JOURNAL ARTICLE
Modelling sustainable land management programme intervention effect on soil loss rate in the watershed region.
Published In: Soil Use & Management, 2025, v. 41, n. 1. P. 1 1 of 3
Database: Environment Complete 2 of 3
Authored By: Shitu, Kasye; Alzahrani, Hassan; Aslam, Rana Waqar 3 of 3
Abstract
This paper modelled the sustainable land management programme intervention effect on soil loss rate in the Hoha and Temba watersheds, Western Ethiopia. In the area, the sustainable land management programme (SLMP) has been doing many soil–water conservation measurements since 2011. However, an assessment of the soil loss rate before and after the implementation of the project has not yet been conducted in the area because of operational issues and the high costs of gathering on‐ground data. Because of this, we have developed a Revised Universal Soil Loss Equation (RUSLE) framework fully integrated with geographic information system (GIS) for high spatial resolution (30 m) soil erosion assessment in 2010 (before SLMP was implemented in the area) and 2015 and 2021 (after SLMP implemented in the area). The results showed that the mean annual soil loss rate of the study area was 13.04, 1.88 and 2.06 t ha−1 year−1 for the Hoha and 9.58, 1.53 and 1.68 t ha−1 year−1 for the Temba watershed in the years, 2010, 2015 and 2021, respectively. In line with this, our results also indicated an increment of forest cover and a reduction of bare land cover in both watersheds throughout the study time. In terms of soil loss reduction, SLMP has a significant role through the improvement in forest cover and reduction in bare land in both watersheds. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Soil Use & Management. 2025/01, Vol. 41, Issue 1, p1
- Document Type:Article
- Subject Area:Environmental Sciences
- Publication Date:2025
- ISSN:0266-0032
- DOI:10.1111/sum.70048
- Accession Number:184140056
- Copyright Statement:Copyright of Soil Use & Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.