Soil erosion in the northwestern Amhara region, Ethiopia has been a subject of anxiety, resulting in a major environmental threat to the sustainability and productive capacity of agricultural areas. This study tried to estimate soil erodibility factor (K-factor) using Universal Soil Loss Equation (USLE) nomograph, and evaluate the spatial distribution of the predicted K-factor in a mountainous agricultural watershed. To investigate the K-factor, the 54km2 study watershed was divided into a 500m by 500m square grid and approximately at the center of each grid, topsoil samples (roughly 10 to 20cm depth) were collected over 234 locations. Sand, silt, clay and organic matter (OM) percentage were analyzed, while soil permeability and structure class codes were obtained using the United States Department of Agriculture (USDA) document. The resulting coefficient of variation (CV) of the estimated K-factor was 0.31, suggesting a moderate variability. Meanwhile, the value of nugget to sill ratio of K-factor was 0.32, which categorized as moderate spatial autocorrelation. Prediction accuracy and model fitting effect of the Gaussian semivariogram approach was best, suggesting that the Gaussian ordinary Kriging model was more appropriate for predicting K-factor. The resulting value of the mean error (ME) was 0 and the mean squared deviation ratio (MSDR) was nearly 1, which indicates the Gaussian model was unbiased and reproduced the experimental variance sufficiently. The values of K-factor were smaller (0.0217 to 0.0188) in the northern part and gradually increased (0.0273 to 0.033MghMJ−1mm−1) towards the central and south of the study watershed

The rapid evolution of smart grids, driven by rising global energy demand and renewable energy integration, calls for intelligent, adaptive, and energy-efficient resource allocation strategies. Traditional energy management methods, based on static models or heuristic algorithms, often fail to handle real-time grid dynamics, leading to suboptimal energy distribution, high operational costs, and significant energy wastage. To overcome these challenges, this paper presents ORA-DL (Optimized Resource Allocation using Deep Learning) an advanced framework that integrates deep learning, Internet of Things (IoT)-based sensing, and real-time adaptive control to optimize smart grid energy management. ORA-DL employs deep neural networks, reinforcement learning, and multi-agent decision-making to accurately predict energy demand, allocate resources efficiently, and enhance grid stability. The framework leverages both historical and real-time data for proactive power flow management, while IoT-enabled sensors ensure continuous monitoring and low-latency response through edge and cloud computing infrastructure. Experimental results validate the effectiveness of ORA-DL, achieving 93.38% energy demand prediction accuracy, improving grid stability to 96.25%, and reducing energy wastage to 12.96%. Furthermore, ORA-DL enhances resource distribution efficiency by 15.22% and reduces operational costs by 22.96%, significantly outperforming conventional techniques. These performance gains are driven by real-time analytics, predictive modelling, and adaptive resource modulation. By combining AI-driven decision-making, IoT sensing, and adaptive learning, ORA-DL establishes a scalable, resilient, and sustainable energy management solution. The framework also provides a foundation for future advancements, including integration with edge computing, cybersecurity measures, and reinforcement learning enhancements, marking a significant step forward in smart grid optimization.

Recent research findings imply that the slope aspect has a great impact on soil genesis and soil microclimate. The microclimate has a significant effect on the soil geobiochemical processes taking place in the soil. However, the slope aspect impact on soil properties has not been yet studied well in Ethiopia, particularly in the northern highlands. This research was initiated to link selected soil physicochemical properties with slope aspects under different land use practices. The research was conducted in Gumara-Maksegnit watershed located at the upper Lake Tana basin, Ethiopia. Four slope aspects, eastward (Ew), northward (Nw), southward (Sw), and westward (Ww), and three land use types at each slope aspect, cropland (Cl), forest land (Fl), and grazing land (Gl), were considered. In total, 36 undisturbed soil samples for bulk density and 36 disturbed soil samples for selected soil properties were collected. Soil particle size (texture), bulk density, electrical conductivity (EC), soil pH, available phosphorus (av. P), available potassium (av. K), total nitrogen (TN), and soil organic carbon (SOC) were analyzed. The resulting analyses showed no significant variation (p<0.05) across both slope aspects and/or land use types for soil pH and EC, whereas the slope aspect showed a significant effect (p<0.05) on SOC, TN, av. K, and av. P. The highest significant (p<0.05) mean value of SOC was observed in the Ww (3.04%) followed by Nw (2.52%) but SOC was not significant (p<0.05) between Sw and Ew. While the highest av. K (1233.2 centimole/kilogram) and av. phosphorus (35.76 ppm) were observed in Nw slope aspect, the highest TN was in the Ww slope aspect (0.24%). The significant effect (p<0.05) of land uses can be summarized as Fl > Gl > Cl for SOC and TN. A strong positive correlation was observed between SOC and TN (R² = 0.997) and av. K and av. P (R² = 0.58) at p<0.05. Generally, the slope aspect, land use types, and their interaction had a significant effect on soil physicochemical properties.

Soil and water conservation (SWC) interventions are needed to control rainfall-driven erosion, and profitability of SWC measures at the sub-catchment scale emerges as the principal reason for their adoption. This study carried out a cost–benefit analysis of SWC measures in mountainous agricultural catchments. Physical data were obtained through field measurements of discharge, sediment and nutrient loss at the sub-catchment scale with and without SWC measures. The major cost benefits of various measures implemented in the study area were quantified using net present value (NPV), and direct market prices were employed in valuing the cost of items required for crop production. The results revealed that sediment loss decreased by 8.78 Mg ha⁻¹ y⁻¹ (46.8%) due to SWC measures, and the cost of production inputs, such as fertilizer (urea) and lime, was reduced by $17.97 ha⁻¹ y⁻¹ and $3.63 ha⁻¹ y⁻¹, respectively. Furthermore, crop yield was enhanced by 13% for teff, 19.4% for sorghum and 19.42% for chickpeas, which is equivalent to economic returns of $102, $96.9 and $140.25 ha⁻¹ y⁻¹, respectively. The total discounted cost of SWC interventions was about $331.74. This includes establishment costs, maintenance costs, input costs and the costs resulting from lost productive land. In sum, the total discounted benefits of SWC measures were the enhancement of crop production, a reduction in lime requirement and a reduction in the loss of total nitrogen and sediment, estimated at about $809.42. Hence, it is possible to deduce that SWC measures reduced nutrient depletion and greatly improved crop yield with a NPV of $477.68 ha⁻¹. The results strongly suggest that SWC measures in the study sub-catchment, as well as in nearby areas with a similar indigenous SWC adoption strategy, topographic conditions and agroclimatic characteristics, should be maintained.

Crop modeling is a powerful tool for estimating yield and water use efficiency, and it plays an important role in determining water management strategies. Under the condition of scarce water supply and drought, deficit irrigation can lead to greater economic gains by maximizing yield per unit of water. Studies have shown that deficit irrigation significantly increased yield, crop evapotranspiration, and water use efficiency as compared to full irrigation requirement. However, this approach requires precise knowledge of crop response to water as drought tolerance varies considerably by growth stage, species and cultivars. This study was conducted in Lasta district, for two successive years to evaluate the effects of water shortage on potato production and water use efficiency, as well as to test the AquaCrop model for potato-producing areas. The irrigation water levels for potatoes were 100%, 75%, and 50% of crop evapotranspiration (ETc). Six treatments were arranged using a randomized complete block design. Climate, soil, and crop data were calibrated using observed weather parameters, and measured crop parameters conducted in the 2018/19 growing season. The model was validated using the observed data conducted in the 2019/20 growing season. The calibration of the model revealed a good fit for canopy cover (CC) with a coefficient of determination ( R ² ) = .98, Root mean square error (RMSE) = 9.6%, Nash-Sutcliffe efficiency ( E) = 0.92, index of agreement ( d) = 0.98, and coefficient of residual moss (CRM) = −0.07, and good prediction for biomass ( R ² = .98, RMSE = 1.8 t ha ⁻¹ , E = 0.96, d = 0.99, CRM = −0.13). Similarly, the validation result showed good fit for CC by 100% water application at development and mid growth season and a 75% water applied at the other stages ( R ² = .98, RMSE = 9.4%, E = 0.94, d = 0.98, CRM = −0.12). The AquaCrop model is simple to use, requires fewer input data, and has a high level of simulation precision, making it a useful tool for forecasting crop yield under deficit irrigation and water management to increase agricultural water efficiency in data-scarce areas.