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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2985

Advisors: Dr.Dagnachew Legesse
Keywords: Mojo Watershed
Land suitability analysis
GIS and Remote Sensing
Multi‐criteria Evaluation
Copyright: Jun-2010
Date Added: 11-May-2012
Publisher: AAU
Abstract: Land suitability analysis is the evaluation and grouping of specific areas of land in terms of their suitability for a defined use. Land suitability potential evaluation for agricultural crops is an important step for sustainable land use planning. Improper land use results in land degradation and decline in agricultural productivity. Hence, in order to get the optimum benefit out of the land, proper utilization of its resources is inevitable. GIS and remote sensing offer a convenient and powerful platform to integrate spatially complex and different land attributes for performing land suitability analysis and allocations. This study intended to analyze and map suitable land areas for agricultural crops in Mojo Watershed using GIS and remote sensing techniques. The study used weighted overlay technique of MCE in a GIS platform to arrive at the final land suitability for agricultural crops. In addition a vector overlay (union) was used for suitable land allocation for the evaluated crops. The factors that were considered for evaluation of the land suitability analysis for agricultural crops are soil (depth, drainage, texture, organic matter, and pH), rainfall, temperature, slope and land use/land cover. The result showed that 27.3%, 35.4%, and 15.7% of the study area are classified as highly suitable for teff, wheat, and chickpea production, respectively. In addition, 65.2%, 60.9%, 77.9% and 80.2% were found to be moderately suitable while 4.1%, 0.6%, 3.3% and 16.5% is marginally suitable land for teff, wheat, chickpea and lentil, respectively. The result indicated that, there is no land that is classified as currently not suitable for wheat. However 0.3%, 0.03% and 0.04% of the study area is classified as currently not suitable for teff, chickpea and lentil, respectively. The vector overlay result indicated that 4.0% of land is highly suitable for both teff and wheat; 11.2% is highly suitable for wheat and chickpea; 2.6 % is highly suitable for teff, wheat and chickpea. In the same manner, 26.8% of the study area is moderately suitable for teff, wheat, chickpea and lentil. Therefore, integrating MCE with GIS for land suitability analysis for agricultural crops for spatial decision making process is a worthwhile technique. Hence, to increase the choice for the stakeholders and decision makers, further analysis for different LUTs is necessary.
URI: http://hdl.handle.net/123456789/2985
Appears in:Thesis - Earth Sciences

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