Land degradation is a serious issue especially in dry and developing countries leading to ecosystem services (ESS) degradation due to soil functions' depletion. Reliably mapping land degradation spatial distribution is therefore important for policy decisions. The main objectives of this paper were to infer land degradation through ESS assessment and compare the modelling results obtained using different sets of data. We modelled important physical processes (sediment erosion and nutrient export) and the equivalent ecosystem services (sediment and nutrient retention) to infer land degradation in an area in the Ethiopian Great Rift Valley. To model soil erosion/retention capability, and nitrogen export/retention capability, two datasets were used: a 'global' dataset derived from existing global-coverage data and a hybrid dataset where global data were integrated with data from local surveys. The results showed that ESS assessments can be used to infer land degradation and identify priority areas for interventions. The comparison between the modelling results of the two different input datasets showed that caution is necessary if only global-coverage data are used at a local scale. In remote and data-poor areas, an approach that integrates global data with targeted local sampling campaigns might be a good compromise to use ecosystem services in decision-making.
Keywords: Ecosystem services modelling; Ethiopia; Global datasets; Land degradation; Nitrogen retention; Soil erosion.
Copyright © 2018. Published by Elsevier B.V.