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- Author or Editor: Phinehas Tukamuhabwa x
The East African region in sub-Saharan Africa (SSA) is widely considered as one of the secondary centers of diversity for sweetpotatoes [Ipomoea batatas (L.) Lam.]. Farmers in the region typically grow landraces, but hybridizations occasionally result in new genotypes. Factors such as regional conflicts, natural disasters, disease, and land pressure continually threaten the SSA sweetpotato gene pool. Despite this threat, very little updated information is easily accessible about SSA germplasm collections. Such information is valuable for purposes of management, exploration, and conservation. Using germplasm collection data from Kenya, Tanzania, and Uganda, we demonstrate how publicly available GIS-based tools, e.g., DIVA-GIS, can be used to document current collections as well as make this information easily accessible, searchable, and portable. First, collection data from each country were compiled and known collection sites were georeferenced using available gazetteers. Following data cleaning and verification, georeferenced data were then converted into a GIS-compliant format, primarily as shapefiles. All files were then copied into storage media for exchange among stakeholders. To further demonstrate the portability of the GIS database files, available World Wide Web GIS web viewers enabled real-time access to GIS files uploaded to an experimental web site. This work demonstrates that with very little expense, access to extant SSA germplasm information for sweetpotatoes can be improved using publicly available GIS tools.
Detailed information on the geographic distribution of a crop is important in planning efficient germplasm conservation strategies but is often not available, particularly for minor crops. Using germplasm collection data from Kenya, Tanzania, and Uganda, we used distribution modeling to predict the distribution of sweetpotato [Ipomoea batatas L. (Lam.)] in sub-Saharan Africa. We used a consensus modeling approach using the following algorithms: genetic algorithm for rule set prediction (GARP), maximum entropy, BIOCLIM, and DOMAIN. The predicted distribution encompasses known sweetpotato production areas as well as additional areas suited for this crop species. New geographic areas where at least three models predicted presence were in Angola, Cameroon, Central African Republic, The Congo, Democratic Republic of Congo, Gabon, Ghana, Angola, Ethiopia, Mozambique, Rwanda, and the Central African Republic. This information can be used to fill gaps in current sweetpotato germplasm collections as well as to further enhance the current presence-only based distribution model. Our approach demonstrates the usefulness of considering several models in developing distribution maps.
Web-based information delivers real-time or near-real-time data to clientele and other stakeholders. Although proprietary methods are available for interactively searching and updating databases through web interfaces, these methods generally require varying costs to maintain licensing agreements. The availability of publicly available software that require minimal or flexible licensing costs provide a cost-effective alternative to institutions that are considering access to databases via a web-accessible interface. For example, if a current web server is already configured to support hypertext preprocessor (PHP) scripts and MySQL databases, all that needs to be installed is a form script to allow the searching, inserting, and deleting of records. We describe procedures, software, and other applications that we used to develop a publicly accessible web interface to an experimental database of representative sweetpotato accessions in Kenya. The web address of this database is http://www.viazivitamu.org. This site also contains links to sweetpotato collection sites in Kenya, Tanzania, and Uganda graphically shown using a public domain GIS viewer. This demonstrates that public domain web-based tools can be configured not only to support collaborative activities among researchers in various locations, but also to provide relevant data to clients and other stakeholders.
The genetic relationships among 192 superior, high–yielding, and disease-resistant sweetpotato [Ipomoea batatas (L.) Lam] accessions from the Ugandan germplasm collection were analyzed using 10 fluorescent labeled simple sequence repeat (SSR) markers. Relatedness among the genotypes was estimated using the Nei and Li genetic distance coefficient, cluster analysis and principle component analysis methods of NTSYS-pc software. The polymorphic information content of the SSR markers used in this study ranged from 0.23 to 0.76 for loci IB-S07 and IB-R12, respectively, with a mean value of 0.62. The number of polymorphic alleles detected per locus ranged from two to six with a mean of four, a confirmation of the effectiveness of microsatellite detection on an automated ABI 3730 sequencer. The mean pairwise genetic distance among the 192 genotypes was 0.57, an indication of moderately high genetic diversity. Cluster analysis divided the accessions into four major groups with no relationship to the district of origin. Two sets of duplicates were identified through SSR genotyping in this study. Up to 190 distinct accessions for use as potential parental genotypes in hybridization schemes for cultivar development in the region were identified.
The amount of genotypic and phenotypic variability that exists in a species is important for selection and initiating breeding programs. Yam bean is grown locally in tropical countries of the Americas and Asia for their tasty storage roots, which usually have low dry matter content. The crop was recently introduced in Uganda and other East and Central African countries to supplement iron (Fe) and protein content in diets. This study aimed to estimate genetic variability for root yield and quality traits among 26 yam bean accessions in Uganda. A randomized complete block design was used with two replications across two ecogeographical locations and two seasons during 2012 and 2013. Near-infrared reflectance spectroscopy (NIRS) was used to determine quality of storage root samples. Significant differences among genotypes were observed for all traits except root protein, zinc (Zn), and phosphorus contents. Genotypic variance components (
