Evaluation of Soil Erosion in Sudan Muhanad Gaafar Osman Space Technology Centre Computer Man College Khartoum, Sudan. Abstract: Erosion is the removal and subsequent loss of soil by the action of water, ice, wind and gravity. It is a process that occurs naturally at a slow rate. Soil erosion is a hazard generally associated with agriculture in tropical and semi-arid areas, but it is becoming increasingly recognized as an important conservation problem related to settlement, communications, and recreation activities as well. The results of soil erosion are; reduced cultivating possibilities on eroded hillsides and sedimentation of water reservoirs which reduces irrigation possibilities and therefore causes decreased agricultural production. An understanding of the mechanics and process of erosion is essential to design a sustainable system of erosion control, but additional information such as factors influencing the soil erosion system (i.e. Vegetation, climate, soil, human activities, and topography), is necessary for the successful planning of soil conservation methods. The use of Geographic Information System for the detection, modelling, and supervision of soil erosion, can offer many advantages for strategies and planning such as: fast, possibilities to investigate huge areas, cost effective estimates, greater possibilities of continuous monitoring of the areas, and possibilities to refine the soil erosion depending on the required output scale. The use of GIS with assessment models of soil degradation enables the simulation of different case scenarios for future land management and decision making. A GIS has ability to store and display the data in spatial manner helps in the organization of data, analysis, and can be used as an interface for the land degradation in large areas. Therefore, GIS is commonly used in land degradation. There are three types of models of soil loss. These models are: conceptual models, empirical or statistical models, and physically based models. The models can be used as conservation planning, productive tools for assessing soil loss, soil erosion inventories and project planning, and tools for understanding erosion processes and their impacts. The Universal Soil Loss Equation (USLE) is a model (empirical) to predict the long term average annual rate of erosion on a field slope based on soil types, topography, rainfall pattern, crop systems, and management practise. There are five major factors used to calculate the soil loss for given site. Each of them is the numerical estimate of the specific condition that affects the severity of soil erosion at a particular area. Generally, the less soil erosion in Sudan is concentrated in the northern section of the country where there is low rainfall or no rainfall. Also, the high soil loss is concentrated in the south western part of the country where there is high rainfall. Furthermore, the work has used five variables of Support Practice (P factor). These variables were Up and Down Slope, Cross Slope, Contour Farming, Strip Cropping Cross Slope, and Strip Cropping Contour. 1. Introduction: The main challenge in future is that 83 per cent of global population (8.5 billion by the year 2025) will be living in developing countries. So, agriculture has to meet this challenge mainly by increasing production on land already in use and by avoiding further encroachment on land that is only marginally suitable cultivation. (kamil 2002) Since 1950 nearly 500 million hectares of land were subjected to soil erosion, 65 per cent of which is agricultural land. During 1980 – 1995 Africa lost 49 million hectares of forest. (kamil 2002) Sudanese economy depends largely on agriculture such as most developing countries. 70 per cent of the economically active population of the country works in agriculture, 90 per cent of them lives in rural areas. Therefore, we could safely say rural development necessitates agricultural development. So, the priority must be on maintaining and improving the capacity of the higher potential agricultural lands to support an expanding population. The main environmental problems in Sudan are: Desertification and land degradation, Deforestation, Wildlife depletion, Use of agrochemicals, Diseases related to water provision, Mismanagement of natural resources, Uncontrolled urbanization, Industrial pollution, Marine and coastal pollution. (UN workshop 2004) The first principle of the Rio declaration: “Human beings are at the centre of concerns for sustainable development. They are entitled to a healthy and productive life in harmony with nature.” The big challenge in the Sudan now is to ensure that the future development is sustainable. That mean economic development and social needs/goals are met without causing damage to human health or environment. Since 1977, many countries work towards sustainable development after the international conference about the desertification. Sudan signed many conventions about environmental protection and natural resource conservation such as Bio-diversity Convention, Convention for Climate change, and International Convention for Combating Desertification. In the six years plan (1977 - 1983), the natural resources and environmental protection were given a high priority, therefore, Sudan established the Administration for Combating Desertification and Drought since 1979. To achieve sustainable development objectives, the integration of economic, environment, institutional and social components are required. These objectives cannot be carried out without greater integration at all policy making and operational level i.e. local communities and NGOs. Many efforts have been done by the government of the Sudan to integrate environmental, economic and social objectives into decision making by elaborating new policies and strategies for sustainable development and adopting existing policies and plans. An Environmental Impact Assessment should be conducted before development projects receive final approval; it is the most important of the requirements. In Sudan, like such developing countries, population growth has led to an increasing demand of basic needs such as food, employment, and cash income. Furthermore, in Sudan, a large proportion of the population live in rural areas (90 per cent out of the total population) , making them subject to rapid economic development such as logging and agricultural development. There are positive impacts of any rural development, but also there are negative impacts such as deforestation, soil erosion, and flooding. 2. Soil degradation: European Environment Information and Observation Network (EIONET) defined the soil degradation: “soil may deteriorate either by physical movement of soil particles from a given site or by depletion of the water-soluble elements in the soil which contribute to the nourishment of crop, plants, grasses, trees, and other economically usable vegetation. The physical movement generally is referred to as erosion. Wind, water, glacial ice, animals and tools in use may be agents of erosion.” Many practices of management can be threatening to soil sustainability. These practices are: over cultivation, decreased or increased water abstraction, under fertilization or over fertilization, careless use of biocides, failure to maintain soil organic matter levels and clearing natural vegetation . During the use of soils for such purposes, soils can suffer various types of degradation that can primarily reduce their ability to produce food resources. The figure (figure 1 -1) below illustrates the main types of soil degradation:  (Figure 1- 1) Source of Data: Oldeman, L.R., R.T.A. Hakkeling, and W.G. Sombroek. 1990. World Map of the Status of Human-Induced Soil Degradation. An Explanatory Note, rev. 2nd edition. International Soil Reference and Information Centre, Wageningen, the Netherlands 2.1. Erosion: Erosion is the removal and subsequent loss of soil by the action of water, ice, wind and gravity. It is a process that occurs naturally at the slow rate. It has been estimated that the average natural geologic rate of soil erosion is 0.2 tons per acre per year. Man’s activities, utilizations (the average soil loss rate in agricultural lands: pastures 1.5 tons per acre per year, cultivated fields 20 tons per acre per year, and managed forests erodes at an average rate is 0.5 tons per acre per year) and disturbance of the lands (mining and construction activities experience soil erosion at even higher rates, unprotected construction sites can cause annual soil loss rate of 150 to 200 tons per acre per year) has increased the rate of soil loss significantly . Soil erosion is a hazard generally associated with agriculture in tropical and semi-arid areas, but it is becoming increasingly recognized as an important conservation problem related to settlement, communications, and recreation activities as well. (Morgan 1979) The results of soil erosion are reduce the ability of cultivating possibilities on eroded hillsides and sedimentation of water reservoirs which reduces irrigation possibilities and therefore causes decreased agricultural production. (Petter Pilesjo 1992) Soil erosion by water is big problem in many parts of the world particularly in tropical and semi-arid (e.g. Sudan). Many factors influenced on the earth can make large areas sensitive to erosion. These factors such as: a greater population, overgrazing, agricultural activities on the steep slopes with marginal soils, in combination with heavy and sporadic rainfall. (Petter Pilesjo 1992) 3. GIS and Soil Degradation models: The use of GIS with assessment models of soil degradation enables the simulation of different case scenarios for future land management and decision making. A GIS has ability to store and display the data in spatial manner and helps in the organization of data, analysis, and can be used as an interface for the land degradation in large areas. Therefore, GIS is commonly using in land degradation . 3.1. The Universal Soil Loss Equation (USLE): The Universal Soil Loss Equation (USLE) is a model to predict the long term average annual rate of erosion on a field slope based on soil types, topography, rainfall pattern, crop systems, and management practise. The model only predicts the amount of soil loss that is produced from sheet or hill erosion on single slope and does not account for additional soil losses that might occur from other types of erosion such as: gully, wind or tillage erosion. This model was created for use in selected cropping and management beside non-agricultural conditions such as constriction sites. The model can be used to compare soil losses from a particular field with specific crop and management system to “tolerably soil loss” rate. (Wischmeier 1978) There are five major factors that are used to calculate the soil loss for given site. Each of them is the numerical estimate of the specific condition that affects the severity of soil erosion at a particular area. The Equation: A: average annual soil loss in tone/acre/year. R: Rainfall and Runoff factor. K: soil erodibility factor. LS: slope length – gradient factor. C: crop/vegetation and management factor. P: support practice factor. 4. The research study area: Sudan lies between latitudes 4 N and 22 N, and between longitudes 22 E and 36 E. Sudan is the largest country in Africa, with a total area of about 2.5 millions km2. The cultivable area is estimated to be 105 million hectare, or 42 per cent out of the total. But the cultivated land is 7.5 million hectares, which is 7 per cent out of the cultivable area. Only about 3 per cent consists of the permanent crops, the remaining area consisting of annual crops.  (Figure 1 – 2) Location of the Sudan 4.1. Soils in Sudan: The most important soil types for farming in Sudan are dominated by expanding clay. Such soils cover most of central Sudan and the eastern plains. They are calcareous and moderately well drained, but generally contain little nitrogen. The eastern plains lying north, southeast and east of the Gadaref town, and the central plains from Gezira to Rosaries on the Blue Nile, are the best example of soils: Loam, Deep and generally well drained. The soils of the northern agricultural zones along the flood plain of the Nile are Loam, stratified and calcareous. 5. Data description and preparation: All data used in this paper are collected from many sources. These sources are Africover organization, Data Exchange Platform for the Horn of Africa (DEPHA), and Sudan Ministries and governmental authorities. Data used in this paper encompass both raster and vector datasets. Raster datasets have values stored in a uniform rectangular array and are typically referred to as grid or Digital Elevation Model (DEM). Victor datasets include points, lines, and polygons and are typically referred to coverage. And it has tables associated with them that describe attributes they represent. (ESRI) 6. Methodology: The methodology of the paper is applying several steps to assessment of soil erosion in Sudan. In this model, the main classes are: rain and surface runoff factor (R), soil erodibility factor (K), slope length factor (L), slope steepness factor (S), vegetation cover factor (C), and practical management factor. The USLE model has been chosen for this paper for many reasons: it gives fast overview, easy to use, easy to update, easy to recalculate (if any factor has changed, it does not affect the other factors), and the time and cost saving implementation in a commercial standard GIS system. Furthermore, the USLE model does not require special hardware and software or special skills to use. And substantially, if accurate maps are available, the model can produce the amount of soil erosion for long term studies within the scope of sustainable development. (Sivertun and Prange 2003) As the USLE is applied only to sheet and rill erosion (Wischmeier 1978) and these two types of erosion occur in Sudan (particularly western and eastern section where the heavy rains is predominate), thus, the use of USLE model to evaluate the soil erosion in Sudan can be adapted for Sudanese conditions. The values of the USLE factors are derived from Wischmeier 1978, Morgan 1979, USDA Agriculture handbook No. 703, 404 pp. Renard, 1997, Petter Pilesjo 1992, and Sivertun in many publications. 7. Implementation: Substantially, the main purpose of procedure of the soil loss prediction is to supply specific and reliable guides for selecting adequate erosion control practices for farm field and construction areas. (Wischmeier 1978) 7.1. Estimating factors: Some of the values of USLE factors are derived in this paper from previous studies, and other from direct calculation by GIS software (i.e. LS factor). In this way, this section concerns the derivation of the factors in the equation as following (about the source of values, see the appendix): 7.1.1. Estimating R: This factor is rainfall erosivity index which is equal to the mean annual erosivity value divided by 100. The R factor values have been shown in Table (1 – 1), (notably, R in this paper does not require computation of EI values as R is already computed).  7.1.2. Estimating K: This factor is the soil erodibility index. It is defined as mean annual soil loss per unit of erosivity for standard condition of bare soil, no conservation practice, 5 degree slope of 22 meters length. The values of K factor for the study area are as following:  7.1.3. Estimating LS: Obviously, this factor is combined slope length (L) with slope steepness (S) in a single index. The proper value of (LS) can be obtained from the equation (1 - 2). The results are calculated using ArcView software. 7.1.4. Estimating C: This factor is the crop/vegetation and management factor. It shows the ratio of soil loss under a given crop to that from bare soil. The values of C factor of the study area are shown in tables below:  7.1.5. Estimating P: This factor is prevention practice factor. Values are obtained from table (1 - 4). The values included: Up and Down slope, cross slope, strip-cropping contour, and strip cropping cross slope.  7.2. Calculating Soil Erosion: For the calculation of soil erosion, the commercial standard GIS software ArcView 3.2 from ESRI is used. It is a broadly used GIS package with all basic GIS functionality. This functionality with extensions (e.g. Spatial Analyst) allows the enhancement and improvement of the applications. Soil erosion needs raster overlay, therefore, The Spatial Analyst extension is needed in USLE model implementation. Much input data used in this paper is available in compatible data formats, and the ArcView is easy to handle and almost it is cheaper, these were main reasons to use the ArcView software for calculation the soil loss. Furthermore, the software and implementation of the USLE model does not required complex computer or special hardware. 7.2.1. Used GIS functions: The functions for the vector to grid conversion (i.e. Convert to Grid) and the imports of ASCII raster data are used in this paper at first step. This function was used to convert the land cover map, Soil type map, and Precipitation map. The most important here is the Map Calculator function, because it allows the calculation of the LS factor. Other useful functions are the possibility to edit attribute tables, join them with external database tables and query for datasets. The slope, flow direction and flow accumulation functions allow the creation of slope length maps from Digital Elevation Models (DEM), while resample function allows the change of pixels size in the raster. The slope length model developed by Mitasova (1999) is used in this paper. After deriving the slope from the DEM, the following expressions were used to compute the slope length factor map in ArcView: ([Suhorn].FlowDirection(FALSE)).FlowAccumulation(NIL) (([flow] * 10/22.1).Pow(0.4))*(((([Slope]*0.01745).Sin)/0.09).Pow(1.4))*1.4 7.2.2. Input data: Most of the input datasets are obtained from FAO Africover organization. All of these datasets are stored in the same coordinate system (Geographic WGS 84). So, no coordinate transformation is necessary to bring the datasets in one system. 8.1. Results: Input data and results of soil Loss using USLE model provides a broad range of information relevant to assessment of erosion risk and preservation planning. These data and information can be treated and communicated in various graphical and numerical forms as maps, statistical characteristics, and summary of reports. The following section illustrates the results of the paper. Generally, less soil erosion in Sudan is concentrated in the northern section of the country where there is low rainfall or no rainfall. While high soil loss is concentrated in the south western part of the country where there is high rainfall. Furthermore, the work used five variables of Support Practice (P factor). These variables were Up and Down Slope, Cross Slope, Contour Farming, Strip Cropping Cross Slope, and Strip Cropping Contour. And their values were 1.0, 0.75, 0.50, 0.37, and 0.25 respectively. The result of this step is that, the Strip Cropping Contour produced less soil loss amount. 8.2. The actual soil loss: The result of this work is the product map by a simple multiplication of five factor maps of the USLE model. This resulting map has a cell size of 86.5 meters. The actual soil loss in study area is the result of multiplying the cell size by number of cells. The table below illustrates that according to the class of soil loss: 
8.3. Erosion in study area. As it has been described before, the erosion in Sudan is concentrated in the south western part of the study area. The map below is shows the erosion in the study area by using USLE model:  8.4. How soil erosion affect study area. The study estimated that in table (4-1), 576133.25 sqm of the study area has eroded. The economic and environmental costs of erosion are severe, taking the economic and environmental toll on every individual in Sudan, in two cases directly and indirectly. 8.5. The environmental and economic impacts. In fact, erosion reduces the productivity of the crops, range, and forest and as a result of that endangers food security, causes displacement of local people and degrades the quality of human life in the affected areas. (Kamil 2002) mentioned in his report, that there is relationship between food security/insecurity and environmental degradation, this result from inappropriate cultivation practices, overgrazing, excessive fuel wood, cutting, burning, and deforestation. Reduction of primary productive systems and therefore accentuates economic crisis. In the other hand, this process resulting high poverty. 9. Conclusion: This project evaluates the soil erosion in Sudan. It demonstrates the practical and proactive approach to environmental conservation. If this approach becomes broadly adopted in the study area, it would significantly reduce the impact of soil erosion by water. Although the project has been done by available data, it requires a further development in the data scales consideration. The weaknesses of the project relate the need for more accurate digital information (i.e. DEM) in the proper format for more widespread provincial coverage. That means the approach of this project works well for areas where the required information is readily available in the proper format. Noting, the approach becomes inconvenient in cases where the data is lacking and has to be derived while executing the project. Accordingly, the following areas of further development would include:
Acknowledgment: This work has been given financial support by the Computer Man College, Khartoum Sudan. I want to extend my warmest thanks for their generosity. Certainly, there are many persons who sacrificed much to make, even in the most difficult circumstances. I so much respect that effort from my uncle Nazar Ali Saeed who assisted me during the data collection phase. I am sincerely grateful to my supervisor Dr. Ake Sivertun, for supervising this paper. Much thanks for his encouragement and efforts during the preparation of this study. References:
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