Sustainable farm animal raising is dependent on the production of sufficient quantities and quality of forages and fodder, especially in dry regions. Improved forage and feed species are an option for these aspects because adequate feed resources enhance soil health and carbon, generate income, and reduce emissions. Therefore, the purpose of this review paper was to investigate the role of climate-smart forage production in sustaining farm animal production and maintaining the environment in arid and semiarid regions. Seasonal fluctuations in feed supply cause temporal scarcity in Ethiopia, with more acute gaps in dry periods, particularly in drought-prone regions. To address these problems in the country, improve forages to strengthen farm productivity, climate change resilience, and environmental sustainability, particularly in arid and semiarid areas. The country has a long history and is of some exemplary practices in indigenous improved forage production, but the input of improved forage to the total biomass production in feed resources is still low due to many factors like scarcity of land and water for irrigation, lack of awareness, forage seed, and policy recommendations. Despite the potential profits of feed and forage grasses and legumes, the availability of species adapted to a wide range of situations in actual use in the livestock sector has been insufficient. Therefore, it is strongly suggested that climate-resilient forage species be popularized to sustain livestock production and the environment, particularly in the country’s arid and semiarid regions.

Air pollution is a critical twenty-first century environmental and public health challenge that is linked to millions of deaths and ecological harm. Accurate prediction of pollutants such as PM₁₀ is vital for mitigation and urban sustainability. This study combines geospatial modeling with three machine learning algorithms (MLAs), Random Forest (RF), K-Nearest Neighbor (KNN), and Naïve Bayes (NB), to identify PM₁₀ hotspots in Addis Ababa, Ethiopia. PM₁₀ data from 11 stations (August 2021–August 2023) were analyzed alongside 25 atmospheric, climatic, anthropogenic, and pollution source predictors. A concentric zonal approach was used to assess spatial variability across radial distances and directional sectors and was supported by 30 m-resolution satellite imagery, climate data, and local geospatial repositories. The model accuracies were 0.95 (KNN), 0.93 (RF), and 0.88 (NB), with distinct performance trade-offs: RF predicted the largest “Good” PM₁₀ zones (78.98 km²), KNN highlighted the most “UnHealSen” areas (279 km²), and NB predict “Moderate” coverage (311 km²). High PM₁₀ concentrations clustered in eastern and northwestern sectors, aligning with industrial zones and traffic density. The results demonstrate the efficacy of MLAs and geospatial integration in producing high-resolution pollution maps. We advocate for targeted emission controls in hotspots, expanding public transit to reduce vehicular emissions, and incorporating air quality metrics into urban planning. This study advances air quality assessment methods for rapidly urbanizing regions, providing data-driven strategies to combat pollution and enhance ecological resilience in African cities.

The present study aimed to evaluate the effect of drinking salinity levels in Lake water on the growth performance, and physiological, and blood constituents of Hararghe-highland lambs. A total of 28 lambs (average IBW of 18 ± 1.18kg) were categorized into four levels of water salinity, namely freshwater (510 mg/l TDS), lake Basaka water (LBW; 2600mg TDS/L); lake Basaka water plus 100% TDS of LBW; 5200mg TDS/L) and lake Basaka water plus 200% TDS of LBW; 7900mg TDS/L). The current results suggest that increased total dissolved solid levels in lake water increase the water intake of lambs while not affecting IBW, FBW, AWG, and FCR. Additionally, drinking saline lake with increasing total dissolved solid levels increased rectal temperature and respiration rate and caused a significant (P < 0.05) change in the concentrations of glucose, albumin, urea, triglycerides, sodium, AST, and ALT. Some hematological variables and biochemical constituents like creatinine, total protein, cholesterol, chlorides, potassium, magnesium, and calcium showed no significant (P > 0.05) differences. In conclusion, Hararghe-highland lambs from Ethiopia could tolerate lake Basaka water with a high salt level (7952 mg/L TDS).

Endemic and endangered bird species, such as Salvadori serin (C. xantholaema), are vulnerable to environmental and anthropogenic changes. Understanding the impact of climate change on ecological niches is essential for effective conservation. This study employed advanced ML algorithms to model the current and future suitability of C. xantholaema under two scenarios (SSP245 and SSP585) for the years 2050 and 2070. The four machine learning models, namely, Maximum Entropy (MaxEnt), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boost (XGBoost), predicted habitat suitability using 188 presence occurrence data and 15 environmental factors. Model performance was assessed using AUC-ROC, accuracy, precision, sensitivity, specificity, kappa, and F1 score, with ensemble modeling techniques enhancing reliability. The current analysis indicated high predictive accuracy, with XGBoost achieving the highest AUC (0.99), followed by RF (0.98), SVM (0.97), and MaxEnt (0.92). Regarding habitat suitability, 75.3% of Ethiopia’s land was unsuitable for C. xantholaema, with only 3.9% classified as highly suitable. By 2050, 61.82% and 57.14% of areas were projected to be unsuitable under SSP245 and SSP585, respectively. By 2070, unsuitable habitats may increase to 65.24% (SSP245) and 60.17% (SSP585), further decreasing habitat suitability. High-suitability habitats are expected to decline by 80.8% in 2050, covering approximately 8,259.95 km², and by 73.2% in 2070, covering about 11,584.6 km². Precipitation during the driest month (Bio14) was the most crucial predictor of habitat suitability, with importance values ranging from 32.5% (XGBoost) to 100% (SVM and RF), while temperature-related factors, particularly annual mean temperature (Bio1), contributed differently across ML models. According to this study, climate factors impact habitat changes. The findings emphasize the urgent need for conservation strategies to mitigate C. xantholaema habitat loss. Future research should include local data and other human-related factors to enhance the effectiveness of conservation efforts and improve predictions.