 TZEI 31 x TZEI 80 142.0 -96.1 46.8 21.2 1.6 25.6 TZEI 31 x TZdEI 120 162.8 -95.4 42.5 13.2 -7.9 19.1 TZEI 31 x TZEI 296 183.6 -45.2 42.5 2.3 2.9 18.2 TZEI 31 x TZEI 326 180.2 -37.4 44.5 6.9 -9.1 15.4 TZEI 31 x TZEI 352 191.6 -64.1 41.1 16.0 4.5 13.0 TZEI 31 x TZdEI 352 183.7 -47.7 29.9 34.3 -5.9 13.2 TZEI 31 x TZEI 355 150.1 -17.2 36.0 15.2 11.3 19.7 TZEI 31 x TZEI 383 117.5 -99.7 36.2 20.0 6.0 16.8 TZEI 31 x TZEI 410 121.8 -54.4 40.5 26.1 11.7 7.9 TZEI 31 x TZdEI 425 210.4 -126.6 45.8 13.8 6.6 3.1 TZEI 56 x TZEI 80 169.6 -51.4 40.2 37.3 12.5 20.0 TZEI 56 x TZdEI 120 145.1 -35.5 46.2 30.2 5.4 15.0 TZEI 56 x TZEI 296 161.9 -43.3 41.9 27.8 11.6 7.6 TZEI 56 x TZEI 326 226.6 2.5 48.6 15.8 -0.9 6.6 TZEI 56 x TZEI 352 144.4 -53.2 40.7 30.9 22.4 3.3 TZEI 56 x TZdEI 352 157.9 -78.2 42.0 34.8 7.4 0.5 TZEI 56 x TZEI 355 144.9 -117.3 42.0 34.1 23.1 7.9 TZEI 56 x TZEI 383 163.3 -120.4 40.1 26.2 16.7 11.2 TZEI 56 x TZEI 410 173.6 -24.9 46.2 23.5 3.0 2.0 TZEI 56 x TZdEI 425 167.7 -68.2 44.0 32.7 10.1 -4.2 TZEI 80 x TZdEI 120 209.5 -35.7 43.0 21.2 -10.0 32.8 TZEI 80 x TZEI 296 300.3 -61.2 42.0 27.5 -1.9 40.2 TZEI 80 x TZEI 326 220.3 -20.0 46.0 23.8 -9.6 30.0 TZEI 80 x TZEI 352 192.8 -13.7 42.1 26.6 -0.7 23.5 TZEI 80 x TZdEI 352 176.2 50.7 40.4 47.4 -5.9 22.4 TZEI 80 x TZEI 355 219.1 -29.2 29.0 31.6 5.1 40.6 TZEI 80 x TZEI 383 183.8 -48.8 38.1 24.2 7.1 34.1 TZEI 80 x TZEI 410 207.3 26.9 34.4 23.8 0.0 23.1 TZEI 80 x TZdEI 425 257.1 -76.6 42.8 31.7 -11.5 24.2 TZdEI 120 x TZEI 296 241.7 -1.0 45.3 10.7 -13.4 21.3 TZdEI 120 x TZEI 326 229.9 -29.4 50.9 0.0 -9.7 30.3 TZdEI 120 x TZEI 352 186.4 -1.6 46.8 21.8 -1.5 12.8 TZdEI 120 x TZdEI 352 134.5 43.8 36.8 44.9 -5.6 5.2 TZdEI 120 x TZEI 355 245.8 -75.3 39.1 20.3 -7.5 17.9 TZdEI 120 x TZEI 383 159.8 -77.8 39.8 12.7 -9.8 11.4 TZdEI 120 x TZEI 410 183.1 4.3 43.9 13.5 -0.1 6.6 TZdEI 120 x TZdEI 425 221.3 16.6 50.9 15.0 -7.9 3.9 117 University of Ghana http://ugspace.ug.edu.gh Appendix 3 cont’d Estimates of heterosis for grain yield and other agronomic traits of 105 hybrids under optimum conditions. PEDIGREE YLD ASI PLHT PASP EASP EPP MP MP MP MP MP MP TZEI 296 x TZEI 326 221.2 -68.2 46.5 15.2 -10.6 24.5 TZEI 296 x TZEI 352 194.4 -26.6 42.9 7.6 7.0 10.8 TZEI 296 x TZdEI 352 289.9 -112.4 25.1 14.7 -20.4 15.8 TZEI 296 x TZEI 355 297.6 -106.2 37.2 18.4 5.2 28.6 TZEI 296 x TZEI 383 210.4 -103.1 37.9 11.4 -1.3 17.7 TZEI 296 x TZEI 410 323.3 -51.9 55.0 9.9 -13.4 14.0 TZEI 296 x TZdEI 425 314.4 -115.6 34.5 -0.7 -23.6 6.7 TZEI 326 x TZEI 352 135.8 -33.2 38.0 23.2 -5.7 2.3 TZEI 326 x TZdEI 352 199.1 15.4 41.3 9.3 -11.0 6.5 TZEI 326 x TZEI 355 121.5 -61.3 36.1 20.9 2.8 13.9 TZEI 326 x TZEI 383 90.4 -45.4 42.7 21.1 3.4 13.7 TZEI 326 x TZEI 410 102.5 -77.7 39.9 27.7 11.7 -3.5 TZEI 326 x TZdEI 425 229.8 34.8 45.0 5.0 -16.0 1.1 TZEI 352 x TZdEI 352 198.9 0.7 46.6 28.6 -12.7 6.5 TZEI 352 x TZEI 355 95.3 554.7 33.9 36.8 11.4 2.3 TZEI 352 x TZEI 383 117.9 -129.0 42.9 15.7 19.8 10.0 TZEI 352 x TZEI 410 118.4 -104.2 44.6 27.9 16.5 2.6 TZEI 352 x TZdEI 425 215.5 -119.3 49.2 11.0 -12.4 1.5 TZdEI 352 x TZEI 355 259.0 -89.3 37.6 19.5 -9.9 12.8 TZdEI 352 x TZEI 383 166.9 -30.7 42.8 33.1 -15.7 10.3 TZdEI 352 x TZEI 410 181.4 -30.0 33.9 38.8 1.8 2.0 TZdEI 352 x TZdEI 425 112.0 2.5 29.3 46.4 19.3 -2.9 TZEI 355 x TZEI 383 112.6 -101.7 33.7 29.2 22.2 17.2 TZEI 355 x TZEI 410 144.4 -86.9 33.9 37.7 24.7 5.2 TZEI 355 x TZdEI 425 225.0 -68.7 39.5 27.4 0.5 0.9 TZEI 383 x TZEI 410 162.4 -22.5 33.5 16.8 16.7 9.9 TZEI 383 x TZdEI 425 184.5 -55.8 38.6 19.4 4.2 5.4 TZEI 410 x TZdEI 425 218.4 -108.1 40.4 15.1 -6.7 -3.3 118 University of Ghana http://ugspace.ug.edu.gh Appendix 4 Estimates of heterosis for grain yield and other agronomic traits of 105 hybrids across research environments. PEDIGREE YLD ASI PLHT EASP EPP MP MP MP MP MP TZEI 5 x TZEI 7 197.2 29.8 38.3 4.3 15.9 TZEI 5 x TZEI 18 176.1 2.1 42.3 -1.1 30.9 TZEI 5 x TZEI 31 166.2 -32.6 32.2 7.6 24.3 TZEI 5 x TZEI 56 165.8 -28.8 37.7 3.2 17.0 TZEI 5 x TZEI 80 164.9 -8.3 29.3 5.0 34.9 TZEI 5 x TZdEI 120 133.6 69.9 39.6 7.1 15.4 TZEI 5 x TZEI 296 206.0 -54.8 41.5 -0.8 33.8 TZEI 5 x TZEI 326 201.0 1.6 44.1 -5.6 11.0 TZEI 5 x TZEI 352 140.3 -36.8 35.8 5.6 20.8 TZEI 5 x TZdEI 352 195.8 -19.5 40.9 -7.7 19.2 TZEI 5 x TZEI 355 223.4 -58.1 49.6 -0.6 32.3 TZEI 5 x TZEI 383 138.4 -62.4 40.4 6.9 20.4 TZEI 5 x TZEI 410 165.4 -24.8 35.8 3.0 12.0 TZEI 5 x TZdEI 425 169.5 -17.6 38.3 6.3 12.8 TZEI 7 x TZEI 18 210.3 -22.4 41.2 -9.5 21.6 TZEI 7 x TZEI 31 189.7 -34.5 32.0 13.6 6.7 TZEI 7 x TZEI 56 170.7 -37.4 42.2 11.7 10.9 TZEI 7 x TZEI 80 239.3 -90.3 37.4 -4.8 23.6 TZEI 7 x TZdEI 120 199.6 -24.3 41.2 11.0 16.0 TZEI 7 x TZEI 296 292.9 -82.1 42.4 -0.2 20.1 TZEI 7 x TZEI 326 208.3 5.6 47.0 9.6 8.6 TZEI 7 x TZEI 352 208.9 -85.1 35.4 6.6 10.1 TZEI 7 x TZdEI 352 204.9 -62.6 36.0 -3.2 9.0 TZEI 7 x TZEI 355 214.6 -124.8 39.3 5.2 13.4 TZEI 7 x TZEI 383 179.8 -102.8 38.1 4.3 14.1 TZEI 7 x TZEI 410 166.2 61.1 38.5 10.4 3.3 TZEI 7 x TZdEI 425 197.5 28.7 38.8 -1.3 1.4 TZEI 18 x TZEI 31 138.5 -36.0 32.6 0.4 17.2 TZEI 18 x TZEI 56 104.2 -28.4 44.0 19.9 10.1 TZEI 18 x TZEI 80 186.5 -75.3 43.3 -0.5 43.0 TZEI 18 x TZdEI 120 198.3 -11.4 38.2 -0.3 23.0 TZEI 18 x TZEI 296 242.3 -7.0 37.1 7.7 29.8 TZEI 18 x TZEI 326 218.0 1.1 54.4 -7.4 15.9 TZEI 18 x TZEI 352 170.3 -56.2 39.3 3.9 12.3 TZEI 18 x TZdEI 352 210.0 -30.8 36.5 -7.3 19.2 TZEI 18 x TZEI 355 188.0 -44.7 43.5 5.5 22.6 TZEI 18 x TZEI 383 173.8 -53.3 37.3 -8.3 23.1 TZEI 18 x TZEI 410 183.9 -64.2 38.6 1.8 17.1 119 University of Ghana http://ugspace.ug.edu.gh Appendix 4 cont’d Estimates of heterosis for grain yield and other agronomic traits of 105 hybrids across research environments. PEDIGREE YLD ASI PLHT EASP EPP MP MP MP MP MP TZEI 18 x TZdEI 425 192.3 38.7 37.2 8.4 10.9 TZEI 31 x TZEI 56 129.0 6.1 42.8 8.3 11.3 TZEI 31 x TZEI 80 125.0 -76.6 48.2 6.3 19.7 TZEI 31 x TZdEI 120 165.6 -53.4 45.5 -1.7 18.8 TZEI 31 x TZEI 296 146.8 -19.7 45.8 9.3 17.4 TZEI 31 x TZEI 326 150.9 -22.3 47.7 -2.0 9.2 TZEI 31 x TZEI 352 171.4 -3.3 31.3 6.8 8.6 TZEI 31 x TZdEI 352 179.0 -21.4 31.4 -1.6 9.4 TZEI 31 x TZEI 355 117.1 -41.6 45.4 17.8 10.8 TZEI 31 x TZEI 383 117.0 -68.3 37.9 5.8 11.0 TZEI 31 x TZEI 410 108.1 -11.0 41.2 12.7 2.5 TZEI 31 x TZdEI 425 193.5 17.0 44.6 4.6 1.7 TZEI 56 x TZEI 80 123.9 -101.1 41.8 11.2 11.2 TZEI 56 x TZdEI 120 126.5 3.1 45.5 10.5 13.5 TZEI 56 x TZEI 296 172.8 -47.5 42.3 6.0 12.0 TZEI 56 x TZEI 326 162.0 -18.4 52.4 10.6 2.9 TZEI 56 x TZEI 352 111.5 -30.3 37.8 25.1 0.1 TZEI 56 x TZdEI 352 131.5 -39.3 46.9 11.9 -1.7 TZEI 56 x TZEI 355 121.6 -112.5 52.1 23.1 11.2 TZEI 56 x TZEI 383 125.0 -89.0 43.3 19.2 7.3 TZEI 56 x TZEI 410 122.9 -17.6 36.7 13.9 0.3 TZEI 56 x TZdEI 425 128.6 -1.7 46.1 16.1 0.4 TZEI 80 x TZdEI 120 248.9 -45.9 42.0 -9.4 40.4 TZEI 80 x TZEI 296 291.6 -59.8 42.6 -5.4 31.5 TZEI 80 x TZEI 326 196.2 1.0 50.5 -6.1 22.3 TZEI 80 x TZEI 352 199.0 50.0 40.4 3.7 20.8 TZEI 80 x TZdEI 352 207.8 -6.6 42.9 -5.4 23.7 TZEI 80 x TZEI 355 237.4 -46.5 47.1 -0.2 34.1 TZEI 80 x TZEI 383 172.2 -47.7 42.7 2.7 23.6 TZEI 80 x TZEI 410 190.5 45.5 35.6 -1.5 15.7 TZEI 80 x TZdEI 425 258.8 -68.4 45.0 -10.0 21.0 TZdEI 120 x TZEI 296 237.4 -0.3 41.3 -7.1 27.5 TZdEI 120 x TZEI 326 222.6 -18.1 52.8 -4.8 27.5 TZdEI 120 x TZEI 352 181.9 16.8 41.5 -3.7 17.7 TZdEI 120 x TZdEI 352 143.5 -0.6 38.9 -1.9 11.9 TZdEI 120 x TZEI 355 230.6 -60.6 40.9 -8.0 20.0 120 University of Ghana http://ugspace.ug.edu.gh Appendix 4 cont’d Estimates of heterosis for grain yield and other agronomic traits of 105 hybrids across research environments. PEDIGREE YLD ASI PLHT EASP EPP MP MP MP MP MP TZdEI 120 x TZEI 383 166.6 -53.8 37.2 -5.4 16.8 TZdEI 120 x TZEI 410 163.3 -55.2 42.5 -2.1 10.5 TZdEI 120 x TZdEI 425 232.1 -35.2 48.5 -5.0 9.8 TZEI 296 x TZEI 326 223.0 -60.9 49.0 -3.0 20.8 TZEI 296 x TZEI 352 171.4 -21.5 36.5 10.0 10.3 TZEI 296 x TZdEI 352 259.2 -51.4 32.3 -13.8 16.1 TZEI 296 x TZEI 355 261.8 -100.4 43.7 4.4 22.8 TZEI 296 x TZEI 383 176.7 -99.2 36.6 -2.8 15.5 TZEI 296 x TZEI 410 232.4 -48.1 46.3 -5.3 10.1 TZEI 296 x TZdEI 425 269.4 -45.9 36.2 -10.5 5.1 TZEI 326 x TZEI 352 120.5 -40.9 34.3 -1.2 2.3 TZEI 326 x TZdEI 352 178.8 -5.5 45.5 -7.6 4.9 TZEI 326 x TZEI 355 91.9 10.4 46.5 9.0 2.0 TZEI 326 x TZEI 383 78.7 -4.1 48.9 7.3 8.8 TZEI 326 x TZEI 410 82.1 48.3 40.6 15.5 -2.7 TZEI 326 x TZdEI 425 176.3 54.7 49.3 -3.6 -3.5 TZEI 352 x TZdEI 352 197.8 -38.5 36.1 -8.5 6.1 TZEI 352 x TZEI 355 82.9 273.5 38.8 18.4 -0.3 TZEI 352 x TZEI 383 113.7 -70.4 37.8 17.1 26.2 TZEI 352 x TZEI 410 97.8 -84.0 33.0 16.4 1.3 TZEI 352 x TZdEI 425 190.7 -65.1 40.5 -4.4 -3.8 TZdEI 352 x TZEI 355 242.7 -70.2 45.3 -4.6 9.6 TZdEI 352 x TZEI 383 155.8 1.1 44.9 -8.5 6.2 TZdEI 352 x TZEI 410 146.3 -66.9 31.1 9.1 1.7 TZdEI 352 x TZdEI 425 103.9 -38.8 30.2 18.2 1.9 TZEI 355 x TZEI 383 82.4 -34.4 40.8 23.3 6.4 TZEI 355 x TZEI 410 124.5 -89.4 40.5 24.0 4.9 TZEI 355 x TZdEI 425 239.3 -60.5 47.6 -3.0 9.8 TZEI 383 x TZEI 410 116.8 -23.0 33.6 20.3 7.2 TZEI 383 x TZdEI 425 157.4 -61.0 39.0 6.8 4.3 TZEI 410 x TZdEI 425 198.9 -112.6 40.5 -2.2 3.3 121 University of Ghana http://ugspace.ug.edu.gh Appendix 5. Dendrogram of the 15 parental early-maturing white maize inbred lines constructed from SCA effects of grain yield (SCA) using Ward’s minimum variance cluster analysis under Striga-infested environment. 122 University of Ghana http://ugspace.ug.edu.gh Appendix 6. Dendrogram of the 15 parental early-maturing white maize inbred lines constructed from SCA and GCA effects of grain yield (HSGCA) using Ward’s minimum variance cluster analysis under Striga-free environments. 123 University of Ghana http://ugspace.ug.edu.gh Appendix 7. Dendrogram of the 15 parental early-maturing white maize inbred lines constructed from SCA and GCA effects of grain yield (HSGCA) using Ward’s minimum variance cluster analysis across research environments. 124