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Data quality assessment and spatial analysis for conservation and sustainable use of Arabica coffee (Coffea Arabica L.) genetic resources in Ethiopia

Author: 
Tamene Yohannes and Sebsebe Demissew
Subject Area: 
Life Sciences
Abstract: 

Poor data quality in database can result from various causes, such as errors in site descriptions, imprecise coordinates or even mistakes. Hence, exploratory data analysis and cleaning are among several range of approaches and methods of data auditing and cleaning, which have been suggested for maintaining or improving data quality. Since its establishment, 1976 the Ethiopian Biodiversity Institute (EBI) previously known as Plant Genetic Resource Centre of Ethiopia/PGRCE (1976), Institute of Biodiversity Conservation/IBC (1994), has been collecting crop germplasm and associated traditional knowledge with special emphasis on major crop species. In 1999 EBI has established a database and documented several biodiversity related information. As of December 2013, the national collection has reached 73,438 accessions from 423 plant species belonging to 196 genera in 54 families. Coffea arabica L. is among these crop species, which Ethiopia has been regarded as the center of origin and center of genetic diversity. The wild coffee populations of Ethiopia are gene pool for several important traits which has national and international importance. Data quality assessment and spatial analysis were done on 5779 Coffee accessions deposited at the EBI, which were collected all over the coffee growing parts of the country and conserved at two field gene banks. The result has showed that, among the 5779 accessions, 2251 (38 %) were entered into the database. Among these 2251 accessions, 977 (43.40%) were georeferenced. The result of visualization map of the 977 C. arabica accessions and quality control based on the comparison of the country administrative unit data in the passport, with that of the country administrative unit information extracted from thematic layers using DIVA-GIS software has showed that, a number of accessions fall out of the geographical region of Ethiopia. Moreover, there are also 11 % erroneous points, which are 107 out of the 977 accessions. Three groups of causes of errors which could be committed either during field data collecting or while data encoding or at both stages were found. All the 107 erroneous points were corrected using different methods and recommendations were given for future use.

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