Open access peer-reviewed chapter

Sustainable Land Use Planning Model in Rural Basins

Written By

Ertuğrul Karaş

Submitted: October 23rd, 2015 Reviewed: April 14th, 2016 Published: August 10th, 2016

DOI: 10.5772/63714

Chapter metrics overview

1,236 Chapter Downloads

View Full Metrics

Abstract

Soil erosion is a common problem that complicates watershed management in Turkey and around the world. The main objective of soil conservation work carried out in basins is to ensure sustainable watershed management. The first operation is to define the current situation in the basin. The initial and fundamental objective of erosion estimation based on existing data is generally deciding how to overcome the problem. However, the treatments carried out in most soil conservation studies are similar to each other. Any common, known, or defined methodology about erosion problems in watersheds has not been improved—until now. Considering this problem, the Sustainable Land Use Planning (SLUP) model was developed to determine soil conservation precautions, to set priorities for decision makers and to produce a common solution for rural watershed in Turkey. While the estimated average soil loss was determined to be 7.66 t ha−1 per year, some land use changes were proposed and land use management priorities were set in the direction of the model results to gain sustainable management in the Çelikli basin. At the end of the study, it was showed that the soil loss can be reduced about the rate of 91.2% applying the SLUP model.

Keywords

  • land use planning
  • soil erosion
  • soil conservation
  • SLUP model

1. Introduction

Soil is an indispensable resource for the continued existence of living organisms on Earth. Today, food security and the environmental sustainability of limited natural resource management have become more important. Population growth and complex situations in the use of natural resources have required skilled land use. As a result, soil erosion, which was an accepted part of the soil degradation process, is observed in various ways and degrees under the influence of factors such as climate, topography, and land use. The biggest change in natural resources in the last 100 years has occurred as a result of changes in land use type and technological improvements. The major factors in accelerated soil degradation processes are improper land use, deforestation, soil erosion, overgrazing, vehicle off-roading, and inappropriate irrigation. The daily displacement of the upper layers of land due to heavy rains, accumulation of the carried soil, plant nutrients in storage structures, sedimentation, and processes such as eutrophication threatens the sustainability of natural resources. No matter the factors, soil degradation processes need to be correctly defined and must be established for the natural balance between living organisms. Today, the estimation of soil loss is one effort to reduce the effects that lead to soil degradation.

USLE, developed by Wishmeier and Smith [1], is globally the foremost and preferred popular model in predicting soil loss. The main objective of RUSLE and WEPP models, which were developed based on USLE, is to estimate soil loss from a given land. Research carried out at the parcel level, the contribution of many scientists [25], is maintained with the support of technology. Today, it is possible to achieve results in a very short time, to forecast the impact of alternative applications, and to analyze and evaluate the results with the help of computers that process data on the Geographic Information System (GIS) environment. With detailed analysis of each factor considered, the effectiveness of these model results is faster and easier. Environmental factors such as land use and soil characteristics can be easily obtained by means of this technique [6]. Thus, the identification of sensitive areas of erosion can offer unique opportunities for exposing priorities and providing measures to decision makers.

Numerous studies have been carried out over the last two decades using USLE-GIS integration in various part of the world [731]. So far, the efforts carried out on the estimation of soil loss have not yet adopted a common point in terms of analysis and evaluation. To keep soil loss under a defined threshold is the point of the alliance. In particular, these efforts have intensified in the last half century and have provided much more to the adoption of the concept of soil loss tolerance [1, 3, 3247]. This is defined as the maximum permission level of soil loss from an area that will not cause any yield reducing. Soil loss tolerance values have been compared with rates of soil formation in many studies [4865].

The first study interested in the SLUP model was applied in the Güvenc basin [66]. In this study, the areal distribution of soil erosion classes and soil conservation precautions was also obtained. According to the results, 64.68% of the basin had a non-existent or too low erosion degree, 9.18% had a low-moderate erosion degree, 7.53% had a moderate-high erosion degree, 4.33% had a high erosion degree, and 14.33% had a very high level erosion degree. It was shown that soil loss can be lowered from 16.30 to 1.44 t ha−1, reduced by approximately 91%, with the proposed approach across the entire basin.

Almost all of the soil conservation studies, carried out in the basins throughout the world up to now, have been focused on the estimation of soil loss. Despite some efforts, any approach that may be a solution offer to soil conservation measures to be taken in the basins has not been developed yet. This is considered as a major absence and requirement. The purpose of this study is to introduce the model called “Sustainable Land Use Management”, which was developed to ensure the most appropriate land use management plan by reducing soil loss in the basins considering the problems and requirements mentioned above.

Advertisement

2. Materials and methods

2.1. The study area

This study was conducted in a catchment known as Celikli, located in the Tokat region of north-east side of central Anatolia, Turkey (Figure 1). The basin is 1041.2 ha in area and has an average elevation of 1300 m above sea level. It is situated in the area transitioning from central Anatolia to the middle Black Sea region (latitude 40° 06 31 N, longitude 36° 21 40 E).

Figure 1.

The location, topographic map and Google Earth view of the Çelikli basin.

The study area has semi-arid climatic conditions. The average annual temperature is 8.1°C, and the mean annual precipitation is 535.9 mm, 84.7% of which falls between October and May [67].

2.1.1. Land use

Current land use for the basin was prepared by a detailed land investigation. The determined land use groups were given in Table 1 and Figure 1. While dry farming areas occupy nearly 68% of the area, pasture and shrub land use have approximately 25 and 5.45%, respectively. Pasture land use, which is not appropriate for tillage due to insufficient soil depth, was left for native usage. In the last 60 years, most of the pastures and some forested areas were converted to agricultural land by ploughing. Consequently, native land use of the basin was considerably changed from pasture and forest to agriculture land use [67].

Land useArea (ha)Area (%)
Dry farming706.967.88
Pasture258.924.86
Shrub56.75.45
Bare rock8.50.82
Water surface10.20.98
Total1041.2100.00

Table 1.

Land use distribution of the basin.

Investigation of pasture composition was done to determine the coverage percentage, dry grass yield, and to describe the species of pasture plants on the selected 27 sample points in the basin. The analysis showed that the coverage rate was about 50% of this pasture area. Although some points are majority Graminea family, most have a mixed composition with Fabaceae, Labiatae, Convolvulaceae, Labiatae, and other species. Vegetation quality of pastures in the basin is generally determined as low [67].

Land Use Capability Class (LUCC) of the basin soils given in Table 2 was also determined with the detailed investigations. LUCC is a classification process made considering soil, topography, climate, environment, crop cover and hydrological conditions. Limiting factors, such as soil properties and slope, are taken into account in favourableness of an area for determining of the most suitable management form such as agricultural, forestland, and pasture. In general, lands are classified as eight groups varying from1 to 8. There are six different LUCC (II, III, IV, VI, VII, and VIII) in the basin except for I and V [67].

Land use capability classesArea, (ha)Area (%)
II11.11.07
III84.38.10
IV52.15.00
VI541.652.02
VII333.432.02
VIII8.50.82
Water surface10.20.98
Total1041.2100.00

Table 2.

Land use capability classes and areal distribution in the basin.

Around 84% of the basin soil is Class VI or VII. Classes II, III, and IV cover 14.07% of the basin and are available for agricultural aims. Although they have some restrictive factors, those areas can be used for agricultural production by carefully choosing plants, applying some special conservation practices, and careful management techniques. About 84.86% of the basin soils (VI, VII, and VIII) are unsuitable for agricultural production, which are favourable mainly for grassland, forestland, or wildlife habitats in terms of LUCC.

When we look at the maps of land use and LUCC together in Figure 2, we can see that most of the basin soil is used for agricultural production (68%), although only 14% of the land is appropriate. The areas suitable for agricultural production (Classes II, III, and IV) are on the north-east side of the basin. Class VI is extensively used for agricultural aims. This current situation reveals that the area is unsuitable and unsustainable for basin management.

Figure 2.

The maps of land use and land use capability class of the basin.

2.1.2. Soil sampling and analysis

Georeferenced soil samples were taken from top soil (0–0.3 m) and subsoil (0.3–0.6 m) in July 2002. In soil samples, organic matter [68], soil pH [69], lime (CaCO3) [68], electrical conductivity (EC) [70], Cation exchange capacity [71], textural distribution [72], saturated hydraulic conductivity [73], and volumetric water content [74] were analyzed. Erodibility was calculated by a soil erodibility nomograph [75]. The sampling points are shown in Figure 3.

Figure 3.

Soil sampling points in the basin.

2.1.3. Soil map

Two satellite images (IRS-1C, with a 5.8 × 5.8 m pixel size and LANDSAT-TM with 30 m × 30 m pixel size) were used to prepare a soil map of the basin, in addition to a cadastral map with a scale of 1/5000. The combined image of the basin obtained from these two images is given in Figure 4.

Figure 4.

Satellite image of the basin.

The detailed soil map was prepared with a scale of 1/5000 and 9 soil series were identified. A description of soil profiles, environmental properties, and physical and chemical characteristics of the soil samples was taken from these profiles. The defined soil series in the basin is given in Table 3.

The soil series in the basin given in Table 3 and Figure 5 were classified according to the basis of soil taxonomy. Three ordos (Entisol, Mollisol, and Alfisol), three subordos (Orthent, Ustoll, and Ustalf), four big groups (Ustorthent, Haplustoll, Haplustalf, and Argiustoll), and three subgroups (Typicustorthent, Lithicustorthent, and Verticargiustoll) were determined using climatological and geological data [67]. Kevenli, Yelten, and Göçyolu are the soil series, which have the most widest in terms of area occupying 29.3, 28.33, and 13.59% of the total area, respectively. Although their average depths are changing from 24 to 67 cm, many soil properties are very close to each other such as exchangeable cations and texture distribution. Other six soil series (Yedikır, Yayla, Alıçlı, Uluyol, Kurtlutepeönü, and Akardere) in the basin include 30.76% of the total area varying from 1.60 to 10.52%

Soil seriesArea (ha)Area (%)
Kevenli305.229.30
Yelten295.028.33
Göçyolu141.513.59
Yedikır109.510.52
Yayla69.86.70
Alıçlı54.75.26
Uluyol20.81.99
Kurtlutepeönü18.01.72
Akardere16.61.60
Water surface10.21.98
Total1041.2100.00

Table 3.

Soil series in the basin.

Figure 5.

Soil series map of the basin.

Some physical and chemical properties of the soil series in the basin are given in the Table 4. Although the soil depths are between 24 and 90 cm, the basin soils have a generally shallow depth, low organic material, low lime content, and slightly alkaline character. Most of the soils have a silty clay loam (SCL) texture, and plant available water content for the mean depth (46 cm) is about 75 mm due to weak soil profile development.

Soil
series
Depth
cm
Salt
%
pHCaCO3
%
Org.
Matter
%
%TextureCation
Exc.
Cap.
me/100g
Exchangable cations me/100g
SandClaySilt Ca + MgKNaTotal
Kevenli67+0.024 7.79 3.21.248.826.324.9SCL33.5231.730.660.0332.42
Yelten31+0.0197.371.11.646.928.324.8SCL30.5728.280.490.0228.79
Göçyolu24+0.0186.780.02.260.718.221.1SL26.9526.140.510.0126.66
Yedikır66+0.0337.141.11.333.641.225.2C44.6840.030.770.0140.81
Yayla51+0.0197.917.71.246.028.725.3SCL21.7520.890.630.0221.54
Alıçlı51+0.0347.698.92.053.126.220.7SCL40.8138.710.700.0139.42
Uluyol65+0.0177.89120.959.521.918.6SCL21.5820.280.470.0120.76
Kurtlute
peönü
67+0.0126.910.00.751.621.826.6SCL22.3921.800.400.0222.22
Akardere90+0.0407.643.02.123.557.019.5C57.5552.250.720.4453.41
Mean48.60.0247.464.11.547.030.023.0SCL

Table 4.

Some physical and chemical properties of soil series in the basin.

2.2. USLE model

Soil loss is estimated using the following equation in USLE [1]:

A=R×K×L×S×C×PE1

where Ais average annual soil erosion per unit area (t ha−1 year−1), Ris rainfall erosivity factor (MJ mm ha−1 h−1 year−1), Kis soil erodibility factor (t ha−1 h ha−1 MJ−1 mm−1), Lis slope length factor, Sis slope steepness factor, Cis cover and management factor, and Pis management practice factor. L, S, C, and Pare all dimensionless.

2.2.1. Rainfall erosivity factor (R)

The rainfall erosivity factor (R) shows the effect of rainfall impact on the amount and rate of runoff calculating the rainfall energy (EI) obtained from a maximum 30-min intensity (I30) having at least 12.7 mm of precipitation during a period of 15 min. The interval duration between two storms must be at least 6 h [1]. After the calculation of the rainfall intensities, rainfall kinetic energy is calculated using the following equations [76]:

Ei=0.019+0.0873log10iE2

i ≤ 76 mm h−1

Ei=0.283i>76mmh1E3

where Eiis the kinetic energy of 1 unit of rainfall (MJ ha−1 mm−1) and iis the rainfall intensity (mm h−1).

The product of the total kinetic energy of rainfall (E) and its peak 30-min intensity (I30):

R=E×(i30100)E4

where Ris the rainfall erosivity factor (MJ ha−1 cm h−1); I30 is the peak 30-min intensity of rainfall (cm h−1); and Eis the total kinetic energy of rainfall (J m−2).

Rfactor value for the whole basin was used as 54.68 MJ ha−1 cm h−1, taken from research carried out in the Tokat province from 1996 to 2005 [77].

2.2.2. Soil erodibility factor (K)

The soil erodibility indicates the erosion susceptibility of the soils, which is a function of soil texture, permeability and organic matter. It is explained using a soil erodibility monograph for farmland and construction sites mathematically [75].

The soil erodibility factor (K) was determined for each soil sample based on analysis of soils in the laboratory. The equation referenced is as follows:

K=((2.17×104)×(M1.14)×(12a)+3.25×(b2)+2.5×(c3))×dE5

where M = (percentage of silt and fine sand) × (100-percentage of clay); ais the organic matter content (%), bis the soil structure (1–4), cis the permeability grade (1–6), and dis the coefficient of converting (d = 1.292).

Soil samples collected from the predetermined points were marked using GPS. The Kfactor values of the soils were determined for each soil series from 142 samples; the Kfactors varied between 0.18 and 0.30. The basin Kfactor map is given in Figure 6.

Figure 6.

Kfactor map of the basin.

2.2.3. Slope length and steepness factor (LS)

Slope length and steepness (LS) factor reflects the impact of topography on soil erosion. With a unit plot of length 22.13 m, the USLE used the equation:

L=(λ22.13)mE6

where Lis slope length factor, λis slope length, and mis a coefficient that changes according to slope [if the slope (S) >4%, m = 0.5, for = 4%, m = 0.4, and if S ≤ 0.3 and m = 0.3].

The equation used to calculate slope steepness factor (S) in the USLE is given below:

S=0.43+(0.30×s)+(0.043×s2)6.574E7

where Sis slope steepness factor and sis slope (%).

Basin slope distribution and slope map are given in Table 5 and Figure 7, respectively.

Slope/land useAgriculturalPastureShrubBare rock
%Slope definitionha%ha%ha%ha%
0–2Flat32.94.665.32.030.00.000.00.00
2–6Slight320.945.4055.221.33.15.493.641.9
6–12Middle266.037.6097.337.520.936.83.237.5
12–20Steep72.610.2071.527.626.046.41.012.0
20–30Very steep12.01.7025.910.06.110.70.67.37
30–45Rough2.10.303.41.320.30.470.11.05
>45Very rough0.30.040.40.140.00.020.00.00
Total706.9100258.910056.71008.5100

Table 5.

Slope distribution of the land use groups.

Figure 7.

Slope map of the basin.

An equation was developed to compute length-slope factor:

LS=(As22.13)m×(sinβ0.0896)nE8

The basin LS factor map was prepared using ArcView Spatial Analyst extension [78] and the digital elevation model (DEM), which was prepared with 10 m interval digitized contours from a 1/25,000 scale topographic map. The grid cell size for this study was chosen as 10 m for purposes of calculation. Therefore, a grid cell area was 100 m2. Flow accumulation and slope steepness values proposed by Moore and Burch [79] were used to calculate the LS factor as grid format. The equation given below calculates the combined LS factor for the basin.

LS=[(Flowaccumulation×cellvalue22.1)0.4]×[(sinslope0.0896)1.3]E9

Basin LS factor values change between 0 and 39.91. The DEM and LS factor maps of the basin are given in Figures 8 and 9.

Figure 8.

Digital Elevation Model (DEM) of the basin.

Figure 9.

LS Factor map of the basin.

2.2.4. Cover and management factor (C)

Cis the crop (cover) management factor, which is an indicator that shows the effectiveness of different crop management systems as comparatively for preventing of reducing of soil loss. It is a relative measurement of soil loss considering between a crop management system and continuously fallow and tilled land. Whereas the USLE was developed for use on agricultural fields, the proper Cfactor values are chosen for nonagricultural conditions.

For this study, Cfactor values were taken from the results of the USLE project [77] carried out at the basin for agricultural areas and based on data published in [80] for pasture and shrub. Cfactors for the basin are given in Table 6 and Figure 10.

Land useQualityChanging intervalSelected value
Agriculture (dry)Poor0.10–10.400.25
PasturePoor0.01–0.050.03
ShrubPoor0.003–0.400.038
Bare Rock0.000.00
Water Surface0.000.00

Table 6.

Cfactor values used for the land use.

Figure 10.

Cfactor map of the basin.

2.2.5. Management practice factor (P)

The effects of soil conservation practices that will reduce soil loss are determined by the management practice factor (P), which represents cropland practices such as contour farming, strip cropping by reducing runoff speed.

There were not any conservation measures for agricultural areas such as contour farming or strip cropping. For that reason, the Pfactor was accepted as 1.00 for the entire basin [67].

2.2.7. Soil loss tolerance

Soil loss tolerance (T) is the permission level of soil loss that will not cause to reduce in productivity as economically [3, 75]. The amount of tolerance value (T) that permitted must be equal to or less than the soil erosion rates [81]. Soil loss tolerance values were determined from according to rooting depth [37], and categorized into five classes, as shown in Table 7.

Rooting depth, cmSoil loss tolerance (t ha−1 year−1)
Renewable soilNon-renewable soil
0–252.22.2
25–504.52.3
50–1006.74.5
100–1509.06.7
>15011.211.2

Table 7.

Implication of soil loss tolerance.

Soil depth and tolerance maps for the basin are in five groups changing from 2.2 to 11.2 t ha−1; their depths are considered in Figure 11.

Figure 11.

Soil depth map and soil loss tolerance map of the basin.

After calculation of the USLE, soil loss and tolerable soil loss rates can be compared to in terms of a specific management system considering their alternative to determine soil conservation measurements in farm planning.

2.2.8. Sustainable land use planning (SLUP) model

A method proposed by Karaş [82] was firstly applied in the Güvenç pond basin in Turkey. The described approach in Table 8 was used for both the soil erosion rates and soil loss tolerance values. The presented table mainly includes all land use types. The principal idea of the table is to apply the required soil conservation precautions (SCP) for land use types according to existing soil erosion and present conditions using only one or a combination of as many precautions as possible. Applying all types of SCP has certain costs. Therefore, SCP practices were ordered from the cheapest to the most expensive. All of the SCP practices were compared to the soil loss tolerance (T) values. Customarily, unused (bare land) and agricultural areas are the most exposed to soil erosion. For example, the first and second SCP at the agricultural land use includes cultural methods, which contain practices such as conservation tillage, strip cropping, and contour farming. If soil erosion is still greater than the soil loss tolerance despite cultural precautions of the agricultural land use, third-degree SCP needs to be applied. Physical applications include terracing or the design and installation of a combined practice to remove settled solids and associated pollutants in all runoff of larger storms. However, if there are no possible events to prevent or reduce soil erosion to the level of Tvalues using the first three SCP, the fourth-degree SCP, which includes changing land use type (natural transformation to pasture, rangeland, and forest), needs to be applied. If it is not possible to reduce soil erosion under a level of Tvalues via the first four SCP practices, the fifth-degree SCP (including the fourth-degree precautions + physical structures) needs to be applied. Physical structures include graded stabilization structures, stream bed improvement, gabion threshold construction, and grassed waterways.

Erosion
degree
The value
of (A/T)
Erosion
escription
Proposed soil conservation precautions
1≤1.0None exists
or too few
First‐Degree Precautions (FDP)
Consider cropping systems that will provide maximum protection
for the soil. Use minimum tillage systems where possible. Soil
management (increasing organic matter content, using soil
stabilisers), crop rotation on agricultural areas, suitable
tillage, minimum tillage, conservation tillage, mulching,
mulch tillage, ridge tillage, strip tillage, fertilising,
controlled grazing, pasture management) using suitable
mechanisation tools for cultivation.
21.0 T – 2.0Low – ModerateSecond-Degree Precautions (SDP)
Use support practices, such as cross slope farming, that will
cause the deposition of sediment to occur close to the source.
(In addition to FDP applications, contour farming, inter cropping,
mixed cropping, agro forestry and shrub establishment
of agricultural areas, continued covering, and developing
rangelands)
32.0–4.0Moderate–highThird-Degree Precautions (TDP) Cultural Precautions
+ Physical Structures (In addition to SDP, strip cropping,
cross slope, wind breaks, drainage, terracing on shrub
land and rangeland) terracing, contour strips, installation of
trench and holes for pasture management
44.0–6.0HighFourth-Degree Precautions (FoDP)
Changing land use type (natural transformation to pasture,
rangeland and forest)
5>6.0Very high
or severe
Fifth-Degree Precautions (FiDP)
Including FoDP + Physical structures
Physical structures (graded stabilisation structures, stream
bed improvement, construction of gabion threshold,
grassed waterways, etc.) Proper forest management,
Reforestation / afforestation, shifting cultivation,
controlled cutting.

Table 8.

Soil conservation precautions according to land use and potential soil loss.

Descriptions: A—potential soil loss; T—soil loss tolerance value


Advertisement

3. Results and discussion

3.1. Soil loss

Potential Soil Loss (PSL) for the basin was estimated to be between 0 and 152.77 t ha−1, applying the USLE equation in the GIS environment. The PSL values for existing land use types were also obtained. The results show that the PSL is of 9.87 t ha−1 for agricultural land use, 3.01 t ha−1 for pasture, and 4.16 t ha−1 for shrub. The mean calculated potential soil loss is 7.66 t ha−1 for the entire basin. Detailed statistical results obtained for each land use type are given in Table 10.

Total soil loss for the general basin is about 7972.86.42 t year−1. While 87.51% of the total soil loss is lost from agricultural areas, pasture and shrub land use also contribute to the rate of 9.51 and 3.58%, respectively. When considered in terms of soil depth in the basin, mean soil loss tolerance values are around 4.5 t ha−1, which are accepted as the threshold level of the basin. This means that 89.79% of the total soil loss occurs over the threshold value.

According to the obtained results, agricultural lands are under a high potential soil loss risk. While the agricultural land use occupies 68% of the total area, 87.51% of total soil loss is sourced from this area. In the basin, the agricultural areas are mainly converted from forest and pasture, which are the native land use. Therefore, most of the soil loss is lost from this area. Furthermore, most of the agricultural land use areas are now class VI in terms of land use capability. Actually, these areas should definitely not be ploughed. The soils in Class VI are generally unsuitable for agricultural production due to their severe limitations such as topographic soil conditions. These areas are generally appropriate for grassland, forestland, or wildlife habitat.

Although only 14% of the soils in the basin are appropriate for agricultural aims, 68% of the basin is used for the agricultural production. In agricultural land use, 31.37% of the area produces 72% of the total soil loss, which represents the soil loss over 11.2 t ha−1 in Table 9.

Land useDescriptive statisticSoil loss (t ha−1 year−1)
0.0–2.22.2–4.54.5–6.76.7–9.09.0–11.2>11.2General
AgricultureMean (t ha−1 year−1)0.523.315.558.0010.0622.699.87
Cell number*18,19210,04883616946497122,17270,690
USLE(t ha−1 year−1)94.59332.58464.03555.68500.085030.826977.78
Area (%)25.7314.2111.839.837.0331.37100.00
PastureMean0.623.255.487.7410.0414.703.01
Cell number14,506527426591584854101325,890
USLE(t ha−1 year−1)89.93171.00145.71122.6085.74148.91763.89
Area (%)56.0320.3710.276.123.303.91100.00
ShrubMean0.903.325.557.6410.0114.324.16
Cell number1887147511577132671715670
USLE(t ha−1 year−1)16.9848.9764.2154.4726.7224.49235.84
Area (%)33.2826.0120.4112.574.713.02100.00
Bare rockMean0.00.00
Cell number850850
USLE(t ha−1 year−1)0.00.00
Area (%)100.00100.00
Water surfaceMean0.00.00
Cell number10301030
USLE(t ha−1 year−1)0.00.00
Area (%)100.00100.00
Basin generalMean0.583.295.547.7710.0522.287.66
Cell number36,46516,79712,1779243609223356104,130
USLE(t ha−1 year−1)211.50552.62674.60718.18612.255203.717972.86
Area (%)35.0216.1311.698.885.8522.43100.00

Table 9.

Soil loss according to land use groups in the basin.

*grid cell size 10 m × 10 m = 100 m2.


The calculated potential soil loss (PSL) using the USLE for the basin is given in Figure 12. PSL values were divided into the appointed soil loss tolerance (T) values for each soil series to determine the soil erosion degree and the proposed soil conservation precautions on current land use, as explained in Table 9. The prepared erosion class map is given in Figure 13.

Erosion degreeA/TrateDescriptionArea (ha)Area (%)
10–1None exist or too few548.452.67
21–2Low–moderate212.820.44
32–4Moderate–high172.016.51
44–6High63.96.13
5>6Severe44.24.25
Total101.3100.00

Table 10.

Areal distribution of soil erosion in Çelikli basin.

Figure 12.

Soil loss map of the basin.

Figure 13.

Erosion class map of the basin.

Areal distribution of soil erosion in Çelikli basin is given in Table 10. Currently, according to the results, soil loss in the 50% of basin has none exist or too few.

While pasture land use produces a total of 763.89 t of soil loss, only about 9.58% of total soil loss came from the pasture areas. Most of the pasture areas are around Çelikli pond and on the northern side of the basin. About 56.03% of soil loss in pasture land use is under 2.2 t ha−1. Soil losses in 20.37% of the pasture areas are between 2.2 and 4.5 t ha−1, which provides 171 t of loss.

Shrub area has 234.8 t of soil loss, which is the 2.95% of total loss. Shrub areas were formerly forested areas, and all of them now include brushwood from cutting down the forests for fuel during the last century. In these areas, the biggest soil loss is between 4.5 and 6.7 t, which is 27% of total soil loss.

3.2. Land use planning of the basin

The natural land usages of the basin are primarily pasture and forest. Areas in the basin under severe erosion risk are mainly agricultural, shrub, and pasture land uses, respectively. Actually, the pasture and forest were the native land use 100 years ago. As mechanization tools were developed, land conversions started to convert to meet agricultural aims and rural needs by cutting down trees for fuel and by cultivating the soil. In that basin, the carrying capacity for grazing was calculated by considering the number of animals. There were a total of 5000 animals in the basin, including 3000 native cows and 2000 sheep and goats, with a 258.9 ha pasture area [67]. In Turkey, a native cow and a sheep are accepted as 0.5 and 0.1 of a big cow as a native unit (BBHB), respectively. Therefore, the grazed animal numbers were calculated as 1700 BBHB for the whole basin. When we calculate for the carrying capacity for each BBHB, the required pasture area is about 6.12 ha per head. Consequently, the needed area for pasture is about (1700 × 6.12) 10,404 ha. This result shows that the existing pasture area is not sufficient for grazing. The number of grazing animals is over the carrying capacity due to insufficient cover rate and forage yield. It is necessary to increase about 40 times the current pasture area for adequate grazing, or to expand it around 20 times by getting additional improvement measures (such as seedlings, fertilizing, controlled grazing) and applying some rain water harvesting techniques (such as constructing micro-basin water harvesting ridges, negarim type micro-basins, flood water harvesting pools, contour trenches, or bunds and terracing), and some planting measures (such as constructing consecutive brushes and trees as barriers for reducing surface runoff).

In agricultural areas, the wheat-fallow system is applied due to insufficient rainfall. The average wheat yield was determined as 1720 kg per ha for the selected 137 points. The average production cost for wheat is around 2960 kg ha−1 in dry areas [83]. Thus, the net income for the Çelikli basin is under the production cost, which is not sustainable for dryland areas. The low productivity is not economical when compared with the obtained income considering soil loss. Moreover, the main source of soil erosion is agricultural fields, which occupy around 88% of total basin area. Currently, 78.34% of arable lands are not appropriate for agricultural usage by ploughing due to improper land use. Therefore, those areas should be converted to pasture land, as they were 100 years ago, for reducing soil loss and meeting the grazing capacity of the basin. This means the pasture areas will increase about 2.13 times, reaching 812.68 ha, which is 78% of the entire basin.

Shrub areas generally have low density coverage and are open to intensive rainfall conditions. Thus, to reduce soil loss they need conservation measures like being converted to forest by increasing the cover density.

Figure 14.

The proposed land use for sustainable management and soil erosion classes in the basin.

Currently, agricultural areas that have LUCC II, III, and IV are appropriate for arable land in the basin. They still unproductively use the wheat-fallow system for agricultural aims. Animal production is a main income for the farmers living in the region. Some forage plants, such as vetch (Vicia sativa), forage pea (Pisum sativum), and sainfoin (Onobrychis sativa), can be grown under the dry conditions in the region. Agricultural production by ploughing risks soil erosion, causing movement of the soil and encouraging soil loss. When natural vegetation is removed for agricultural aims by ploughing, soil surface is exposed to intensive rainfall. Alternatively, the forage crops have the basic requirements for feeding the animals and are grown in the Çelikli province.

The research, carried out in the region, showed that sainfoin is one of the most favourable plants, which permanently covers the soil surface. Sainfoin is a perennial legumes-forage crop, which can be grown in poor soil and dry regions, where the rainfall is 300–400 mm per year for grazing animals. While it can obtain a harvest in dry regions in general, hay production can be changed between 2000 and 6000 kg per ha with a fertilization and maintenance. It is accepted as a rotation crop in arid regions and convenient for reducing soil erosion from the fields and ensuring nitrogen [84]. Sainfoin is advised to grow in areas that have low phosphorus soils [85]. It protects animals against bloat due to having tannins and it helps to increase protein absorbsion. It also keeps the soil in place thanks to main and side roots [86].

The land use proposed by the SLUP model was applied in the basin by considering the land use changes, the applications previously mentioned for agricultural, forest, and pasture land use. A final soil loss map of the basin and soil loss distribution is given in Figure 14 and Table 11.

Agricultural land use in class VI was converted to pasture by reducing 79.13% of the total arable land. Current land use (wheat-fallow) was changed to permanent forage crops (sainfoin, O. sativa)applying no tillage or minimum tillage, strip cropping with contour farming, and using suitable mechanization tools for cultivation to reduce soil losses. The Cfactor was 0.25 for O. sativa. The Pfactor was 0.37 for strip cropping with contour farming. While the Pfactor was 1.0 with fall plough, the soil tillage method factor was 0.25 for no tillage farming. Therefore, the Pfactor was decreased from 1.00 to 0.0925. Soil losses on the agricultural land decreased from 9.87 to 0.24 t ha−1, and the reduction rate was approximately 97.56% considering those applications.

In pasture areas, first- and second-degree precautions were applied to reduce soil loss in addition to the installation of some permanent vegetation, such as buffer strips (e.g. Saltbush) situated in short intervals, including physical structures (such as terracing, contour strips, and installing trenches and holes) to intercept storm water runoff and minimize soil erosion. In pasture land use, overgrazing management and improvement studies (seeding, fertilizing) on poor areas are proposed for sustainable management. Pasture areas increased by converting them from agricultural land at a rate of 216% in the basin. The Cfactor for pasture area was selected as 0.025. Soil loss in pasture land use was decreased from 3.01 to 0.83 t ha−1 by means of grazing management, improvement studies, and including some physical precautions.

Shrub land areas were another source of soil loss. This land was a forest before the trees were cut down. After planning, the shrub land was converted to its natural cover plant. The Cfactor was selected as 0.001 applying forest management. Therefore, soil loss in this land decreased from 4.16 to 0.05 t ha−1. Overall, soil loss was lowered from 9.87 to 0.67 t ha−1 with the SLUP model across the entire basin, reducing it by approximately 91.25%. After planning, land use changes are given in Table 12.

Land useDescriptive statisticSoil loss (t ha−1 year−1)
0.0–2.22.2–4.54.5–6.76.7–9.09.0–11.2>11.2General
Agriculture
(forage crop)
Mean (t ha−1 year−1)0.232.430.24
Cell number*14,7133714,750
USLE (t ha−1 year−1)33.840.9033.81
Area (%)99.750.25100.00
PastureMean0.592.95.167.4710.0924.540.83
Cell number75,119640314877235981,830
USLE (t ha−1 year−1)443.20185.697.645.752.3214.48659.08
Area (%)91.598.020.190.100.030.07100.00
ForestMean0.050.05
Cell number56705670
USLE (t ha−1 year−1)2.842.84
Area (%)100.00100.00
Bare rockMean0.00.00
Cell number850850
USLE (t ha−1 year−1)0.00.00
Area (%)100.00100.00
Water surfaceMean0.00.00
Cell number10301030
USLE (t ha−1 year−1)0.00.00
Area (%)100.00100.00
Basin generalMean0.492.905.167.4710.0924.540.67
Cell number97,3826440148772359104,130
USLE (t ha−1 year−1)479.88186.597.645.752.3214.48696.66
Area (%)93.516.180.140.070.020.06100.00

Table 11.

Soil loss according to proposed land use groups in the basin.

*grid cell size 10 m × 10 m = 100 m2.


Land useCurrent land use (ha)Proposed land use (ha)Difference (ha)
Agriculture (wheat-fallow)706.9−706.9
Agriculture (forage crops)147.5+147.5
Pasture258.9818.3+559.4
Shrub56.7−56.7
Forest56.7+56.7
Bare rock8.58.5
Water surface10.310.3
Total1041.31041.3

Table 12.

Land use changing before and after planning in the basin.

Advertisement

4. Conclusion

Sustainable land use planning (SLUP) model was applied in a semi-arid basin, having different land use. Main problem in the basin had soil erosion due to land use problems such as improper land use, deforestation, and overgrazing. The grazing capacity for feeding animals is not sufficient due to poor vegetation and cover rate. It is necessary to increase the pasture area about 40 times for adequate grazing, or to expand it around 20 times by getting additional improvement measures and applying some rain water harvesting techniques and some planting measures. When it was evaluated the basin in terms of land use capability classes, some land use problems were determined. Although only 14.07% of the basin is available for cultivation, around 68% of the basin has been used for agricultural aims for years. The USLE and GIS were used to estimate soil loss in the basin. While the average soil loss was calculated as 7.66 t ha−1 for the entire basin, soil loss for agricultural, pasture, and shrub had 9.87, 3.01 and 4.16 t ha−1, respectively, varying between 0 and 152.77 t ha−1 yearly. Total soil loss for the general basin is about 7972.86 t per year. While 87.52% of the total soil loss is lost from agricultural areas, pasture and shrub land use also contribute to the rate of 9.58 and 2.95%, respectively. When considered in terms of soil depth in the basin, mean soil loss tolerance values are around 4.5 t per ha, which are accepted as the threshold level of the basin. This means that 89.79% of the total soil loss occurs over the threshold value.

The land use proposed by the SLUP model was applied in the basin by considering the land use changes, the applications previously mentioned for agricultural, forest, and pasture land use. A final soil loss map of the basin and soil loss distribution was prepared. Agricultural land use in class VI was converted to pasture by reducing 79.13% of the total arable land. Current land use (wheat-fallow) was changed to permanent forage crops (sainfoin, O. sativa)applying no tillage or minimum tillage, strip cropping with contour farming, and using suitable mechanization tools for cultivation to reduce soil losses. While Cfactor was 0.25 for sainfoin, Pfactor was 0.37 for strip cropping with contour farming. While the Pfactor was 1.0 with fall plough, the soil tillage method factor was 0.25 for no tillage farming. Soil losses on the agricultural land decreased from 9.87 to 0.24 t ha−1, and the reduction rate was approximately 97.56% considering those applications.

In pasture areas, first- and second-degree precautions were applied to reduce soil loss in addition to the installation of some permanent vegetation to intercept storm water runoff and minimize soil erosion. It was also applied an overgrazing management and improvement studies (seeding, fertilizing) on poor areas are proposed for sustainable management. Pasture areas were increased by converting them from agricultural land at a rate of 216% in the basin. The Cfactor for pasture area was selected as 0.025. Soil loss in pasture land use was decreased from 3.01 to 0.83 t ha−1 by means of grazing management, improvement studies, and including some physical precautions.

Shrub land areas were another source of soil loss. This land was a forest before the trees were cut down. After planning, the shrub land was converted to its natural cover plant. The Cfactor was selected as 0.001 applying forest management. Therefore, soil loss in this land decreased from 4.16 to 0.05 t ha−1.

Overall, soil loss was lowered from 7.66 to 0.67 t ha−1 with the SLUP model across the entire basin, reducing it by approximately 91.25%.

References

  1. 1. Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses: a guide to conservation farming, USDA Handbook: No. 537.
  2. 2. Koreleski K (2008) The influence of field factors on the intensity of water erosion exemplified by a mountain village (in Polish). Infrastructure and Ecology of Rural Areas, 3, 5–12.
  3. 3. Pretorius JR, Cooks J (1989) Soil loss tolerance limits: an environmental management tool. GeoJournal, July 1989, 19(1), 67–75. doi:10.1007/BF00620551.
  4. 4. White AF (2002) Determining mineral weathering rates based on solid and solute weathering gradients and velocities: application to biotite weathering in saprolites. Chemical Geology 190, 69–89.
  5. 5. Williams JR, Dyke PT, Jones CA (1982) EPIC a model for assessing the effects of erosion on soil productivity. Analysis of ecological systems: state of the art in ecological modelling, pp. 553–572, Elsevier, Amsterdam.
  6. 6. Jain SK, Dolezal F (2000) Modeling soil erosion using EPIC supported by GIS, Bohemia, Czech Republic. Journal of Environmental Hydrology, 8, 1–11.
  7. 7. Arghinus C, Arghinus V (2011) The quantitative estimation of the soil erosion using USLE type ROMSEM model. Case-study—the Codrului Ridge and Piedmont (Romania). Carpathian Journal of Earth and Environmental Sciences, 6(2), 59–66.
  8. 8. Bathrellos GD, Skilodimou HD, Chousianitis KG (2010) Soil erosion assessment in Southern Evia Island using USLE and GIS. Bulletin of the Geological Society of Greece. In: Proceedings of the 12th International Congress, Patras, May, (3), 1572.
  9. 9. Bosco C, Rusco E, Montanarella L, Panagos P (2009) Soil erosion in the Alpine area: risk assessment and climate change. Studi Trentini di Scienze Naturali, 85, 117–123.
  10. 10. Cai C, Ding S, Shi Z (2000) Study of applying USLE and geographical information system IDRISI to predict soil erosion in small watershed. Journal of Soil and Water Conservation, 14, 19–24
  11. 11. Demczuk P (2009) Conform model USLE to automatic mapping intensity of soil erosion in the Bystrzanka Mountain Catchment (Flysh Carpathian) (in Polish). In: W. Bochenek and M. Kijowska (Eds.) The integrated monitoring of the environment, pp 239–244. Szymbark: IGiPZ PAN.
  12. 12. Devatha CP, Deshpande V, Renukaprasad MS (2015) Estimation of Soil loss Using USLE Model for Kulhan Watershed, Chattisgarh—a case study. In: International conference on water resources, coastal and ocean engineering.
  13. 13. Drzewiecki W (2006) GIS and remote sensing data application to the assessment of land-use conditions (in Polish). Geoinformatica Polonica, 8, 7–22.
  14. 14. Elci S, Selcuk P (2014) Effects of basin activities and land use on water quality trends in Tahtali Basin, Turkey. Environmental Earth Sciences (2013)68, 1591–1598.
  15. 15. Erdogan EH, Erpul G, Bayramin İ (2007) Use of USLE/GIS methodology for predicting soil loss in a semiarid agricultural watershed. Environmental Monitoring and Assessment, August, 131(1), 153–161.
  16. 16. Fıstıkoglu O, Harmancioglu NB (2002) Integration of GIS with USLE in assessment of soil erosion. Water Resources Management, 16, p.447.
  17. 17. Igwe CA, Akamigbo FOR, Mbagwu JSC (1999) Application of a SLEMSA and USLE erosion models for potential erosion hazard mapping in south-eastern Nigeria. International Agrophysics, 13, 41–48.
  18. 18. Irvem A, Topaloglu F, Uygur V (2007) Estimating spatial distribution of soil loss over Seyhan River Basin in Turkey. Journal of Hydrology, 336, 30.
  19. 19. Karaburun A (2010) Estimation of C factor for soil erosion modeling using NDVI in Buyukcekmece watershed. Ozean Journal of Applied Sciences, 3(1).
  20. 20. Karaş E (2005) Sustainable management of Küçükelmalı and Güvenç basins according to water and sediment yield. PhD. Thesis. Ankara University, Graduate School of Natural and Applied Sciences, Department of Agricultural Structures and Irrigation, pp 236, Ankara (in Turkish).
  21. 21. Karaş E, Öztürk F (2013) Land use planning of Küçükelmalı Pond Basin according to soil conservation measures. Journal of Agricultural Faculty of Gaziosmanpasa University, 2011, 28(2), 127–134, Tokat (in Turkish).
  22. 22. Koreleski K (2008) The influence of field factors on the intensity of water erosion exemplified by a mountain village (in Polish). Infrastructure and Ecology of Rural Areas, 3, 5–12.
  23. 23. Lastoria B, Miserocchi F, Lanciani A, Monacelli G (2008) An estimated erosion map for the Aterno-Pescara river basin. European Water, 21/22, 29–39.
  24. 24. Mellerowicz KT, Rees HW, Chow TL, Ghanem I (1994) Soil conservation planning at the watershed level using the Universal Soil Loss Equation with GIS and micorocomputer technologies: a case study. Journal of Soil and Water Conservation, March-April, 49(2), 194–200.
  25. 25. Pacheco FAL, Varandas SGP, Sanches LF, Valle Junior RF (2014) Soil losses in rural watersheds with environmental land use conflicts. Science of the Total Environment, 1 July, 485–486, 110–120. doi:10.1016/j.scitotenv.2014.03.069.
  26. 26. Perović V, Životić L, Kadović R, Đorđević A, Jaramaz D, Mrvić V, Todorović M (2013) Spatial modelling of soil erosion potential in a mountainous watershed of South-eastern Serbia. Environmental Earth Sciences, January, 68(1), 115–128. doi:10.1007/s 12665-012-1720-1.
  27. 27. Ştefanescu L, Constantin V, Surd V, Ozunu A, Vlad ŞN (2011) Assessment of soil erosion potential by the USLE method in Roşia montană mining area and associated natech events. Carpathian Journal of Earth And Environmental Sciences, 6(1), 35.
  28. 28. Wang X, Zhao X, Zhang Z, Yi L, Zuo L, Wen Q, Liu F, Xu J, Hu S, Liu B (2016) Assessment of soil erosion change and its relationships with land use/cover change in China from the end of the 1980s to 2010. Catena, February, 137, 256–268. doi:10.1016/j.catena.2015.10.004.
  29. 29. Yue-qing X, Jian P, Xiao-mei S (2009) Assessment of soil erosion using RUSLE and GIS: a case study of Maotiao River watershed, Guizhou Province, China. Environmental Geology, 56, 1643–1652. doi:10.1007/s10661-007-9894-9.
  30. 30. Zhu M (2015) Soil erosion assessment using USLE in the GIS environment: a case study in the Danjiangkou Reservoir Region, China. Environmental Earth Science 73, 7899–7908. doi:10.1007/s12665-014-3947-5.
  31. 31. Životić L, Perović V, Jaramaz D, Đorđević A, Petrović P, Todorović M (2012) Application of USLE, GIS, and remote sensing in the assessment of soil erosion rates in Southeastern Serbia. Polish Journal of Environmental Studies, 21(6), 1929–1935.
  32. 32. Bhattacharyya P, Bhatt VK, Mandal D (2008) Soil loss tolerance limits for planning of soil conservation measures in Shivalik–Himalayan region of India. Catena, 73(1), 117–124. doi:10.1016/j.catena.2007.10.001.
  33. 33. Johnson LC (1987) Soil loss tolerance fact or myth. Journal of Soil and Water Conservation, 42, 155–160.
  34. 34. Jones OR, Eck HV, Smith SJ, Coleman GA, Hauser VL (1985) Runoff, soil, and nutrient losses from rangeland and dry-farmed cropland in the southern high plains. Journal of Soil and Water Conservation, January/February, vol. 40(1), 161–164.
  35. 35. Mandal D, Sharda VN, Tripathi KP (2010) Relative efficacy of two biophysical approaches to assess soil loss tolerance for Doon Valley soils of India. Journal of Soil and Water Conservation, January/February, 65(1), 42–49.
  36. 36. Mannering JV (1981) The use of soil loss tolerances as a strategy for soil conservation. In: Morgan, R.P.C. (Eds.) Soil conservation. Problems and prospects, pp 337–350. John Wiley, Chichester.
  37. 37. McCormack DE, Young KK, Kimberlin LW (1981) Technical and societal implications of soil loss tolerance. In: R.P.C. Morgan (ed.) Soil conservation, problems and prospects. John Wiley and Sons, New York, NY.
  38. 38. Pierce FJ, Larson WE, Dowdy RH (1984) Soil loss tolerance: maintenance of long-term soil productivity. Journal of Soil and Water Conservation, 39, 136–138.
  39. 39. Schertz DL (1983) The basis for soil loss tolerances. Journal of Soil and Water Conservation, 38(1), 10–14
  40. 40. Skidmore EL (1982) Soil loss tolerance. In: Determinants of soil loss tolerance, pp 87–93. ASA Spec. Publ. 45. ASA, Madison, WI.
  41. 41. Smith RM, Stamey WL (1964) How to establish erosion tolerances. Journal of Soil and Water Conservation, May-June, 19(3).
  42. 42. Smith RM, Stamey WL (1965) Determining the range of tolerable erosion. Soil Science, 100, 414–424.
  43. 43. Sparovek G, DeMaria IC (2003) Multiperspective analysis of erosion tolerance. Scientia Agricola, Abr/Jun, 60(2), 409–416, doi:10.1590/S0103-90162003000200029.
  44. 44. Sparovek G, De Yong VLQ (1997) Definition of tolerable soil erosion values. Revista Brasileira de Ciência do Solo, 21, 467–471.
  45. 45. Sparovek G, Schnug E (2001) Temporal erosion-induced soil degradation and yield loss. Soil Science Society of America Journal, 65, 1479–1486.
  46. 46. Sparovek G, Weill MM, Ranieri SBL, Schnug E, Silva EF (1997) The life-time concept as a tool for erosion tolerance definition. Sci. Agric. Piracicaba, 54 (Numero Especial), 130–135.
  47. 47. Verheijen FGA, Jones RJA, Rickson RJ, Smith CJ (2009) Tolerable versus actual soil erosion rates in Europe. Earth-Science Reviews, 94(1–4), May, 23–38. doi:10.1016/j.earscirev.2009.02.003.
  48. 48. Alewell C, Egli M, Meusburger K (2014) An attempt to estimate tolerable soil erosion rates by matching soil formation with denudation in Alpine grasslands. Journal of Soils and Sediments (H. Special issue: soil formation and weathering in time and space), 15(6), 1383–1399. doi:10.1007/s11368-014-0920-6.
  49. 49. Anderson SP (2005) Glaciers show direct linkage between erosion rate and chemical weathering fluxes. Geomorphology, 67, 147–157. doi:10.1016/j.geomorph.2004.07.010.
  50. 50. Bouchard M, Jolicoeur S (2000) Chemical weathering studies in relation to geomorphological research in southeastern Canada. Geomorphology 32, 213–238. doi:10.1016/S0169-555X(99)00098-7.
  51. 51. Friend JA (1992) Achieving soil sustainability. Journal of Soil and Water Conservation, 47, 156–157.
  52. 52. Fujisaka S (1994) Learning from six reasons why farmers do not adopt innovations intended to improve sustainability of upland Agriculture. Agricultural Systems, Edinburgh, 46, 409–425.
  53. 53. Green EG, Dietrich WE, Banfield JF (2006) Quantification of chemical weathering rates across an actively eroding hillslope. Earth and Planetary Science Letters, 242, 155–169.
  54. 54. Heimsath AM, Chappell J, Dietrich WE, Nishiizumi K, Finkel RC (2002) Late Quaternary erosion in southeastern Australia: a field example using cosmogenic nuclides. Quaternary International, 83–85, 169–185.
  55. 55. Heimsath AM, Dietrich WE, Nishiizumi K, Finkel RC (1997) The soil production function and landscape equilibrium. Nature, 388, 358–361.
  56. 56. Heimsath AM, Dietrich WE, Nishiizumi K, Finkel RC (1999) Cosmogenic nuclides, topography, and the spatial variation of soil depth. Geomorphology, 27, 151–172.
  57. 57. Heimsath AM, DietrichWE, Nishiizumik K, Finkel RC (2001) Stochastic processes of soil production and transport: erosion rates, topographic variation and cosmogenic nuclide in the Oregon coast range. Earth Surface Processes and Landforms, 26, 531–552.
  58. 58. Kliment'ev AI, Tikhonov VE (2001) Ecohydrological analysis of soil loss tolerance in agrolandscapes. Soil Erosion, 34(6), 673–682.
  59. 59. Miklos AAW (1992) Biodynamique d'une couverture pédologique dans da region de Botucatu (Bresil-SP). Ph D Thesis, Université de Paris. Paris, France, 1995.
  60. 60. Minasny B, McBratney AB (2001) A rudimentary mechanistic model for soil formation and landscape development II. A two-dimensional model incorporating chemical weathering. Geoderma, 103, 161–179.
  61. 61. Owens LB, Watson JP (1979) Rates of weathering and soil formation on granite in Rhodesia. Soil Science Society of America Journal, 43(1), 160–166. doi:10.2136/sssaj1979.03615995004300010031x.
  62. 62. Peter BS, Donald MF, Thomas WG, Katherine M, Susan LB (2004) Rate of weathering rind formation on Costa Rican basalt. Geochimica ET Cosmochimica Acta, 68(7), 1453–1472. doi:10.1016/j.gca.2003.09.007.
  63. 63. Small EE, Anderson RS, Hancock GS (1999) Estimates of the rate of regolith production using 10Be and 26Al from an alpine hillslope. Geomorphology, 27(12), 131–150.
  64. 64. Wakatsuki T, Rasyidin A (1992) Rates of weathering and soil formation. Geoderma, 52, 251–264. doi:10.1080/00380768.1993.10416984.
  65. 65. White AF (2002) Determining mineral weathering rates based on solid and solute weathering gradients and velocities: application to biotite weathering in saprolites. Chemical Geology, 190, 69–89.
  66. 66. Karaş E, Oğuz İ (2015) A new approach to determine land use planning and soil conservation measures based on soil erosion classification. Carpathian Journal of Earth and Environmental Sciences, 10(2).
  67. 67. Oğuz İ, Karaş E, Susam T, Tetik A, Noyan ÖF, Akar Ö (2006) Tokat—Artova Çelikli havzasında toprak bozulmasının belirlenerek, sürdürülebilir bir tarım için havzanın planlanması, Tarım ve Köyişleri Bakanlığı, Tarımsal Araştırmalar Genel Müdürlüğü, TAGEM–BB-TOPRAKSU-2006/19, Enstitü Yayın No: 230, Teknik Yayın No: 45, 116 s. Tokat (in Turkish).
  68. 68. Nelson DW, Sommers LE (1982) Total carbon, organic carbon, and organic matter. In: Methods of soil analysis, part 2, 2nd ed., ed. A. L., pp 539–579. Madison: ASA and SSSA.
  69. 69. McLean EO (1982) Soil pH and lime requirement. In: Methods of soil analysis, part 2, 2nd ed., ed. A. L., pp 199–224. Madison: ASA and SSSA.
  70. 70. Richards LA (1954) Diagnosis and improvement saline and alkali soils (USDA Agricultural Handbook No. 60). Washington, D.C.: U.S. Government Printing Office.
  71. 71. Rhoades JD (1982) Cation exchange capacity. In: Methods of soil analysis, part 2, 2nd ed., ed. A.L., pp 149–157. Madison: ASA and SSSA.
  72. 72. Gee GW, Bauder JW (1986) Particle size analysis. In: Methods of soil analysis, part 1, 2nd ed., ed. A. Klute, pp 383–411. Madison: ASA.
  73. 73. Black CA (1965) Methods of soil analysis, part 2: Chemical and microbiological properties. Madison: ASA.
  74. 74. Klute A (1986) Water retention: laboratory methods. In: Black, C.A., ed. Methods of soil analysis. I. Physical and mineralogical methods. Madison: American Society of Agronomy, Soil Science Society of America, pp 635–662.
  75. 75. Wischmeier WH, Johnson CB, Cross BV (1971) A soil erodibility monograph for farmland and construction sites. Journal of Soil and Water Conservation, 26, 189–193.
  76. 76. Foster GR, McCool DK, Renard KG, Moldenhauer WC (1981) Conversion of the universal soil loss equation to SI metric units. Journal of Soil and Water Conservation, 36(6), 355–359.
  77. 77. Oğuz İ (1997) K, R, C and P factors of USLE equation in Koluvial soil group at Tokat province. Research Report, 102, 69–79, Ankara (in Turkish).
  78. 78. ESRI (2003) GIS standards and interoperability. ArcNews, ESRI, Spring, 25(1),.
  79. 79. Moore I, Burch G (1986) Physical basis of the length-slope factor in the universal soil loss equation. Soil Science Society of America Journal, 50, 1294–1298.
  80. 80. Çanga MR (1995) Soil and water conservation. University of Ankara, Faculty of Agriculture, Publication no. 1386, p 118. Ankara. (in Turkish).
  81. 81. Stamey WL, Smith RM (1964) A conservation definition of erosion tolerance. Soil Science, 97(3), 183–186.
  82. 82. Karaş E (2007) Application of SWAT, USLE and RUSLE on Güvenç basin. Soil and Water Resources Research Institute of Eskişehir, Research report: TAGEM-BB-TOPRAKSU-2007/43. p 123. Eskişehir (in Turkish).
  83. 83. Altıntaş G (2014) Production inputs and costs of some agricultural productions grown in Tokat, Amasya, Yozgat and Sivas provinces. Agricultural Research Institute of Tokat, No: 261-P23. Tokat (in Turkish).
  84. 84. Elçi Ş, Ekiz H, Sancak C (1996) Problems of sainfoin production in Turkey. The third congress of forage crops, June 16–18, Erzurum (in Turkish).
  85. 85. Miller DA, Hoveland CS (1995) Other temperate legumes. In: R. F. Barnes, D. A. Miller and C. J. Nelson (ed.) 5th ed. Forages: an introduction to grassland agriculture. p. 276. Iowa State Univ. Press, Ames.
  86. 86. Anonymous (2015b)Onobrychis. https://en.wikipedia.org/wiki/Onobrychis. Accessed 08 November 2015.

Written By

Ertuğrul Karaş

Submitted: October 23rd, 2015 Reviewed: April 14th, 2016 Published: August 10th, 2016