Integrating GIS and Remote Sensing Technology for Managing Tef Production in Ethiopia

The Pressure on global food system will increase and food production must increase as well to meet global demand for food. More food must be produced sustainably through implementation of existing knowledge, technology and best practice, and by investment in new science and innovation. Precision agriculture provides a means to monitor the food production chain and manage both the quantity and quality of agricultural product. Resource misallocation has serious impacts on sustainability and food security. One of the answers to this problem is the adoption of precision agriculture. This study deals with development and adaptation of precision agriculture tools for sustainable food production in Ethiopia, specifically to facilitate the production of existing tef crops and encouraging establishment of new ones. Geographic Information Systems provide ideal environment for spatial analysis to be performed. Ethiopia’s climate and environment conditions were aggregated and formed the basis of tef suitability mapping for respective data layers in the GIS system. Additional detailed local scale soil survey data were collected and entered into the GIS tool. These large data bases of information were collected from the Ethiopian Ministry of W ater Resources and Ethiopian Institute of Agricultural Research, Debre Zeit Agricultural Research Center (EIAR-DZARC). Soil sample and data were also collected and analyzed at approximately 50 sample sites in the study area. The analysis of all these data sets provided insights critical to farmers and politicians making decision on establishing new tef crops or choosing the most appropriate crop with respect to projected local conditions for maximum production of tef. Another part of this study used high spectral resolution imagery with Geoeye-1 and Rapideye remote sensing systems to identify tef crop conditions. Using object- based classification and change detection analysis of multi-temporal data, tef crops were mapped within the study area. This methodology showed the potential for regional scale mapping and analysis that are important for tef production estimation, planning and food security assurance. Recommendations were made for adapting this methodology to other areas of Ethiopia and implementing a tef crop monitoring system by integrating hyperspectral data analysis and field sampling to improve overall tef production within Ethiopia.