Gis and parametric based coffee site suitability zonation in North Shewa zone of Oromia region, central Ethiopia

Several studies are now being undertaken to examine land suitability using GIS and soil data. This study mapped and assessed the sustainability of coffee farming in Ethiopia's central highlands using GIS and parametric decision-making criteria. The method combined geographic, meteorological, and soil quality data. We obtained spectral and DEM data from the USGS and climate data from EMSA. To evaluate soil suitability for coffee, we established 120 soil plots using a stratified random sampling method based on grids and transects. Samples were obtained from depths ranging from 0 to 30 cm and evaluated in the laboratory. The suitability metrics were generated using GIS and multi-criteria decision-making analysis, with the Storie and square root methods used to assess topographic, climatic, and soil suitability. Our findings revealed that Arabica coffee may be grown in the central highlands, with approximately 27 % (303,421.5 ha) of land classified as highly to marginally suitable (S1-S3) based on minimal coffee production standards. Conversely, 73 % of the region's land (821213 ha) was rated unsuitable due to lower temperatures or major production constraints. Our methodology also effectively assessed the suitability of present coffee-growing areas at higher elevations, where it was previously thought that coffee could not be grown. We conclude that our findings have the potential to influence coffee producers, policymakers, and specialists, serving as a resource for coffee production in highland areas by identifying viable land suitability classes that could benefit from improvement efforts for sustainable and productive coffee farming. • Land suitability for coffee for 303,421.5 ha , of central highlands of Ethiopia is described. • Digital map of Climate suitability, topographic suitability, soil quality suitability are mapped. • Unsuitable area extent (821213 ha) and map for coffee are estimated. • Gis, parametric and Mulit-critrira approach are demonstrated. • Temperature from Spectrum bands of satellite image are extracted.