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Applied and Environmental Soil Science
Volume 2014 (2014), Article ID 646502, 14 pages
Research Article

Soil Quality Assessment Strategies for Evaluating Soil Degradation in Northern Ethiopia

1College of Agriculture, Aksum University-Shire Campus, 314 Shire, Ethiopia
2Center for Development Research (ZEF), University of Bonn, Walter-Flex Street No. 3, 53113 Bonn, Germany

Received 28 June 2013; Revised 17 November 2013; Accepted 22 November 2013; Published 4 February 2014

Academic Editor: William Horwath

Copyright © 2014 Gebreyesus Brhane Tesfahunegn. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Soil quality (SQ) degradation continues to challenge sustainable development throughout the world. One reason is that degradation indicators such as soil quality index (SQI) are neither well documented nor used to evaluate current land use and soil management systems (LUSMS). The objective was to assess and identify an effective SQ indicator dataset from among 25 soil measurements, appropriate scoring functions for each indicator and an efficient SQ indexing method to evaluate soil degradation across the LUSMS in the Mai-Negus catchment of northern Ethiopia. Eight LUSMS selected for soil sampling and analysis included (i) natural forest (LS1), (ii) plantation of protected area, (iii) grazed land, (iv) teff (Eragrostis tef)-faba bean (Vicia faba) rotation, (v) teff-wheat (Triticum vulgare)/barley (Hordeum vulgare) rotation, (vi) teff monocropping, (vii) maize (Zea mays) monocropping, and (viii) uncultivated marginal land (LS8). Four principal components explained almost 88% of the variability among the LUSMS. LS1 had the highest mean SQI (0.931) using the scoring functions and principal component analysis (PCA) dataset selection, while the lowest SQI (0.458) was measured for LS8. Mean SQI values for LS1 and LS8 using expert opinion dataset selection method were 0.874 and 0.406, respectively. Finally, a sensitivity analysis (S) used to compare PCA and expert opinion dataset selection procedures for various scoring functions ranged from 1.70 for unscreened-SQI to 2.63 for PCA-SQI. Therefore, this study concludes that a PCA-based SQI would be the best way to distinguish among LUSMS since it appears more sensitive to disturbances and management practices and could thus help prevent further SQ degradation.