DOI: 10.5281/zenodo.17036321
title: Potential annual soil loss by erosion (RUSLE), Rwanda

description: Average annual soil loss (t ha-1 yr-1) calculated using the Revised Universal Soil Loss Equation (RUSLE): A = R × K × LS × C × P. This model estimates sheet and rill erosion risk based on five factors: rainfall erosivity (R), soil erodibility (K), topography (LS), cover-management (C), and support practices (P). The resulting map supports erosion risk assessment and soil conservation planning in Rwanda.
Each input layer (R, K, LS, C, P) was derived as a separate spatial dataset as follows:







R factor: Rainfall erosivity factor (MJ mm ha⁻¹ h⁻¹ yr⁻¹). Derived by clipping the global rainfall erosivity dataset of Panagos et al. (2017, https://doi.org/10.1038/s41598-017-04282-8), as published in Panagos et al. (2023, https://doi.org/10.1016/j.dib.2023.1094820), to the administrative country boundary of Rwanda.

K factor:  Soil erodibility factor ((Mg/ha)[(MJ/ha)(mm/h)]⁻¹), calculated following the method of Torri et al. (1997, https://doi.org/10.1016/S0341-8162(97)00036-2).

The input sand, silt, clay and soil organic carbon maps were generated using digital soil mapping as implemented in the seedling workflow (https://doi.org/10.17027/ISRIC-FSX2-2691). The input soil profile data for these were:



Sand, Silt & Clay: AfSIS (URI:b88870b4-6af8-4e78-a3ac-38871d757525), Marshland & MINAGRI (provided by Rwanda Agricultural and Animal Resources Development  Board (RAB))

 Soil Organic Carbon: CATALIST (provided by the IFDC CATALIST project) & Marshland (shared by RAB)









LS factor: Topographic factor computed using slope and flow accumulation following the method of Luvai et al. (2021, https://doi.org/10.7176/JEES/11-16-06), and applied to areas with slope <50% in accordance with Panagos, Borrelli, and Meusburger (2015, https://doi.org/10.1016/j.scitotenv.2015.01.008). The LS factor was derived from the MERIT Digital Elevation Model (https://doi.org/10.1002/2017GL072874).

C factor: Cover-management factor, calculated following the method of Negese (2024: https://doi.org/10.1016/j.rsase.2023.101089), using NDVI data derived from Landsat 8 Surface Reflectance Tier 1 Collection 2 imagery (2018–2023) (https://www.usgs.gov/landsat-missions/landsat-8).

P factor: Support practices factor. P = 1 due to data gaps.




This research was carried out for the LSC-IS hubs project under the funding program Development Smart Innovation through Research in Agriculture (DeSIRA), European Union. EU Contribution Agreement to MinBUZA: FOOD/2020/419-433 ; MinBUZA to WUR Grant number: 4000004100.

 



Coordinate Reference System - EPSG:32736
description: DISCLAIMER:

These soil property maps were generated at a resolution of 100m, with the best available data at the time of production, including global datasets and legacy national level data, using digital soil mapping and GIS modelling. The derived products are provided 'as-is' without any warranty, regarding accuracy, completeness or fitness for a particular purpose. Users are advised to verify the information independently before making decisions based on it. Additionally, users should assess the local 'predictive' accuracy of the maps prior to using them for making recommendations at local (or field) level.

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