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.

Irrigation dams and irrigation suitability analysis is important for optimal water management, crop selection and productivity, water conservation, environmental sustainability, and economic viability in agriculture arena. Thus, the main objectives of this study were to identify a suitable dam site and irrigation area in the Gedeb River, Ethiopia, using Multi-Criteria Decision-Making analysis and 3D Visualization techniques. To identify a suitable dam site, various parametrs such as rainfall, runoff, stream flow, mineral site, faulting areas, landslide site, rock types, elevation points, relief features, soil types were used while to identify a suitable irrigation area, different parametrs such as altitude, slope, soil, geological structure, distance, and land use land cover datasets were used. The necessary dataset which were used to identify a suitable dam site and irrigation area collected from Ethiopian Mapping Authority (EMA), Ethiopian irrigation and energy ministry freely. In addition, for the final irrigation dam site selection and suitable irrigation area in the Gedeb watershed, multi-criteria decision-making method with expert judgment were applied respectively. Based on the study's findings, a suitable irrigation water reservoir dam covering an area of 1886 ha, with a potential water holding capacity of 2,961,145,697 cubic meters was identified. The results also revealed a highly suitable area of 18,362.05 ha, a moderately suitable area of 19,204.05 ha, a marginally suitable area of 2095.25 ha, and a not suitable area of 2.89 ha for the aforementioned purpose. The methodological approach and research findings presented in this study can greatly assist government and non-governmental organization planners and decision-makers in the development of irrigation projects.

Plantation forests provide vital social, economic, and environmental benefits to surrounding communities. However, intensive forest exploitation and harvesting practices, which are often conducted without regard for age or species differences, pose significant setbacks to the sustainability of these ecosystems. There is still a major gap in fully understanding and mapping forest age and species. Using locally developed spectral signatures that are specific to age and species can improve the accuracy of forest monitoring and assessment. This approach is crucial for assessing current forest management practices and their alignment with sustainable forest ecosystem contributions. Thus, this study investigates how the intensive forest exploitation and harvesting bring age and species forest dynamics from 2016 to 2024 in the Yeraba plantation forest. To achieve the intended objective, 15 age and species forest samples were collected from the forest purposively, while high-resolution multispectral imageries for 2016, 2020, and 2024 were freely downloaded from Maxar (via Bing Maps). The collected forest samples spectral signatures were measured using an RS-3500 spectroradiometer, and the spectral libraryies were developed using R3.4. Additionally, the preprocessing and image classification of forest species and age were performed using the SAM algorithm referenced with the locally developed spectral libraries. As a result, the forest species and ag classification maps for 2016, 2020, and 2024 were created. The results showed that harvested areas in the Yeraba plantation forest sharply declined from 74.83 ha (23.94%) in 2016 to 7.49 ha (2.4%) in 2024, reflecting a net reduction of 67.34 ha (21.45%) at an average rate of 7.48 ha (2.39%) per year. The overall accuracy for the forest species- and age-classified maps for the years 2016, 2020, and 2024 ranged between 90.48% and 96.61%, with a 95% confidence interval(CI) ranging between 88.42% and 98.51% and an uncertainty level ranging between ± 1.9% and ± 3.07%. The developed ag and species specific forest spectral libraries will serve as a foundational step toward building a classification system tailored to specific age and species compartments within the forest.