Land Suitability Mapping for Production of Chickpea, Faba Bean and Malt Barley Varieties in Ethiopia
MetadataShow full item record
Demeke Nigussie, Wondafrash Kabthiymer, Adamu Molla Tiruneh, Zewdie Bishaw, Chandrashekhar Biradar. (30/3/2019). Land Suitability Mapping for Production of Chickpea, Faba Bean and Malt Barley Varieties in Ethiopia.
Ethiopia's agriculture has been facing recurrent challenges and the country remains food insecure due to its ever-increasing population and chronically low agricultural productivity despite its high biophysical potential. The situation is exacerbated by inappropriate use of agricultural land leading to land degradation, as well as recurrent droughts superimposed by climate variability and change. These challenges require the potential and the constraints of agricultural land to be properly identified for appropriate decision-making for land use planning and sustainable farming. Different land areas have varying potential and constraints for appropriate and sustainable agricultural use. Information on the potential and constraints of the land will help to identify and develop appropriate technology to target location specific interventions. For crop technology targeting and scaling, the potential of the different areas need to be properly identified and mapped for better crops and crop varieties. Land suitability analysis work enables identification of where and how much potentially suitable land for a crop and crop variety exists in a specific location or in the country at large. It is, therefore, very important to identify and map the extent and distribution of land area that is potentially suitable for a specific crop and crop variety. Cognizant of these facts, the land suitability mapping for selected varieties of chickpea, faba bean and malt barley was initiated to analyze and delineate the land suitability in Ethiopia. Land suitability analysis is an evaluation and decision-making process involving several biophysical (soils, topography and climatic) factors. Accordingly, the main factors considered in this analysis include climate layers (rainfall and temperature during the growing period and length of growing period-LGP), topography (digital elevation models. i.e. altitude and slope data), soil types and soil properties (pH, depth, texture, and drainage). For classification of the data layers according to the degree of favorability for each variety, existing maps, reports, and other relevant information were reviewed and used in defining the limits of the suitability ranges of the crop varieties. Then, environmental requirements of varieties were defined by means of a set of critical values, which determine the limits between the land suitability levels (classes). The suitability classes were set as S1 (very suitable), S2 (moderately suitable), and S3 (marginally suitable) and N (unsuitable). The biophysical criteria for specific crops and crop varieties were assigned at pixel level in each layer to reclassify layers for weighted rates. Following this process, each layer was compared among themselves and ranked. The suitability criteria layers were assigned weights to account for their relative importance. The analytic hierarchy process, which relies on pairwise comparison, was used to calculate the weights for the different criteria. The pairwise comparisons scales were assigned through discussion with biophysical experts. The overall suitability is computed by multiplying the selected criteria weight by the assigned sub-criteria score and summing these values in the spatial modeling in the ArcGIS domain (ESRI GIS package). Lands occupied by forests, woodlands and towns (except Addis Ababa, Dire Dawa and Harari) are not excluded in this analysis. Moreover, this work focused only on rainfed areas of the country. The analysis results show the extent and patterns of the suitable land area available for selected crop varieties of chickpea (Cicer arietinum L.), faba bean (Vicia faba L.), and malt barley (Hordeum vulgare L.). The results are presented in the form of tabular data, maps and graphs.
- Agricultural Research Knowledge 
Tiruneh, Adamu Mollahttps://orcid.org/0000-0002-8555-5827