Background:The demand for meeting local food production has caused farmlands to expand at the cost of natural forests and grasslands in the Ethiopian highlands. However, empirical evidences on rate and patterns of LULC dynamics, and major driving forces in highlands of Ethiopia at catchment level were rare to contribute to design effective land management options. This study was to analyze the rate and patterns of LULC dynamics, and identify major driving forces in the Gelda catchment.Results: Six different LULC maps derived from aerial photographs and Landsat images were produced, and comparisons were made. The results indicated that the study catchment has undergone significant LULC alterations and transformations since late 1950s. Farmlands and settlement were expanded by 57.7% while shrubs, forests and grasslands were declined by 18.6, 83.8 and 53.5% over the entire study period, respectively. The magnitude of initial grasslands and farmlands converted into degraded land seems small; however these can significantly cause an irreversible damage to the soil resources. The combinations of land reform of 1975, forest development and villagization program 1980s, civil war, frequent changes in political structure, and population pressure were the major driving forces of LULC change. Conclusion:Therefore, the GIS and remote sensing based change detection matrix analysis technique could provide useful baseline information to understand the spatiotemporal patterns of land use transitions caused by the major driving forces thereby sustainable land management planning is possible.

The present study was carried out to examine the suitability status of plots of land for selected land utilization types (teff -Eragrostis tef, maize -Zea mays and finger millet -Eleusine coracana). The land mapping units of the study area, prepared from land resource survey, were used for the purposes of land evaluation. The methodology used for land suitability evaluation was GIS-based multi-criteria evaluation following FAO (1976) guidelines involving matching diagnostic land qualities against crop requirements and assigning suitability rates for each land qualities. The weighted overlay analysis combining diagnostic soil, climate and topographic factors showed that the largest coverage (76.04, 69.52 and 67.79%) of the study area is classified as moderately suitable for teff, maize, and finger millet cultivation, respectively. The vector overlay analysis results revealed that about 20.25 and 63.92% of the catchment are moderately suitable and marginally suitable for cultivation of all selected land utilization types. This showed that competitions for the same parcel of land by different uses were possible. Thus, farmers could have freedom to choose a range alternative land utilization types with the same suitability level and allocate land utilization type that best meet his/her interest. Therefore, land suitability analysis for agricultural crops using multi-criteria evaluation in a GIS environment is a strong tool for measuring and valuating land in terms of the varying importance to decision makers for sustainable rainfed agriculture.

Abstract. Up-to-date digital soil resources information, and its comprehensive understanding, is crucial to support crop production and sustainable agricultural development. Generating such information through conventional approaches consumes time and resources, which is difficult for developing countries. In Ethiopia, the soil resource map that was in use is qualitative, dated (since 1984), and small-scale (1:2 M) which limits its practical applicability. Yet, a large legacy soil profile data accumulated over time and the emerging machine learning modelling approaches can help in generating a high-quality quantitative digital soil map that can provide accurate soil information. Thus, a group of researchers formed a coalition of the willing for soil and agronomy data sharing and collated about 20,000 soil profile data and stored them in a central database. The data were cleaned and harmonized using the latest soil profile data template and prepared 14,681 profile data for modelling. Random Forest was used to develop a continuous quantitative digital map of 18 WRB reference soil groups at 250 m resolution by integrating environmental variables-covariates representing major Ethiopian soil-forming factors. The validated map will have tremendous significance in soil management and other land-based development planning, given its improved spatial nature and quantitative digital representation.