ASSESSMENT OF SOIL EROSION INTENSITY IN KOLUBARA DISTRICT, SERBIA

© by PSP Volume 22 – No 5a. 2013 Fresenius Environmental Bulletin

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ASSESSMENT OF SOIL EROSION INTENSITY IN KOLUBARA DISTRICT, SERBIA

Snezana Belanovic1, Veljko Perovic2,*, Dragana Vidojević3,

Stanimir Kostadinov1, Milan Knežević1, Ratko Kadović1 and Olivera Košanin1

1 University of Belgrade, Faculty of forestry, Kneza Višeslava, 1, 11030 Belgrade, Serbia 2 Institute of Soil Science, Teodora Drajzera 7, 11000 Belgrade, Serbia

3 Agency for Environmental Protection, Ministry of Environment, Mining and Spatial Planning, Ruze Jovanovic 27a, 11 160 Belgrade, Serbia

ABSTRACT

The paper shows spatial estimation of soil loss apply-ing USLE equation in the area of Kolubara district. Apart from the erosion factor and vegetative cover factor, soil erodibility (K factor) is a key parameter for soil erosion modelling. The erodibility factor in the area of Kolubara district was calculated using the Wishmaier and Smith method. The land in the study area is mainly used for agriculture and C factor was determined by Corine Land Cover 2000. The objective of this study was to assess the applicability of a simple methodology, based on the ap-plication of USLE model, to determine the erosion inten-sity of certain soil types. In the area of Kolubara district the most common soil category is the one not threatened by water erosion covering 53.9% of the total territory. 15.6% is low threatened, 14.7 % moderately threatened, 9.51% is highly threaten and very high threatened is 6.23% of the total territory. Compared to the area they cover, ex-tremely endangered soils are Haplic Cambisol (Dystric), Haplic Cambisol (Eutric, Skeletic) and Haplic Cambisol (Eutric).

Low and very low erosion category in the study area were noted in previous researches and the tendency of erosion reduction resulted from the change in land use, i.e. abandoning large agricultural areas.

KEY WORDS: erosion, K factor, USLE, spatial modelling, soil type

1 INTRODUCTION

Soil erosion by water is one of the most important types of soil degradation in Serbia, and 86% of central Serbian territory is endangered by various processes of soil erosion by water [1]. Soil erodibility is defined as soil resis-tance to detachment and further transport [2,3]. Erodibility is the function of dynamic soil properties. Land use regime also has an impact on soil erodibility [4,5]. The differences * Corresponding author

in natural soil resistance to erosion could hardly be quan-tified based on filed studies since soil with low erodibility factor can show the signs of strong erosion and vice versa since erosion level is in the function of topographic influ-ences and agents of erosion. K-factor measurements are ex-pensive, complex, and require complex equipment and ex-tensive time, and therefore the equations were calculated using mechanical soil properties and organic matter content as basic parameters. Soil erodibility could be shown through the values of K-factor from USLE equation [6] in a satis-factory manner.

Soil erosion modelling is difficult primarily because the data on soil is unavailable. Soil erodibility for the wider European area [7] is shown using Wishmeier equation.

Erosive processes in Kolubara district, the north-west part of the Republic of Serbia, are the subject of the re-search starting with relief properties of Kolubara basin [8, 9] to pluvial erosion [10, 11] by modelling the erodibility factor. Running USLE model in GIS software was ana-lyzed in many papers [12, 13, 14]. The advantages of GIS tools in USLE method were emphasized when calculating topographic factor from DEM (Digital Elevation Model) using geostatistics for spatial data processing in remote sensing especially when calculating the factor of land use [12].

The objective of this study is to assess the applicabil-ity of a simple methodology, based on the application of USLE model to determine the erosion intensity of certain soil types.

2 MATERIALS AND METHODS

2.1 Study Area

The Kolubara district is located in the north-west part of Serbia covering an area of 2.474 km2. According to the data of the Statistical Office of the Republic of Serbia [15] 191 (174 according to 2011 census) inhabitants live in the district which is 2% of the total number of inhabi-tants. Metal industry, agriculture, and food industry are developed in this area. The number of agricultural households is 29,846 with 170,514 ha of agricultural land


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divided into 102,731 ha of arable land, 18,390 ha of or-chards, 25,689 ha of meadows, and 23,274 ha of pastures. Its characteristic climate is moderate continental. The following soil types are found in the study area: lepto-soles, cambisols, fluvisols, and planosols.

FIGURE 1 - Study area

2.2. The Calculation of Annual Soil Loss

Soil erosion is estimated as a product of empirical co-efficients, which must therefore be accurately evaluated. Four main factors are generally considered: soil, topogra-phy, land use and climate [6]. The model determines aver-age annual soil erosion as a the product of six erosion fac-tors, and average soil loss was estimated on a 30x30 m cell basis resolution. The well known form of USLE equation is given as:

A=R· K ·C· LS· P Where:

A – Average annual soil loss rate (t·ha−1·yr−1), R- rainfall erosivity factor (MJ·mm·ha−1·h−1·yr−1), K - soil erodibility factor (t·ha·h·ha−1·MJ−1·mm−1), LS - topog-raphic factor (dimensionless), C - crop management factor (dimensionless), and P – conservation supporting practice factor (dimensionless) are considered to have the value one since erosive measures in the study area were not taken into consideration.

2.2.1 Rainfall Erosivity Factor (R)

R factor was determined using historical weather data-sets from Valjevo, Loznica, RC Valjevo, Belgrade, Ljubo-vija, Krupanj, Bajina Bašta, Zlatibor, Kragujevac, and Požega meteorological stations which are located in the study area or in the vicinity of the study area. The lack of dense meteorological network and fine rain intensity

measurements made us use simplified model GJRM to obtain R values.

The mean annual rainfall from period 1949-2010 was used to obtain average R values for meteorological stations. R-factor is a function of the mean annual rainfall (mm) and was calculated using the following formula [16]:

R=b0 Pm Where: R – Rainfall erosivity (MJ·mm·h-1·ha-1·month-1);

Pm is the mean annual rainfall (mm); b0 – empirical coeffi-cient (MJ·h-1·month-1), which has values from 1.1 – 1.5, and it was adopted to be 1.3 by Grimm et al. [16] and used in our study.

The application of interpolation models has an in-creasing significance in describing spatial variability of natural conditions such as soil properties, meteorological data, elevations above sea level, etc. The values of such occurrences are obtained by sampling in certain areas, and their mapping, i.e. their estimation on all other locations is conducted by interpolation. Ordinary kriging was used for obtaining the final rainfall erosivity map (R factor). Ordi-nary kriging is an extremely suitable interpolation method due to its simplicity and reliability. The method is based on the determination of optimum weight coefficients which are joined with known (sampled) values from variogram.

Spatial distribution for R factor is shown in Figure 2.

2.2.2 Erodiblity Factor (K)

Factor K was calculated based on Wishmeier and Smith (1978) formula [6, 17]:

( ) ( ) ( )3103,32103,412108,2 3314,14 −⋅⋅+−⋅⋅+⋅−⋅⋅= −−− psMOMKWS

Where: OM is organic matter (%), s is soil structure class, p is permeability class; M is textural factor: silt per-centage of fine sand fraction content multiplied by 100 - clay fraction.

K factor units were properly converted to the SI and expressed in SI units of t ha·h·ha-1·MJ-1·mm-1. For calcu-lating soil loss and erodibility the data from soil profile data base – Information System on Soils from the Envi-ronmental Agency of the Republic of Serbia were used as well as some data obtained in 2011. All data from the data base were analysed for the layer depth of 30 cm including the content of organic carbon and partial particle size dis-tribution expressed as percentages (%) of clay (0.0002-0.002 mm), silt (0.002-0.063 mm) and sand (0.063-0.2 mm).

Spatial distribution of the K factor is shown in Figure 3.

2.2.3 Topographic Factor (LS)

Topographic factor presented length factor and slope degree factor. This factor could be calculated in various ways depending on data availability. Generating the LS values is the largest problem in using the USLE method [18, 19]. Therefore, many algorithms for calculating this factor were developed [19-23]. The basic input for generat-ing LS factor grid in GIS was 30 m DEM dataset. A DEM of the area was generated by spatial interpolation of


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FIGURE 2 - The map of rainfall erosivity factor of Kolubara district

FIGURE 3 - Soil erodibility map of Kolubara district


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FIGURE 4 - Topographic factor map of Kolubara district

of contour lines from Survey of Serbia’s toposheets 1:50,000 scale. The basic input for generating LS factor grid in GIS was 30 m DEM dataset. The L-factor and S factor were calculated [20]:

L=(λ/22,13)m Where: λ is horizontal projected slope length, m is

slope length exponent. S = 10.8sinθ + 0.03 for slopes < 9 % S = 16.8sinθ + 0.50 for slopes > 9 % Where: θ is slope angle (0). The automated extraction of topographic parameters

from DEMs provides faster and more accurate measure-ment compared to traditional techniques. Wu et al. [24] explained that the estimate of soil loss is considerably reduced with higher DEM resolution. The model used in this paper was primarily written in AML language [21,25], and was later on updated to be more efficient [26].

Spatial distribution for factor LS is shown in Figure 4.

2.2.4 Cover Management Factor (C)

Cover management factor represents the connection between soil, crops, productivity level, duration of vegeta-tive period, measures performed on cultures, crop residue management, and rainfall distribution [27]. Remote sens-ing and its interpretation provide a wealth of information on the status and quality of the environment. C factor was

determined on a basis of CORINE Land Cover (1992) database on a scale of 1:100000. Mapping of land cover in CORINE program aims to provide consistent informa-tion on land cover in European countries through CLC data bases for the purpose of easier environment man-agement.

Kolubara district was divided into 16 land cover clas-ses (Table 1), and for each of those an appropriate C fac-tor value was allocated [28-30].

Spatial distribution of factor C is shown in Figure 5. TABLE 1 - CORINE land use and corresponding C values

CLC code Description C - factor 112 Discontinuous urban areas 0 121 Industrial or commercial areas 0 131 Mining 0 141 Green urban areas 0.005 142 Sport and leisure facilities 0.005 211 Non-irrigated arable land 0.4 231 Pastures 0.02 242 Complex cultivation patterns 0.15

243 Land principally occupied by agriculture, with significant areas of natural vegetation 0.15

311 Broad-leaved forest 0.005 312 Coniferous forest 0.004 313 Mixed forest 0.004 321 Natural grasslands 0.05 324 Transitional woodland-shrub 0.007 333 Sparsely vegetated areas 0.3 512 Water bodies 0


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FIGURE 5 - Map of cover management factor C of Kolubara district.

3 RESULTS AND DISCUSSION

Spatial distribution of annual soil loss in Kolubara district was determined based on all described parameters. It could be concluded that the intensity of erosive proc-esses belong to the weak erosion (Table 2, Fig. 6).

TABLE 2 - Area under different classes of soil erosion

Erosion categories

Numeric range (t ha-1 yr-1)

Area (km2)

Area percentage (%)

Very low I 0 - 3 1311,52 53,01 Low II 3 - 10 392,88 15,88 Moderately III 10 - 20 361,04 14,59 High IV 20 - 40 176,24 9,39 Very high V >40 75,52 7,12

R factor went from 1109.7 mm·h-1·ha-1·month-1

to 963,1 MJ·mm·h-1·ha-1·month-1, with a value decrease going from west to north. LS factor [13] had a great effect on current erosion intensity in the basin. LS factor in that area ranged from 0 – 97.47 (Fig. 4). Comparing LS factor and the incline map shows that the LS factor was sensitive to steep slopes and that its values increased as the incline increased. Higher values of LS factor were in the south area of the district where the highest annual land losses were calculated.

Erodibility factor went from 0.01 to 0.04 and when compared to the Soil map, it could be noted that K factor values depended on the change in soil type (Fig. 3).

Cover management factor in Kolubara district went from 0 to 0.4 (Fig. 5). Lower values of C factor were found in forest areas. Forests are nevertheless the best soil pro-tection and all researches so far showed a lower erosion intensity in forests [27].

In the area of Kolubara district the most common soil

category was the one not endangered by water erosion covering 53.9% of the total territory, weakly endangered is 15.6%, mean endangerment was on 14.7%, highly endangered was 9.51%, and extremely endangered 6.23% of total territory (Table 3, Fig. 6). The most common soil types in Kolubara district were Planosol, Luvic Planosol, Haplic Cambisol (dystric and eutric), and Haplic Fluvisol (siltic). Compared to the area they cover the following soils fall into the category of highly endangered soils: Haplic Cambisol (Dystric) – 12.9% Haplic Cambisol (Eutric, Skeletic)– 12.1% and Haplic Cambisol (Eutric) – 10.8%.

According to Dragićević [11] research, conducted by

applying Gavrilović’s method [31], 66.7 % of Kolubara basin fell into the category of low to very low erosion and the tendency of erosion reduction was evident. The stud-ies conducted in the area of Kolubara district when com-pared to erosion maps [32] showed a reduction in erosion intensity. The reduction of erosive processes resulted from the changes in land use, i.e. abandoning large agri-cultural areas and rural households [33].


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TABLE 3 - Soils endangered by water erosion according to WRB classification

Erosion category Total Total WRB classification (34) I II III IV V (km2) (%) Haplic Fluvisol (Clayic) 7,50 1,09 0,29 0,14 0,03 9,05 0,37 Haplic Fluvisol (Siltic) 126,17 31,88 10,59 7,72 3,09 179,45 7,25

Haplic Fluvisol (Arenic) 5,77 3,03 1,77 1,79 1,04 13,41 0,54 Coluvic Regosol 10,13 3,85 4,06 2,21 0,93 21,17 0,86 Mollic Leptosol 0,76 0,36 0,08 0,07 0,05 1,32 0,05

Haplic Leptosol (Eutric) 10,96 1,95 0,71 0,74 0,97 15,34 0,62 Haplic Leptosol (Eutric, Skeletic) 73,60 6,31 2,76 2,48 0,84 85,99 3,48

Cutanic Cambisol (Eutric ) 57,03 14,43 5,82 2,41 0,79 80,48 3,25 Haplic Glaysol 2,22 0,20 0,08 0,00 0,00 2,50 0,10

Planosol 262,26 79,23 83,91 34,17 18,10 477,67 19,31 Luvic Planosol 235,89 69,56 83,62 46,27 12,46 447,80 18,10

Haplic Glaysol (Clayic) 9,53 0,37 0,07 0,04 0,00 10,01 0,40 Litic Leptosol 26,70 4,16 2,36 3,01 3,54 39,77 1,61

Rendzic Leptosol (Calcaric, Skeletic) 5,14 0,71 0,05 0,10 0,09 6,09 0,25 Mollic Leptosol (Eutric,Skeletic) 4,20 1,77 1,29 0,81 0,29 8,36 0,34

Haplic Cambisol (Dystric) 143,97 30,64 43,80 41,44 38,62 298,47 12,06 Haplic Cambisol (Eutric) 171,80 64,29 85,48 57,48 45,94 424,99 17,18

Haplic Cambisol (Dystric, Skeletic) 79,90 24,27 10,90 16,90 18,21 150,19 6,07 Haplic Cambisol (Eutric, Skeletic) 44,61 8,52 4,22 4,41 4,83 66,58 2,69

Molic Vertisol- Gleyic Pheozem (Pachic) 10,45 3,39 0,66 0,23 0,08 14,80 0,60 MollicVertisol 45,94 35,89 21,54 12,92 4,26 120,55 4,87

Total (km2) 1334,54 385,90 364,06 235,33 154,17 2474,00 Total (%) 53,94 15,60 14,72 9,51 6,23 100,00

FIGURE 6 - The map of average annual soil loss in Kolubara district

4. CONCLUSIONS

Quantitative estimate of potential average annual soil loss was analysed in 30 x 30 m grid cells and based on it

the spatial distribution of potential erosion in study area was obtained. Around 69.5 % of the area is low or very low exposed to erosive processes. Moderate, high, and very high erosion intensities were estimated to appear on


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753.5 km2, which is 30.46 % of the district area. Low and very low erosion category in the study area were even noted in previous researches and the tendency of erosion reduction resulted from the change in land use, i.e. aban-doning large agricultural areas.

Shown methodology deals with the determination of each erosion factor individually within GIS and facilitates the work on each erosive factor in USLE method, and by it on the final map of the soil loss was estimated during the erosion processes.

Due to inability to perform detailed filed investiga-tion, the use of remote sensing, digital evaluation model analysis, the application of geostatic, and other GIS seg-ments turned out to be priceless factors in perceiving erosive processes and its use is required in soil and water conservation, both from the point of view of precision and required time.

ACKNOWLEDGEMENTS

This paper was realized as a part of two projects: "Studying climate change and its influence on the environ-ment: impacts, adaptation and mitigation" (43007) and "Impact of soil quality and irrigation water quality on agri-cultural production and environmental protection" (37006) financed by the Ministry of Education and Science of the Republic of Serbia for the period 2011-2014.

REFERENCES

[1] Đorović, M. (2005) Water and wind erosion, Acta Biological Yugoslavia, Yugoslav Society of Soil Science, Commission of water and soil conservation. (in Serbian)

[2] Kadović, R. (1999) Antierosion agroecosistems - Soil con-servation, Faculty of Forestry University of Belgrade. (in Serbian)

[3] Morgan, R.P.C. (2005) Soil Erosion and Conservation. third edition, National Soil Resources Institute, Cranfield Univer-sity, Blackwell, ISBN 1-4051-1781-8.

[4] Yılmaz, M., Yılmaz, F., Karagul, R. and Altun, L. (2008) Change in Erodibility Indices and some Soil Proporties ac-cording to Parent Materials and Land Use Regimes in Erfelek Dam Creek Watershed (Sinop, Turkey). Fresenius Environ-mental Bulletin 17, 2083 – 2090.

[5] Efe, R., Ekinci, D. and Curebal, I. (2008) Erosion analysis of Findikli Creek catchment (NW of Turkey) using GIS based on RUSLE (3d) method. Fresenius Environmental Bulletin 17(5), 568-576.

[6] Wischmeier, W.H. and Smith, D.D. (1978). Predicting rain-fall erosion lossess-a quide for conservation planning. USDA, Agricultural Handbook 537, Washington, DC.

[7] Panagos P., Meusburger, K., Alewell, C. and Montanarella, L. (2012) Soil erodibility estimation using LUCAS point survey data of Europe, Environmental Modelling &Software 30, 143-145.

[8] Cvijić, J. (1909) Karst of Lelic, Bulletin of the Serbian Geo-graphical Society, Belgrade. (in Serbian)

[9] Menković, L.J. (1995) Geological substrate and relief of Mi-onica, Municipal Mionica, specal edition, Geographical Insti-tute ''Jovan Cvijić'', Serbian Academy of Sciences and Arts, book 42, Belgrade. (in Serbian)

[10] Dragićević, S. (2001) Pluviometric regime and its influence on erosive process in the Kolubara river basin, Bulletin of the Serbian Geographical Society, Belgrade. (in Serbian)

[11] Dragićević, S (2007) Dominant Processes of Erosion in Kolubara River Basin, Faculty of Geography, University of Belgrade, monograph, Belgrade. (in Serbian)

[12] Perović, V., Đorđević, A., Životić, Lj., Nikolić, N., Kadović, R. and Belanović, S. (2012) Soil erosion modelling in the complex terrain of pirot municipality. Carpathian Journal of Earth and Environmental Sciences 7, 93 – 100.

[13] Dabral P.P., Baithuri, N. and Pandey, A. (2008) Soil erosion assessment in a hilly catchment of north eastern India using USLE, GIS and Remote Sensing. Water Resour Manage 22, 1783-1798.

[14] Tošić, R., Dragicevic, S, Kostadinov, S. and Dragovic N., (2011) Assessment of Soil Erosion potential by the USLE method: Case study, Republic of Srpska – BIH. Fresenius Environmental Bulletin 20, 1910 – 191.

[15] Statistical Office of the Republic of Serbia (2012): 2011 Cen-sus of Population, Households and Dwellings in the Republic of Serbia, Population, Age and Sex, Data by settlements, ISBN 978-86-6161-028-8, Supported by the EU Belgrade.

[16] Grimm, M., Jones, R.J.A., Rusco, E. and Montanarella, L. (2003) Soil erosion risk in Italy: a revised USLE approach. EUR 20677 EN, Office for Official Publications of the Euro-pean Communities, Luxemburg, Luxemburg.

[17] Foster, G.R., Mc Cool, D.K., Renard, K.G. and Moldenhauer, W.C. (1991) Conversion of the universal soil loss equation to SI metric units. J Soil Water Consev 36, 356–359.

[18] Renard, K.G., Foster, G.R. and Weesies, G.A. (1991) RUSLE: revised universal soil loss equation. J Soil Water Conserv 46(1), 30–33.

[19] Moore, I.D. and Wilson, J.P. (1992) Length–slope factors for the reviseduniversal soil loss equation: simplified method of estimation. J Soil Water Conserv 47(5), 423–428.

[20] McCool, D.K., Foster, G.R., Mutchler, C.K. and Meyer, L.D. (1987) Revised slope steepness factor for the universal Soil Loss Equation. Trans of ASAE 30(5), 1387–1396.

[21] Hickey, R., Smith, A. and Jankowski, P. (1994) Slope length calculations from a DEM within ARC/INFO GRID. Com-puters, Environment, and Urban Systems 18 (5), 365–380.

[22] Mitasova, H., Hofierka, J., Zlocha, M. and Iverson, L. (1996) Modelling topographic potential for erosion and deposition using GIS. Int J GIS 10(5),629–641.

[23] Moore, I. and Burch, G. (1986) Physical basis of the length-slope factor in the Universal Soil Loss Equation. Soil Sci Soc Am J 50(5), 1294–1298.

[24] Wu, S., Li, J. and Huang, G. (2005) An evaluation of grid size uncertainty in empirical soil loss modeling with digital elevation models. Environmental Modeling and Assessment 10, 33-42.

[25] Hickey, R. (2000) Slope angle and slope length solutions for GIS. Cartography 29 (1), 1–8.


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[26] Van Remortel, R., Maichle, R. and Hickey, R. (2004) Com-puting the RUSLE LS Factor based on Array-based Slope Length Processing of Digital Elevation Data Using a C++ Executable. Computers and Geosciences 30, 1043-1053.

[27] Kоstаdinоv, S. (2008) Torrents and erosion, Faculty of For-estry, Belgrade, ISBN 8672991478, 9788672991475. (in Serbian)

[28] Bathrellos, G.D., Skilodimou, H.D., and Chousianitis, K.G. (2010) Soil erosion assessment in southern Evia Island using USLE and GIS. Bulletin of the Geological Society of Greece, 2010. Proceedings of the 12th International Congress, Patras, May, 2010, XLIII, No 3, 1572-1581.

[29] Lastoria, B., Miserocchi, F., Lanciani, A. and Monacelli, G. (2010) An estimated erosion map for Aterno-Pescara River Basin. European Water 21-22, 29-39.

[30] Sivertun, A. and Prange, L. (2003) Non-point source critical area analysis in the Gisselo watershed using GIS. Environ-mental Modelling & Software 18, 887–898.

[31] Gavrilović, S. (1972) Torrential flow engineering and erosion processes. Construction, special edition. (in Serbian)

[32] Lazarevic R (1983) Map of erosion SR Serbia 1:500,000. Belgrade: Institute of forestry and timber industries. (in Ser-bian)

[33] Dragićević, S. and Stepić, M. (2006) Changes of intensity of Ljig river basin erosion - influence of Anthropogenic factors, Bulletin of the Serbian Geographical Society 2, Belgrade 17-25. (in Serbian)

[34] IUSS Working Group WRB. (2006) World Reference Base for Soil Resources 2006, World Soil Resources Reports No. 103. FAO, Rome.

Received: August 22, 2012 Accepted: November 22, 2012 CORRESPONDING AUTHOR

Veljko Perovic

Institute of Soil Science Teodora Drajzera 7 11000 Belgrade SERBIA E-mail: [email protected]

FEB/ Vol 22/ No 5a/ 2013 – pages 1556 - 1563

ASSESSMENT OF SOIL EROSION INTENSITY IN KOLUBARA DISTRICT, SERBIA

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