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Soil erosion prediction using RUSLE for central Kenyan highland conditions

Angima, S.D., Stott, D.E., O'Neill, M.K., Ong, C.K., Weesies, G.A.
Agriculture, ecosystems & environment 2003 v.97 no.1-3 pp. 295
agricultural watersheds, water erosion, prediction, highlands, equations, mathematical models, slope, crop rotation, Kenya
Soil erosion by water is serious global problem. In Africa, about 5 Mg ha-1 of productive topsoil is lost to lakes and oceans each year. This study was conducted at the Kianjuki catchment in central Kenya to predict annual soil loss using the Revised Universal Soil Loss Equation (RUSLE Version 1.06) to determine the erosion hazard in the area and target locations for appropriate initiation of conservation measures. All factors used in RUSLE were calculated for the catchment using local data. The rainfall erosivity R-factor was 8527 MJ mm ha-1 h-1per year and the annual average soil erodibility K-factor was 0.016 Mg h MJ-1 mm-1. Slopes in the catchment varied between 0 and 53% with steeper slopes having overall LS-values of over 17. The C-factor values were computed from existing cropping patterns in the catchment, including corn-bean (Zea mays-Phaseolus vulgaris) 1-year rotation, coffee (Coffea arabica), and banana (Musa sapientum). Support practice P-factors were from terraces that exist on slopes where coffee is grown. Total annual soil loss predictions varied from one overland flow segment to the next and ranged from 134 Mg ha-1 per year for slopes with average LS-factors of 0-10 to 549 Mg ha-1 per year for slopes with average LS-factors of 20-30, which is more than the estimated soil loss tolerance (T) for the area of 2.2-10 Mg ha-1 per year. Using 3 years of field data, the RUSLE model was able to pinpoint site-specific erosion hazards associated with each overland flow segment in the catchment for different cropping patterns and management practices. This work highlights the severity of erosion in tropical highlands of east Africa and gives suggestions on possible intervention strategies; however, there is still a need for developing more long-term data to validate the model to suit local agro-ecological conditions.