Impact Evaluation of Sustainable Land Management (SLM) Options to Contribute to Land Degradation Neutrality in Rmel Catchment in Northeastern Tunisia

cg.contactKhaoula.karaoud@gmail.comen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerUniversity of Tunis El Manar, The National Engineering School of Tunis - UTM - ENITen_US
cg.contributor.crpCGIAR Research Program on Water, Land and Ecosystems - WLEen_US
cg.contributor.funderDeutsche Gesellschaft für Internationale Zusammenarbeit - GIZen_US
cg.contributor.projectImpact evaluation of SLM options to achieve land degradation neutralityen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.countryTNen_US
cg.coverage.regionNorthern Africaen_US
cg.subject.agrovocland degradationen_US
cg.subject.agrovocgisen_US
cg.subject.agrovocsoil erosionen_US
cg.subject.agrovocsustainable land managementen_US
dc.creatorKaraoud, Khaoulaen_US
dc.date.accessioned2019-07-03T08:41:38Z
dc.date.available2019-07-03T08:41:38Z
dc.description.abstractSoil erosion is a natural process causing serious land degradation problems. In Tunisia, soil erosion represents a serious environmental problem. The Rmel watershed located in the Governorate of Zaghouan in north-eastern Tunisia and covering an area of 679 square kilometers, suffers from this problem. It was thus selected to estimate annual soil loss using the Revised Universal Soil Loss Equation (RUSLE), and geographic information system (GIS). RUSLE model’s parameters (R, K, LS, C, and P) were derived from digital elevation model (DEM), average annual precipitation, soil type map and land cover map. They were computed as raster layers in a GIS environment, then multiplied together to predict soil erosion rates, and to generate a soil erosion risk map. Based on this study, the annual soil loss varies across the Rmel watershed from 0 to 186 ton ha - 1 -1 year . The average and total annual soil loss potential of the study watershed was 2.18 ton ha - 1 -1 , respectively. About 85% of the watershed was categorized low risk class, 9.5% moderate class and 5.5% high to severe erosion risk classes. The predicted amount of soil loss and its spatial distribution could provide indications to plan sustainable land use and management, by showing where the potential erosion hotspots are. The data generated by this project can contribute to improve current land management and related economic activities by making available an impact evaluation tool for sustainable land management (SLM) practices, to achieve sustainable economic development. Sustainable Land Management (SLM) practices have been advocated by several worldwide partnerships – such as the CGIAR, the United Nations Convention to Combat Desertification (UNCCD) and Economics of Land Degradation (ELD) – but still selectively applied. This research has been conducted in the frame of the project “Impact Evaluation of SLM Options to Achieve Land Degradation Neutrality” a holistic, innovative initiative aiming at providing solid geoinformatics guidance for planning and impact evaluation of SLM practices.en_US
dc.formatPDFen_US
dc.identifierhttps://mel.cgiar.org/reporting/downloadmelspace/hash/ea3f5d972809a8e55092b2da46d11625/v/a12dd3fe839a57e4381672276cb87fc7en_US
dc.identifier.citationKhaoula Karaoud. (9/7/2018). Impact Evaluation of Sustainable Land Management (SLM) Options to Contribute to Land Degradation Neutrality in Rmel Catchment in Northeastern Tunisia.en_US
dc.identifier.statusOpen accessen_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/10100
dc.languageenen_US
dc.rightsCC-BY-NC-4.0en_US
dc.subjectrusleen_US
dc.titleImpact Evaluation of Sustainable Land Management (SLM) Options to Contribute to Land Degradation Neutrality in Rmel Catchment in Northeastern Tunisiaen_US
dc.typeThesisen_US
dcterms.available2018-07-09en_US
mel.funder.grant#Deutsche Gesellschaft für Internationale Zusammenarbeit - GIZ :81204252en_US
mel.project.openhttp://geoc.mel.cgiar.orgen_US

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