Soil erosion in Ethiopia harms agricultural production, water quality, and overall ecosystem health. Effective soil loss estimation can help minimize these outcomes. The implementation of the Revised Universal Soil Loss Equation (RUSLE) in Ethiopia has not been sufficiently evaluated. Thus, the main objective of this review is to systematically review the comprehensive strengths and limitations of the RUSLE model applications for soil loss estimation. Specifically, it evaluates the specific parameters used, the type of input data, the validation approaches, and the role of GIS and remote sensing in the computation process of the RUSLE Model. An extensive search method was employed to classify suitable research papers about applying the RUSLE model in estimating soil erosion in Ethiopia. The review underscores that different approaches have been used to compute the five parameters of the RUSLE model. Key strengths of the RUSLE model include its widespread acceptance and adaptability to various environmental conditions; however, notable limitations are evident, regarding the validation and calibration processes. This review highlights that the scale of study areas varies widely, from 18.33 to 1,119,683 km². Soil loss estimates across the reviewed studies ranged from 12.94 to 576 t ha⁻¹ yr⁻¹. The findings of this review presented here are critical for using the RUSLE model to estimate soil loss integrating it with other process-based models and the advanced machine learning technologies. Ultimately, this review aids in improving soil resource management and ecological sustainability in Ethiopia.

Humans benefit significantly from rangelands in terms of both economic and environmental aspects. However, climate variability and anthropogenic causes can hurt rangeland production. Rangeland suitability analysis is a critical step in rangeland management. The study sought to analyze the feasibility of rangelands for livestock production in agro-pastoral areas of Afar, Ethiopia’s pastoral regions. Natural pasture was the predominant feed source for animals, and feed shortage was the primary obstacle to livestock productivity, followed by drought in pastoral and agro-pastoral systems, respectively. The five suitability parameters assessed were altitude, land use or land cover, soil, water availability, and slope. ArcGIS 10.7.1 was used to process vector and raster data layers. Finally, suitability classes were determined by conducting a weighted overlay of spatial analysis, which relied on the reclassified raster layer incorporating all parameters, along with the outcomes of multicriteria analysis. The study’s outcomes revealed that 1.20%, 8.49%, 20.08%, 69.26%, and 0.95% of the study area were categorized as highly suitable, moderately suitable, marginally suitable, presently unsuitable, and permanently unsuitable for cattle production, respectively