African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6589

Full Length Research Paper

Evaluation of sweetpotato accessions for end-user preferred traits improvement

Ernest Baafi
  • Ernest Baafi
  • CSIR-Crops Research Institute, P. O. Box 3785, Kumasi, Ghana
  • Google Scholar
Vernon E. Gracen
  • Vernon E. Gracen
  • West Africa Centre for Crop Improvement, University of Ghana, Legon
  • Google Scholar
Essie T. Blay
  • Essie T. Blay
  • West Africa Centre for Crop Improvement, University of Ghana, Legon
  • Google Scholar
Kwadwo Ofori
  • Kwadwo Ofori
  • West Africa Centre for Crop Improvement, University of Ghana, Legon
  • Google Scholar
Joe Manu-Aduening
  • Joe Manu-Aduening
  • CSIR-Crops Research Institute, P. O. Box 3785, Kumasi, Ghana
  • Google Scholar
Edward E. Carey
  • Edward E. Carey
  • International Potato Centre (CIP), Ghana
  • Google Scholar

  •  Received: 30 May 2015
  •  Accepted: 16 October 2015
  •  Published: 10 December 2015


This study assessed the genetic diversity and differentiation in sweetpotato accessions in Ghana to guide selection for genetic improvement on beta-carotene, dry matter and sugar contents to promote increased utilization. One hundred and fifteen sweetpotato accessions from four different sources, which were the International Potato Centre (CIP) collection, local collection from farmers’ field, local improved varieties, and local and exotic collections from the National Agricultural Research Programmes were studied using 40 agro-morphological and physico-chemical traits, and 25 SSR markers. Variability was obtained for 13 agro-morphological traits and all the physico-chemical traits. Significant genetic diversity indicates existence of a high degree of agro-morphological and physicochemical variation. Within Group variation (97%) accounted for most of the diversity indicating a broad genetic base. The divergence indicates that breeders can form different populations with significant levels of genetic variation to exploit heterosis and improvement of populations. A strong negative relationship was found for sugar content and dry matter content and indicates a possible development of non-sweet high dry matter sweetpotato varieties. However, developing non-sweet, high dry matter and high beta-carotene sweetpotato varieties could be challenging due to the strong negative association between dry matter content and beta-carotene content, and the positive association existing between beta-carotene and sugar content. This study has in addition confirmed the breeding potential of sweetpotato accessions in Ghana and the probability of providing useful genetic variation for the development of farmer preferred cultivars.


Key words: Analysis of Molecular Variance (AMOVA), diversity, end-user, simple sequence repeats (SSR) markers, Sweetpotato, traits.


Sweetpotato is a major staple crop in developing countries all over the world because of its diverse uses. These include use in many food and industrial products such as tarch, sweeteners, noodles, citric   acid, soft drinks, desserts, flour, industrial alcohol, ethanol fuel and livestock feed. Despite its importance, the level of utilization in Ghana is very low and it is not well integrated into Ghanaian diets (Adu-Kwarteng et al., 2002). This is because consumers in Ghana prefer sweetpotato with dry mealy flesh, non-sweet, and high nutritive value (Sam and Dapaah, 2009; Baafi et al., 2015), but locally available varieties are sweet that limits consumption as a staple food (Missah and Kissiedu, 1994). In addition, the recently introduced orange-flesh genotypes, identified as a cheapER source of Vitamin A, are low in dry matter content. These factors have led to the low adoption of the 13 varieties released to date. There is, therefore, the need to incorporate non-sweetness, high dry matter, and/or high beta-carotene contents into the existing genetic background of high yielding and early maturing cultivars which are resistant to biotic and abiotic stresses.
A prerequisite for genetic improvement of sweetpotato is knowledge of the extent of genetic variation present in the germplasm. Information on genetic diversity guides selection of divergent parents to broaden genetic base of a breeding population and produce progenies with heterosis (Manosh et al., 2008). Identification of populations with high frequencies of favourable alleles for desirable traits is an important step in the development of improved varieties (Gasura et al., 2008). Understanding the genetic diversity is also critical to find new alleles for desirable traits (Warburton et al., 2002). Since the amount of genetic diversity within populations determines the extent of response in traditional breeding through selection, genetically diverse breeding populations are needed (Bos et al., 2000). Morphological characterization has been used extensively in diversity studies for various crop plants including sweetpotato (Bos et al., 2000; Kaplan, 2001; K’opondo, 2011).
Agro-morphological and physicochemical traits are important diagnostic features for distinguishing among sweetpotato accessions. The use of these traits as genetic markers can speed up selection in sweetpotato improvement. SSR markers have been used to study genetic diversity in sweetpotato (Buteler et al., 1999; Diaz and Gruneberg, 2008; Tumwegamire et al., 2011; Somé et al., 2014). SSR markers are multi-allelic, highly polymorphic, highly reproducible, co-dominant and provide rich genetic information with good genome coverage (Kawuki et al., 2009; Sree et al., 2010). The SSR markers are affordable and amenable to most breeding procedures and applicable in public breeding programmes that may not be able to afford expensive diversity assessment techniques (Turyagyenda et al., 2012). Application of both phenotypic and genetic markers is important in obtaining full knowledge of genetic diversity in sweetpotato germplasm.
The objective of this work was to characterize sweetpotato germplasm in Ghana using phenotypic and SSR markers with focus on enhancing end-user characteristics of sweetpotato for increased utilization in Ghana.


Agro-morphological and physico-chemical characterization
Germplasm collection and evaluation
Germplasm was collected from the major sweetpotato growing areas in Ghana in 2010. These were the Northern, Upper East, Upper West, Volta, Eastern, Central and the Brong Ahafo Regions. Collections from the CSIR-Crops Research Institute, Kumasi and the CSIR-Plant Genetic Resource Institute, Bunsu, were also included. In addition, accessions were collected from the Crop Science Department, University of Ghana and the International Potato Centre (CIP) gene bank in Accra and Kumasi. Thus, a total of 115 sweetpotato accessions (Table 1) were collected. These represent four groups, which were local accessions (32), local improved varieties (13), exotic and local accessions in National Agricultural Research Systems (NARS) or programmes (43), and exotic accessions from CIP, Kumasi germplasm (27). Evaluation of the sweetpotato germplasm was carried out under rain-fed conditions using Randomised Complete Block Design (RCBD) in three replications at CSIR-Crops Research Institute research fields at Fumesua (forest ecozone) in 2011, after carrying out planting material multiplication in 2010. Planting distance was 1 m between ridges and 0.3 m within row of ridge length 3.6 m.
Data collection
Data collection was done based on the sweetpotato descriptor for field phenotyping (CIP/AVRDC/IBPGR, 1991)as well as storage root quality traits as shown in Table 2. Harvesting was done at three and half months after planting. At harvest, data were taken on storage root yield and its components and a random sample of storage roots (one small, one medium and one large) were taken for physico-chemical analysis. Storage roots considered for the yield data were those over 0.3 m in diameter and without cracks, insect damage or rotten parts (Ekanayake et al., 1990). With the exception of the dry matter content, all the storage root quality traits were determined using the near-infrared reflectance spectroscopy (NIRS) which uses the work flow of the Quality and Nutrition Laboratory of CIP Lima, Peru. Fifty grams fresh sample was used. It was freeze-dried for 72 h using a freeze dryer. Dry matter content was determined after freeze drying as ratio of dry weight to fresh weight of sample expressed as a percentage. These were determined at CIP Laboratories in Kumasi, Ghana and Lima, Peru.
Data analysis
Data were subjected to Principal Component Analysis (PCA) and Cluster Analysis using Genstat version (Genstat, 2007). The PCA was done based on the correlation matrix. Data for beta-carotene, dry matter and total sugar contents were subjected to an Analysis of Variance (ANOVA) using Genstat version (Genstat, 2007). Based on the mean performance of these traits, the top 10 and the bottom 10 accessions were selected to construct a dendrogram and a GGE Biplot using the most important traits for PC1 and PC2. The dendrogram was constructed based on the hierarchical, single link method using Euclidean test. The biplot was constructed to depict the phenotypic relationships among the accessions, their correlation with the traits significant for PC1 and PC2, as well as the association among the traits. The biplot was constructed using GGE Biplot software (Yan and Kang, 2003).
Molecular characterization using SSR markers
Genetic material
A total of 76 sweetpotato accessions were used for the study (Table 1). These represent four groups, which were collections from International Potato Centre (CIP) gene bank in Ghana (19), local collection from farmers’ field (19), local improved varieties (12), and local and exotic collections sourced from the National Agricultural Research Systems (NARS) or Programmes (26). These were planted at the CSIR-Crops Research Institute research field at Fumesua which is in the forest ecozone.
DNA extraction
This was  done  at  the  Molecular  Laboratory  of  the  CSIR-Crops Research Institute, Fumesua using the method of Egnin et al. (1998), in 2012. Two hundred milligram of young tender leaf tissue was weighed into 2 ml Eppendorf tube and was ground to powder after freeze drying with liquid nitrogen. Eight hundred microliter (800 μl) of buffer A [1M Tris HCl (pH 8) = 50 mM, 5 M NaCl = 300 mM, 0.5M EDTA (pH 8) = 20 mM, PVP = 20%, Sodium Metabisulphate = 1 g/100 ml, 20% Sercosine = 1.5] was added and incubated at 90°C for 10 min, and vortexed every 5 min. The suspension was cooled at room temperature for 2 min after which 400 μl of 5 M potassium acetate was added and then gently mixed by inversion 5 to 6 times. The suspension was then incubated on ice for 30 min with continuous shaking, followed by centrifuging at 13,000 rpm for 10 min. The upper phase was transferred to a new Eppendorf tube. One volume of cold isopropanol and 1/10th of 3 M sodium acetate was added and mixed 10X by inverting the tube. This was followed by incubation at -20°C for 1 h, and centrifuging at 13,000 rpm for 10 min. The supernatant was poured off, the pellets were washed with 800 μl, 80% ethanol, and centrifuged at 14,000 rpm for 5 min. The alcohol was then discarded and the   pellets were dried. 
Five hundred microliter (500 μl) of 1X TE buffer was used to dissolve the pellets, followed by the addition of 4 μl RNase A, and incubation at 37°C for 30 min. This was followed by addition of 250 μl of 7.5 M ammonium acetate. The suspension was incubated on ice for 3 min, and centrifuged at 13,000 rpm for five minutes, and then transferred into a new 1.5 ml tube. Seven hundred microliters (700 μl) of isopropanol was added, mixed by inversion (ice inversion), and centrifuged at 13,000 rpm for 15 min. The supernatant was discarded and the pellets were washed with 1 ml 80% ethanol by centrifuging at 14,000 rpm for five minutes. Again the supernatant was discarded, followed by drying of the pellets at room temperature. The DNA pellets were then dissolved in 200 μl 1X TE buffer, and its quality was checked on 0.8% agarose gel.
Genotyping with simple sequence repeats (SSR) markers
The genotyping was carried out at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India in 2012. A 3 ng sample of total genomic DNA from each of the samples was used for the polymerase chain reactions (PCRs). Twenty-five pairs of SSR markers confirmed for sweetpotato DNA amplification (Buteler et al., 1999; Diaz and Gruneberg, 2008; Tumwegamire et al., 2011)were used (Table 3). A final volume of the reaction mixture of 10 μL, which contains 25 mM MgCl2, 10x buffer, 10 mM deoxyribonucleotide triphosphate (dNTPS), 1 μM M13 FORWARD 700/800, 1 μM forward primer, 1 μM reverse primer, 5 U μL–1 Taq polymerase, 3 ng μL–1 DNA, and a double distilled water were used for the PCR. The amplification conditions were set up at 94°C for four minutes and denaturation at 94°C for one minute; annealing at between 56.0 to 62.0°C (depending on the annealing temperature of the primer); and polymerization at 72°C for one minute. Step 2 annealing was 56.0 to 62.0°C (depending on the annealing temperature of the primer) and was repeated 30 times, and a final extension at 72°C for 7 min. Amplification products were analyzed and read on a computer automated Licor (4300) DNA Analyzer (Licor Biosciences, Lincoln, NE) for 25 pairs of SSR primers.
Simple sequence repeats data scoring and analysis
Accessions amplified were noted and used to estimate percent accessions amplified. The number of alleles for each marker was noted and recorded. Markers that showed variation in at least 25% of the accessions were noted and their alleles were recorded as unique alleles. Percent unique alleles were computed as the ratio of number of unique alleles to the total number of alleles. Genotypes were scored for the presence (1) or absence (0) of each fragment. NTSYSpc software version 2.1 (Rohlf, 1993, 2002)was used to run the binary data. Jacard’s coefficients (Jaccard, 1908)were used to construct a similarity matrices from the binary data by using SIMQUAL algorithm. This was followed by construction of a dendrogram using the unweighted paired group method average (UPGMA) applying the SHUAN algorithm. Principal Coordinate Analysis (PCoA) was performed from Jacard’s coefficients using Genstat (Genstat, 2007). The polymorphic information content (PIC) was determined  based on the approach and method of Weir (1996) as presented below:
PIC = 1 - ΣPi2
Where, Pi is the frequency of the ith allele.
Analysis of Molecular Variance (AMOVA) was also performed using Arlequin 3.1 version computer software (Excoffier et al., 2005), to quantify the genetic variation and relationship existing between and among the sweetpotato and the four population groups studied.


Phenotypic variation
The first six Principal Components (PCs) with Eigen values greater than 1.0 jointly explained 54.86% of the total variation in the accessions based on the 40 agro-morphological and physicochemical traits studied (Table 4). The traits of importance for the first component involved root traits of commercial interest. Beta-carotene, dry matter and total sugar contents were of importance for PC2.
The mean performance of the top 10 and the bottom 10 selected accessions for beta-carotene, dry matter and sugar contents are presented in Table 5. Significant differences were observed between the accessions for the traits. The range of values obtained for beta-carotene content was 6.83 - 33.67 (mg/100 g) DW. For dry matter content the range was 27 - 50%, and for sugar content the range was 9.83 - 30.34%. Ogyefo and Apomuden had the lowest and highest values for beta-carotene content. Apomuden had the lowest dry matter content whilest FA-10-026 had the highest dry matter content. CRIWAC 19-10 and CIP 442850 gave the lowest and highest sugar contents, respectively.
The dendrogram separated the selected accessions with a Euclidean similarity distance ranging from 1.00 to 0.93 (Figure 1).  At 1.00 level of similarity, all the accessions were distinct from each other except BOT 03- 030 and CIP 442896. Conversely, at about 0.93 levels of significance, two clusters were identified with all the accessions being similar except for CRIWAC 12-10. Five main clusters A, B, C, D, and E at 94.5% (0.945) level of significance were identified. The first four clusters contained 1 to 5 accessions per cluster while the fifth cluster (E) had 26 accessions.
The distribution of PC1 and PC2 among the correlated traits, the selected accessions as well as between the selected accessions and the correlated traits are shown in Figure 2. Three groups were observed for the correlated traits. Beta-carotene, fructose, total sugars, calcium (Ca), and magnesium (Mg) were grouped together in Quadrant 1. Storage root yield traits were grouped in Quadrant 2, while only dry matter was found in Quadrant 3. Four groups were detected for the accessions. Beauregard and Apomuden were the most distantly related accessions in Quadrant 1, whilest CIP 440032 and CIP 442264 were the most distantly related accessions in Quadrant 2. The most distantly related accessions in the third and fourth quadrants were Histarch and Ogyefo, and CIP 442850 and TAG 03-030, respectively.
Genotypic variation
Out of the 25 SSR markers used to assess the genetic diversity of the sweetpotato accessions, only 20 produced amplifications. The five markers that did not produce amplification were IbS01, IbS07, IbS10, IbCIP2 and IbR20. A total of 87 polymorphic alleles were observed across the accessions and loci. These ranged from two to six with mean of 4.25. Markers IbS18 and IbR21 recorded the lowest number of alleles while Ib3/24, Ib316, Ib-297, IbC12, IbS11, J10A and J116A recorded the highest number of alleles (Table 6). Out of the 87 alleles revealed by the 20 SSR markers across accessions and loci, 40 (45.98%) were unique alleles and the average number of unique alleles was two. IBCIP-1, IbC12 and J67 produced no unique alleles while Ib3/24 recorded the highest number (5) of unique alleles followed by Ib-297 and J10A with 4 unique alleles. However, Ib3/24 obtained the highest percent polymorphism (83.33%), followed by IbR14 (75.00%). The range and the average percent polymorphism were 0 to 83.33 and 45.50%, respectively. The PIC values were high and ranged between 0.62 for J67 and 0.96 for IbR16 and IbR19, with a mean of 0.84. The highest amplification was recorded by IbR14 (90.91%) followed by IbR316 and J67 with value of 77.92%. IbR16 recorded the lowest amplification. Base range for the markers was highest and lowest for IbR03 (262-277) and J175 (133-147). 
IbS11 recorded the highest number of loci (1- 6) across accessions followed by IbC12 (2 - 6). The lowest number of loci (1-2) across accessions was produced by Ib3-24, IbS18, IbR14 and IBR21.  
Principal coordinate analysis (PCoA), which was determined from the similarity coefficients is graphically presented in Figure 3 (showing diversity in sweetpotato accessions), and Figure 4 (showing diversity in the group structure of the sweetpotato accessions). The two axes explained 45.21% of the total similarity (54.79% of total variation) with the first axis (PCoA1) accounting for 28.08% and the second (PCoA2) accounting for 17.13%. The 76 sweetpotato accessions investigated by PCoA did not form clear groups according to the group structure both within and between.
The dendrogram constructed separated the 76 sweetpotato accessions into major clusters at different similarity levels ranging from 0.00 to 1.00 (Figure 5). At slightly greater than 0.00 similarity level, two major clusters were observed. CIP 6 (CIP 442462) constitutes the first cluster while the second cluster consisted    of the other 75 accessions. At 0.25 similarity level, seven major clusters were observed while 17 were found at 0.50 similarity level. The markers fully discriminated the 76 sweetpotato accessions by the 1.00 level of similarity except for two improved cultivars LOCIMP2 (Santompona) and LOCIMP10 (Otoo). The primers, however, did not fully discriminate the accessions into the different group structures. Significant differences were observed between the sweetpotato accession within the groups (P<0.01) as well as between the  groups  (P<0.05)as shown in Table 7. The differences observed within the groups however accounted for a greater percentage (97.12%) of variation observed than that found between the groups (2.88%).


Variability was observed in all the physico-chemical traits and 20 out of the 27 agro-morphological traits. This indicates a high degree of agro-morphological and physicochemical polymorphism among the accessions. Diversity in flesh colour (beta-carotene content) of sweetpotato cultivars has been reported (Warammboi et al., 2011). Sugar content in sweetpotato is also reported to be cultivar-dependent (Ravindran et al., 1995; Aina et al., 2009), and showed high levels of polymorphism with SSR markers. This confirms the discriminatory capacity of the SSR markers on sweetpotato (Gichuru et al., 2006; Tumwegamire et al., 2011). High level of polymorphism was observed in this study with an allele range of two to six alleles per SSR marker and this is in agreement with Yada et al. (2010). Buteler et al. (1999)obtained high polymorphism with an allele range of 3 to 10. Somé et al. (2014), also reported 1 to 8 alleles. A range of 2 to 11 alleles was reported by Tumwegamire et al. (2011). A lower level of polymorphism, ranging between  one  and four alleles per SSR locus has also been reported (Hwang et al., 2002). Differences observed may be attributed to the use of different SSR primers, sweetpotato genotypes and annealing temperatures. Varying number of SSR primers used in diversity studies may also account for the differences in observations.
Hwang et al. (2002) attributed high level of polymorphism to large genome size and heterozygosity of sweetpotato. It should also be noted that genetic diversity due to heterozygosity in sweetpotato is driven by both the mating system (outcrossing in combination with self-incompatibility) and the high ploidy level of the crop (autohexaploid) (Tumwegamire et al., 2011). The AMOVA and ANOVA results also indicated significant differences within and between the different sweetpotato groups studied. These results demonstrate significant genetic diversity and indicates that meaningful selection and improvement of these traits is possible (Mohammed et al., 2012; Nwangburuka and Denton, 2012). Furthermore, these demonstrate the existence of diversity at the individual genotype level that can be exploited to obtain trait combinations in specific varieties. In addition, the divergences indicate that it is possible to select contrasting parents from these accessions for improvement of beta-carotene, sugar and dry matter contents in sweetpotato. These results agree with results of other researchers (Zhang et al., 2000; 2001; Gichuki et al., 2003; Gichuru et al., 2006; Abdelhameed et al., 2007; Grüneberg et al., 2009; Tumwegamire et al., 2011).
PIC is a measure of the discriminatory capacity of a marker (Jia et al., 2009). According to Heng-Sheng et al. (2012), a PIC value greater than 0.5 is high, and any marker with such value may be effective in genetic diversity study. In this study, the PIC value for all the markers that showed amplification were greater than 0.5. This implies that the values which ranged from 0.62 to 0.96 with mean of 0.84 were very high indicating a high discriminating power of the SSR markers used.  These values are greater than range and mean of 0 to 0.88, and 0.72 reported by Somé et al. (2014). Based on the number of unique alleles and the PIC values, all the SSR markers that showed amplification were very effective in discriminating among the sweetpotato accessions. In spite of this, the markers did not discriminate between cultivars LOCIMP2 (Santompona) and LOCIMP10 (Otoo) at 1.00 level of similarity even though these cultivars are agro-morphologically distinct.  It is probable that no were repeats found that could differentiate the two cultivars and therefore, more SSR markers need to be used in the future to have a full diversity study.
Genetic relationships between traits may result from pleiotropic gene effects, linkage of two genes, linkage disequilibrium and epistatic effects of different genes or environmental influences (Falconer and Mackay, 1996). The strong negative relationship found for sugar content and dry matter content as depicted in the GGE biplot indicates that it is possible to develop non-sweet high dry matter sweetpotato varieties. A similar observation was made by Gruneberg et al. (2009), who also reported that development of non-sweet sweetpotato varieties should not be too difficult. However, developing non-sweet, high dry matter and high beta-carotene sweetpotato varieties could be challenging due to the strong negative association between dry matter content and beta-carotene content, and the positive association existing between beta-carotene and the sugar content. Breeding for such cultivars may require many cycles of selection and hybridization to break genetic linkages associated with the traits. However, beta-carotene seems to be controlled by a limited number of genes and should be easy to manipulate.


This study provides estimate on the level of genetic variation among sweetpotato accessions in Ghana. Significant genetic diversity was found between the accessions for dry matter, beta-carotene and sugar content. This information can be used in sweetpotato germplasm management and improvement in Ghana. The study also affirmed the discriminatory capacity of the SSR markers, and the agro-morphological and physico-chemical markers for sweetpotato characterization especially for breeding programmes with limited resources.
Sufficient useful genetic variation is present in the accessions studied which may be exploited to provide for substantial amount of improvement through selection of superior genotypes. The strong negative association between dry matter and sugar content indicates that it is feasible to develop non-sweet high dry matter sweet potato cultivars which are the preferred sweetpotato varieties in Ghana. However, developing non-sweet, high dry matter and high beta-carotene sweetpotato varieties may require many cycles of selection due to the strong negative association between dry matter content and beta-carotene content.


The authors declare they have no conflict of interests.


Many thanks to the Alliance for a Green Revolution in Africa (AGRA) for sponsoring this study through West Africa Centre for Crop Improvement (WACCI). Thanks to my supervisors and all staff of CSIR-Crops Research Institute, Fumesua, Ghana and staff of the Molecular Biology Laboratory of the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India for their support.


Abdelhameed E, Fjellheim S, Larsen A, Rognli OA, Sundheim L, Msolla S, Masumba E,Mtunda K (2007). Analysis of genetic diversity in sweetpotato (Ipomoea batatas L. Lam) germplasm collection from Tanzania as revealed by AFLP. Genet. Resour. Crop Evol. 55:397-408.


Adu-Kwarteng E, Otoo JA, Osei CK,Baning IS (2002). Sweetpotato: The crop of the future. Factsheet. CSIR-Crops Research Institute. November 2002.


Aina AJ, Falade KO, Akingbala JO, Titus P (2009). Physicochemical properties of twenty-one Caribbean sweetpotato cultivars. IJFST 44:1696-1704.


Baafi E, Manu-Aduening J, Carey EE, Ofori K, Blay ET, Gracen VE (2015). Constraints and breeding priorities for increased sweetpotato utilization in Ghana. Sustain. Agric. Res. 4(4):1-16.


Bos HJ, Vos J, Strulk PC (2000). Morphological analysis of plant density effects on early leaf area growth in maize. Neth. J. Agric. Sci. 48:199-212.


Buteler M, Jarret R,La Bonte D (1999). Sequence characterization of microsatellites in diploid and polyploidy ipomoeas. Theor. Appl. Genet. 99:123-132.


CIP/AVRDC/IBPGR (1991). Descriptors for sweetpotato. Huaman, Z, editor. International Board for Plant Genetic Resources, Rome, Italy.


Diaz F, Gruneberg YW (2008). Variabilidad genetitica de clones avanzados y variedades cultivadas de Ipomoea batatas L. Determinado mediante marcadores SSR. Vi congreso peruano de genetica y 13th congreso latinoamericano de genetica, Lima, Peru. 4-8 May, 2008.


Egnin M, Mora A, Prakash CS (1998). Factors enhancing agrobacterium tumefaciens. Mediated gene transfer in peanut (Arachis hypoges L.). In vitro Cell. Dev. Biol. Plants 34:310-318.


Ekanayake IJ, Malagamba P, Midmore DJ (1990). Effect of water stress on yield indices of sweetpotatoes. In: Howeler, R.H. (ed.). Proceedings 8th symposium of the International Society for Tropical Root Crops. Bangkok, Thailand. 724p.


Excoffier L, Laval G, Schneider S (2005). Arlequin version 3.1. An integrated software package for population genetic analysis. Evol. Bioinform. Online 1:47-50.


Falconer DS, Mackay TFC (1996). Introduction to quantitative genetics. 4th edn., Longman, England.


Gasura E, Mashingaidze AB, Mukasa SB (2008). Genetic variability for tuber yield, quality, and virus disease complex in Uganda sweetpotato germplasm. Afr. Crop Sci. J. 16:147-160.


Genstat (2007). For windows release Genstat-ninth edition @ 2007, Lowes Agricultural Trust.


Gichuki ST, Barenyi M, Zhang D, Hermann M, Schmidt J, Glossl J, Burg K (2003). Genetic diversity in sweetpotato [Ipomoea batatas (L.) Lam.] in relationship to geographic sources as assessed with RAPD markers. Genet. Resour. Crop Evol. 50:429-437.


Gichuru V, Aritua V, Lubega GW, Edema R, Adipula E, Rubaihayo PR (2006). A preliminary analysis of diversity among East African sweetpotato landraces using morphological and simple sequence repeats (SSR) markers. Acta Hortic. 703:159-164.


Grüneberg W, Robert M, Maria A, Harrison D (2009). Unleashing the potential of sweetpotato in sub-saharan Africa: Current challenges and way forward. Challenge them paper 1: Sweetpotato breeding. CIP - Social Sciences Working Paper 2009-1 pp. 1-42.


Heng-Sheng L, Chih-Yun C, Song-Bin C, Gwo-Ing L, Chang-Sheng K (2012). Genetic diversity in the foxtail millet (Setaria italica) germplasm as determined by agronomic traits and microsatellite markers. Aust. J. Crop Sci. 6(2):342-349.


Hwang SY, Tseng YT, Lo HF (2002). Application of simple sequence repeats in determining the genetics relationships of cultivars used in sweetpotato polycross breeding in Taiwan. Sci. Hortic. 93:215-224.


Jaccard P (1908). Nouvelles recherches sur la distribution florale. Bull. Soc. Vaud. Sci. Nat. 44:223-270.


Jia X, Zhang Z, Liu Y, Zhang C, Shi Y, Song Y, Wang T, Li Y (2009). Development and genetic marpping of SSR markers in foxtail millet [Sataria italica (L.) p. Beauv.]. Theor. Appl. Genet. 118:821-829.


K'opondo FB (2011). Morphological characterization of selected spiderplant (Cleome gynandral.) types from western Kenya. Ann. Biol. Res. 2:54-64.


Kaplan DR (2001). The science of plant morphology: Definition, history, and role in modern biology. Am. J. Bot. 88(10):1711-1741.


Kawuki SR, Morag F, Maryke L, Liezel H,Dong-Jin K (2009). Identification, characterisation and application of single nucleotide polymorphisms for diversity assessment in cassava (Manihot esculenta crantz). Mol. Breed. 23(4):669-684.


Manosh K, Biswas MA, Mondal A, Hossain M, Islam R (2008). Utilization of genetic diversity and its association with heterosis for progeny selection in potato breeding programs. AEJAES 3(6):882-887.


Missah A, Kissiedu AFK (1994). Effect of time of harvesting on the yield and pest incidence of two sweetpotato varieties in the forest zone of Ghana. In: Proceedings of the 5th symp. of ISTRC AB. pp. 276-280.


Mohammed A, Geremew B, Amsalu A (2012). Variation and association of quality parameters in Ethiopian durum wheat (Triticum turgidum L. Var. Durum) genotypes. IJPBG 6(1):17-31.


Nwangburuka CC, Denton OA (2012). Heritability, character association and genetic advance in six agronomic and yield related characters in leaf corchorus olitorius. Int. J. Agric. Res. 7:367-375.


Ravindran V, Ravindran G, Sivakanesan R, Rajaguru SB (1995). Biochemical and nutritional assessment of tubers from 16 cultivars of sweetpotato. J. Agric. Food Chem. 43:2646-2651.


Rohlf FJ (2002). Ntsyspc: Numerical taxonomy and multivariate analysis system. Version 2.1. Exter software, Setauker, New York.


Rohlf JF (1993). Ntsys-pc numerical taxonomy and multivariate analysis system. Version 1.80. Department of ecology and evolution, State University of New York, Stony Brook, NY.


Sam J, Dapaah H (2009). West Afriican Agricultural Productiiviity Programme (WAAPP), Ghana Baseline Survey Report pp. 46-48.


Somé KGV, Asante I, Danquah EY, Ouedraogo TJ, Baptiste TJ, Jerome B, Vianney TM (2014). Diversity analysis of sweetpotato (Ipomoea batatas [L.] Lam) germplasm from Burkina Faso using morphological and simple sequence repeats markers. AJB 13(6):729-742.


Sree L, Santha S, Pillai V, Sree KJ (2010). Molecular genotyping of Indian cassava cultivars using SSR markers. Adv. Environ. Biol. 4(2):224-233.


Tumwegamire S, Rubaihayo PR, Labonte DR, Diaz F, Kapinga R, Mwanga RO, Gruneberg WJ (2011). Genetic diversity in white- and orange-fleshed sweetpotato farmer varieties from East Africa evaluated by simple sequence repeat markers. Crop Sci. 51:1132-1142.


Turyagyenda LF, Kizito EB, Ferguson ME, Baguma Y, Harvey JW, Gibson P, Wanjala BW, Osiru DSO (2012). Genetic diversity among farmer-preferred cassava landraces in Uganda. Afr. Crop Sci. J. 20(s1):15-30.


Warammboi JG, Dennien S, Gidley MJ, Sopade P (2011). Characterization of sweetpotato from Papua New Guinea and Australia: Physicochemical, pasting and gelatinisation properties. Food Chem. 126:1759-1770.


Warburton ML, Xianchun X, Crossa J, Franco J, Melchibger A. E., Frisch M (2002). Genetic characterization of CIMMYT inbred lines and open pollinated populations using large scale fingerprinting methods. Crop Sci. 42:1831-1840.


Weir BS (1996). Genetic data analysis. II. Methods for discrete population genetic data. Sinauer Associates, Inc., Sunderland, MA.


Yada BP, Tukamuhabwa P, Wajala B, Kim DJ, Skilton RA, Alajo A, Mwanga R (2010). Characterizing ugandan sweetpotato germplasm using fluorescent labeled simple sequence repeat markers. Hortscience 45(2):225-230.


Yada BP, Tukamuhabwa P, Wajala B, Kim DJ, Skilton RA, Alajo A, Mwanga R (2010). Characterizing Ugandan sweetpotato germplasm using fluorescent labeled simple sequence repeat markers. Hortscience 45(2):225-230.


Yan W, Kang MS (2003). GGE biplot analysis: A graphical tool for breeders, geneticist and agronomist. CRC Press, Boca, Raton, FL.


Zhang D, Cervantes J, Huaman E, Carey E, Ghislain M (2000). Assessing genetic diversity of sweetpotato (Ipomoea batatas (L.) Lam.) cultivars from tropical America using AFLP. Genet. Resour. Crop Evol. 47:659-665.


Zhang DP, Carbajulca D, Ojeda L, Rossel G, Milla S, Herrera C, Ghislain N (2001). Microsatellite analysis of genetic diversity in sweetpotato varieties from Latin America. In: Program Report 1999-2000, International Potato Center, Lima pp. 295-301.