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Soil Quality Assessment Strategies for Evaluating Soil Degradation in Northern Ethiopia. College of Agriculture, Aksum University- Shire Campus, 3. Shire, Ethiopia. 2Center for Development Research (ZEF), University of Bonn, Walter- Flex Street No. Bonn, Germany. Received 2.
June 2. 01. 3; Revised 1. November 2. 01. 3; Accepted 2. November 2. 01. 3; Published 4 February 2. Copyright . 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 2. 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 8. LUSMS. LS1 had the highest mean SQI (0.
PCA) dataset selection, while the lowest SQI (0. LS8. Mean SQI values for LS1 and LS8 using expert opinion dataset selection method were 0. Finally, a sensitivity analysis (S) used to compare PCA and expert opinion dataset selection procedures for various scoring functions ranged from 1. SQI to 2. 6. 3 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. Introduction. Globally, declining in soil quality (SQ) has posed a tremendous challenge to increasing agricultural productivity, economic growth, and healthy environment . The underlying causes for SQ degradation are largely related to inappropriate land use and soil management, erratic and erosive rainfall, steep terrain, deforestation, and overgrazing .
Most of the causes are resulted from a desperate attempt by farmers to increase production for the growing population which aggravate SQ degradation more in the developing countries, which mainly depend on natural resources (agriculture) . Misuse of natural resources that leads to degradation can also be stimulated by socioeconomic and political issues, for example, land tenure, capital, and infrastructure . SQ degradation by soil erosion such as soil nutrient depletion and changes in soil physical indicators is largely recognized as a principal cause aggravated by the effect of inappropriate land use and soil management in the developing countries like Ethiopia . In normal conditions, the soil can maintain equilibrium by pedogenetic processes .
However, this equilibrium is easily disturbed by anthropogenic activities (e. In order to make sound decisions regarding sustainable land use systems, knowledge of SQ related to different land use scenarios is essential . It is therefore most important to assess SQ degradation of different land use and soil management systems using soil quality index (SQI) since many of the factors that influence sustainable productivity are related to SQ. Information on SQI can support to further prioritization and then device management strategies that improve soil resources sustainably . To do so, applying the concept of SQI is desirable as individual soil properties in isolation may not be sufficient to quantify changes in SQ related to land use and soil management systems .
In line to this, many studies reported that indexing SQ indicators based on a combination of soil properties could better reflect the status of SQ degradation as compared to individual parameters . Previous studies reported that different methods of minimum dataset selection (MDS), scoring, and SQ indexing have been applied but SQI results varied even for the same conditions . The most widely reported MDS methods of SQ indicators are expert opinion and statistical tools (e. PCA)) . An expert can generate a list of appropriate SQ indicators on the basis of ecosystem processes and functions and other decision rules such as management goals for a site associated with soil functions as well as other site- specific factors, like region or crop sensitivity as selection criteria . Studies elsewhere compared the two scoring methods to represent soil system function but the value of nonlinear scoring method was reported higher than the linear method . There are different types of linear and nonlinear scoring functions, even though none of the previous studies have evaluated them all simultaneously . Different SQ indexing methods have been also used by different researchers .
The same authors have reported that there are differences in SQI values among the various SQ indexing methods (e. Despite the fact that there is diversity in data selection, scoring, and SQ indexing methods, previous studies have limitation in evaluating the methods using the same data simultaneously in a similar field conditions. Regardless of the above limitation, having SQI of long- term land use and soil management systems is necessary in order to locate areas to be carefully managed for sustainable development. The use of site- specific SQI can help planners and decision makers to evaluate which land use and management system is most sustainable and vice- versa in a given situation . These authors also noted that SQI can reflect the extent of SQ degradation and thereby give support to suggest appropriate remedial measures such as optimum fertilizer rates and planning of other suitable land management practices considering potentials and constraints of different fields at large scale such as a catchment. In general, SQI is a useful assessment tool that may help move soil conservation and resource management beyond assessments of soil erosion and changes in productivity .
SQI can thus provide the necessary information for planners and decision makers to make informed decisions against SQ degradation using the introduction of appropriate interventions. Despite such importance of SQI in combating SQ degradation, only few studies have been reported in relation to various land use and soil management systems.
This indicated that research on SQI has been mostly neglected for unknown reasons, with the most probable reason which could be technical and financial limitations. Many approaches assessing SQ degradation using the concept of SQI have been already developed and applied elsewhere . In this study, such concepts are adopted and evaluated to narrow the knowledge/information gap of SQI across different land use and soil management systems in the northern Ethiopia. The objective of this study was to assess and identify an effective SQ indicator dataset among 2. SQ indexing method to evaluate soil degradation across the LUSMS in the Mai- Negus catchment of northern Ethiopia. Materials and Methods.
Study Area. This evaluation was conducted in the Mai- Negus catchment in Tigray regional state, northern Ethiopia (Figure 1). The catchment covers an area of 1. Land use is dominantly arable with teff (Eragrostis tef) being the primary crop on > 8. The remainder of the catchment is pasture with scattered patches of mixed tree, bush, and shrub cover.
The major rock types are lava pyroclastic and metavolcanic. According to FAO- UNESCO Soil Classification System, soils are dominantly Leptosols at very steep positions, Cambisols on middle to steep slopes, and Vertisols on flat areas . Annual rainfall averages 7. Mean annual temperature was 2. Selection of Land Use and Soil Management Systems.
Eight land use and soil management systems (LUSMS) were selected on the basis of three steps. First, information on historical and existing LUSMS in the catchment was collected and described. Soil sampling units were then identified across each LUSMS. Finally, composite soil samples were collected, processed, and analyzed for several SQ indicators using standard laboratory procedures.
The first step is described below, with details for the second and third steps given in Sections 2. Field reconnaissance surveys and informal group discussions were conducted in June 2. The six farmers were selected purposively because the large group size made it impractical for all the household heads to participate and doing so would have been problematic for discussion and consensus building.
The dominant cropping history and soil management practices for each LUSMS were identified and described by the team. In addition, terrain characteristic and soil factors were documented for each rain- fed agricultural LUSMS.
All eight LUSMS were selected as much as possible to be from similar soil type (Cambisols) and a range in slope gradient in the catchment. Topographical characteristics of each sampling units are presented in Table 1. Table 1: Topographic characteristics of each sampling unit in the eight LUSMS selected for soil quality assessment within the Mai- Negus catchment in northern Ethiopia. Based on land use information acquired in the study, eight LUSMS that represent the best and worst management practices being used throughout the study catchment were identified and are described (Table 2).