Enrichment of water bodies with nutrients from wastewater is one of the causes of eutrophication to aquatic ecosystems. This study investigated the use of biochar derived from rice husk, coconut husk, and coffee husk in adsorbing nitrates (NO3-N) and nitrites (NO2-N) from slaughterhouse wastewater. It also explored the desorption efficiencies of the adsorbed nutrients to ascertain the applicability of the enriched biochars as slow-release fertilizers. To characterize the physicochemical properties of the biochars, scanning electron microscopy (SEM) was used. Fourier transforms infrared spectroscopy (FTIR), elemental analysis (CHNO) Langmuir and Freundlich, and the isotherm models were employed to fit the experimental equilibrium adsorption data. It was observed that the Langmuir isotherm model has the best fit of NO3- N and NO2- N on all the biochars. And this was based on the coefficient of correlation values. Also, the coconut husk biochar has the highest adsorption capacities of NO3-N and NO2-N at 12.97 mg/g, and 0.244 mg/g, respectively, attributing to its high porosity as revealed by the SEM images. The adsorption capacities for the rice husk char were 12.315 and 0.233 mg/g, while that for coffee husk char were12.08 mg/g and 0.218 mg/g for NO3-N and NO2-N, respectively. The relatively higher amount of NO3-N adsorbed to that of NO2-N could be attributed to its higher initial concentration in the solution than nitrite concentration. The desorption efficiencies of nitrates were 22.4, 24.39, and 16.79 %, for rice husk char, coconut husk char and coffee husk char, respectively. For the rice husk char, coconut husk char and coffee husk char, the nitrites desorption efficiencies were 80.73, 91.39, and 83.62 %, respectively. These values are good indicators that the studied biochar can be enriched with NO3- N and NO2- N and used as slow-release fertilizers.

The Mkurumudzi River originates in the Shimba hills and runs through Kwale County on the Kenyan Coast. Study on this river has been informed by the many economic activities that the river supports, which include sugarcane plantations, mining, tourism and subsistence farming. The main objective of this study was to use the soil moisture accounting (SMA) model specified in the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) settings for the continuous modeling of stream flow in the Mkurumudzi catchment. Data from past years were compared with observed stream flow data in order to evaluate whether the model can be used for further prediction. The calibration was performed using data from 1988 to 1991 and validation for the period from 1992 to 1995 at a daily time step. The model performance was evaluated based on computed statistical parameters and visual checking of plotted hydrographs. For the calibration period of the continuous modeling, the performance of the model was very good, with a coefficient of determination R² = 0.80, Nash-Sutcliffe Efficiency NSE = 0.80, index of agreement d = 0.94, and a Root Mean Squared Error (RMSE)/observations’ standard deviation ratio—RSR = 0.46. Similarly, the continuous model performance for the validation period was good, with R² = 0.67, NSE = 0.65, RSR = 0.62 and d = 0.88. Based on these performance results, the SMA model in the HEC-HMS was found to give a satisfactory prediction of stream flow in the Mkurumudzi Catchment. The sensitivity analysis of the model parameters was performed, and the different parameters were ranked according to their sensitivity in terms of percent change in simulated runoff volume, peaks, Nash-Efficiency, seven-day low flow and base flow index. Sensitivity analysis helped to understand the relationships between the key model parameters and the variables.

Land and water degradation caused by soil erosion and climate change pose major environmental threats, particularly in agricultural watersheds. Soil erosion in a catchment leads to low crop yields due to declining soil quality (SQ), productivity and sustainability. However, very few studies have been done to assess soil health in Kenya, and none in Narok County. Thus, the aim of this study was to evaluate the soil sustainability status in Kakia-Esamburmbur catchment, based on the identification of key indicators (IKI) from a large dataset (LDS) of 23 indicators, across three land use types designated as grass land (GL), crop land (CL) and forest land (FL). To achieve the stated objective, two soil quality indexing methods were employed: the Additive Soil Quality Index (A-SQI) using the LDS; and the Weighted Soil Quality Index (W-SQI) using Principal Component Analysis (PCA) as a reduction tool to obtain the IKI set. The results show that at a depth of 20 cm, the catchment's soils characteristics did not differ significantly. The two methods (A-SQI and W-SQI) resulted in FL having the highest SQI mean values (0.61, 0.57), followed by CL (0.59, 0.55), while the lowest SQI mean value was recorded in GL (0.58, 0.53). Additionally, the sensitivity analysis showed W-SQI as the most sensitive and superior method in the evaluation of SQI changes due to its high sensitivity and coefficient of variation (CV), at 2.25 and >12 %, respectively. Among the ten IKI, CEC made the greatest contribution to SQ (18.68 %), followed by BD (15.61 %), BIR (14.71 %), Mg (14.26 %), MBN (8.30 %), MBC (8.26 %), Sand (6.77 %), Moisture (5.75 %), TOC (5.16 %), and PMN (2.63 %). The findings show that the catchment belongs to the "medium" category of SQ. The IKI can help save time and reduce the cost of intensive lab works for temporal assessment and monitoring of the effects of different land use on SQ.

The inefficient water use, and variable and low productivity in Kenyan public irrigation schemes is a major concern. It is, therefore, necessary to periodically monitor and evaluate the performance of public irrigation schemes. This prompted evaluation of performance of three rice growing irrigation schemes in western Kenya using benchmarking and principal component analysis. The aim of the study was to quantify and rank the performance of selected irrigation schemes. The performance of the irrigation schemes was evaluated for the period from 2012 to 2016 using eleven performance indicators under agricultural productivity, water supply and financial performance categories. The performance indicators were weighted using principal component analysis and combined to form a single performance score using linear aggregation method. The average performance in the Ahero, West Kano and Bunyala irrigation schemes was 48%, 49% and 56%, respectively. Based on performance score, the Bunyala irrigation scheme is the highest performing rice irrigation scheme in western Kenya. The three irrigation schemes have an average performance. Operation and management measures to improve the current performance of the irrigation schemes are needed.