Revisiting the questioned reliability of the revised universal soil loss equation (RUSLE) for soil erosion prediction in the tropics
Department of Soil Science, University of Calabar, Nigeria
Department of Soil Science, Faculty of Agriculture, University of Nigeria, Nsukka, Nigeria
Department of Soil Science, University of Nigeria, Nsukka, Nigeria
Data nadesłania: 04-06-2023
Data ostatniej rewizji: 27-05-2024
Data akceptacji: 01-06-2024
Data publikacji online: 01-06-2024
Data publikacji: 01-06-2024
Autor do korespondencji
Sunday E. Obalum   

Department of Soil Science, Faculty of Agriculture, University of Nigeria, Nsukka, Obola-Afor, 410101, Nsukka, Nigeria
Soil Sci. Ann., 2024, 75(2)189538
Soil erosion is one of the greatest environmental degradations challenges the tropical region and the world in general are facing. Tropical soils are less erodible than the temperate, but climatic erosivity is higher in the tropical region due to high erosive power of rain than the temperate region. In the past and present, physical processes of soil erosion and its control have been firmly assessed using the revised universal soil loss equation (RUSLE) model. This paper review aims at revisiting the questioned reliability of the revised universal soil loss equation (RUSLE) for soil erosion prediction in the tropics. The RUSLE model estimate the long-term average annual soil loss (A) by multiplying factors of rainfall erosivity (R), soil erodibility (K), slope length and slope steepness factors (LS), land cover and management factor (C) and the soil conservation or prevention practices (P). The RUSLE model had least data demanding and often used than any other tool available because of high degree of flexibility, data accessibility and suitability in computer program. As a drawback, the development of the model is site specific and often criticized, in that, a mere empirical erosion model cannot be applied in soil erosion predictions. However, amongst all others soil erosion prediction tools, the model is dependable and reliable, especially now, that it combines with remote sensing (RS), digital elevation model (DEM) and geographical information system (GIS) to estimates annual soil loss on a pixel-by-pixel basis and spatial distribution of the soil erosion.
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