An accurate estimation of a soil wetting front is important for improving water-use efficiency under drip irrigation. This study is aimed at determining the wetting front of sandy soil under drip irrigation using the existing and modified empirical equations and optimizing the input parameters using the response surface methodology (RSM). This study was conducted during the dry season in sandy soil containing a hard pan at a soil depth of about 30 cm. The soil wetting front was measured using measuring tape at two emitter discharges under a point-source drip irrigation system type. An evaluation of the models for the wetting fronts during calibration and validation showed that the coefficient determination (r²) ranged between 77–98% and 79–99% for the empirical and modified models, respectively. The normalized root mean square error (NRMSE) was less than 20% and greater than 20% for the modified and existing equations, respectively, thus emphasizing that the modified model produced a better result. Using the RSM approach, the linear + interaction produced the best result, and the optimization result revealed an optimum irrigation time and volume of 75 min and 225 cm³, corresponding to a maximum wetting depth and width of 26.8 and 21.7 cm, respectively.

Wastewater treatment necessitates the use of an appropriate method to achieve satisfactory results. The conventional method of Alum addition has been widely used for years, but it is prohibitively expensive. This study uses Moringa oleifera, an inexpensive and readily available plant, as a natural coagulant to treat wastewater collected from university dormitories. Physicochemical parameters such as pH, Turbidity, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Dissolved Oxygen (DO) were examined based on appropriate standards. Wastewater treatment with varied coagulant dosages of 50, 100, and 150 mg.L-1) was monitored using a standard jar test device with an initial wastewater perturbation at 100 rpm for 5 min was reduced to 50 rpm in 10 minutes with a rest time of 30 min. The results showed that the quality of the physicochemical properties of the water improved. The percentage increase in the water quality is; BOD (92%), COD (92%), and TDS (52-64%), with an increase in Moringa coagulant achieving a reduction of 96% of Turbidity. While the DO improved (79%), the pH remained below acceptable limits (6.73-7.56) for effluent disposal. The treated water showed clarity (colorless) and no odor compared to the wastewater. Hence, Moringa oleifera seeds cake residue can be an effective coagulant for wastewater treatment.

Mulching is a major agronomic practice that improves agricultural productivity and mitigates the effects of climate change. It is also an effective soil and water conservation method, as it minimizes evaporation, regulates soil temperature, and enhances water retention, particularly in dry and semi-arid areas, which constitute approximately 40% of the world's land area. Organic and inorganic mulching materials differ in their contributions: organic mulches are more beneficial for enhancing soil fertility through nutrient cycling and enhanced microbial activity, whereas inorganic mulches, such as plastic films, are more effective for immediate moisture retention but have adverse environmental effects, such as plastic pollution. Biodegradable mulches and hydrogel-based materials are examples of new products and solutions that offer eco-friendly alternatives, reducing ecological hazard. Mulching has been reported to enhance crop production and water-use efficiency when combined with precision irrigation methods such as drip irrigation. In addition, mulching lowers greenhouse gas emissions by regulating soil microbial activity and increasing organic carbon sequestration in the soil, especially when used in tandem with other amendments such as biochar. Although mulching has been shown to improve resilience to climate variability among smallholder farmers, particularly in sub-Saharan Africa and Asia, socioeconomic and technological challenges remain. Climate-smart agriculture can be advanced by incorporating mulching into climate-sensitive agricultural systems, supported by digital technologies and policymakers, to promote sustainable intensification and food security worldwide. This review demonstrates, in a multifaceted way, the importance of mulching for climate resilience, sustainable production, and environmental stewardship.

Improper disposal or open burning of agricultural waste releases smoke, particulate matter, and greenhouse gases, contributing to air pollution and climate change. The utilization of agricultural wastes as substrates for biofertilizer production offers a sustainable approach to waste valorization and nutrient recycling. This study evaluated the effects of agricultural waste type and contact time on the nutrient composition of Trichoderma sp.-based biofertilizer produced from sawdust, rice husk, and guinea corn husk. A factorial experiment was conducted using four substrates and four incubation periods (0, 5, 10, and 15 days). Nutrient parameters, including total nitrogen (TN %), phosphorus (P %), and potassium (K %), were determined and subjected to analysis of variance. The biofertilizer exhibited high microbial viability, with a highest propagule density of 6.46 × 10¹¹ CFU ml⁻¹, indicating successful fungal colonization. Significant (p < 0.0001) effects of substrate type, contact time, and their interaction were observed for all nutrient parameter accumulation. Total nitrogen increased with incubation time, reaching a maximum of 0.901% in guinea corn husk after 15 days. Rice husk recorded the highest phosphorus concentration (2.497%) at 15 days, while sawdust produced the greatest potassium concentration (2.389%) at 15 days. The results indicate that Trichoderma sp. effectively bio-converts agricultural residues into nutrient-rich biofertilizers, with substrate type influencing nutrient profiles. This technology represents a promising strategy for sustainable soil fertility management and circular agricultural production systems, depending on substrate type used and incubation time.

Accurate prediction of wetted width and wetted depth is essential for optimizing water use efficiency in drip irrigation systems. Existing empirical models are often restricted to specific soil textures and cannot adequately capture the complex nonlinear interactions among soil hydro-physical and chemical properties, irrigation variables, and different soil textures. This study evaluated four machine learning algorithms—Linear Support Vector Machine (Linear SVM), Medium Gaussian Support Vector Machine (Medium Gaussian SVM), Matern 5/2 Gaussian Process Regression (GPR), and Boosted Tree Regression—for predicting wetted width and wetted depth in sand and sandy loam soils. Model inputs included emitter discharge, irrigation duration, and selected soil hydro-physical and chemical properties. Models were developed using a 70% training dataset and validated with the remaining 30%. The Matern 5/2 GPR achieved the highest training accuracy for wetted width (R² = 0.99; RMSE = 0.74) and wetted depth (R² = 0.98; RMSE = 0.90), but validation errors increased to RMSE values of 2.27 and 3.84, respectively. Medium Gaussian SVM yielded the lowest validation RMSE (2.11) for wetted width, whereas Boosted Tree Regression achieved the best wetted depth prediction (RMSE = 2.11; MAE = 1.69). These findings demonstrate the importance of model-specific selection for reliable irrigation management.