Identification of soil erosion hotspot areas for sustainable land management in the Gerado catchment, North-eastern Ethiopia

Remote Sensing Applications: Society and Environment 13 (2019) 306–317

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Identification of soil erosion hotspot areas for sustainable land management in the Gerado catchment, North-eastern Ethiopia

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Legass Bahir Asmamaw , Assen Ahmed Mohammed Department of Geography and Environmental Studies, Addis Ababa University, Ethiopia

A R T I C LE I N FO

A B S T R A C T

Keywords: Soil loss tolerance RUSLE2 Erosion factors Management prioritization

In Highland Ethiopia, soil erosion is one of the main forms of land degradation which has a wide range of undesirable on-site and off-site impacts. It is therefore essential to mitigate soil erosion through site-specific and problem-oriented management practices. The research employed Revised Universal Soil Loss Equation (RUSLE2) model to identify hotspot areas of soil erosion and prioritize land management intervention in the Gerado catchment, North-eastern Ethiopia. The parameters required for the model were acquired from different sources and integrated with ArcGIS tools to estimate soil loss rates of the study catchment. Mean annual soil loss rates were estimated to be between 5 and 100 t ha–1 yr–1 on flatter and steeper slopes respectively. Over 75% of the catchment area had an average soil loss above the estimated tolerance soil loss rate of 18 t ha–1 yr–1 for the country. In order to identify hotspot areas, the catchment was classified into severe, very high, high, medium, low and very low erosion risk categories. Based on the study result, it is recommended that areas with severe, very high and high erosion risk having estimated soil loss of 25 t ha–1 yr–1 or over are prioritized for land management intervention. Areas which require the immediate implementation of soil management approximately accounted for 75% (5025 ha) of the total catchment. The results showed that the severity of erosion was linked to the slope steepness, steep slope cultivation, absence/lack of effective conservation measures and sparse nature of the steep slope vegetation cover.

1. Introduction Soil erosion has been a severe problem of many countries since the beginning of land cultivation (Morgan, 2005; Chen et al., 2007). In Ethiopia, soil erosion is considered to be as old as the history of agriculture (Hurni, 1993). At the contemporary time “much of the earth is degraded, is being degraded or is at a risk of degradation” (Barrow, 1991: 1). In most agricultural lands the soil loss rate ranges from 13 to 40 t soil ha−1 yr−1 and 13–40 times faster than the very slow soil formation rate (Pimentel and Kounang, 1998). However, the soil loss rate by water in Ethiopia ranges from 16 to over 300 t soil ha−1 yr−1 mainly depending on the degree of slope gradient, intensity and type of land use/cover and nature of rainfall intensities (Hurni, 1988; Tamirie, 1995; Bewket and Teferi, 2009; Abate, 2011; Daniel et al., 2015). The total annual soil loss of cultivated, range and pasture lands, covering an area of 780,000 km2, was estimated to range from 1.3 to 23.4 billion t soil yr−1 (Tamirie, 1995). In many African countries including Ethiopia, weather changes between dry and wet seasons accelerates the occurrence of high erosion at the onset of rainy season; especially, in areas with insufficient land cover (Mati and Veihe, 2001).

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In the Highlands of Ethiopia, soil erosion is mainly induced by overgrazing, rampant deforestation, poor farming systems and lack of soil management practices (Tamirie, 1997). The RUSLE2 model makes it possible to assess the degree of soil erosion, which varies with rainfall erosivity, soil erodibility, slope length and steepness, land use/land cover and soil conservation practices (Prasannakumar et al., 2012; Arora, 2003). Such research at the local level is fundamental to explore the spatial variation of soil loss and prioritize the implementation of land management in highly soil erosion vulnerable areas (Abate, 2011; Bewket and Teferi, 2009; Gebreyesus and Kirubel, 2009). Soil erosion and the consequent decline in soil fertility, has significantly reduced the agricultural productive capacity of land resources in many parts of the country (Hurni, 1993). As a result, most farmers who largely depend on the intrinsic of land resources are unable to ameliorate the depletion of soil fertility (Sonneveld et al., 1999). An average soil loss of 42 t soil ha−1 yr−1 from cropland in Ethiopia reduces up to 2% of the total annual agricultural production (Hurni, 1993). Severe water erosion mainly in the highlands of Ethiopia results in huge sediment accumulation in reservoirs, lowering water holding capacity, shortages of water supply and hydroelectric power generation

Correspondence to: Department of Geography and Environmental Studies, Addis Ababa University, P.O. Box-150249 (6 Kilo campus), Addis Ababa, Ethiopia. E-mail addresses: [email protected], [email protected] (L.B. Asmamaw).

https://doi.org/10.1016/j.rsase.2018.11.010 Received 3 February 2018; Received in revised form 19 November 2018; Accepted 26 November 2018 Available online 30 November 2018 2352-9385/ © 2018 Elsevier B.V. All rights reserved.

Remote Sensing Applications: Society and Environment 13 (2019) 306–317

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highland climate (EMA, 1979; EMS, 1979). Meteorological records from the nearby Dessie representative station (2540 masl; 11° 05′ N and 39° 40′ E) indicate the mean monthly temperature ranges from 12.6 °C in December to 18.4 °C in June. The highest mean maximum monthly temperature (26.5 °C) occurs during the wet summer in July. The lowest mean minimum monthly temperature (4.7 °C) was recorded in December. The mean annual temperature was 15.7 °C with low annual range. The mean annual rainfall of 33 years’ rainfall records (from 1976 to 2008) was computed to be 1171 mm. The variability of the mean annual rainfall ranges from 706.8 mm in 1984 to 1486.3 mm in 1998 (Fig. 2) with high inter annual variability. The moisture regime is dominated by one major rainy season (kiremt) extending from July to September and accounting for 52.8% of the total annual rainfall. The short rainy season extends from March to May and accounts for 21% of the total annual rainfall. The rainfall of the main (kiremt) and small (belg) seasons permits rainfed crop cultivation. The dry season occurs between October and February with very little rainfall. The highest monthly rainfall, 294 mm, is recorded in July with the lowest rainfall in November and December. Frost and erratic nature of both major (summer) and minor (spring) rainfall patterns are climatic limiting factors, affecting agricultural production in the study catchment, as elsewhere in Ethiopia’s central highlands (Woldamlak, 2012). The study area has been covered by seven land use/land cover types. The cultivated & rural settlement and shrublands which accounted 59.7% (4005.8 ha) and 13.2% (882.7 ha) of the total study area were the major land use/land cover types. The remaining parts of the study area were covered by 6.2% (418.8 ha) wood land, 6% (403.1 ha) bare land, 5.8% (347.3 ha) urban built up area, 5.7% (379.6 ha) forest land and 4.1% (272.5 ha) grass land. The presences of various land use/land covers have different soil and water protection capacity from erosion (Table 3, Fig. 6). In the study area, strip cultivation, stone cover, contour cultivation, soil bunding, terracing and check damming are practiced by the local communities in conserving their soil and water resources. Subsistence rainfed crop production, supplementary traditional irrigation and livestock husbandry are the main livelihood sources. The farmers cultivate a variety of cereals, pulses, oil seeds, and vegetables and root crops. Wheat (Tiriticum aestivum), maize (Zea mays L.), teff (Eragrostis tef ZUCC.), horse beans (Vicia faba), field peas (Pisum sativum), linseed (Linum usitattissimum), fenugreek (Trigonella foenum grecum) and chick peas (Cicer aritinum) are the main cultivated food crops. The first three cereals are cultivated with rainfed agriculture in summer and supplementary irrigation in spring. The local farmers also cultivate a variety of vegetables and root crops such as head cabbage (Brassica oleracea capitata), beetroot (Beta vulgaris rubra), carrot (Daucus carota sativa), onion (Allium cepa), and potato (Solanum tuberosum) through traditional irrigation. There is also cultivation of sorghum (Sorghum bicolor L.), finger millet (Eleusine coracana), vetch (Vicia sativa), barley (Hordeum vulgare), emmer wheat (Avena sativa), and soya bean (Glycine max) in the catchment.

(Haregeweyn et al., 2001; Tamene et al., 2011). The large removals of cultivated soils in Umbolo catchment of Southern Ethiopia through the gradual incision of the lands have resulted in the development of large gullies (Moges and Holden, 2008). Furthermore, as already underlined by Pimentel and Kounang (1998) nearly two decades ago, soil erosion is one of the greatest challenges in providing sufficient food crop production for a rapidly growing human population among many developing countries. Under the Ethiopian conditions, approximately 200 kg of organic matter, 30 kg of nitrogen and 75 kg of phosphorous ha−1 yr−1 are estimated to be lost as a result of soil erosion processes (Tamirie, 1995). This reduces available soil nutrients and causing low crop productivity. Moreover, previous studies have shown that about 26% of the dry lands of the country have shallow soil depth of less than 50 cm (Tamirie, 1997). As Menale et al. (2009) points out “inadequate nutrient supply, depletion of soil organic matter, and soil erosion continue to present serious challenges to crop production in Ethiopia.” Increasing population and climate change combined with limited capacity of the people to respond to shocks has increased the land degradation-poverty-land degradation feedback which has resulted in major physical, social and economic crisis among many areas and communities of Ethiopia (Daniel et al., 2015; Hurni, 1993). Thus, consolidated effort and improved organization are needed to conserve and increase the productive capacity of agricultural soils. Appropriate soil conservation and effective policy measures are needed to harness the increasingly high rates of soil degradation observed in Ethiopia as well as to reclaim affected areas and bring them back into production (Tamirie, 1997; Sonneveld et al., 1999). A thorough understanding of soil erosion processes plays a pivotal role to lower the current rates of soil loss by planning, designing and implementing appropriate conservation strategies (Tamene and Vlek, 2007). In most parts of Ethiopia, similar land management methods were and still are practiced without considering variations in degrees of soil erosion, climate, topography, soils and land use/land cover. Therefore, the central thesis of this study was to estimate and quantify the average annual soil loss rate and identify soil erosion hotspot areas using the Revised Universal Soil Loss Equation (RUSLE2) model (Renard et al., 1996). The results coming from this study will contribute to the development of site specific and problem oriented land management strategies. Hence, the outcome of the research can play an important role in properly implementing land management intervention on the bases of the priority of soil erosion problem. Geographically, the study was conducted in the Gerado catchment, South Wello, which is one of the most soil erosion affected areas in Ethiopia. 2. Materials and methods 2.1. Description of the study area The Gerado catchment which is found in the Highlands of South Wello, North-eastern Ethiopia forms the upper part of the Blue Nile drainage system. It is located between 11° 3′ 00′′ -11° 8′ 30′′ N and 39° 32′ 20′′- 39° 39′10′′ E (Fig. 1) and is covering 6700 ha of land. The relief configuration of the catchment was mainly carved by tectonic forces of territory period in Cenozoic era (Mohr, 1971). The study catchment is characterized by steep mountains at the periphery and conforms a flat plain at the center; it has an elevation ranging from 2, 174 to 3, 032 m above mean sea level. The gradient varies from flat slope (0–2%) at the central parts of the study area to very steep slopes (> 70%) on the mountain ranges. The master plan of the Blue Nile River Basin (1:250,000 scale) classifies the soils of Gerado catchment as Eutric Vertisols and Eutric Leptosols. The latter soils occupy the steep mountains while the formers are situated on flat and gentle slopes of the study area (BCEOM, 1998). The study area falls within moist dega (temperate) and moist weyna dega (subtropical) agro-climatic zones (Hurni, 1995). It has tropical

2.2. The revised universal soil loss equation Erosion processes do not equally affect all areas of a landscape. It is also not economically feasible, socially sounding and technically possible to conserve all soil erosion areas at once. Measuring and experimenting soil erosion in all degraded areas of any country especially in developing regions is impractical. Predicting the location of excessive erosion hazard is therefore relevant to identify and prioritize management areas, properly manage degraded lands, make successful erosion control, and improve the wellbeing and development of people (Thompson and Troeh, 1978). Soil erosion models are commonly used to achieve these goals (Wischmeier and Smith, 1978; Renard et al., 1996). The absence of appropriate erosion assessment model for tropical and subtropical countries forced many researchers to rely on 307

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L.B. Asmamaw, A.A. Mohammed

Fig. 1. Location Map of the Gerado catchment, North-eastern Ethiopia (EMA, 1991).

Fig. 2. Mean, maximum and minimum monthly temperature (2008) and mean annual rainfall records (1976–2008) of Dessie meteorological station (2540masl; 11° 07′ N and 39° 38′ E), North-eastern Ethiopia (NMSA, 2008, unpublished document).

valued to employ RUSLE2 model as a pivotal tool in identifying the spatial variation of soil loss and erosion prone hotspot areas for soil and water management priority. As a result, it enables in implementing integrated structural and biological soil conservation techniques in appropriate priority sites depending on the degree of the problem and available resources to conserve the land from water erosion hazard (Pimentel et al., 1995). In the developing countries, shortage of data limits the application of data intensive research models. However, the compatibility of the RUSLE2 model with GIS & RS technology as well as its limited data input requirement to estimate soil loss rate, identify hotspots of soil degradation and priority of land management in a wide range of areas (Shi et al., 2004; Fu et al., 2005; Karaburun, 2009) enables researchers to apply the model. TheRUSLE2 model can be used to calculate the soil loss rate of the catchments based on the product of six different data

RUSLE2 or other soil loss assessment models developed for temperate countries. These models, however, can be adapted to the existing local condition and erosion hazard assessment (Hurni, 1985a). However, the implementation of erosion models might face uncertainty in data inputs and outputs. For, example, the use of limited data, for example, dependence on limited climatic & soil data and generalization undertaken in processing the six RUSSLE2 model factors can be the cause of inputs and outputs uncertainty. Such issues should be considered as the limitation of such model research. Furthermore, critical care has to be taken in processing the input variables at all stages and while interpreting the RUSLE model outputs so as to enable in minimizing the degree of uncertainties. Human, financial, and physical resources required for soil management are not equally available to all areas at the same time. This research was not carried to improve the RUSLE2 model. Rather it was 308

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L.B. Asmamaw, A.A. Mohammed

Fig. 3. Required inputs and geoprocessing of RUSLE2 model factors to produce soil loss rate and erosion hazard map of the Gerado catchment, North-eastern Ethiopia.

Wischmeier and Smith (1978) method that considers variation of slope classes and two major land uses, agricultural and non-agricultural lands (Table 4, Fig. 7). In addition, key informant interviews and focus group discussion were made with agricultural development agents and purposely selected members of the local community based on their better experience and knowledge to understand severity and spatial variation of soil erosion in the study area.

sets: rainfall intensity (R), soil erodibility (K), slope length and steepness (LS), land use/ land cover (C) and support practices (P) factors (Renard et al., 1996). Once the data of these erosion factors are available, the average annual soil loss rates (A), based on RUSLE2, can be calculated as shown in Eq. (1) (after Hudson, 1995; Renard et al., 1996):

A = R*K *LS *C *P

(1)

where A is the mean annual soil loss (t soil ha−1yr−1) caused by sheet and rill erosion; R is the rainfall erosivity factor (MJ mm ha−1 h−1 yr −1 ); K is the soil erodibility index (t ha h ha−1 MJ−1 mm−1); LS is the combination of slope length (L) and slope steepness (S) factor; C is the land use/cover factor; and P is the support practice factor.

2.3.1. Rainfall-Runoff erosivity (R) factors Rainfall erosivity measures the erosive forces of rainfall to detach and transport soil particles in a given area (Barrow, 1991; Hudson, 1977, 1995). In RUSLE2 model, the combined product of kinetic energy (E) of rainfall and maximum intensity of rain in 30 min (I30) is used to calculate rainfall erosivity (R) factor (Renard et al., 1996). However, data for kinetic energy and intensity of rainfall are not available for developing regions and remote areas. In such instances, total annual rainfall has been calibrated to estimate R-factor for different regions (Hellden, 1987; Bewket and Teferi, 2009; Gebreyesus and Kirubel, 2009). For the Ethiopian conditions, the R-factor was estimated from the mean annual rainfall of many years of rainfall records (Fig. 2) using Eq. (2) developed by Hurni (1985b):

2.3. Data acquisition and modeling of soil loss rates The required inputs of the RUSLE2 model have been collected from different primary & secondary sources (Fig. 3) and calculated with the GIS and RS technologies to estimate and prepare the soil loss rates map of the Gerado catchment. Soil survey was conducted to collect thirtyfour disturbed soil data that were used to generate the soil erodibility (K) factor of the model. The rainfall records of the nearby Dessie station were collected from National Meteorological Office to calculate the rainfall erosivity (R) factor. ASTER satellite image was used to calculate slope length and steepness (LS) factors of the catchment. The 2006 SPOT-5 satellite image was bought from the Ethiopian Mapping Agency to compute the land use/land cover (C) factors of the study area. The support practices (P) factors of the area were calculated using

R = −8.12 + 0.562P

(2)

where R is the calculated rainfall-runoff erosivity factor and P is the mean annual rainfall (mm). 2.3.2. Soil erodibility (K) factors Soil erodibility (K) factor refers to the inherent soil susceptibility to 309

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2.3.4. Land use/land cover (C) factors Different land use/covers have various degree of soil protection against soil erosion (Morgan, 1977). Land use/covers with better vegetation have lower C value and low degree of soil erosion hazard and vice versa. By intercepting rainfall drops and reducing velocity of runoff, plant cover protects the soil against erosion (Morgan, 1977). On the contrary, the removal of vegetation for cropping and/or grazing leads to the depletion of soil organic matter (SOM) and increased runoff and soil erosion. The rate of soil loss is, therefore, likely to increase rapidly under bare soil conditions (Morgan, 1995). Furthermore, cultivation breaks soil aggregate particles, reduces the amount of soil organic matter (SOM) and then raises the degree of soil erodibility (Elwell, 1986). The C factor reflects the extent of protection given to soil by the vegetation cover (Hudson, 1995). To account for such differences, the land use/cover of the study area was derived from a supervised land cover classification of the 2006 SPOT-5 satellite image and verified with field information collected from 120 samples taken from training sites. The seven land use/land covers were identified, at 88.67% overall classification accuracy, using ERDAS imagine 9.2 and mapped with their corresponding C values (Table 3, Fig. 6).

Table 1 Soil erodibility (K) factor values of different textures and organic matter content. Source: Randolph (2004: 354) Textural class

Fine sand Very fine sand Loamy sand Loamy very fine sand Sandy loam Very fine sandy loam Silt loam Clay loam Silty clay loam Silty clay

Organic Matter (%) ≤2

2–4

≥4

0.16 0.42 0.12 0.44 0.27 0.47 0.48 0.28 0.37 0.25

0.14 0.36 0.10 0.38 0.24 0.41 0.42 0.25 0.32 0.23

0.10 0.28 0.08 0.30 0.19 0.33 0.33 0.21 0.26 0.19

the forces of erosion by rainfall (Hudson, 1995; Batjes, 1996). In the RUSLE2 model, soil erodibility can be computed from soil texture class, soil organic matter content, soil structure and soil permeability data (Renard et al., 1996). However, due to the high costs of obtaining the data of all the four soil parameters, decision was made to consider only the textural class and soil organic matter (Randolph, 2004) of the top 20 cm soils to determine soil erodibility (K) factor (Tables 1 and 2). Even some researchers have estimated soil erodibility only from soil colour (Hellden, 1987; Bewket and Teferi, 2009; Gebreyesus and Kirubel, 2009).

2.3.5. Support practice (P) factors For cultivated lands, the most important support practice includes contour cultivation, terracing and strip cropping (Mather, 1986; Renard et al., 1996). Within the study catchment, soil bunding, terracing, traditional ditch, check damming and tree planting conservation measures are practiced at a limited scale (Bahir, 2010). The support practice (P) factor refers to the ratio of soil loss with a specific support practice to the corresponding loss with up and down slope tillage (Renard et al., 1996). According to Wischmeier and Smith (1978), the support practices (P) factors can be computed for agricultural lands of different slope classes but non-agricultural lands are aggregated together. The P-values range from 0 to 1 depending on the slope classes of agricultural lands and non arable lands. The P values gradually increase with the rising in the slope of agricultural lands. However, on the non-arable lands with no conservation measures, the P value of 1 is used (Morgan, 1980; Table 4). Accordingly, to determine the support practice (P) factors of the catchment, the seven land use/ covers of the study area were reclassified as agricultural and non-arable lands. All the non-arable land use/covers were merged using GIS technology. The agricultural and non-arable lands were again overlapped with the slope classes to determine the respective P factor values of the catchment (Table 4). Finally, the corresponding P values of agricultural lands with six slope classes and non-arable lands have been determined (Table 4, Fig. 7). Similar approach has been employed to determine the support practice (P) factors of the RUSLE2 erosion model by Bewket and Teferi (2009) in the Chemoga Watershed, Blue Nile Basin and Abate (2011) in Borena Woreda of South Wollo highlands, Ethiopia.

2.3.3. Slope length and steepness (LS) factors The Digital Terrain Model (DTM) of ASTER satellite image of 30 by 30 m pixel size resolution has been used to calculate slope length and steepness (LS) factors of the catchment (Fig. 5). The LS factors were calculated based on the principle of stream transport capacity index of Eq. (3): n Sin β ⎤ A LS = (m + 1) × ⎡ S ⎤ × ⎡ 0.0896 ⎣ 22.13 ⎦ ⎣ ⎦

(3)

where m and n are empirical constants known as slope length and slope angle coefficients. For the purpose of this study, and based on the values reported by Moore and Wilson (1992), values of m = 0.4, and n = 1.3 were used. AS is the specific upslope contributing area per unit length of contour; β is local slope gradient in degrees. The LS factors in Eq. (3) are found to be more appropriate than the USLE based LS since the former considers the whole terrain configuration rather than slope length-steepness alone (Moore and Burch, 1986). The LS determines the volume of water flow and its impact at different landscape positions through the specific catchment area (AS) which is calculated based on multiple flow routing algorithm. This is justified as it better accounts for the role of flow convergence and divergence, on soil erosion processes compared with the single flow algorithm (Moore et al., 1991):

AS =

1 bi

3. Results and discussion 3.1. RUSSLE2 values and estimated annual soil loss rate

N

∑i =1 aiui

(4) The soil loss rate and erosion hazard of the Gerado catchment were determined by integrating the six soil erosion factors of RUSLE2 model in a GIS environment. Eq. (2) was used to estimate R factor based on the mean annual rainfall data (1171 mm) of Dessie town metrological station. This dataset was deemed to be representative since the town is located 10 km from the center of the Gerado catchment and the spatial variability of rainfall is limited within and around the catchment. Therefore, the R-value of the research area was estimated to be 650 mm for all pixels of the Gerado catchment. Generally, the R value equivalent to 800 or lower indicates the low erosivity of the rainfall to erode soil resources (Batjes, 1996).

where ai corresponds to the area of the ith grid cell, b is the contour width of the ith cell, which is approximated by cell resolution; ui is the weight which depends on the runoff generating mechanism and the infiltration rates, and N is the number of grid cells draining into grid cell i. Generally, larger values of slope length exponent are associated with increasing of concentrated flow and rill erosion. Hence, the amount of erosion would normally be expected to increase with slope length and steepness which enhance the velocity and volume of surface runoff (Hudson, 1995; Morgan, 1995). 310

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The clay soils of the flat arable and grassland areas of the Gerado catchment, however, were less vulnerable to erosion than sandy clay loam soils of the steep mountains. This is due to the lower gradient and cohesive nature of clay of the toe slope soils to generate low erosion hazard. The soils occupied by permanent grasslands in the flat part of the catchment were found to have the lowest K-value (0.19) given their relatively high top soil organic matter content (8.95%). The highest erodibility (0.42) of the shrubland soils of steep mountains was partly resulting from the medium organic matter content of the topsoil and high impact of the steep slope on loam textural soils. The estimated Kvalues of the Gerado catchment (Table 2) are considered to be reasonable since they are equivalent to the experimental plot results (Kvalue) of Maybar area located south of Dessie town (Weigel, 1986; Mulugeta, 1988). To determine the LS factor of the Gerado catchment as shown in Fig. 5, ASTER satellite image of 30 by 30 m pixel size resolution has been employed using Eq. (3) (Section 2.3.3). The larger values of slopelength exponent are associated with increasing of concentrated flow and rill erosion. Hence, the amount of erosion would normally be expected to increase with slope length and steepness which enhance the volume and velocity of surface runoff (Hudson, 1995; Morgan, 1995). To determine the RUSSLE2 model C factors, the seven land use/land covers of the research area were classified from the 2006 SPOT-5 satellite image and mapped with their corresponding C-values obtained from previous literature (Table 3, Fig. 6). The different land use/cover patterns have various degree of soil protection against soil erosion (Morgan, 1977). By intercepting rainfall drops and reducing velocity of runoff, land use/land covers with better vegetation protects the soil against erosion (Morgan, 1977) and has low degree of soil erosion hazard. On the contrary, the removal of vegetation for cropping and/or grazing leads to the depletion of soil organic matter (SOM) and increase runoff and soil erosion. Therefore, cultivation that breaks soil aggregate particles and reduces the amount of soil organic matter raises the

Table 2 Soil texture, organic matter content and estimated values of soil erodibility (K) factor of the Gerado catchment, North-eastern Ethiopia. Point data

T1P1 T2P1 T2P2 T2P3 T2P4 T2P5 T3P1 T3P3 T3P4 T4P1 T4P2

UTM reading Easting

Northing

565319 564054 564127 564303 563151 563820 567344 567950 567116 566005 565904

1227159 1226935 1225935 1227545 1229520 1227535 1224851 1226447 1224927 1225386 1225845

Top soil texture class

Organic matter (%)

K Value

C C C SCL C Cl L L CL L SiC

2.31 8.95 2.33 1.37 1.64 3.14 3.84 4.96 2.23 2.41 2.33

0.23 0.19 0.23 0.28 0.25 0.23 0.42 0.33 0.25 0.24 0.23

C-clay, SCL-Sandy clay loam, L-Loam, CL-Clay loam & SiL-Silty clay.

As indicated in Table 1 (Section 2.3.2), the equivalent K-value of silty clay, clay loam and silt loam/sandy loam and the respective amount of organic matter in respective texture classes of the Gerado catchment were used to estimate the soil erodibility values of clay, sandy clay loam and loam soils respectively (Batjes, 1996; Randolph, 2004). However, the clay loam and silty clay textural class of Randolph (2004) in Table 1 and the respective amount of organic matter were as well directly used to estimate their erodibility value (Table 2). Similar to the K-values of tropical soils that ranges from 0.06 to 0.48 (El-Swaify et al., 1992), most soils of Ethiopia have the K values of 0.05–0.6 (FAO/ UNDP, 1984) and have shown relatively wide variability in their vulnerability to soil erosion. As estimated from the topsoil textural classes and soil organic matter content, the K-values of the Gerado catchment ranged from 0.19 to 0.42 and were highly erodible and very vulnerable to soil erosion (Fig. 4).

Fig. 4. Soil erodibility (K) factors of the Gerado catchment, North-eastern Ethiopia. 311

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Fig. 5. Slope length and angle (LS) factors in the Gerado catchment, North-eastern Ethiopia.

in close agreement with other prior findings made for similar soils and environmental conditions in Ethiopia. For example in the Chemoga watershed of north-west Ethiopia (Bewket and Teferi, 2009) and Borena district of South Wello, Ethiopia (Abate, 2011), the rate of soil loss ranges from less than 10 t to over 80 t soil ha−1 yr−1. The study made by Tamene et al. (2017) in the Bosena district of central highlands of Ethiopia found the net soil loss rate ranging from 0.4 to 88 t ha−1 yr−1 Furthermore, in over 50% and 75% of the total catchments of Borena district (Abate, 2011) and Chemoga watershed (Bewket and Teferi, 2009), the rate of soil loss exceeds the tolerable soil loss rate estimated for the country. However, in about 11,000 ha (70%) of the total study area of Bosena district (15,250 ha) in the central highlands of Ethiopia, the average soil loss rate was estimated to be < 1 t ha−1 yr−1 which is different and low compared with many research results estimated in different parts of Ethiopia. Because many soil erosion researches have studied gross soil loss without adjusting for the sediment deliver ratio that leads to relatively higher soil loss estimation in some cases and the small areal coverage of (mostly up to 5000 ha) of study areas (Tamene et al., 2017). The soil loss that exceeds rate of soil formation degrades the existing production systems (Elwell, 1986) and results in decreased soil productivity (Morgan, 1980; Hurni, 1983b; Hellden, 1987). Therefore, the major menace of soil erosion in the agricultural production systems of Ethiopia requires greater attention of soil resource conservation. Hence, the priority of appropriate land management intervention has to be planned according to the magnitude of the estimated soil loss, extent of erosion hazard, and availability of labour and required inputs.

Table 3 Land use/land cover (C) value of the Gerado catchment (Hurni, 1985a), North-eastern Ethiopia. Land use/cover

C-value

Cultivated Shrubland Woodland Bare land (hard) Grassland Urban built up area Plantation forest

0.15 0.01 0.01 0.05 0.01 0.05 0.01

degree of soil erodibility (Elwell, 1986, Table 3). Furthermore, the rate of soil loss is likely to increase rapidly under bare soil conditions (Morgan, 1995). Even though there are different techniques, the Wischmeier and Smith (1978) methods that considers only two types of land usesagricultural and non-arable lands and variation in slope classes of an area (Table 4, Fig. 7) were used to determine the support practice (P) factors of the Gerado catchment. The arable lands were classified into six slope categories and the corresponding P values have been assigned. As shown in Table 4, the P values gradually increase with the rising of agricultural slopes. However, the P value of 1 is used for all the nonarable lands with no conservation measures (Morgan, 1980; Table 4). As a product of the six different data sets, the RUSLE2 model has revealed that the mean annual soil loss of different land use/covers ranged between 5 and 100 t soil ha−1 yr−1 for the toe and degraded steep gradients respectively (Fig. 8). Approximately, 75% of the total catchment area was found to have soil loss rates above 25 t soil ha−1 yr−1; these exceeded both the soil loss tolerance of 18 t soil ha−1 y−1 (Hurni, 1983a, 1983b) and the estimated soil formation rate ranging from 2 to 22 t soil ha−1 y−1 (Hurni, 1983a, 1983b). The estimated soil loss rate of the Gerado catchment was found to be

3.2. Spatial pattern of soil loss For effective land management interventions, the availability of information on the spatial variability of soil loss appears to be more valuable than the gross annual soil loss rate. Tamene et al. (2006) similarly argued the crucial relevance of the sound knowledge in the 312

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Fig. 6. Land use/land cover (C) factors of the Gerado catchment, North-eastern Ethiopia.

zones identified topography (slope length and angle) and land use/ cover factors as the major agents of soil erosion (Hurni, 1985a). In the present study, the severity of erosion hazard is governed by the combined interaction of environmental (e.g. topographic) and anthropogenic (e.g. land use/cover impact) factors. Variation in the rate of soil formation in the highlands of Ethiopia, amount of annual soil loss and priority of land management of the catchment have been used to classify the degree of severity of erosion into five classes (Table 5). Singh and Phadke (2006) have as well used similar erosion severity classes in their soil loss study in the Jamni River Basin, Bundelkhand region of India. In approximately 5000 ha, the rate of soil loss was over 25 t soil ha−1 yr−1 (Table 5). The cultivated and poorly vegetated steep slopes covered 2659 ha (40%) of the total catchment and was affected by severe soil erosion and high rate of soil loss exceeding 100 t soil ha−1 yr−1 (Table 5, Fig. 8). High soil loss from steep slope areas was due to high run off flow from steep gradients. As a result, the mean annual soil loss rate ranges from less than 5 t soil ha−1 yr−1 on the flat lands to over 100 t soil ha−1 yr−1 on steep slopes. However, the study explored that all steep slope areas in the catchment do not have high soil loss rate since some of them are covered by resistant lithology and a relatively dense plant cover. The dense wood/ shrub lands of the steep slopes experience moderate soil erosion and rate of soil loss ranging from 10 to 25 t soil ha−1 yr−1. The relatively inaccessible areas are under less stress of cultivation and over grazing which thereby reducing the risk of soil erosion. Generally, areas that experienced higher soil loss rate (> 25 t soil ha−1 yr−1) than the tolerable limit (18 t soil ha−1 yr−1) are in need of immediate land management intervention and could be considered as soil erosion hotspot areas and designated as conservation priority sites. As the result of RUSLE2 model confirmed very low, low and moderate soil loss rate that accounted for 736 ha (11%), 254 ha (4%) and 665 ha (10%) of the total catchment in that order. These portions of the landscape require low management priority (Table 5). However, low soil erosion prone areas would not be totally neglected from land

Table 4 The P-values of agricultural and non arable lands of the Gerado catchment, North-eastern Ethiopia. Source: Wischmeier and Smith (1978). LULC

Slope (%)

P-Value

Agricultural land

0–5 5–10 10–20 20–30 30–50 50–100 All

0.10 0.12 0.14 0.19 0.25 0.33 1

Non arable lands

spatial variation of soil erosion in planning conservation efforts. The quality of the RUSLE2 model to estimate quantitative soil loss will also be questionable due to the potential errors that might be introduced when deriving the different erosion factors. It is therefore essential to focus on the relative differences of erosion hazard over the various landscape positions, land use/land covers and slope forms (Fig. 8). The study depicted that the Northern, Northeastern and Southwestern parts of the catchment were found to experience high soil loss rates compared with the other parts due to poor vegetation, high slope length and steepness. However, the flat slope over the East-West central position of the Gerado catchment showed low (5–10 t soil ha−1 yr−1) soil erosion risk from other parts of the study area. The group discussion as well explored that the local people have understood the spatial variability of erosion and loss of soil as predicted by the RUSLE2 model. The cultivated, sparse vegetation cover and steep gradient landscape positions experienced high soil loss rate (25–50 t soil ha−1 yr−1) and erosion hazard in many parts of the catchment (Fig. 8). Similarly, the experimental plot-based studies carried from 1981 to 1984 in different research centers of Ethiopia located in various agro-ecological 313

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Fig. 7. Conservation practice (P) factors of the Gerado catchment, North-eastern Ethiopia.

Fig. 8. Potential mean annual soil loss rate and distribution of hotspot erosion hazard areas in the Gerado catchment, North-eastern Ethiopia. 314

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in the basin (Daniel et al., 2015). It may be necessary to plan alternatives for the local farmers, especially grazing lands for their livestock or to breed limited but productive animals. To avoid resource demand conflict, local farmers have to be allowed to use grass resources of the steep mountain, shrub and woodlands through cut and carry pasture use system. The construction of hillside terraces is important to reduce slope length and gradient and the consequent soil erosion by powerful seasonal water runoff. These practices can be implemented on cultivated areas as well as on steep slopes. With all these conservation strategies and policy measures, the participation of community has to be considered as a major component of environmental planning and management in rehabilitating degraded lands, sustainably managing the existing environmental resources and improving the livelihoods of the present and future generation of the catchment.

Table 5 Annual soil loss rate, erosion severity classes and priority areas of land management in the Gerado catchment, North-eastern Ethiopia. Soil loss (t ha−1y−1)

0–5 5–10 10–25 25–50 50–100 > 100

Severity Classes

Very low Low Moderate High Very high Severe

Priority classes

VI V IV III II I

Area Hectare

Percent

736 245 665 1054 1351 2659

11 4 10 16 20 40

management interventions. The low soil erosion risk areas of the catchment should as well be managed after completing the land management intervention of the hotspot soil erosion/land degradation areas. Areas with slope of < 5% have resulted in the lowest soil erosion hazard mainly due to its lower LS factor. Likewise, Zhou et al. (2013) explored low soil erosion risk in most of the central and southern parts of the upper Mekong river basin of Yunnan Province in China where the LS factors are low and vice versa. Generally, the same study revealed that the amount of soil loss in tons ha−1 yr−1 increased with the slope steepness. The average amount of soil erosion was 11.6 t soil ha−1 yr−1 when the slope steepness was lower than 100. However, it reached 59.7 t soil ha−1 yr−1 when the slope steepness was greater than 40° (Zhou et al., 2013). The flat areas with slope gradient of 0–2% (FAO, 2006) in the Gerado catchment are generally depositional zones where eroded alluvial materials from up slopes have accumulated and enriched the soil fertility. Due to water logging, most of the low erosion prone lands are left uncultivated during summer. However, through supplementary traditional irrigation and spring rainfall, these areas are beneficial for root and some horticultural crops in the autumn, and cereal crop production mainly teff, wheat, barley and maize in the spring. Some parts of the steep slopes covered with better vegetation have moderate degree of soil loss and erosion hazard (Fig. 8). These areas do not require immediate intervention measures. However, care should be taken to avoid cultivation and overgrazing as these areas are prone to severe soil degradation by water erosion. Once the hotspot areas that require prior management intervention are identified, the second step will be to subscribe appropriate management interventions. The measures should be those intended to tackle processes that accelerate soil detachment and transportation. By manipulating the land use/cover and other erosion factors of the Gerado catchment using various land management intervention, it would be possible to modify the impacts of LS, R and K and reduce the hazard of soil erosion. The type of management intervention should also consider the position of the landscape, land use/cover characteristic and availability of land as well as the willingness of people to adopt sustainable soil management practices. For instance, erosion prone steep slopes could be used for perennial tree plantation or agroforestry systems coupled with terracing, restriction of cultivation and grazing. Since those areas are not under extensive cultivation, the management measures will not compete with current farmers needs and therefore will be acceptable. The regional and national agricultural policy enforcement has to be strong to restrict cultivation on erosion prone steep slope areas. The shrub and woodland areas have to be protected from animal and human interference observed and reported during the field survey so as to enhance the rehabilitation of plant biodiversity and maintain the ecological balance of the catchment. The study carried by Daniel et al. (2015) in the upper Blue Nile River Basin of Ethiopia has confirmed the reduction of soil erosion following improvement in the soil organic matter content. To the contrary, the scenario analysis for 20% and 50% SOM reduction had proved the relative acceleration of soil loss

4. Conclusions and policy implication The study has identified severe soil erosion prevailing in most parts of the Gerado catchment of North-eastern Ethiopia. The amount of soil loss and degree of erosion hazard was high to severe on sloping/ strongly sloping cultivated lands and poorly vegetated steep gradients. Thus, the present study raises the possibility that soil loss rates rapidly accelerate to unacceptably high levels with the clearing of steep land covers into cultivated plots. The finding, suggests that spatial variability in the severity of soil loss within the study catchment indicates the hotspots of soil erosion where there is a need to prioritize land management interventions. In approximately 75% of the study area, soil loss was estimated to be in excess of 25 t soil ha−1 yr−1 and well above the suggested soil loss tolerance limit of 18 t soil ha−1 yr−1 for the country as well as the soil formation rate (2–22 t soil ha−1 yr−1) estimated for the highlands of Ethiopia. These areas are in need of immediate action for soil conservation practices. The land management strategies to be implemented should match the characteristics of the topography, land use/cover and interest of the local community. Agroforestry, terracing, cut and carry system of mountain pasture use, for instance, can be integrated in order to sustainably manage erosion prone areas of steep mountains ecosystems. The regional and national agricultural policy enforcement has to be practically implemented to restrict unsustainable cultivation and grazing practices on the shallow soils. Therefore, uncontrolled grazing practiced over steep slope fragile environment of the catchment should be banned through awareness creation and community participation. Depending on the problem, the rules and regulation of environmental management have to be put on ground as pivotal component of degraded land resources reclamation. In order to effectively implement soil conservation, the regional and/or federal government should design strategies to provide incentives and alternative ways of livelihoods for land managers who are prohibited to utilize marginal and sloping lands for cultivation and grazing purposes. The soil erosion hazard of sloping and/or strongly sloping cultivated lands should be minimized through the implementation of soil bunding, terracing, strip cultivation and check damming. The water logging constraint of the flat slopes (< 2%) in the catchment can be tackled through the cultivation of appropriate crops like rice that can suitability match with the characteristics of the landscape and hydrology. As additional alternative, the waterlogged areas should get enough time to infiltrate and/or evaporate the accumulated water for the cultivation of fast maturing pulses like chickpea and lentils. The combination of all these land management strategies in the long run can restore degraded lands, minimize soil erosion hazard, reclaim the ecology of the catchment and ultimately improve the livelihood of the local communities. Generally, appropriate soil and water conservation practices should be taken first in erosion hot spot areas and then continue to the others depending on the magnitude of the constraints and available resources.

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The authors are extremely grateful to the financial contribution of the Office of Vice President for Graduate Studies and Research (OVPGSR), Addis Ababa University (AAU), Ethiopia. The authors extend our gratitude to the Ethiopian Mapping Agency in extracting the Location Map of the Gerado catchment from their topographical sheet. We appreciate the invaluable academic comment of Professor Hans Hurni and assistance of Yelekal Yetayew and Abebe Mohammed in supporting to prepare the required maps of the study area. We thank Dr. Diogenes Antille in thoroughly improving both the content and the language of the article. The local communities and agricultural development agents are highly appreciated for their significant role played in providing valuable information during interview and group discussions. 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