The effectiveness of tropical grass species in strips of different length in trapping sediment from cropland was assessed, and the influence of filter length was determined. The assessment was made under natural rainfall which induced sheet and rill erosion in run‐off plots and then using simulated run‐off which caused concentrated erosion. The evaluated grasses were elephant grass, lemon grass, paspalum and sugarcane. Run‐off plots were on a 10% slope in a randomized complete block design replicated three times. Filter lengths were 2.5, 5 and 10 m against a 10‐m‐long sediment source area planted with maize on a clay loam soil. The results show that sediment trapping effectiveness (TE) increases nonlinearly with increasing filter length for all grasses. Under natural rainfall, more than 70% of sediment was trapped in the first 5 m, and lengthening the strip to 10 m only resulted in a marginal increase in TE. With concentrated run‐off, more than 70% of sediment was trapped in the first 5 m and lengthening the strip to 10 m resulted in a significant increase in TE. Paspalum and lemon grass performed significantly better than other grasses (P < 0.05), owing to their spreading growth pattern over the soil surface. Paspalum also has the highest root density in the upper 0.3‐m layer of the soil followed by lemon grass, hence offering the greatest resistance to erosion from concentrated flow. The results demonstrate that tropical grass filter strips provide a viable means for reducing the sediment flux from cropland.

The Soil and Water Assessment Tool (SWAT) is a versatile model presently used worldwide to evaluate water quality and hydrological concerns under varying land use and environmental conditions. In this study, SWAT was used to simulate streamflow and to estimate sediment yield and nutrients loss from the Murchison Bay catchment as a result of land use changes. The SWAT model was calibrated and validated for streamflow for extended periods. The Sequential Uncertainty Fitting (SUFI-2) global sensitivity method within SWAT Calibration and Uncertainty Procedures (SWAT-CUP) was used to identify the most sensitive streamflow parameters. The model satisfactorily simulated stream discharge from the catchment. The model performance was determined with different statistical methods. The results showed a satisfactory model streamflow simulation performance. The results of runoff and average upland sediment yield estimated from the catchment showed that, both have increased over the period of study. The increasing rate of runoff can lead to severe and frequent flooding, lower water quality and reduce crop yield in the catchment. Therefore, comprehensive water management steps should be taken to reduce surface runoff in the catchment. This is the first time the SWAT model has been used in the Murchison Bay catchment. The results showed that, if all uncertainties are minimised, a well calibrated SWAT model can generate reasonable hydrologic simulation results in relation to land use, which is useful to water and environmental resources managers and policy and decision makers.

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Sustainable land use systems planning and management requires a wider understanding of the spatial extent and detailed human-ecosystem interactions astride any landscape. This study assessed the extent of historical, current, and future land use systems in Uganda. The specific objectives were to (i) characterize and assess the extent of historical and current land use systems, and (ii) project future lan use systems. The land use systems were defined and classified using spatially explicit land use/cover layers for the years 1990 and 2015, while the future prediction (for the year 2040) was determined using land use systems datasets for both years through a Markov chain model. This study reveals a total of 29 classes of land use systems that can be broadly categorized as follows: three of the land use systems are agricultural, five are under bushland, four under forest, five under grasslands, two under impediments, three under wetlands, five under woodland, one under open water and urban settlement respectively. The highest gains in the land amongst the land use systems were experienced in subsistence agricultural land and grasslands protected, while the highest losses were seen in grasslands unprotected and woodland/forest with low livestock densities. By 2040, subsistence agricultural land is likely to increase by about 1% while tropical high forest with livestock activities is expected to decrease by 0.2%, and woodland/forest unprotected by 0.07%. High demand for agricultural and settlement land are mainly responsible for land use systems patchiness. This study envisages more land degradation and disasters such as landslides, floods, droughts, and so forth to occur in the country, causing more deaths and loss of property, if the rate at which land use systems are expanding is not closely monitored and regulated in the near future.