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- Author or Editor: Simon C. Jeremiah x
Africa represents a unique secondary site of genetic diversity for the sweetpotato [Ipomoea batatas (L.) Lam.]. Despite the genetic resources available for sweetpotato breeding and cultivar selection, regional conflicts and adverse weather in the last two decades have accelerated the risk of germplasm loss, particularly in East and Central Africa. A cooperative research project is currently underway to assess genetic diversity as well as help conserve sweetpotato germplasm in East Africa. One of the tools that are currently being used is a web-accessible GIS database that enables access to spatial and temporal data by project investigators and other stakeholders. Although proprietary methods are available for delivering GIS data through web interfaces, these methods often require expensive licensing agreements. The use of ALOV Map, a freely available Java® application for publishing vector and raster maps, along with basemaps and other thematic maps downloaded from publicly accessible web sites, helped provide the framework for a web-accessible GIS database. DIVA-GIS, a free desktop based GIS software was used to generate shapefiles as well as preview files prior to uploading. This demonstrates that the availability of publicly available software requiring minimal or flexible licensing costs provide a cost-effective alternative to institutions that are considering access to GIS databases via a web-accessible interface. We describe procedures, software, and other applications that we used to develop a publicly accessible web interface to a GIS database of sweetpotato germplasm collections in Kenya and Tanzania.
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.