Genetic Variation Diversity and Genotype by Environment Interactions of Nutritional Quality traits in East African Sweetpotato i Genetic Variation, Diversity and Genotype by Environment Interactions of Nutritional Quality traits in East African Sweetpotato Silver Tumwegamire M.Sc. Agric. – Crop Science, B.Sc. Hons Agric. Department of Agricultural Production, College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda A thesis submitted to Makerere University Kampala for the award of Doctorate Degree of Philosophy in Agriculture July 2011 ISBN 978-92-9060-408-2 ii Declaration I wish to declare to the best of my knowledge that that the research presented in this thesis is original and conducted by myself and has not been presented for a degree award before. Signed Date 19 / 8 / 11 Silver Tumwegamire M.Sc. Agric. – Crop Science, B.Sc. Hons Agric. The thesis has been submitted for examination with our approval as University supervisors Signed Date 4 / 10 / 11 Professor Patrick R. Rubaihayo Department of Agricultural Production, College of Agricultural and Environmental Sciences, Makerere University Signed Date 19 / 8 / 11 Professor Don R. LaBonte Louisiana State University, AgCentre, iii Dedication To my loving family, parents, sisters and brothers for their support during the study. iv Acknowledgements The research was part of International Potato Centre’s (CIP) sweetpotato biofortification breeding project funded under a wider Harvest plus Biofortification challenge Program. I am especially grateful to Dr. Regina Kapinga, former SSA sweetpotato breeder, who helped to convince CIP to allow this research for a PhD thesis. I appreciate her motherly support and guidance during the entire study period. My deep gratitude goes to Professors Patrick Rubaihayo of Makerere University and Don La Bonte of Louisiana State University for their supervision and guidance of the study. It has been a rewarding experience to working with them, and I commend them for their patience when even the processes seemed to be slow. I would like also to express my sincere thanks to Dr. Grüneberg Wolfgang and Dr. Robert Mwanga for their technical guidance and advice during the entire study. Many other different people supported this work at different stages. My gratitude goes to Gabriella Burgos, Thomas Zum Felde, Eduardo Porras, Willy Alarcon, Federico Diaz, Raul Eyzaguirre and Lius Gutierrez Walhoff all from CIP head quarters for their kind support in different components of the research. Also Agnes Alajo, Yakubu Ssekamwa, Joweria Namakula, Rose Makumbi, and Moses Mwondha all from National Crops Resources Research Institute are appreciated for immense help during experiments and data collection in Uganda. Pheona Nabukaru and Joseph Ndunguru also helped with technical advice during molecular characterization of the germplasm v Contents List of Tables vii List of Figures ix Acronyms x Abstract xi INTRODUCTION 1 Origin and Importance of Sweetpotato 1 Micronutrient Deficiency Problems 1 Control strategies for Micronutrient Deficiencies 2 Problem Statement 2 Justification of the Study 3 Objectives of the Study 3 LITERATURE REVIEW 5 Sweetpotato Germplasm 5 Genetic Diversity Studies of the Sweetpotato Germplasm 5 Germplasm Characterization for Quality Traits among Staple Crops 6 Application of Near Infrared Reflectance Spectroscopy (NIRS) in Rapid Screening of Quality Traits in Staple Crops 7 Genetic and Environmental Interactions for Micronutrient Traits 7 Cited Literature 9 CHAPTER ONE 15 Evaluation of Dry Matter, Protein, Starch, ß-carotene, Iron, Zinc, Calcium and Magnesium in East African Sweetpotato [Ipomoea batatas (L.) Lam] Germplasm 15 Abstract 16 Introduction 17 Materials and Methods 19 Results 25 Discussion 33 Cited Literature 37 CHAPTER TWO 41 Genotype x Environment Interactions for East African Orange-fleshed Sweetpotato Clones Evaluated across Varying Ecogeograhic Conditions in Uganda 41 Abstract 42 Introduction 43 Materials and Methods 45 Results 48 Discussion 61 vi Cited Literature 66 CHAPTER THREE 69 Genetic Diversity in White- and Orange-fleshed Sweetpotato Farmer Varieties from East Africa evaluated by Simple Sequence Repeat (SSR) Markers 69 Abstract 70 Introduction 71 Materials and Methods 73 Plant Material 73 DNA extraction 73 Simple Sequnce Repeat Amplification 76 Simple Sequnce Repeat data scoring and analysis 78 Results 79 Discussion 85 Cited Literature 88  vii List of Tables Table 1.1 List of sweetpotato varieties used for quality characterization at Namulonge and Kachwekano in Uganda during 2005/06. 20 Table 1.2 Description of locations used for the evaluation of farmer varieties. 22 Table 1.3 Experimental means ( x ), coefficient of variation (CV %), minimum (min) and maximum (max) genotypic values for observed traits at locations. 25 Table 1.4 Estimated variance components, variance component ratios in brackets, and operational broad-sense heritabilities of observed traits 26 Table 1.5 Clone means of farmer varieties for observed traits across locations. 27 Table 1.6 Pearson correlation coefficients among observed traits in East African sweetpotatoes. 29 Table 1.7 Clone means of farmer varieties for contribution to recommended daily intake (RDA) of micro-nutrients based on 250 g fresh sweetpotato root consumption per day 31 Table 2.1 Description of clones used for the GxE analysis (CT, Cultivar type; FV, Farmer variety; MV, Modern variety; IO, Intermediate orange; DO, Deep orange; LO, Light orange; SPVD, Sweetpotato virus disease) 45 Table 2.2 Description of locations used for the GxE analysis 46 Table 2.3 Environmental means for observed traits across genotypes [harvest index (HI), % dry matter (DM), Iron (Fe), Zinc (Zn), β-carotene (BC), Calcium (Ca), Magnesium (Mg) and Sucrose (SUC)] 48 Table 2.4 Clone means for observed traits across environments [harvest index (HI), % dry matter (DM), Iron (Fe), Zinc (Zn), β-carotene (BC), Calcium (Ca) Magnesium (mg) and % Sucrose (SUC)]. 49 Table 2.5 Variance components and operational broad-sense heritabilities for observed traits 51 Table 2.6 An ANOVA for genotype (G) by Environment (E) interaction (GxE) with subdivision (SUB) of GxE interaction using regression analysis for storage root yield, iron, zinc, calcium and magnesium contents of storage roots (Het. R. = heterogeneity due to regression, Dev. R. = deviation from regression lines 52 Table 2.7 Estimates obtained using the dynamic concept of genotype x environment interaction for storage root yield, iron (Fe), zinc (Zn), calcium (Ca) and magnesium (Mg) content of storage roots 53 Table 2.8 Estimates obtained using the static concept of genotype x environment interaction for storage root yield, iron (Fe), zinc (Zn), calcium (Ca) and magnesium (Mg) content of storage roots 54 Table 2.9 Pearson correlation coefficients among observed traits 60 Table 3.1 Description of clones used for the genetic diversity study in farmer varieties from East Africa and 7 non-African varieties as checks 74 viii Table 3.2 Description of SSR markers used to characterize sweetpotato genotypes by currently used names, motifs, forward and reverse primers, and annealing temperature 77 Table 3.3 Number of polymorphic alleles and their bp range generated by SSR markers in farmer varieties from East Africa and check. 80 Table 3.4 Analysis of Molecular Variance (AMOVA) of 92 sweetpotato accessions grouped into East African versus non-African germplasm 83 Table 3.5 Analysis of Molecular Variance (AMOVA) of 92 sweetpotato accessions grouped into OFSP versus WFSP germplasm 84 Table 3.6 The average genetic distances among sweetpotato accessions 84 ix List of Figures Figure 2.1 Storage root yield of ten clones of sweetpotato used for analysis of genotype x environment interactions across eight environments: KA = Kachwekano, S = Serere, NM = 1 Namulonge, MBK = Mobuku, S1 = season 1, and S2 = season 2. 50 Figure 2.2 The AMMI bi-plot of 10 sweetpotato clones evaluated for storage root yield in 8 environments in Uganda 55 Figure 2.3 The AMMI biplot of 10 sweetpotato accessions evaluated for iron storage root content in 8 environments in Uganda 56 Figure 2.4 The AMMI biplot of 10 sweetpotato accessions evaluated for zinc storage root content in 8 environments in Uganda 57 Figure 2.5 The AMMI biplot of 10 sweetpotato accessions evaluated for calcium storage root content in 8 environments in Uganda 58 Figure 2.6 The AMMI biplot of 10 sweetpotato accessions evaluated for magnesium storage root content in 8 environments in Uganda 59 Figure 3.1 Frequency distribution of pairwise SSR similarity coefficients among 85 EA farmer varieties and 7 non-African varieties 81 Figure 3.2 Dendrogam of the UPGMA cluster analysis on the basis of Jaccard’s SSR based genetic similarities among 85 EA farmer varieties and 7 varieties of non-African origin used as check clones 82 x Acronyms AMMI Additive Multiplicative Main Interactions AMOVA Analysis of Molecular Variance ANOVA Analysis of Variance CIAT International Centre for Tropical Agriculture CIMMTY International Maize and Wheat Research Centre CIP International Potato Centre CV Coefficieint of Variation DM Dry matter EA East Africa ECA East and Central Africa HPLC High Performance Liquid Chromatography IARC International Agricultural Research Centres IITA International Institute of Tropical Agriculture NaCRRI National Crops Resources Research Institute ng Nanogram NIRS Near Infra-red Reflectance Spectroscopy OFSP Orange-fleshed sweetpotato PC Principal Component PCR Polymerase Chain Reaction PLABSTAT Plant Breeding Statistical program RDA Recommended Daily Allowance SSA Sub-Saharan Africa SSR Simple Sequence Repeats UPGMA Unweighted Pair Group Method Analysis UBOS Uganda Beareau of Statistics VAD Vitamin A dificiency WFSP White/cream-fleshed sweetpotato xi Abstract Sweetpotato is one of the staples that have been earmarked by the global initiatives to fight micronutrient deficiency, particularly vitamin A deficiency. The present study sought to contribute to the pre-breeding knowledge base required for the improvement of sweetpotato nutritional quality targeting β-carotene, dry matter, starch, sucrose and minerals (i.e Fe, Zn, Ca and Mg) as a sustainable strategy to reduce the problems associated with the micronutrient deficiencies and malnutrition among people in developing countries. The specific objectives of the study were to i) characterize selected East African sweetpotato accessions for storage root quality (dry matter, protein, starch, sucrose, ß-carotene, iron, zinc, calcium and magnesium) ii) determine the magnitude of GxE variation in orange-fleshed sweetpotato (OFSP) varieties of East African origin for yield and nutritional traits conducted across ecogeograhic zones of Uganda; and iii) study genetic relationships among and between OFSP and white-fleshed sweetpotato (WFSP) farmer varieties gene pools, and how these two phenotypic groups compare with non-African OFSP and WFSP accessions. For the micronutrient profiling study, 89 (White/cream- and orange-fleshed) landraces, plus one introduction, Resisto, were evaluated at Namulonge and Kachwekano research stations in Uganda. Roots were analyzed for β-carotene, iron, zinc, calcium, magnesium, protein and starch content using the Near Infrared Refractance Spectroscopy (NIRS) procedure. The 2 G variance was significant (p < 0.01) for all the traits except sucrose content. Overall, the farmer varieties had higher dry matter, higher starch, and lower sucrose contents than the check. It is these qualities that make sweetpotato attractive as a starchy staple in EA. A low population’s mean of β-carotene content was observed. However, deep orange-fleshed farmer varieties, ‘Carrot_C’, ‘Ejumula’, ‘Carrot Dar’, ‘Mayai’ and ‘Zambezi’ had β-carotene content that can meet ≥350% of recommended daily allowance (RDA) with 250 g serving to a 5 – 8 year old child. More, but light orange- fleshed farmer varieties ‘ARA244 Shinyanga’, ‘HMA493 Tanzania’, ‘K-118’, ‘K-134’, ‘K-46’, ‘PAL161’, ‘Sowola6’, ‘SRT52’, and ‘Sudan’ can provide 50 - 90% RDA of the child. The root minerals’ content was generally low except for magnesium, the content of which can meet ≥ 50% RDA in many farmer varieties. However, in areas with high sweetpotato consumption, varieties ‘Carrot_C’, ‘Carrot Dar’, ‘KRE Nylon’, ‘MLE163 Kyebandula’ and ‘SRT49 Sanyuzameza’ can improve iron, zinc, calcium, and magnesium intake. In conclusion, some EA farmer varieties can contribute greatly to alleviation of vitamin A deficiency and meaningful mineral intakes. The GxE analysis was conducted with regression, and additive main effects and multiplicative interaction (AMMI). The environment effects were significant (p < 0.05; or < 0.01) for root yield, harvest index, and all quality traits except dry matter. The genotypic effects were significant (p < 0.05; or < 0.01) for all traits except root yield, iron and magnesium. Accessions, ‘Ejumula’, ‘SPK004/6’, and ‘SPK004/6/6’ had higher root yields xii than the check, Resisto, while ‘Naspot_5/50’ had the lowest root yields. The former three accessions are released in Uganda, and represent the potential gains in breeding for orange-fleshed sweetpotato clones with high root yields, dry matter and β-carotene. The σ2 GxE components were not significant (p>0.05) for β- carotene and starch root content. The σ2 GxE components were highly significant (p<0.01) for dry matter but fractional (0.4) compared to the corresponding σ2 G component. These results suggest traits can be improved with high selection efficiency in the early stages of a sweetpotato breeding program. The σ2 GxE: σ2 G ratio was close to 1 for harvest index and sucrose content, and large (> 2) for storage root yields and all mineral contents. Like for yield, the results suggest that breeding for elevated mineral levels in sweetpotato is complex and requires information about the causes of GxE interactions before the breeder can embark on enhancing these minerals. However, medium to high positive correlations among mineral traits simplify selection aiming at elevated mineral contents in sweetpotato and it merits research if the trait complex of minerals can be improved more efficiently by an index. For the genetic diversity study, eighty five East African farmer varieties (29 OFSPs and 56 WFSPs) and 7 varieties of non-African origin as check clones were analyzed using 26 simple sequence repeat (SSR) markers. A total 158 alleles were scored with an average of 6.1 alleles per SSR loci. The mean of Jaccard’s similarity coefficients was 0.54. The unweighted pair group method analysis (UPGMA) revealed a main cluster for EA germplasm at a similarity coefficient of 0.52. At a similarity coefficient of about 0.56 sub clusters within the EA germplasm were observed, but these were neither country nor flesh color specific. Analysis of molecular variance (AMOVA) found a significant difference between EA and non-African germplasm, and a non significant difference between OFSP and WFSP germplasm. In conclusion, the EA germplasm appears to be distinct from non-African germplasm, and OFSP and WFSP farmer varieties from EA are closely related. OFSP farmer varieties from EA might show similar adaptation to SSA environments as WFSP and a big potential in alleviating vitamin A deficiency (VAD). 1 Introduction Origin and Importance of Sweetpotato Sweetpotato [Ipomoea batatas (L.) Lam] belongs to the family Convolvulaceae. It is hexaploid, and usually considered the only species of Ipomoea of economic importance. It is of neotropical origin and crossed the Pacific via Polynesia before the discovery of the new world (Huaman et al., 1999; Zhang et al., 2000). In Africa it was introduced by explorers from Spain and Portugal during the 16th century (O’Brien, 1972; Zhang et al., 2000; Zhang et al., 2004). Based on the presence of large numbers of varieties, East Africa, is one of the areas suggested as secondary centres of diversity (Gichuki et al., 2003). With an annual production of 124 million tones, sweetpotato is the world’s seventh most important food crop after wheat, rice, maize, potato, barley and cassava (FAOSTAT, 2007), and the third most important tuberous root crop (Gibson et al., 2002). It is widely adapted in the tropics, sub-tropical and warm temperate regions where it is grown by smallholder farmers on marginal land with minimal inputs (Bashasha et al., 1995; Kapinga et al., 1995). Developing countries account for 98% of the world’s sweetpotato production. Africa produces only about 6% of the world crop, and almost all the crop is consumed directly by humans, hence the crop has a relatively large nutritional impact (Gibson et al., 2002). Indeed in East and Central Africa where over 70% of the Sub-Saharan Africa (SSA) regional sweetpotato is produced and daily per capita intake is high [e.g. about 240g in Uganda (FAOSTAT, 2007)], the potential to contribute to solving the problem of VAD has been shown to be greatest (Low et al., 2001). Micronutrient Deficiency Problems The pro-vitamin A and minerals (Fe, Zn, Ca, and Mg) are critical and deficient in human food supply (Frossard et al., 2000). Worldwide 100 million (Black, 2003) children under the age of five are vitamin A deficient and suffer high death rates due to diarrhea, measles and malaria. Also, 2 billion people, mostly infants, children and women of childbearing age in developing countries, are anemic (Frossard et al., 2000) due to Fe deficient diets. In the developing world, Fe and Zn deficiencies are implicated in 700,000 and 800,000 deaths per year, respectively (Black, 2003; WHO, 2002). According to Black (2003) 2.4%, 1.8% and 1.9% of the global disease burden is attributable to Fe deficiency, vitamin A deficiency (VAD) and Zn deficiency, respectively. In Uganda, about 20% of children and 19% of women are vitamin A deficient; and 73% of children and 49% of women are anemic (UBOS and Macro International Inc., 2007). The levels of anemia are higher among pregnant (64%) and breast feeding (53%) mothers. Overall, severe micronutrient malnutrition damages the cognitive development, lowers disease resistance in children and reduces the likelihood that mothers survive childbirth (Frossard et al., 2000). 2 Control strategies for Micronutrient Deficiencies Three broad strategies, namely; supplementation with pharmaceutical preparations, food fortifications, and dietary diversification have been adopted worldwide to avert the effects of micronutrient malnutrition (Frossard et al., 2000). Although notable reductions in prevalence levels have been achieved due to the above interventions, malnutrition remains high in remote areas of developing countries. The strategies have proved costly and less sustainable (Bouis, 2003; HarvestPlus, 2003). Food staples enriched with micronutrients through plant breeding have been adopted as a new but complementary strategy to avert the effects of micronutrient malnutrition by many International Agricultural Research Centers (IARC) and their partners in developing countries including SSA (Bouis, 2003; and HarvestPlus, 2003; Welch and Graham, 2004). The strategy is potentially sustainable because the staples are already part of the diets of the majority of the people (Frossard, et al., 2000; Harvest Plus, 2003) and high levels of the micronutrients have been identified in the staples. For example, high contents of Fe and Zn have been observed in the edible parts of such staple foods as rice, maize, beans and wheat (Gregorio, et al., 1999; Gregorio, 2002). It is within this IARC’s main framework to improve the nutritional quality of major staples that International Potato Centre (CIP) and its partners are aiming at improvement of sweetpotato nutritional quality, targeting β-carotene, starch, dry matter, protein, sucrose and minerals (i.e Fe, Zn, Ca and Mg) (Grüneberg et al., 2009). Problem Statement The breeding goals for nutrition quality in sweetpotato cannot be fully met with the current pre-breeding knowledge gaps (Grüneberg et al., 2005). For example whereas cultivars rich in β-carotene have been identified (Hagenimana et al., 1999; Laurie 2008; Mwanga et al., 2007; Mwanga et al., 2009), scanty information exists on the sources of mineral nutrients (Fe, Zn, Ca and Mg) among sweetpotato germplasm. Woolfe (1992) on average reported up to 0.69 and 0.24 mg/100g amounts of Fe and Zn, respectively. These levels are very low and comprehensive screening studies are required (Grüneberg et al., 2005 unpublished) to identify cultivars with higher levels. At the same time there is conflicting information on the extent to which this genetic variation of these micronutrients in sweetpotato germplasm interacts with environment (GxE). Previous studies (Woolfe, 1992; Ngeve, 1993, Ravindran et al., 1995) on several traits have shown that sweetpotato is sensitive to environmental variation, despite wide adaptability to harsh growing conditions. Preliminary findings (Grüneberg et al., 2005) show extremely low GxE interactions for the quality traits, β- carotene, Fe and Zn while Manrique and Hermann (2000) observed increased concentrations of ß-carotene at high altitudes among the studied clones. GxE interactions are of great importance when evaluating the stability of breeding clones under different environmental conditions. Of additional importance, especially to multi-trait breeding objectives of the micronutrients in sweetpotato, is the understanding of the genetic correlations of the target quality traits. All this information is currently lacking. 3 CIP’s overall goal of multi-trait selection for nutrient dense sweetpotato varieties, builds on the progress so far registered in the development of sweetpotato cultivars rich in ß-carotene. In SSA, breeding for ß- carotene rich cultivars has been faced with moderate rates of acceptability (due to low dry matter) and high susceptibility to viruses and drought of the introduced OFSP varieties. At the same time, CIP and partners in the region have identified what are considered as African ß-carotene rich farmer varieties, which are more adapted and are looked at as important gene pool to enhance the breeding objectives for quality sweetpotato in Africa. However, the genetic variation and distinctiveness of this group of OFSP farmer varieties are not understood. This knowledge is important for efficient rationalization and utilization of this germplasm (Zhang et al., 1998; LaBonte et al., 1997), designing appropriate plant breeding programs, as well as in making choice of parent genotypes for population development. Justification of the Study It has already been demonstrated that micronutrient enrichment traits are available within genomes of the major staple food crops including sweetpotato. However, research to identify accessions high in different nutritional qualities (dry matter, protein, starch, sucrose, ß-carotene, Fe, Zn, Ca and Mg) has been initiated by CIP for germplasm in genebank and breeding. But such characterization needs to be done for the germplasm from the Eastern Africa sub-region. The identified accessions could be promoted as superior varieties to farmers or used as parents in a comprehensive breeding program for improved nutrition in sweetpotato varieties without negatively impacting crop yields (Grüneberg et al., 2005). Apart from identifying varieties rich in the nutrients, there is a need to understand the GxE as well as the stability of the nutrient traits across diverse environments to guide future choice and use of appropriate breeding strategies for the improvement of sweetpotato (Grüneberg et al., 2005). Such an understanding would also allow making informed choices regarding which locations and input systems to be used in breeding efforts for improved nutrient levels in sweetpotato. Stability for β-carotene in sweetpotato cultivars has been reported (Manrique and Hermann, 2000) while no reports exists for mineral traits (Fe, Zn, Ca and Mg). African OFSP farmer varieties are a new sweetpotato population whose genetic diversity and distinctiveness are not understood. This is crutial if such varieties are to be maximally utilized for breeding. Objectives of the Study A study was therefore undertaken with the overall objective of contributing to the pre-breeding knowledge base required for the improvement of sweetpotato nutritional quality targeting β-carotene, dry matter, starch, sucrose and minerals (i.e Fe, Zn, Ca and Mg) as a sustainable strategy to reduce the problems associated with the deficiencies. The specific objectives of the study were to i) characterize selected East African sweetpotato accessions for storage root quality (dry matter, protein, starch, sucrose, ß-carotene, iron, zinc, calcium and magnesium); ii) determine the magnitude of GxE variation in OFSP varieties of East African origin for yield and nutritional traits conducted across ecogeograhic zones of Uganda; and iii) study 4 genetic relationships among and between OFSP and WFSP farmer varieties gene pools, and how these two phenotypic groups compare with non-African OFSP and WFSP accessions. 5 Literature review Sweetpotato Germplasm Sweetpotato is one of the major world staples with rich germplasm diversity (He et al., 1995). Nearly 8000 accessions of sweetpotato have been collected and maintained at various gene banks worldwide (Zhang et al., 2000) though this may represent a fraction of existing diversity. The majority of the accessions (5526) are being maintained in vitro at the CIP gene bank in Peru and these have been collected from 57 countries (Huaman and Zhang, 1997; Huaman et al., 1999; Zhang et al., 2000). A total of 2589 accessions have been collected from Latin America most of which are landraces and farmers’ varieties. In Papua New Guinea alone, there are about 5000 estimated cultivars (Takagi, 1988). Other sizable collections exist in China, Indonesia (CIP, Bogor) and the United States (National Plant Germplasm System collection, Griffin Georgia). Genetic Diversity Studies of the Sweetpotato Germplasm Genetic diversity studies have enhanced greater understanding of the extent of variation within the germplasm collections and required management practices. The information has been crucial in the development of core collections of different crops (Zhang et al., 2000) and tailoring germplasm exploration to focus on those areas with maximal genetic diversity (Wilde et al., 1992; Graner et al., 1994). The information has also been useful for the optimal design of plant breeding programs, influencing the choice of genotypes to cross for development of new populations (Zhang et al., 2000). In sweetpotato, a lot of germplasm diversity assessments have been based on morphological and agronomic traits as well as reaction to pests, diseases and other stresses (CIP/AVRDC/IBPGR, 1991). These traits, however, vary a lot with cultivars, environment, stage of growth, and cultural practices (Jarret et al., 1992; Gichuru, 2003) and hence unreliable when correct identification of germplasm is desired. Molecular markers supplant morphological characterization for traits that are environmentally unstable. They are powerful and reliable tools for discerning variation within crop germplasm and studying evolutionary relationships (Jarret et al., 1992; Gepts, 1993). Although, no practical use of molecular markers exists in sweetpotato improvement to date, studies in phylogenetics and gene pool evaluation, (Jarret et al., 1992; Jarret and Bowen, 1994; He et al., 1995; Zhang et al., 1998; Zhang et al., 2001), genomic characterization (Villordon and La Bonte, 1995), finger printing (Conolloy et al., 1994), map-making strategies (Krienger et al., 2001), and a marker for root- knot nematode resistance (Ukoskit et al., 1997) are reported. Zhang et al. (2001) studied genetic diversity of 113 accessions from Latin America using SSR markers. Results showed that three regions, Mesoamerica (Guatemala, Mexico, Nicaragua, Panama, El Salvador), Peru and Ecuador, and Colombia and Venezuela, were distinct from one another based on alleles unique in each of the three areas. Mesoamerica was found to possess the most allelic diversity and hence warrants consideration as the primary source of genetic 6 diversity in sweetpotato. Earlier, dispersal studies by Zhang et al. (1998) showed that Pupua New Guinea sweetpotato cultivars were distinct from those in Peru. On the other hand Rossel et al. (2001) showed that accessions from Oceania are likely to have originated from Mesoamerica and not from Peru Ecuador. Based on molecular classification, Fajardo (2000) identified a core collection of 12 genotypes from a collection of 141 genotypes from Papua New Guinea. High genetic diversity has been observed among the sweetpotato germplasm in East African region (Gichuki et al., 2003; and Gichuru, 2003; Abdelhameed et al., 2007; Yada et al., 2010) with the majority being farmers’ varieties (Bashasha et al., 1995; Kapinga et al., 1995; Abidin, 2004) existing under different names. None of these studies has reported genetic diversity of OFSP farmer varieties. Under this study, a sample of what is considered African OFSP farmer varieties was assessed for genetic relatedness with counterpart white or cream-fleshed cultivars. Germplasm Characterization for Quality Traits among Staple Crops Germplasm characterization studies for quality traits are reported by various Consultative Group on International Agricultural Research centres for different staple crops. CIAT (International Centre for Tropical Agriculture) scientists have characterized various bean (for Fe and Zn) and cassava (for β-carotene) accessions. In over 1000 bean accessions evaluated, Fe concentrations ranging between 34 and 89 μg/g (average = 55 μg/g) and Zn concentrations ranging between 21 and 54 μg/g (average = 35 μg/g) are reported (Graham et al., 1999; Beebe et al., 2000). In cassava, β-carotene levels ranging between 0.1 and 2.4 mg/100 are reported for 630 core cassava genotypes from about 5500 CIAT’s global collection (Iglesias et al., 1997). The genotypes containing the highest levels of β-carotene were collected from the Amazon region of Brazil and Colombia, where the indigenous farmers prefer yellow root lines. At CIMMYT (International Maize and Wheat Improvement Centre), accessions of wheat and maize have been assessed for Fe and Zn. Monasterio and Graham (2000), revealed wheat grain Fe and Zn concentrations ranging between 28 to 56.5 μg/g (average 37.3 μg/g) and 25.2 – 53.3 μg/g (average 35.0 μg/g), respectively. The species Triticum doccum had the highest concentrations of Fe and Zn. On the other hand Fe and Zn concentrations in maize kernels seem not to be as high as in other cereals though improvement is possible (Welch and Graham, 2004). Twenty lines from South African germplasm showed a range between 16.4 and 22.9 μg/g (mean 19 μg/g) for Fe, and between 14.7 and 24.0 μg/g (mean of 19.8 μg/g) for Zn (Bazinger and Long, 2000). At IITA (International Institute of Tropical Agriculture), scientists observed Fe and Zn concentration ranges between 15.5 – 19.1 μg/g and 16.5 – 20.5 μg/g, respectively, among a number of early maturing lines of maize in Nigeria. Additional 1814 accessions from CIMMTY and evaluated in Zimbabwe and Mexico between 1994 and 1999 (Bazinger and Long, 2000), showed Fe and Zn concentrations ranges of 9.6 to 63.2 μg/g (average 23.76 μg/g) and 12.9 to 57 μg/g (average 33.27μg/g), respectively. 7 In sweetpotato germplasm, considerable variability of different nutritional traits has been reported (Woolfe, 1992; Ravindran et al., 1995; Saad, 1996; Laurie, 2008; Grüneberg et al., 2009b). Also extreme high genetic variation has been observed for ß-carotene among orange-fleshed types by CIP and partners (Manrique and Hermann, 2000; Grüneberg et al., 2005). Up to 8000 g of ß-carotene per 100g of fresh weight have been recorded in some sweetpotato varieties tested by CIP (Hagenimana et al., 1999; Grüneberg et al., 2009b). However, scanty preliminary studies (Grüneberg et al., 2005) show low to medium values and high genetic variation for Fe, Zn, and Ca content in sweetpotato storage roots. More characterization studies have been recommended. Application of Near Infrared Reflectance Spectroscopy (NIRS) in Rapid Screening of Quality Traits in Staple Crops Successful selection for quality traits in plants and animals require adequate analytical procedures to measure them. Chemical analyses are expensive and often a few samples can be analyzed per unit time (Zum Felde et al., 2009). Yet breeding studies involve large populations that must be analyzed. NIRS has proven an accurate, precise, and rapid alternative to wet chemistry procedures for determining concentrations of major classes of chemical compounds in organic materials (Baye and Becker, 2004). It is a non-destructive, reliable and rapid method to determine quality traits simultaneously as an early screening method in many agricultural products. The method utilizes reflectance signals resulting from bending and stretching vibrations in molecular bonds between carbon, nitrogen, hydrogen and oxygen. Calibration is required to correlate the spectral response of each sample at individual wavelengths to known chemical concentrations from laboratory analyses. The technique has had a broad range of analytical applications. NIRS has been used to measure protein, oil and starch content in agricultural and food industries due to its convenience and easy sampling. In breeding studies the technique has equally had a broad range of applications. It has been effectively used to achieve rapid screening of germplasm (Baillères et al., 2002; Baye and Becker, 2004), assessing genetic control and heritability studies (Raymond, 2002) as well as prediction of disease/pest resistance (Cao et al., 2002). In sweetpotato, preliminary studies have been done to screen sweetpotato germplasm for micronutrients Fe, Zn and β-carotene concentrations (Grüneberg et al., 2009b). Genetic and Environmental Interactions for Micronutrient Traits Genotype by environment (GxE) interaction is the differential response of crop genotypes to changing environmental conditions. Such interactions complicate testing and selection in breeding programs and result in reduced overall genetic gains of the desired traits (Shafii and Price, 1998). They are of great interest when evaluating the stability of breeding clones under different environmental conditions. Understanding of GxE therefore, allows making of informed choices regarding which locations and input 8 systems to be used in the breeding efforts (Grüneberg et al., 2005). In spite of wide adaptability to harsh growing conditions, GxE studies on several traits (Collins et al., 1987; Bacusmo et al., 1988; Woolfe, 1992; Ngeve, 1993, Ravindran et al., 1995; Grüneberg et al., 2005, Ndirigwe, 2005) have shown that sweetpotato is sensitive to environmental variation. For example, sweetpotato root yield and yield components have been shown to be highly sensitive to changes in environment (Bacusmo et al., 1988; Manrique and Hermann, 2000; Grüneberg et al., 2005). GxE interactions for quality traits such as dry matter, starch, total protein, sugar and ß-carotene have been studied with contrasting findings. Li (1976) observed environmental influence on protein, sugar, and ß-carotene contents of sweetpotato, and none for dry matter. Contrastingly, Jones et al. (1986) observed that breeding for quantitative traits like root dry matter in hexaploid sweetpotato has partly been inhibited by the significant GxE interactions. In Rwanda, Ndirigwe (2005) observed significant GxE interactions for β-carotene levels with the increasing trend in the high altitudes. Zhang and Collins (1995) found significant GxE interactions for trypsin inhibitor activity, crude protein, and true protein. However, a significant proportion of the GxE interaction could be explained by linear environment effect. Recent studies (Grüneberg et al., 2009b, Grüneberg et al., 2005) agree with some studies and disagree with others depending on the quality traits. Grüneberg et al. (2005) showed general low GxE interaction effects for nutritional traits root dry matter, starch, root and leaf ß- carotene content, as well as chlorophyll content. Earlier, Manrique and Hermann (2000) equally reported low GxE interaction effects of ß-carotene content in sweetpotato. However, it is important to observe that few GxE studies are reported for mineral nutrients (e.g. Fe and Zn) in sweetpotato. Yet mineral nutrients are part of the breeding targets for sweetpotato by CIP and partners. In other staple crops significant GxE for both Fe and Zn are reported in beans (Beebe et al., 2000) and wheat grains (Monasterio and Graham, 2000). 9 Cited Literature Abdelhameed, E., S. Fjellheim, A. Larsen, O. A. Rognli, L. Sundheim, S. Msolla, E. Masumba, and K. Mtunda. 2007. Analysis of genetic diversity in sweetpotato (Ipomoea batatas L. Lam) germplasm collection from Tanzania as revealed by AFLP. Genet. Resour. Crop Evol. 55 (3): 397-408. Abidin, P.E. 2004. Sweetpotato breeding for north eastern Uganda: Farmer varieties, farmer participatory selection and stability performance. Wageningen University, PhD Thesis. pp 152. Bacusmo, J.L., W.W. Collins, and A. Jones. 1988. Effects of fertilization on stability of yield and yield components of sweetpotato. J. Amer. Soc. 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November 2 – 9, 2009. 14 15 CHAPTER ONE 01 Evaluation of Dry Matter, Protein, Starch, ß-carotene, Iron, Zinc, Calcium and Magnesium in East African Sweetpotato [Ipomoea batatas (L.) Lam] Germplasm Silver Tumwegamire1, Regina Kapinga2 International Potato Center (CIP), P.O Box 22274, Kampala, Uganda. Patrick R. Rubaihayo Crop Science Department, Makerere University, P.O Box 7062 Kampala, Uganda. Don R. LaBonte Louisiana State University, AgCenter, 104B M.B. Sturgis Hall, LSU Campus, Baton Rouge, LA 70803, USA. Wolfgang J. Grüneberg International Potato Center, Apartado 1558, Lima 12, Peru. Robert O.M. Mwanga National Agricultural Research Organization (NARO), National Crops Resources Research Institute (NaCRRI), Namulonge, P.O. Box 7084, Kampala, Uganda. The research was part of CIP’s sweetpotato biofortification breeding project funded under the HarvestPlus Biofortification challenge Program. We obtained the sweetpotato germplasm used in the study from different national sweetpotato programs in Uganda, Kenya, Tanzania and Zambia. The paper is part of a PhD thesis to be submitted to Makerere University, Kampala, Uganda by Tumwegamire Silver. Additional Index words: Biofortified crops, protein, starch, sucrose, β-carotene, iron, zinc, calcium, and magnesium contents, Near infrared reflectance spectroscopy (NIRS) technology, Ipomea batatas. Published by American Society for Hortcultural Science March 2011. HortSci. Vol 46(3): 348 – 357. 1 To whom the reprint requests should be addressed. Email: s.tumwegamire@cgiar.org 2 Former Regional Sweetpotato Breeder for sub-Saharan Africa, currently program officer, Bill Gates Foundation Seattle, USA 16 Abstract The present study evaluated selected East African (EA) sweetpotato varieties for storage root dry matter and nutrient content, and obtained information on the potential contributions of the varieties to alleviate vitamin A and mineral deficiencies. Roots obtained from 89 farmer (white- and orange-fleshed) varieties and one introduced variety (‘Resisto’), were analyzed for storage root quality using Near Infrared Reflectance Spectrometry. Location differences were only significant for starch content. The 2 G variance was significant (p < 0.01) for all the traits except sucrose content. Overall, the farmer varieties had higher dry matter, higher starch, and lower sucrose contents than the check, ‘Resisto’. It is these qualities that make sweetpotato attractive as a starchy staple in EA. A low population’s mean β-carotene content (19.0 ppm) was observed. However, deep orange-fleshed farmer varieties, ‘Carrot_C’, ‘Ejumula’, ‘Carrot Dar’ ‘Mayai’ and ‘Zambezi’ had β-carotene content that can meet 350% or greater recommended daily allowance (RDA) with 250 g serving to a 5 – 8 year old child. More but light orange-fleshed farmer varieties K-118’, ‘K-134’, ‘K-46’, ‘KMI61’, ‘MLE162 Nakahi’, ‘PAL161’, ‘Sowola6’, ‘Sponge’, ‘SRT34 Abuket2’, ‘SRT35 Anyumel’, ‘SRT52’ and ‘Sudan’ can provide 50 - 90% RDA of the child. The root minerals’ content was generally low except for magnesium whose content can meet 50% or greater RDA in many farmer varieties. However, in areas with high sweetpotato consumption, varieties ‘Carrot_C’, ‘Carrot Dar’, ‘KRE Nylon’, ‘MLE163 Kyebandula’ and ‘SRT49 Sanyuzameza’ can make good intakes of iron, zinc, calcium, and magnesium. In conclusion, some EA farmer varieties can contribute greatly to alleviation of vitamin A deficiency and substantial mineral intakes. 17 Introduction Sweetpotato [Ipomoea batatas (L.) Lam] ranks fifth in importance for its caloric contribution in developing countries after rice, wheat, maize and cassava (CIP, 2005). In some areas of East Africa (EA) the crop has become a staple (Scott et al., 2000). For example, in Uganda the daily intake of sweetpotato is estimated to be 240 g per day per person (FAOSTAT, 2007). Information about quality attributes of African sweetpotato germplasm is very limited. The average storage root dry matter (DM) of the cultivated sweetpotato clones of the world is ≈30% (Woolfe, 1992; Bradbury and Holloway, 1988). Two main taste groups can be distinguished: (i) white- and cream-fleshed sweetpotatoes usually with DM contents of ≈25 to 35% and (ii) orange-fleshed, sweetpotatoes (OFSP) with DM of ≈20 to 30% and high provitamin A carotenoids (Grüneberg et al., 2009; Martin and Jones, 1986). The taste preference in Sub-Saharan Africa is clearly the dry and low sweet type, which is nearly exclusively white-fleshed. Carotenoid pigments provide OFSP storage roots the orange flesh color. More than 60 mg total carotenoids in 100 g DM have been reported (Woolfe, 1992). A constant high proportion (≈90%) of β-carotene in relation to total carotenoids in OFSP has been known for decades (Ezell and Wilcox, 1958; Purcell, 1962; Purcell and Walter, 1968; Haggenimana et al., 1998), and currently OFSP is considered a complementary food approach to alleviate vitamin A deficiency (VAD) in the world (Low et al., 2001, 2007). Modern OFSP varieties that are more adapted to African consumer preferences than traditional moist and sweet OFSP have been bred and released in Uganda (Mwanga et al., 2007, 2009). Also, OFSP farmer varieties that meet local consumer preferences have been found in EA (Tumwegamire et al., 2004; CIP, 2005). Approximately 80 to 90% of sweetpotato storage root DM is made up of carbohydrates, mainly starch (≈60 to 70% of DM) and sugars (≈15 to 20% of DM with a wide range from ≈5 to 40% of DM), and lesser amounts of pectins, hemicelluloses and cellulose (Woolfe, 1992). Usually white- and cream-fleshed varieties have higher starch (≈50 to 80% of DM) and lower sugar contents (≈5 to 15 % of DM) compared with OFSP genotypes, which have lower starch (≈45 to 55 % of DM) and higher sugar contents (≈10 to 20 % of DM) (Woolfe, 1992). Additionally, the storage root of sweetpotato also contains reasonable amounts of protein [≈5% of storage root DM] (Woolfe, 1992). Studies on sweetpotato storage root mineral contents (especially trace minerals) 18 are limited, particularly for African sweetpotato germplasm. Bradbury and Holloway (1988) reported storage root mineral content ranges of ≈75 to 740 ppm calcium, ≈180 to 350 ppm magnesium, ≈1.6 to 9.4 ppm iron, and ≈2.7 to 18.9 ppm zinc in sweetpotato accessions from the South Pacific. Courtney (2007) observed up to ≈10 ppm iron and ≈6.4 ppm zinc in fresh storage roots for North American breeding material. The pro-vitamin A and minerals (Fe, Zn, Ca, and Mg) are critical and deficient in human food supply (Frossard et al., 2000; Munoz et al., 2000). In Uganda, ≈20% of children and 19% of women are vitamin A deficient; and 73% of children and 49% of women are anemic (UBOS and Macro International Inc., 2007). The levels of anemia are higher among pregnant (64%) and breast feeding (53%) mothers. Worldwide, 127 million preschool children and more than 7.2 million pregnant women in developing countries suffer from vitamin A deficiency (VAD) (Bouis, 2003; West, 2002) and approximately 2 billion people are anemic (Frossard et al.; 2000). Another 13.5 million pregnant women have low vitamin A status (West, 2002). Globally, 800,000 and 700,000 deaths per year are attributed to Fe and Zn deficiencies, respectively (Black, 2003). According to Black (2003) 2.4%, 1.8% and 1.9% of the global disease burden is attributable to Fe deficiency, vitamin A deficiency and Zn deficiency, respectively. OFSPs have been demonstrated to have a great potential to alleviate VAD around the Lake Victoria region and East African highlands (Low et al., 2001). However, the majority of sweetpotato varieties consumed in EA are white-fleshed. Also, the traditional OFSP with their moist and sweet taste are unlikely to be accepted on a broad basis in EA. Fortunately, African OFSP farmer varieties and modern breeding lines have been identified and are currently being promoted by CIP and HarvestPlus in Uganda and Mozambique (Mwanga et al., 2009). The present study evaluated selected East African sweetpotato accessions for storage root quality (dry matter, protein, starch, sucrose, ß-carotene, iron, zinc, calcium and magnesium), and obtained information on the potential contributions of the accessions to alleviate vitamin A and mineral deficiencies in EA region. 19 Materials and Methods Ninty sweetpotato accessions were used in this study (Table 1.1). All varieties were farmer varieties from EA, except the modern variety ‘Resisto’ from the United States of America. Non-Ugandan accessions had been introduced for regional trials during early 2005. The variety, ‘Resisto’, was used in this study as a check to compare OFSP varieties of African origin with the typical moist and sweet OFSP type of non-African origin. It should be noted that several nutritional studies have used ‘Resisto’ to investigative effects on human vitamin A status due to OFSP consumption (Low et al. 2007; van Jaarsvield et al., 2005). Thirty-two of the farmer varieties were OFSP cultivars, with varied intensities of orange flesh color. One cultivar, ‘Kwezikumwe’, was purple-fleshed. The remaining accessions were cream, white or yellow-fleshed varieties. Sixty five farmer varieties were from Uganda, 19 from Kenya, four from Tanzania and one from Zambia. The field trials were planted at the National Crops Resources Research Institute (NaCRRI) at Namulonge close to lake Victoria (1150 m.a.s.l), and Kachwekano Zonal Agricultural Research Institute (2220 m.a.s.l) in the south western highlands of Uganda (Table 1.2). Namulonge has a bimodal rainfall pattern of 1270 mm per year, annual mean temperature of 22.2 oC (mean maximum temperature = 28.4oC, mean minimum temperature = 15.9oC), ferralitic soils (red sandy clay loams) and soil pH 4.9 to 5.0. Kachwekano has a bimodal rainfall of 1319 mm per year, annual mean temperature of 18oC, latosolic soils (sandy clay loam), and soil pH 5.8 – 6.2. During the second rain season of 2005 (starting in October), each variety was planted on two-row plots using 20 vines placed 30 cm apart. The rows were 1 m apart and each variety was planted with two plot replications in a randomized complete block design. The plots were kept weed free and no fertilizer or other agro-chemicals were applied. Harvest was carried out five months after planting at Namulonge and seven month after planting at Kachwekano, using the local practice of sweetpotato crop duration in these different eco-geographic zones. 20 Table 1.1. List of Sweetpotato varieties used for quality characterization at Namulonge and Kachwekano in Uganda during 2005/06. Variety name CIP Plant Country Cultivar Storage root code type Origin Type Flesh Skin Form color colour Obuogo1 i.p. n.a Kenya FV Cream Cream n.a KBL640 Africare No Semi-erect Uganda FV LO Cream Long elliptic APA343 No Semi-erect Uganda FV LO Cream Round elliptic APA348 Liralira No Semi-erect Uganda FV Cream Purple red Elliptic APA352 Oketodede i.p. Semi-erect Uganda FV Cream Cream Round elliptic APA365 Anam Anam i.p. Erect Uganda FV Cream Cream Elliptic ARA208 Ombivu i.p. Semi-erect Uganda FV Cream Purple red Round elliptic ARA214 i.p. Semi-erect Uganda FV LO Cream Round elliptic ARA244 Shinyanga i.p. Semi-erect Uganda FV LO Cream Long elliptic Bunduguza i.p. Spreading Uganda FV Cream Purple red Round elliptic Bungoma No Semi-erect Uganda FV Cream Purple red Elliptic Carrot_C i.p. Spreading Tanzania FV DO Cream Long irregular Carrot Dar i.p. Semi-erect Tanzania FV DO Cream Long elliptic Dimbuka No Semi-erect Uganda FV Cream Cream Obovate Ejumula No Spreading Uganda FV DO Cream Long irregular HMA490 Kawogo No Semi-erect Uganda FV Cream Brown Ovate HMA493 Tanzania i.p. Spreading Uganda FV LO Cream Long elliptic IGA963 Nyongerabarenzi No Erect Uganda FV Cream Cream Round elliptic K-118 i.p. Semi-erect Kenya FV LO Cream Long elliptic K-134 No Erect Kenya FV LO Purple red Round elliptic K-207 No n.a Kenya FV Yellow Cream n.a K-37 i.p. n.a Kenya FV LO Cream Elliptic K-46 i.p. Semi-erect Kenya FV Orange Purple red Round elliptic KBL627 Mukazi i.p. Spreading Uganda FV Cream Cream Long elliptic KBL632 Nyinakamanzi No Spreading Uganda FV Cream Purple red Round KMI56 Opira No Erect Uganda FV Cream Brown Long irregular KMI59 Kampala i.p. Semi-erect Uganda FV Cream Purple red Long elliptic KMI61 i.p. Semi-erect Uganda FV Orange Cream Long elliptic KMI78 Osukari No Semi-erect Uganda FV Cream Purple red Obovate KMI81 Ikala i.p. Semi-erect Uganda FV LO Cream Round elliptic KMI83 Ikala2 i.p. Semi-erect Uganda FV LO Cream Elliptic KML883 Silkempya No Semi-erect Uganda FV White Cream Elliptic KRE716 Nylon No Spreading Uganda FV Cream Cream Ovate KRE726 Kwezikumwe No Semi-erect Uganda FV Purple Purple red Elliptic KRE733 Kitambi i.p. Spreading Uganda FV Cream Purple red Round KSR673Mabereikumi i.p. Semi-erect Uganda FV Cream Cream Ovate KSR652 Mugumire i.p. Semi-erect Uganda FV Cream Cream Long elliptic KSR662 Kakoba i.p. Semi-erect Uganda FV Yellow Purple red Elliptic KSR664 Mulerabana No Semi-erect Uganda FV Cream Pink Long elliptic KSR675 NoraII i.p. Semi-erect Uganda FV Cream Cream Round Kunykubiongo No Erect Kenya FV Cream Purple red Elliptic Kyabafuriki No Spreading Uganda FV Cream Cream Round elliptic LIR257 Otada No Semi-erect Uganda FV Cream Cream Round elliptic LIR296 i.p. Semi-erect Uganda FV PY Purple red Round elliptic 21 Table 1.1. Continued. Variety name CIP Plant Country Cultivar Storage root code type Origin Type Flesh Skin Form color colour Mayai No Semi-erect Tanzania FV DO Cream Long elliptic MBR 539 Kitekamaju i.p. Semi-erect Uganda FV White Cream Elliptic MBR524 Nkwasahansi i.p. Semi-erect Uganda FV Cream Purple red Long irregular MBR536 Karebe i.p. Semi-erect Uganda FV Cream Cream Elliptic MBR552 Kahungezi No Semi-erect Uganda FV Cream Purple red Ovate MBR560 Mugurusi No Semi-erect Uganda FV Cream Cream Long irregular MBR580 Nylon No Semi-erect Uganda FV Cream Purple red Long elliptic MBR600 Kisakyabikiramaria No Semi-erect Uganda FV Cream Cream Long elliptic MLE166 No Semi-erect Uganda FV LO Purple red Round MLE162 Nakahi No n.a Uganda FV LO Cream n.a MLE163 Kyebandula i.p. Semi-erect Uganda FV Cream Cream Long elliptic MLE165 Namafumbiro No Semi-erect Uganda FV Cream Cream Elliptic MLE173 Kijovu i.p. Semi-erect Uganda FV Cream Purple red Long irregular MLE184 Manafayereta No Semi-erect Uganda FV White Pink Long irregular MSK1025 Bitambi i.p. Erect Uganda FV Cream Brown Long irregular MSK1047 Bwanjure i.p. Semi-erect Uganda FV White Purple red Long irregular Nyaguta i.p. n.a Kenya FV Cream Pink Long elliptic Nyandere i.p. n.a Kenya FV PY Purple red Elliptic Nyathiodiewo No Spreading Kenya FV LO Purple red Round Nyatonge i.p. n.a Kenya FV Cream Cream n.a Obuogo2 No n.a Kenya FV White Purple red n.a Oguroiwe i.p. Semi-erect Kenya FV Cream Cream Long elliptic PAL153 Abukoki i.p. Semi-erect Uganda FV Cream Cream Elliptic PAL161 i.p. Semi-erect Uganda FV LO Cream Elliptic Pipi i.p. n.a Tanzania FV LO Cream n.a Resisto 440001 Semi-erect USA MV DO Brown Ovate Sowola (389A) No Semi-erect Uganda FV Cream Brown Elliptic Sowola 6 No Semi-erect Uganda FV LO Cream Long irregular Sponge No Semi-erect Kenya FV LO Purple red Round elliptic SRT14 Nora No Erect Uganda FV Cream Purple red Elliptic SRT01 Osapat i.p. Erect Uganda FV Yellow Cream Obovate SRT02 Araka White i.p. Semi-erect Uganda FV Cream Cream Ovate SRT30 Nyara No Semi-erect Uganda FV LO Cream n.a SRT33 Abuket1 i.p. Semi-erect Uganda FV Orange Pink Long elliptic SRT34 Abuket2 i.p. Semi-erect Uganda FV LO Cream Elliptic SRT35 Anyumel No Erect Uganda FV LO Cream Round elliptic SRT39 Rwanda No Semi-erect Uganda FV Orange Cream Round elliptic SRT40 Mary No Semi-erect Uganda FV Cream Cream Long elliptic SRT49 Sanyuzameza No Erect Uganda FV Yellow Purple red Long oblong SRT52 No Erect Uganda FV Orange Cream Oblong Sudan No Spreading Uganda FV LO Cream Long elliptic Tororo 3 i.p. Semi-erect Uganda FV Cream Cream Long elliptic Ukerewe i.p. Semi-erect Tanzania FV Yellow Purple red Elliptic Wagaborige i.p. Spreading Uganda FV Cream Cream Round Wera i.p. n.a Kenya FV Yellow Cream n.a Zambezi i.p. Semi-erect Zambia FV DO Purple red Round elliptic 22 i.p. = designation of CIP code in process, No = no acquisition from the gene bank at CIP. FV = Farmer variety; MV Modern variety. DO = Deep orange, LO = Light orange, PY = Pale yellow. Plots were harvested by uprooting the center of each row, leaving a plant at both ends of each row. The harvested roots were collected into a composite pile and a sample of five roots each between 100 and 300 g weight was taken for dry matter, protein, starch, sucrose, β-carotene, Fe, Zn, calcium, and magnesium determination. The roots were washed of soil particles and rinsed with abundant tap water, peeled, and each root cut longitudinally into four sections. Two opposite sections of each of the sectioned roots were taken to prepare a 100 g compound sample that was placed in transparent polythene bags, and freeze dried at -31oC for 72 hours. Dry samples were weighed, milled into flour in a stainless steel mill and stored in Kraft paper bags. Percent root dry matter was calculated from flesh and dry weight estimates. Near infrared reflectance spectroscopy (NIRS) technology (Shenk and Westerhaus, 1993) was used to determine protein, starch, sucrose, β-carotene, Fe, Zn, calcium and magnesium in milled samples of freeze dried storage root samples. NIRS technology has been used to screen for macro-nutrients in root and tuber crops (Haase, 2006; Young et al., 1997; Mehrübeoglu and Coté, 1997) including sweetpotato (Lebot et al., 2009; Lu et al., 2006), and has been tested for minerals in agricultural commodities (Cozzolino and Moron, 2004, Halgerson et al., 2004). Also the technology has become a standard fast screening method for mirco-nutrients (pro-vitamins A, iron and zinc) (Zum Felde et al., 2009; Pfeiffer and McClafferty, 2007). Each milled sample material (two times 3 g) was analysed by NIRS within the range of 400 to 2500 nm on a NIRS monochromator model 6500 (NIRSystems, Inc. Silver spring, MD) using small ring cups with sample autochanger. Near-infra-red spectra of each sample were stored in a computer file and in 2009 these spectra were again used to determine protein, starch, sucrose, β-carotene, Fe, Zn, calcium and magnesium with the latest calibration version for sweetpotato freeze dried samples (Zum Felde, 2009). In this version the correlations in cross validation Table 1.2. Description of locations used for the evaluation of farmer varieties. Temperature† oC Location Ecogeographic region Soil types Altitude (m.a.s.l) Rainfall† (mm) Mean Range Namulonge Tropical rain forest Sandy clay soils (pH 4.9 to 5.0) 1150 359.0 23.1 16.1 - 30.1 Kachwekano Tropical mountain region Sandy clay Loam (pH 5.8 to 6.2) 2220 423.1 18.1 11.9 - 24.2 †Rainfall (mm) and temperature experienced during the crop growing period: Oct. 2005 to Feb. 2006 at Namulonge; Oct. 2005 to Apr. 2006 at Kachwekano. 23 between standard laboratory reference methods and NIRS are 0.95, 0.96, 0.80, 0.97, 0.80, and 0.89, for protein, starch, sucrose, β-carotene, iron, and zinc, respectively (Zum Felde, 2009) and 0.92 and 0.78 for calcium and magnesium, respectively (Zum Felde pers. Comm.). The reference methods for NIRS calibration were Dumas according to Sweeney and Rexroad (1987) for crude protein, polarimetrically by hydrochloric acid dissociation according to ICC No. 123/1 (ICC, 1994) for starch, high performance liquid chromatography (HPLC) according to Rodriguez-Amaya and Kimura (2004) for β-carotene, inductively coupled plasma argon optical emission spectrometer (ICP-OES), according to Bridger and Knowles (2000) and reviewed by Aceto et al (2002) for Fe, Zn, calcium and magnesium. For sucrose determination we used a procedure in which a water extract of the freeze-dried samples (0.1 g in 100 mL) was used: (i)The samples were incubated in a water bath at 60°C for 1 h and afterwards, they were treated with each 0.2 mL Carrez I and Carrez II solution to remove proteins. (ii) Samples were purified by centrifugation (Sorvall RC-5B Refrigated Superspeed, GMI, Ramsay, USA) for 10 min and 20°C with 10000 rpm, total sugars were determined from the membrane-filtered supernatant (pores size 0.45 μm), and sucrose, glucose, fructose, maltose and galactose were separated using a LiChrospher 100 NH2 (5 μm) 4 x 4 mm pre-column in combination with a LiChrospher 100 NH2 (5 μm) 4 x 250 mm separation column (Merck KGaA, Darmstadt, Germany) and an acetonitrile - pure water solution (80:20 v/v) as mobile phase (flow rate 1.0 mL min-1) at 20 °C and an injection volume of 20 μL. Sugars were detected with a Knauer differential refractometer 198.00 (Knauer, Berlin, Germany). Statistical analyses were conducted using PLABSTAT (Plant Breeding Statistical Program) computer package (Utz, 2001) and SAS6.12 (SAS Institute 1988; 1997). Data were classified relative to varieties or genotypes (G), locations (L), and blocks or replications (R). In an analysis of variance (ANOVA), each trait xi (namely, protein, starch, sucrose, ß-carotene, Fe, Zn, calcium and magnesium) was analyzed from each experimental site separately to determine outliers, experimental means, coefficients of variation, minimum and maximum values using the SAS procedure GLM and the model statement Xi = G + R, which corresponds to the statistical model Yijl = i + gij + blil + ijl, where Yijl is the plot value of the ith trait of the jth genotype and the lth block, i is the trial mean of the ith trait, gij, is the effect of genotypes, blil is the effect of blocks, and ijl is the plot error. For the analysis across locations an ANOVA was carried out for each trait xi using PLABSTAT, with the model statement Xi = G + L + GL + R: L + RGL, which corresponds to the statistical model ijklkilijkikijiijkl lblgllgY   )()( where lik and glijk are the effects of locations and genotype-location interactions, respectively, and other effects as designated above. In the first analysis all effects were considered random in order to use the ANOVA to estimate the magnitude and significance of variance components for 2 G , 2 L , 2 GxL , and 24 2  . In a second analysis the effects gij, lik and glijk were considered as fixed to estimate the least significant difference (LSD) to compare means among varieties and locations for each trait. Correlations among traits were carried out by SAS procedure CORR and the optional statement PEARSON. The correlations were calculated for each location and replication separately, followed by calculating the average correlation between each trait pair across locations and replications using the statement BY in SAS procedure CORR. These correlations are still phenotypic correlations, but can be considered as a good approximation of genotypic correlation estimates (Hill et al., 1998). In the final analysis the contribution of sweetpotato to the recommended daily allowance (RDA) for β- carotene, Fe, Zn, calcium and magnesium were calculated by assuming an intake of 250 g fresh sweetpotato storage root per day (comparable to the consumption estimates for Uganda). The RDAs for school age children from five to eight years were based on the Institute of Medicine in the United States (National Academy of Sciences, 2004) statistics. These RDA per day are: 400 μg Retinol, which corresponds to 4.8 mg β-carotene, 10 mg iron, 5 mg zinc, 800 mg calcium, and 130 mg magnesium. For each, β- carotene, Fe, Zn, calcium and magnesium data value, the corresponding % RDA were calculated by: % RDA = nutrient content in 250 g fresh weight basis (fwb) / RDA * 100. To compare varieties for their value in RDA contribution the LSDs were calculated for % RDA as described for other traits above. 25 Results Differences in the experimental means between locations were not large for all the traits, except storage root starch content (Table 1.3). Storage root yield means were 7.5 t ha-1 for Namulonge and 10.0 t ha-1 for Kachwekano. However, some accessions had higher storage root yields than respective means at both locations. At Kachwekano, storage root starch and sucrose contents were respectively higher and lower than at Namulonge (Table 1.3). The lowest storage root sucrose contents for farmer varieties were 4.3% and 4.7% at Namulonge and Kachwekano, respectively. Table 1.3. Experimental means ( x ), coefficient of variation (CV %), minimum (min) and maximum (max) genotypic values for observed traits at locations. Namulonge Kachwekano Trait x CV % Min Max x CV % Min Max Storage root yield, t ha-1 7.5 47.8 0 18.1 10.0 56.0 0.2 21.3 Dry matter content of storage roots, % 32.3 5.5 19.4 38.3 31.7 6.8 20.8 36.7 Protein content of storage roots, % DM 6.8 13.4 4.0 9.2 6.5 16.0 3.8 9.5 Starch content of storage roots, % DM 60.5 3.2 30.1 68.2 68.2 3.2 62.2 73.4 Sucrose content of storage roots, % DM 11.4 14.6 4.3 48.7 9.4 18.0 4.7 13.8 β-carotene content of storage roots, ppm DM 36 40.6 0 338 24 65.9 0 295 Iron content of storage roots, ppm DM 23.7 8.9 17.3 33.2 19.5 11.1 14.7 26.9 Zinc content of storage roots, ppm DM 12.3 11.0 9.5 17.8 9.5 12.1 5.9 12.7 Calcium content of storage roots, ppm DM 1980 21.2 929 4411 1880 18.0 1029 3795 Magnesium content of storage roots, ppm DM 569 25.4 169 1416 676 25.12 363 1392 DM = dry matter At Namulonge, means for protein, β-carotene, Fe, Zn, and calcium were slightly higher than at Kachwekano. Maximum values for β-carotene were high at both locations, while the mean values for β- carotene were low (approximately two-thirds of the farmer varieties used in the study were white-fleshed). The CV (CV given as a percentage) values for observed traits were low to moderate, except storage root yield and β-carotene content of storage roots (greater than 30%). 26 Table 1.4. Estimated variance components, variance component ratios in brackets, and operational broad-sense heritabilities of observed traits†. Trait 2 G 2 L 2 GL 2  h2 Storage root yield, t ha-2 8.01** (1) -4.56 (-0.56) 4.83* (0.60) 22.05 (2.75) 0.50 Dry matter content of storage roots, % 4.94** (1) -0.97 (-0.20) 3.62** (0.73) 3.89 (0.79) 0.64 Protein content of storage roots, % DM 0.32** (1) 0.06 (0.18) 0.34** (1.05) 0.94 (2.94) 0.44 Starch content of storage roots, % DM 5.31** (1) 29.78** (5.61) 13.88** (2.62) 4.24 (0.80) 0.40 Sucrose content of storage roots, % DM 1.30 (1) 1.86* (1.44) 12.09** (9.32) 2.81 (2.16) 0.16 β-carotene content of storage roots, ppm DM 4362** (1) 60** (0.01) 430** (0.10) 183 (0.04) 0.94 Iron content of storage roots, ppm DM 1.97** (1) 8.72** (4.42) 2.63** (1.33) 4.54 (2.30) 0.45 Zinc content of storage roots, ppm DM 0.81** (1) 3.80** (4.70) 0.64** (0.79) 1.59 (1.97) 0.53 Calcium content of storage roots, ppm DM 52643* (1) -23551 (-0.45) 137272** (2.61) 145497 (2.76) 0.33 Magnesium content of storage roots, ppm DM 15008** (1) 4306 (0.29) 15355** (1.02) 24607 (1.64) 0.52 * Significant at the 0.05 level. ** Significant at the 0.01 level. † Variance components: 2 G = genotypes, 2 L = locations, 2 GxL = genotype-location interactions, 2  = error; h2 = operational broad-sense heritability. ‡ DM dry matter. The 2 G , variance component was significant (p < 0.01) for all traits, except storage root sucrose content (Table 1.4). For several observed traits the 2 L , variance component was not significant (p>0.05), except starch, β-carotene, Fe and Zn. In contrast the 2 GxL variance component was significant (p < 0.01) for all traits. The 2 G : 2 GxL ratios were high (1: 0.1 for β-carotene content) to extremely low (1: 9.32 for sucrose content). It should be noted that the 2 G : 2 GxL ratio for sucrose is extreme for a quality trait. Mainly due to the magnitude of 2 G : 2 GxL ratios within the interval 1 : 0.5 and 1 : 3.0 for most traits as well as the number of locations (2) the operational broad sense heritabilities (h2) were moderate (0.3 to 0.6) for most traits, and only high for β-carotene content of storage roots (0.94). The population means (across varieties, locations and replications) for storage root yield were low (8.6 t.ha-1) (Table 1.5), but higher than the national average of 4.2 t.ha-1(Yanggen and Nagujja, 2006). Compared with averages given for cultivated sweetpotato clones of the world, higher population means for storage root dry matter (32.1%) and starch content (64.4%) were observed. In contrast, sucrose population’s mean (10.3%) was clearly low. The population mean values observed for storage root Fe, Zn, calcium, and 27 magnesium were 21.6 ppm, 10.9 ppm, 1950 ppm, and 626 ppm, respectively. However, an important finding was that nearly all light to deep OFSP farmer varieties clearly contain pro-vitamin A β-carotene. For the OFSP check (‘Resisto’) a storage root β-carotene content of 271 ppm was observed. Table 1.5. Clone means of farmer varieties for observed traits across locations. Observed traits† Farmer variety YLD (tha-1) DM (%) PRO (%) STA (%) SUC (%) BC (ppm) Fe (ppm) Zn (ppm) Ca (ppm) Mg (ppm) APA343 5.3 32.5 5.7 63.9 11 12 19.7 9.7 1582 523 APA348 Liralira 11.2 39.0 6.8 69.9 - 0 20 9.7 1748 607 APA352 Oketodede 13.9 32.4 5.9 65.8 10.1 0 19.6 10.3 1587 478 APA365 Anam Anam 12.3 33.0 5.7 67.1 8.9 0 20.1 10.4 1789 540 ARA208 Ombivu 12.2 29.6 8.2 66.3 9.9 0 23.2 12.4 1550 401 ARA214 8.4 31.9 6.2 64.3 9.6 27 19.9 10.3 1500 520 ARA244 Shinyanga 14.3 24.7 5.3 57.7 14 64 20.2 9.3 1685 561 Bunduguza 5 35.3 6 66.6 10.7 0 19.8 8.6 2033 660 Bungoma 11.9 33.4 6.2 67.5 6.4 0 20.8 10.1 1699 644 Carrot C 5.5 33.2 8.2 58.7 13.7 259 26.1 12.7 2591 924 Carrot Dar 7.8 31.1 8 58.2 13.7 272 28.4 14.4 2232 981 Dimbuka 16.8 32.2 7.5 67.1 8.3 0 21.2 11.3 1778 539 Ejumula 8.4 32.7 7.4 58 13.4 240 23.8 11.4 2263 848 HMA 490 Kawogo 1.3 32.6 6.1 65.5 8.6 0 20.1 10.4 1709 456 HMA493 Tanzania 1.8 33.4 6.1 65.8 8.4 29 20.2 9.7 1996 682 IGA963 Nyongerabarenzi 15.5 30.7 6.6 66.2 9.5 1 20.6 10.8 1525 374 K-118 5.5 30.7 7.2 62.8 11.6 38 21.4 11.5 1350 470 K-134 10.3 31.9 6.7 64.5 10.5 40 21.1 11.1 1885 616 K-207 5.8 37.4 5.8 67.5 7.4 0 20.4 9.7 2888 857 K-37 2.8 34.1 4.7 66.6 7.4 25 18 8.2 2426 665 K-46 4.5 33.7 6.6 62.9 11.3 48 21.3 10.7 2522 730 KBL627 Mukazi 8.1 35.8 6.6 64.4 12.4 0 22.7 10.9 2053 747 KBL632 Nyinakamanzi 7.1 31.3 6.6 68 7.4 1 20.9 11.4 2128 694 KBL640 Africare 15.3 30.2 6.5 63.6 11.4 15 20.2 9.9 1392 467 KMI56 Opira 10.6 31.3 6.8 65 9.5 0 22.9 11.2 1978 763 KMI59 Kampala 11.6 32.6 7.1 63.8 9.8 0 22.9 10.1 1904 651 KMI61 7.9 33.4 7.4 64.4 10.5 75 23 11.3 1333 496 KMI78 Osukari 6.5 31.5 8.4 66.1 8.6 0 22 11.8 1543 531 KMI81 Ikala 11.7 25.6 7.3 60 12.1 28 22.7 12.6 1594 495 KMI83 Ikala2 11.9 29.9 7.4 63.2 11.7 11 21.2 10.8 1620 507 KML883 Silkempya 13.8 35.1 6.2 68.7 8 0 20.1 9.4 1931 485 KRE716 Nylon 2.5 36.1 6.9 66.7 8.3 -1 22.8 11.6 2569 816 KRE726 Kwezikumwe 16.6 31.7 5.9 69.3 8.3 9 19.2 10.7 1426 445 KRE733 Kitambi 3.8 35.2 6.3 66.4 8.6 0 21.9 11 1977 661 KSR673 Mabereikumi 6.6 33.5 6.2 67.4 7.5 0 21.1 11.3 1505 365 KSR652 Mugumire 1.9 34.9 7.5 66.9 8.9 1 21.1 10.9 2029 412 KSR662 Kakoba 7.5 32.7 4.8 68 7.9 1 18.3 9.6 2313 568 KSR664 Mulererabana 5.8 31.4 7.5 66.8 8.4 1 22.7 10.9 2056 738 28 Table 1.5. Continued. Observed traits† Farmer variety YLD (tha-1) DM (%) PRO (%) STA (%) SUC (%) BC (ppm) Fe (ppm) Zn (ppm) Ca (ppm) Mg (ppm) KSR675 Nora II 7.2 33.2 6.9 64.7 10.5 0 24 11.1 1897 730 Kunykubiongo 15 29.3 6.2 63.4 10.1 0 21.3 10.2 1866 565 Kyabafuriki 10.3 27.4 7.5 63.4 10.2 0 22.8 12.4 1969 492 LIR257 Otada 11.7 32.7 6.7 67.8 6.7 0 20.3 9.5 1810 582 LIR296 15.5 32.6 6.5 63.2 11.6 0 19.4 9.7 1742 564 Mayai 6.8 33.2 7.3 66.6 9.8 264 22.5 10.8 2177 761 MBR539 Kitekamaju 6.5 32.5 6.7 69.3 5.6 1 20.8 11.5 1934 530 MBR524 Nkwasahansi 1.1 31.5 7.5 62.1 12.1 1 25.2 11.7 1904 639 MBR536 Karebe 15 31.3 5.1 65.3 10.7 0 19.4 10.7 2120 633 MBR552 Kahungezi 9.3 33.9 7.3 66.8 9.8 0 21.2 11.2 1986 644 MBR580 Nylon 4.8 27.4 6.5 58.4 13.7 0 27.4 14.8 3290 1152 APA343 5.3 32.5 5.7 63.9 11 12 19.7 9.7 1582 523 PAL161 6.8 35.5 6.8 65.2 9.3 33 20.2 10.5 1609 602 Pipi 7.7 33.2 6.4 65 10.2 13 19 10 1903 602 Resisto 7.8 24.8 7.6 53.5 15.7 271 24.1 12.7 1821 646 Sowola (389 A) 13.2 33.3 7.2 66.4 9.8 0 21.1 11.1 1603 469 Sowola6 9.9 30.6 8 62.2 10.3 54 24.6 12.5 2302 770 Sponge 11.4 32.4 6 65.1 10 48 19.7 10.2 2039 595 SRT 14 Nora 14.1 32 6.6 62.8 11.7 0 21.2 9.5 2156 593 SRT01 Osapat 9.5 33.6 6.2 66.8 9.6 0 19.5 10 1660 525 SRT02 Araka white 10.3 33 7.3 65.8 9.6 0 22.5 11.5 1608 440 SRT30 Nyara 13.9 29 7.6 58.3 13.8 22 22.9 11.7 1891 644 SRT33 Abuket1 11.6 27.7 7.1 58.2 14.6 159 23.1 12.4 1938 670 SRT34 Abuket2 13.2 31 6.6 59.5 14.5 51 20.3 10.1 1762 587 SRT35 Anyumel 9.4 31.6 6.9 63.8 10.6 82 22.7 11.2 1871 636 SRT39 Rwanda 12.2 20.1 5.4 51.1 17 169 22.7 10.7 2179 771 SRT40 Mary 10.6 35.9 6.4 64.7 10.7 -1 22.1 10.8 2916 918 SRT49 Sanyuzameza 5.8 35.3 8 65.7 9.3 0 24.3 12.3 2414 976 SRT52 4.1 32.5 7.5 64.2 11 35 23.4 11.7 2644 892 Sudan 6.1 32.2 6.3 64.6 10.6 44 20.6 10.2 1571 646 Tororo3 5.1 32.9 5.2 65.2 9.3 0 18.1 7.9 2238 537 Ukerewe 13.7 34.6 5.7 65.7 11.2 0 18.4 9.6 1536 421 Wagaborige 7.3 32.2 6 65.2 11.9 0 19.9 8.7 1493 498 Wera 4.2 29.9 7 50.1 28.5 1 26.1 13.1 2544 1004 Zambezi 6.7 29.5 7 62 11.1 233 22.9 12.9 2631 884 LSD (0.05) 6.6 2.8 1.4 2.9 2.5 19 3.0 1.8 534 219 Population mean 8.6 32.1 6.7 64.4 10.3 30.6 21.6 10.9 1950 628 † Observed traits: YLD = Storage root yield, t ha-1; DM = dry matter content of storage roots, %; PRO = protein content of storage roots, % DM; STA = starch content of storage roots, % DM; SUC = sucrose content of storage roots, % DM; BC = β-carotene content of storage roots, ppm DM; Fe = iron content of storage roots, ppm DM; Zn = zinc content of storage roots, ppm DM; Ca = calcium content of storage roots, ppm DM; Mg = magnesium content of storage roots, ppm DM. 29 Several OFSP farmer varieties, namely ‘Carrot_C’ (259 ppm), ‘Carrot Dar’ (272 ppm), ‘Ejumula’ (240 ppm), ‘Mayai’ (264 ppm), and ‘Zambezi’ (233 ppm) exhibited similar or slightly different β-carotene contents as the check. For these OFSP accessions high storage root dry matter contents (≈33%), elevated storage root starch contents (≈58% to 66.6 % dry weight basis), and low to moderate sucrose contents (≈9.8 and 13.7% dry weight basis) were also observed. However, low storage root sucrose contents (6.4 to 7.4%) were also observed in several white-fleshed varieties such as ‘Bungoma’, ‘K-207’, ‘K-37’, and ‘KBL632 Nyinakamanzi’. Two OFSP varieties (‘Rwanda’ = 169 ppm; ‘Abuket1’ = 159 ppm) were observed with moderately high β-carotene contents. It should be noted that for these two varieties, only low to medium storage root DM contents were observed [in the case of ‘Rwanda’ significantly (P<0.05) lower than ‘Resisto’]. Additional 12 farmer varieties [‘K- 118’, ‘K-134’, ‘K-46’, ‘KMI61’, ‘MLE162 Nakahi’, ‘PAL161’, ‘Sowola6’, ‘Sponge’, ‘SRT34 Abuket2’, ‘SRT35 Anyumel’, ‘SRT52’ and ‘Sudan’] were observed with significant but low β-carotene contents, and high to very high storage root DM contents. Relative high values for minerals were observed in OFSP (e.g. ‘Carrot Dar’ with values that correspond to 8.8 ppm Fe, 4.5 ppm Zn, 695 ppm calcium and 305 ppm magnesium on fresh weight basis) as well as white-fleshed farmer varieties (i.e. ‘MBR580 Nylon’ with values that correspond to 7.5 ppm Fe, 4.1 ppm Zn, 901.5 ppm calcium and 315.6 ppm magnesium on fresh weight basis). Moderate to high positive correlations were observed between trait pairs for dry matter and starch (r = 0.620), protein and Fe (r = 0.810), protein and Zn (r = 0.796), Fe and Zn (r = 0.859), Fe and magnesium (r = 0.633), and calcium and magnesium (r = 0.837) in storage roots on basis of all accessions (N=90) used in the study (Table 1.6). Table 1.6. Pearson correlation coefficients among observed traits in East African sweetpotatoes. YLD DM PRO STA SUC BC Fe Zn Ca Estimates based on all farmer varieties (N=89) DM -0.178 PRO -0.095 0.018 Sta -0.015 0.620 -0.265 Suc -0.023 -0.472 0.147 -0.885 BC -0.061 -0.275 0.131 -0.467 0.351 Fe -0.188 -0.176 0.810 -0.505 0.368 0.232 Zn -0.123 -0.205 0.796 -0.361 0.231 0.205 0.859 Ca -0.299 0.088 0.228 -0.214 0.163 0.149 0.428 0.276 Mg -0.276 0.067 0.397 -0.301 0.234 0.232 0.633 0.433 0.837 Estimated based on orange fleshed farmer varieties (N =32 ) DM -0.258 PRO -0.148 0.064 Sta -0.071 0.708 -0.199 Suc 0.050 -0.586 0.181 -0.917 BC -0.188 -0.209 0.213 -0.521 0.470 Fe -0.172 -0.205 0.805 -0.493 0.429 0.446 Zn -0.140 -0.167 0.855 -0.375 0.325 0.379 0.899 Ca -0.355 0.050 0.315 -0.137 0.061 0.374 0.469 0.324 Mg -0.350 0.006 0.505 -0.232 0.151 0.428 0.705 0.560 0.857 30 † Observed traits: YLD = Storage root yield, t ha-1; DM = dry matter content of storage roots, %; PRO = protein content of storage roots, % DM; STA = starch content of storage roots, % DM; SUC = sucrose content of storage roots, % DM; BC = β- carotene content of storage roots, ppm DM; Fe = iron content of storage roots, ppm DM; Zn = zinc content of storage roots, ppm DM; Ca = calcium content of storage roots, ppm DM; Mg = magnesium content of storage roots, ppm DM. A high negative correlation was observed for starch and sucrose (r = -0.885) on basis of all accessions used in the study. A separate analysis with only OFSP varieties (N=32 clones) revealed that there are positive correlations between β-carotene and mineral (Fe r = 0.446; Zn r = 0.379; Mg r = 0.374; Ca r = 0.428) and sucrose (r = 0.470) contents although these are not strong (Table 1.6). Also a moderate negative correlation between β-carotene and starch (r = -0.521) was observed. The %RDA under the condition of a high intake (250 g fresh storage roots) and consumers 5 to 8 years old was high for β-carotene (350 to 450) in deep OFSP farmer varieties [i.e. ‘Carrot_C’, ‘Carrot Dar’, ‘Ejumula’, ‘Mayai’, and ‘Zambezi’]. It should be noted that the estimated %RDA β-carotene for the check clone (‘Resisto’) was 350%. Estimates of ≈50% (results not presented) for %RDA β-carotene were obtained with small intakes (≈30 g fresh storage roots per day) of deep OFSP farmer varieties (variety names given above). Many OFSP farmer varieties with light orange color and high dry matter and starch contents in storage roots were observed with %RDA β-carotene estimates of 50 to 90% (‘Shinyanga’, ‘HMA493 Tanzania’, ‘K- 118’, ‘K-134’, ‘K-46’, ‘PAL161’, ‘Sowola6’, ‘SRT52’, and ‘Sudan’). On average, low to medium %RDA were observed for Fe and Zn (17.5%), calcium (20%), and magnesium (40%) (Table 1.7). Several accessions were observed with %RDA between 20 and 22% for Fe and Zn, 25 to 33% for calcium, 50 to 66% for magnesium, which were significantly different from accessions below the population mean (LSD test). 31 Table 1.7. Clone means of farmer varieties for contribution to recommended daily intake (RDA) of micro-nutrients based on 250 g fresh sweetpotato root consumption per day. RDA contribution (%) Farmer varieties β-carotene Iron Zinc Calcium Magnesium APA343 21 16 15.7 16 32.7 APA348 Liralira 0 19.5 18.9 21.3 45.5 APA352 Oketodede 0 15.9 16.6 16.1 29.8 APA365 Anam Anam 0 16.6 17.1 18.4 34.3 ARA208 Ombivu -0.1 17.2 18.4 14.4 22.9 ARA214 44.4 15.8 16.3 14.9 31.8 ARA244 Shinyanga 82.7 12.4 11.5 13 26.6 Bunduguza -0.2 17.5 15.2 22.4 44.8 Bungoma 0 17.3 16.8 17.7 41.3 Carrot C 447.6 21.6 21 26.8 58.9 Carrot Dar 440.7 22.1 22.4 21.7 58.7 Dimbuka -0.2 17.1 18.2 17.9 33.4 Ejumula 409.4 19.5 18.7 23.1 53.4 HMA490 Kawogo 0 16.4 16.9 17.4 28.6 HMA493 Tanzania 50.9 16.8 16.2 20.8 43.7 IGA963 Nyongerabarenzi 1.5 15.8 16.6 14.6 22 K-118 60.3 16.4 17.7 12.9 27.7 K-134 65.6 16.8 17.7 18.8 37.8 K-207 -0.2 19 18.2 33.8 61.7 K-37 45.1 15.4 13.9 25.8 43.5 K-46 83.2 17.9 17.9 26.5 47.3 KBL627 Mukazi 0 20.3 19.5 23 51.4 KBL632 Nyinakamanzi 1.5 16.4 17.8 20.8 41.7 KBL640 Africare 23.6 15.2 15 13.2 27.1 KMI56 Opira 0 17.9 17.6 19.3 45.9 KMI59 Kampala 0 18.6 16.4 19.4 40.7 KMI61 130.2 19.2 18.8 13.9 31.9 KMI78 Osukari 0.1 17.3 18.6 15.2 32.2 KMI81 Ikala 37.4 14.6 16.1 12.8 24.4 KMI83 Ikala2 16.7 15.8 16.1 15.1 29.1 KML883 Silkempya 0 17.7 16.5 21.2 32.8 KRE716 Nylon -1.7 20.6 20.9 29.0 56.7 KRE726 Kwezikumwe 14.2 15.2 16.9 14.1 27.1 KRE733 Kitambi 0 19.2 19.3 21.7 44.7 KSR673 Mabereikumi 0 17.6 18.8 15.7 23.5 KSR652 Mugumire 1.7 18.4 19.0 22.1 27.6 KSR662 Kakoba 1.7 14.9 15.7 23.6 35.7 KSR664 Mulererabana 1.5 17.8 17.0 20.2 44.6 KSR675 Nora II 0 19.9 18.3 19.7 46.5 Kunykubiongo 0 15.6 14.9 17.1 31.8 Kyabafuriki 0 15.6 17.0 16.9 25.9 LIR257 Otada -0.2 16.6 15.4 18.5 36.6 LIR296 0 15.8 15.8 17.7 35.4 Mayai 456.5 18.6 17.9 22.6 48.6 MBR539 Kitekamaju 1.4 16.9 18.6 19.6 33.2 MBR524 Nkwasahansi 1.5 19.8 18.3 18.7 38.7 MBR536 Karebe 0 15.2 16.7 20.7 38.1 MBR552 Kahungezi 0 18.0 18.9 21.0 42.0 MBR560 Mugurusi 0 18.2 18.9 19.0 37.1 MBR580 Nylon -0.1 18.7 20.3 28.1 60.6 MBR600 Kisakyabikiramaria 0 18.1 19.0 15.5 30.9 MLE166 9.8 18.8 18.9 24.4 49.8 MLE162 Nakahi 77.1 17.0 16.6 18.5 38.7 MLE163 Kyebandula -1.7 22.3 22.0 27.3 60.8 MLE165 Namafumbiro 0.1 16.8 17.7 22.4 44.6 MLE173 Kijovu -1.4 17.6 16.9 17.2 28.8 MLE184 Manafayereta 0 18.8 21.7 18.3 25.7 MSK1025 Bitambi 0 17.1 17.9 27.7 51.1 MSK1047 Bwanjure 0 17.0 17.1 15.8 25.8 Nyaguta 0 20.6 18.8 27.6 66.8 Nyandere 0 17.0 18.2 16.6 31.7 32 Table 1.7. Continued. RDA contribution (%) Farmer varieties β-carotene Iron Zinc Calcium Magnesium Nyathiodiewo 0 14.8 15.3 15.5 32.8 Nyatonge 0 16.3 16.5 20.9 40.6 Obuogo1 0 20.7 18.3 23.5 54.5 Obuogo II 0 18.9 16.8 18.9 43.6 Oguroiwe 0 16.7 17.6 12.9 23.2 PAL153 Abukoki -1.4 12.3 13.4 15.2 17.7 PAL161 60.7 17.9 18.7 17.9 41.1 Pipi 22.5 15.8 16.6 19.7 38.4 Resisto 350.1 14.9 15.7 14.1 30.8 Sowola (389A) 0 17.5 18.4 16.7 30.0 Sowola_6 86.3 18.8 19.1 22.0 45.3 Sponge 80.6 16.0 16.6 20.6 37.1 SRT14 Nora -0.1 17.0 15.2 21.5 36.5 SRT01 Osapat 0 16.4 16.8 17.4 34.0 SRT02 Araka white -0.2 18.5 18.9 16.6 27.9 SRT30 Nyara 32.5 16.6 16.9 17.2 35.9 SRT33 Abuket_1 228.8 16.0 17.1 16.7 35.6 SRT34 Abuket_2 82.2 15.7 15.6 17.0 35.0 SRT35 Anyumel 134.7 17.9 17.7 18.4 38.6 SRT39 Rwanda 176.5 11.4 10.7 13.7 29.7 SRT40 Mary -1.7 19.9 19.3 32.7 63.4 SRT49 Sanyuzameza 0 21.5 21.8 26.7 66.3 SRT52 59.3 19.0 19.0 26.8 55.7 Sudan 72.9 16.6 16.4 15.8 39.9 Tororo3 0.2 14.9 13.0 23.0 33.9 Ukerewe 0 15.9 16.6 16.6 28.0 Wagaborige 0 16.0 14.0 15.0 30.8 Wera 1.4 19.5 19.6 23.8 57.7 Zambezi 357.6 16.9 19.1 24.3 50.2 LSD (0.05) 27.8 2.7 3.0 6.0 14.9 Population mean 47.9 17.3 17.4 19.6 38.9 33 Discussion This study focused on β-carotene, DM, sucrose, protein, starch, and minerals contents in EA sweetpotato against the background of the contribution of sweetpotato to food supply. Whereas levels of root β- carotene and DM contents are fairly well documented for African germplasm, other quality traits are not, thus making results of this study the first of its kind. The more pronounced differences between locations for starch content in our study (Table 1.3) extend our knowledge by documenting the magnitude of this variability (Saad, 1996; Grüneberg et al., 2005) but could have also resulted from small plots used in the present study. Experiments with large plots would be needed to generate reliable data for root yield performance of the accessions. The CV for all traits at both locations were low (Table 3), except storage root yields and storage root β-carotene contents. High CV values for storage root yield have been previously reported for sweetpotatoes (Grüneberg et al., 2005). The high CV values for storage root β-carotene contents in this study can be explained by the low population mean (for all accessions including white- and cream-fleshed), whereas mean estimates for β-carotene contents varied considerably between accessions. The variance component 2 GxL was unexpectedly higher for starch and sucrose content of storage roots (Table 1.4). However, CV values for both traits at each location were low. The locations belong to different agro-ecological zones and differ greatly in altitude and crop duration for harvest (Table 1.2), which might be the reason for high 2 GxL estimates for starch (Grüneberg et al., 2005) and sucrose. Such extreme locations, which are useful in testing accessions’ adaptability and resistance to pests and diseases, might be less useful for nutritional quality breeding (Grüneberg et al., 2009). The extreme locations result in lower heritabilities in programs focusing on improvement of quality traits, which was also observed in this study (Table 1.4). This merits further studies with a fraction of the varieties used in this study. Nevertheless, the variance component 2 G was significant (p < 0.01) for all the observed traits except storage root sucrose contents, which indicates significant differences between accession means. Owing to the magnitude of 2 GxL estimates and that locations were in distinct eco-geographical zones, genotype and location were set as fixed factors for a multiple comparison of accessions by the LSD-test. Hence, LSD values at the 0.05 level might be under estimated, which does not affect the evidence that differences below the LSD values given (Table 1.5) are not significantly different.