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.

Vulnerability to climate change has become the greatest challenge in our world due to its adverse impacts on ecosystems, economies, and societies. Thus, these studies assessed rural households’ vulnerability to climate variability and adaptation responses in Guna Begemdir District. Primary data was collected from three kebeles by selecting 120 sample households using the systematic random sampling method. Moreover, farmers’ perceptions, adaptation strategies, and socioeconomic and institutional factors were determined through household surveys, key informant interviews, and secondary data collected through document analysis. Monthly, seasonal, and annual rainfall and temperature data were collected for two metrological stations for the period 1983–2018 to assess climate variability and identify climate-related risks. To assess farmers’ perceptions and adaptation strategies being used by farmers. Finally, a logistic regression model was employed to identify factors that affect farmers’ decisions to adapt and implement given adaptation strategies to tackle the impacts of climate variability. Results from the study showed the presence of significant rainfall variability during Belg months (March–May months) compared to Kiremt months (June–August). The rainfall series for the annual time scale showed mixed trends (increase and decrease). However, the majority of farmers in Guna Begemdir District perceived that the rainfall had been decreasing for the last 36 years. They also perceived an increase in temperature, a delay in rainfall onset during the growing season, rainfall occurrence during harvesting time, an early end to rainfall during the growing season, pest occurrence, and a rainfall shortage. The overall LVI and IPCC-LVI score in this study is 0.49, which implies that the study area is vulnerable to the impacts of climate variability. In response to the perceived climate risks (water shortage, flood risks, and soil erosion), farmers in the study area adopted many types of soil and water conservation strategies. Thus, the study identified gender of household heads, size of active household members, household distance from farmland and market, access to credit, extension support, and agroclimate zone type as significant determinant factors that affect farmers’ adoption of climate change and variability.

A severe and frequent rise in temperature and the failure of rainfall cause a scarcity of water. The objective of this study is to assess rural households’ vulnerability to climate variability-induced water scarcity and adaptation strategies in Jawi District, Northwest Ethiopia. The primary data were gathered from 120 randomly selected respondents for household survey questions, while secondary sources were used for the region. Temperature and rainfall data were gathered from the National Meteorological Agency at the Beles Research sub-station from 2005 to 2020. However, the limited climate data, which is only for 15 years, affected the findings of the study. Simple linear regression, a standardized precipitation index, and a water vulnerability index were applied to analyzed quantitative data. The overall WVI-IPCC result placed the study area at a moderate level of vulnerability with an index value of 0.030. A binary logistic regression model was also used to identify the determinants of the farmer’s choice of adaptation strategy to climate variability. The descriptive statistics result showed that 54.2% and 60% of the respondents perceived the existence of temperature and rainfall variability, respectively, and the overall water vulnerability index score of Jawi district was 0.437, which is 437, which is high based on standards. In the study area, the temperature and rainfall trend results show that there is an increase in temperature and a decline in rainfall, indicating high variability. To cope with such variability, the commonly used adaptation practices that are employed by the majority of rural households in Jawi district are water harvesting, water and soil conservation, irrigation, and forest conservation. The key priority findings of this study call for designing and implementing appropriate adaptation strategies in response to climate variability. Top-down policies and strategies for climate variability adaptation should be implemented across the critical area.