Soil Quality Assessment Strategies for Evaluating Soil Degradation in Northern Ethiopia
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 as...
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Wiley
2014-01-01
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| Series: | Applied and Environmental Soil Science |
| Online Access: | http://dx.doi.org/10.1155/2014/646502 |
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| author | Gebreyesus Brhane Tesfahunegn |
| author_facet | Gebreyesus Brhane Tesfahunegn |
| author_sort | Gebreyesus Brhane Tesfahunegn |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-58c5912fb5a44a678441611858fc706d |
| institution | Kabale University |
| issn | 1687-7667 1687-7675 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied and Environmental Soil Science |
| spelling | doaj-art-58c5912fb5a44a678441611858fc706d2025-02-03T01:12:12ZengWileyApplied and Environmental Soil Science1687-76671687-76752014-01-01201410.1155/2014/646502646502Soil Quality Assessment Strategies for Evaluating Soil Degradation in Northern EthiopiaGebreyesus Brhane Tesfahunegn0College of Agriculture, Aksum University-Shire Campus, 314 Shire, EthiopiaSoil 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.http://dx.doi.org/10.1155/2014/646502 |
| spellingShingle | Gebreyesus Brhane Tesfahunegn Soil Quality Assessment Strategies for Evaluating Soil Degradation in Northern Ethiopia Applied and Environmental Soil Science |
| title | Soil Quality Assessment Strategies for Evaluating Soil Degradation in Northern Ethiopia |
| title_full | Soil Quality Assessment Strategies for Evaluating Soil Degradation in Northern Ethiopia |
| title_fullStr | Soil Quality Assessment Strategies for Evaluating Soil Degradation in Northern Ethiopia |
| title_full_unstemmed | Soil Quality Assessment Strategies for Evaluating Soil Degradation in Northern Ethiopia |
| title_short | Soil Quality Assessment Strategies for Evaluating Soil Degradation in Northern Ethiopia |
| title_sort | soil quality assessment strategies for evaluating soil degradation in northern ethiopia |
| url | http://dx.doi.org/10.1155/2014/646502 |
| work_keys_str_mv | AT gebreyesusbrhanetesfahunegn soilqualityassessmentstrategiesforevaluatingsoildegradationinnorthernethiopia |