Ethiopia aims to restore 15 million ha degraded forests and woodlands, but effects on the potentially contrasting goals of long-term carbon storage, biodiversity and sustainable livelihoods are unknown. To quantify the effects of grazing exclusion on vegetation and fire behaviour, we established six 30 × 30 m fenced exclosures with grazed controls, in a mesic wooded savanna. Experimental burns were done after 1.5 years. Tree seedlings were few but more common inside fences. Field layer cover and biomass increased inside fences, and grass species increased in numbers and cover. Fire intensity was higher inside fences, killing shrubs and saplings but not mature trees. Interviews confirmed that overgrazing has resulted in “cool fires”, causing shrub encroachment. High-intensity fires occurred in the 1980s after a zoonotic disease killed most livestock. Short-term increase in carbon storage through fire and grazing exclusion may lead to loss of pasture, and in the long-term increased wildfire risk.

National parks are areas of land protected to conserve native plants and animals and their habitats, places of natural attractiveness, historic heritage and native cultures. The objective of this review paper was to identify challenges affecting conservation and management of national parks in Ethiopia and based on review results, to suggest management strategies that can bring solutions to the problems. Lack of sense of ownership, limited awareness, population growth, lack of coordination, conflicts over resources, Issues of boundary/Lack of Boundary, invasive species, illegal charcoal production, climate change, and poverty are the identified challenges that are affecting the conservation and management of national parks in Ethiopia. Developing sense of ownership within community, awareness creation and development, collaborative approach and consultation among stakeholders, co-management and resolution of border issues, reduction of free grazing, invasive species utilization, care during introduction and biological control, reduction of fuel-wood consumption and increase carbon sequestration, improving incomes, institutional and policy reforms are the suggested strategies that can bring solutions to the problems.

In various areas of Ethiopia, different plant species have been reported having a medicinal role for ailment tonsillitis. However, the ethnomedicinal information on those medicinal plant species that cure the ailment is not summarized in a way that gives general information and initiates further study. Therefore, the objective of this review paper was to summarize the ethnomedicinal information about medicinal plant species that used to treat tonsillitis in Ethiopia. The result of the review indicated that herbs and leaves are the most utilized growth forms and part of the plants respectively during remedy preparation for the ailment tonsillitis in Ethiopia. Similarly, fresh plant materials, and water are the most applied condition of plant parts and diluent added correspondingly. Chewing and oral route of application are the most widely used methods of remedy preparation and administration correspondingly, for the ailment in the country. Utilization of herbs and leaves can be seen as an advantage from the conservation point of view of medicinal plant species that used to treat the ailment since herbs can be cultivated when they are in short supply and are also available as compared to other growth forms. Similarly, use of leaves can reduce damage to the medicinal plant species as compared to the use of other plant parts such as roots. However, the utilization of fresh materials should be reduced as much as possible. Rhamnus prinoides L'Hér. and Zingiber officinale Roscoe are the most commonly used medicinal plant species for the ailment in Ethiopia.

Soil organic carbon (SOC) stock is a key component of terrestrial ecosystems, playing a critical role in climate regulation and ecosystem productivity. Despite its economic importance, the impacts of the expansion of khat (Catha edulis) cultivation at the expense of other land uses and its intensive management practices on the depletion of soil carbon content are overlooked in Eastern Ethiopia. This study aimed to estimate and map SOC stocks using multispectral Sentinel-2 and RapidEye imagery, combined with environmental, soil, and topographic variables, across khat-dominated landscapes in the Haramaya District of Eastern Ethiopia. A total of 88 soil samples were collected and analyzed to quantify SOC stocks. Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms were employed to predict SOC stocks. The dataset was stratified into training (70%) and an independent validation (30%) subset. Model development was performed using five-fold cross-validation on the training dataset, while final performance was assessed on the independent validation set using the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). Laboratory-measured SOC stocks ranged from 24.99 to 65.94 Mg C ha⁻¹, with a mean value of 36.88 Mg C ha⁻¹. The predicted spatial SOC stocks ranged from 30.4 to 50.4 Mg C ha⁻¹ using RapidEye data and from 32.8 to 51.5 Mg C ha⁻¹ using Sentinel-2, with Sentinel-2 producing slightly higher mean estimates. The lowest SOC stocks were consistently observed in bare, grass, and shrub land-use types across both datasets. RF demonstrated superior performance compared with XGBoost, achieving moderate predictive performance for both the RapidEye (R² = 0.56, RMSE = 5.91 Mg C ha⁻¹) and Sentinel-2 (R² = 0.42, RMSE = 6.90 Mg C ha⁻¹) datasets. This result indicates that RF provided greater robustness for SOC stock prediction under the heterogeneous environmental conditions of khat-dominated agricultural landscapes. Topographic and soil-related variables, particularly the Topographic Wetness Index (TWI), land surface temperature (LST), and clay content, were identified as the most influential predictors in both models. Although less consistent, remote sensing indices such as GNDVI, BSI, and NDWI also contributed to SOC prediction. While both sensors proved effective for SOC mapping, a measurable sensor-related effect was observed. The findings demonstrate the effectiveness of integrating multisource remote sensing, environmental, soil, and topographic variables with machine learning for SOC stock mapping in khat-dominated landscapes. This approach provides valuable spatial information to understand SOC variability and support sustainable land management and climate change mitigation strategies in Eastern Ethiopia.

Climate change is expected to cause latitudinal and elevational shifts in where crops can grow, with major consequences for land use, rural livelihoods and biodiversity. Shifts in agroforestry systems can be especially transformative, as this alters tree cover. We used semi-structured interviews and vegetation surveys to investigate the patterns, drivers, consequences and future trajectories of an upward shift of Arabica coffee agroforestry within its native range in the Afromontane mountains. We observed a climate-, income- and crop raiding-driven upward shift of coffee production into a novel elevational range, with positive feedback mechanisms enhancing the future adoption of agroforestry. Forest and open-land-derived agroforests showed similar woody plant species richness and large overlaps in community composition. Open-land-derived coffee agroforestry is, and is expected to increasingly be, the dominant coffee agroforestry type in the novel range, with positive implications for tree cover, biodiversity and ecosystem services in one of the world’s biodiversity hotspots.

Soil organic carbon (SOC) is a key component of terrestrial ecosystems, serving as an energy source for soil microorganisms and playing an essential role in climate regulation and ecosystem productivity. However, SOC stocks are highly influenced by land-use and land-cover changes. This study aims to estimate and map SOC using multispectral Sentinel-2 and RapidEye imagery combined with environmental, soil, and topographic variables across Khat-dominated landscapes in the Haramaya district, eastern Ethiopia. A total of 88 soil samples were collected and analyzed in the laboratory for organic carbon estimation. Two machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGB), were applied to predict SOC, and their performances were evaluated using the coefficient of determination (R²), root mean square error (RMSE and mean absolute error (MAE). Laboratory-measured SOC values ranged from 0.83% to 3.9%. Both satellite datasets produced comparable predictions, with Sentinel-2 estimating slightly higher mean SOC values (~40mg/ha), while RapidEye(~38mg/ha) provided more spatially detailed and accurate maps due to its finer resolution. Among the algorithms tested, RF outperformed XGB, showing higher predictive accuracy and stability, particularly under heterogeneous landscape conditions. The results suggest that both Sentinel-2 and RapidEye data are suitable for SOC estimation and mapping, with higher-resolution imagery preferred for detailed spatial analysis. Future research should focus on optimising predictor selection and assessing the potential impacts of Khat cultivation on SOC variability and spatial distribution.