A Framework for National Assessment of Land Degradation in the Drylands: A Case Study of Somalia

Abstract

Land degradation is a gradual, negative environmental process that is accelerated by human activities. Its gradual nature allows degradation to proceed unnoticed, thus reducing the likelihood of appropriate and timely control action. Presently, there are few practical frameworks to help countries design national strategies and policies for its control. The study presented here developed a framework for the national assessment of land degradation. This framework is envisaged to support governments in formulating policies on land degradation. It uses time‐series remote sensing data to identify the rate and extent of land degradation, local experts to identify prevalent degradation types and drivers of the degradation and field observations to validate the overall assessment. Its simplicity, use of freely downloadable input data and self‐triangulation of the assessment methods make it suitable for rapid assessment of land degradation on a national scale. It was tested in Somalia, where it exhibited accuracy greater than 60 per cent when assessing land degradation. This framework is relevant for designing national strategies and policies that address land degradation and provides an opportunity for accurate identification of areas to target with comprehensive local assessment. Testing of the framework in Somalia showed that about one‐third of the country was degraded because of loss of vegetation cover, topsoil loss and to the decline of soil moisture. Overgrazing, excessive tree cutting and poor agronomic practices in agricultural areas were identified as the primary drivers of the country's land degradation. These drivers are encouraged by the prevailing communal land tenure practices, poor governance and civil war. Copyright © 2011 John Wiley & Sons, Ltd.

Quantifying the impact of land degradation on crop production: the case of Senegal

“…An expert judgement-based inventory (Sonneveld, 2003;Omuto et al, 2014) under the Land Degradation in Dryland Areas (LADA) project (FAO/UNEP) shows that currently 34 % of the national territory and 58 % of the agricultural areas are affected by a degradation process. Sonneveld et al (2012) tested the consistency of these expert judgements by a cross-comparison of mapping units with identical characteristics for annual rainfall, soil suitability, slope, population density, and livestock density.…”

“…Finally, the weighted values were normalized to let the range of the land degradation index fluctuate between 0 and 1. We acknowledge that the created land degradation index cannot be tested in full, yet combining classes and area shares in a single land degradation index has been used in many other peer-reviewed studies (Leiwen et al, 2005;Pace et al, 2008;Sonneveld and Dent, 2009), which gives us, jointly with our empirical results, sufficient confidence to apply the index for our analysis.…”

Unveiling soil degradation and desertification risk in the Mediterranean basin: a data mining analysis of the relationships between biophysical and socioeconomic factors in agro-forest landscapes

“…SD&D processes occur in both developed and emerging countries and affect arid, dry and even sub-humid areas (Strijker 2005;Koulouri and Giourga 2007;Helming et al 2011;Corbelle-Rico, Crecente-Maseda, and Sante-Riveira 2012;Omuto, Balint, and Alim 2013;Stringer and Harris 2014). Although research has been carried out with the aim to identify the spatial determinants of SD&D and to classify them into homogeneous classes (Salvati et al 2011 of regional-scale drivers and to rank the importance of a number of site specific biophysical and socioeconomic factors deserves further investigation (Salvati and Zitti 2009;Bisaro et al 2013).…”

Spatial Assessment of Land Degradation Risk for the Okavango River Catchment, Southern Africa

“…A link of degradation assessments to climate information is also in high demand (Sivakumar & Stefanski, ). While remote sensing data, such as temporal trends in the normalized difference vegetation index (NDVI), can provide important insights in past and present states of land degradation (Higginbottom & Symeonakis, ; Omuto et al ., ; Niklaus et al ., ; Vicente‐Serrano et al ., ), for a comprehensive assessment of exposure to future degradation it is not sufficient. In this study, remote‐sensing data is brought together with results of regionalized climate simulations for the Okavango region (Weinzierl et al ., ) and socioeconomic as well as pedologic data in a multi‐component GIS‐based assessment of degradation risk factors.…”