modeling the usle k-factor for calcareous soils in northwestern iran

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2008) 414–423www.elsevier.com/locate/geomorph

Geomorphology 97 (

Modeling the USLE K-factor for calcareous soilsin northwestern Iran

A.R. Vaezi a, S.H.R. Sadeghi b,⁎, H.A. Bahrami a, M.H. Mahdian c

a Department of Soil Science, Tarbiat Modares University, Tehran 14115-336, Iranb Department of Watershed Management Engineering, Tarbiat Modares University, Noor 46417-76489, Iran

c Institute of Soil Conservation and Watershed Management, Tehran, 13445-1136, Iran

Received 11 March 2007; received in revised form 25 August 2007; accepted 30 August 2007Available online 9 September 2007

Abstract

Soil erodibility defines the resistance of soil to detachment by rainfall impact and/or surface flow force. In the Universal SoilLoss Equation (USLE), the soil erodibility (K) is estimated using the texture, organic matter content, permeability and structure of asoil. The USLE was originally developed for non-calcareous soils in the USA. However, in calcareous soils, calcium is animportant factor affecting soil structure and hence may influence soil erodibility. The application of the USLE to calcareous soilstherefore requires a reassessment of K. The present study evaluates K and identifies factors affecting K for calcareous soils inHashtrood City, northwestern Iran. The soils contain 13% lime and 1% organic matter, and are mainly utilized for wheat dryfarming. A square agriculture area of 900 km2 was selected and then divided into 36 grids of 5×5 km. The erosion unit plots atthree replicates with 1.2 m spacing were installed in each grid. K was measured based on soil loss and the rainfall erosivity indexfrom March 2005 to March 2006. The rate of soil loss resulting from 23 natural rainfall events during the study period wasmeasured at the unit plot scale. Various soil properties including the contents of sand, silt, silt+very fine sand, clay, gravel, organicmatter, lime, and potassium as well as aggregate stability and permeability were measured in the vicinity of each plot. The resultsshow that K significantly correlates with the contents of sand, silt, silt+very fine sand, organic matter, and lime as well as water-aggregate stability and permeability. The application of principal component analysis (PCA) also indicates that the contents of clayand lime as well as permeability strongly control K. The contents of clay and lime, which have not been well considered in USLEstudies, significantly decrease K due to their strong effects on aggregate stability and water infiltration into soil. K can be estimatedusing a linear regression equation based on the contents of sand, clay and lime.© 2007 Elsevier B.V. All rights reserved.

Keywords: USLE; Erodibility factor; Calcareous soil; Erosion modelling; Iran

1. Introduction

Soil erosion is one of the most important envi-ronmental problems in the world, causing great eco-nomical losses every year and threatening sustainable

⁎ Corresponding author.E-mail address: [email protected] (S.H.R. Sadeghi).

0169-555X/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.geomorph.2007.08.017

development (Jianping, 1999). About 85k of globalland degradation is associated with soil erosion, mostof which occurred after World War II, causing a 17%reduction in crop productivity (Oldeman et al., 1990;Biot and Lu, 1995; Bruce et al., 1995) and environ-mental damage. For this reason, prevention of soilerosion is of paramount importance in the managementand conservation of natural resources (Morgan, 1995;

mailto:[email protected]

http://dx.doi.org/10.1016/j.geomorph.2007.08.017

415A.R. Vaezi et al. / Geomorphology 97 (2008) 414–423

Sadeghi et al., 2007). Predicting soil erosion and properevaluation of the main erosional factors in an area ofinterest therefore provide the first step in choosingproper strategies for soil erosion control.

The Universal Soil Loss Equation (USLE; Wischmeierand Smith, 1978) and its revised versions are widely usedto predict soil loss and to plan soil conservation works(Renard et al., 1991; Sadeghi et al., 2004). The USLE is astatistically-based water erosion model related to sixerosional factors (Wischmeier and Smith, 1978):

A ¼ R � K � L � S � C � P ð1Þ

where A is the mean annual soil loss, R is the rainfallerosivity factor, K is the soil erodibility factor, L is theslope length factor, S is the slope steepness factor, C is thecrop management factor and P is the erosion controlpractice factor.

Soil erodibility is conceived of as the ease with whichsoil is detached by splash during rainfall and/or surfaceflow (Renard et al., 1997). It is generally considered asan inherent soil property with a constant value. Thisfactor reflects the fact that different soils erode at dif-ferent rates when the other factors that affect erosion arethe same (Kirkby and Morgan, 1980). In the USLE, theconcept of soil erodibility was introduced as the K fac-tor, which was defined as the mean rate of soil loss froma unit (standard) plot divided by the rainfall erosivityindex:

K ¼ AR

ð2Þ

The unit plot is 22.1 m long and 1.83 m wide with auniform slope of 9% in continuous clean-tilled fallowwith tillage performed in the upslope/downslopedirection. In metric units, A is in t ha−1 per year and Ris in MJ mm (ha h)−1 per year, and therefore K is in t h(MJ mm)−1.

To estimate K from measurable soil properties, a soilerodibility nomograph was developed in the early 1970s(Wischmeier et al., 1971). Main factors considered inthe soil erodibility calculation in the USLE include soilparticle compositions (percentages of sand, silt, veryfine sand+silt, and clay), percentage of organic matter,the soil structure code and the soil permeability class(Wischmeier and Mannering, 1969; Wischmeier et al.,1971; Wischmeier and Smith, 1978). In recent years,many authors have used K as an indicator for soil ero-sion (Barthès et al., 1999; Parysow et al., 2003), becauseit is a measure of soil susceptibility to erosion. Theparticle detachment and subsequent transport are in-

fluenced by soil properties, such as particle size dis-tribution, structural stability, organic matter content,soil chemistry, clay mineralogy and water transmissioncharacteristics (Lal, 1994). Many studies indicated thatdifferent soil properties affect soil erodibility, andinfluential soil properties are texture and structure(Troeh et al., 1980), infiltration rates and permeability(Kirkby and Morgan, 1980; Santos et al., 2003; Yu et al.,2006; Zhang et al., 2007), aggregation and its stability(Duiker et al., 2001; Barthès and Roose, 2002; Zhanget al., 2007), organic matter content (Rodríguez et al.,2006), and type of organic matter (Tejada and Gonzalez,2006). The effectiveness of some of these factors can begreatly influenced by soil lime content, although stud-ies on this topic have been very limited. Some reportsshowed that polyvalent cations (especially Ca2+) sig-nificantly control flocculation of colloids and resistanceto erosion (Orts et al., 2000; Charman and Murphy,2000; Duiker et al., 2001).

A limited number of soil erosion studies have beenconducted in Iran where water erosion-related processescause land degradation in many parts (Mahdian, 2005).Our review of research on soil erosion in Iran showedthat investigation on effects of soil properties on erod-ibility and applicability assessment of the USLE beganin early 1996 (Karimzadeh and Hajabbasi, 1996). Thesubsequent studies showed the significant relationshipof erodibility with various factors such as land use andsoil management (Bahrami et al., 2005) soil textureand organic matter content (Ghasemi and Mohammadi,2003), and particle size distribution (Ghaderi andGhoddosi, 2005; Ghorbani and Bahrami, 2005).

From our review of the literature, it is also evidentthat although many soil erosion studies have been con-ducted throughout the globe, almost no study has per-formed soil erodibility assessment for calcareous soilsmainly found in arid and semi-arid regions. In cal-careous soils, calcium is an important factor determiningaggregate stability and consequently infiltration ratesthat can significantly affect soil erodibility. Therefore,the application of Wischmeier et al.'s (1971) nomographto calcareous soils in arid and semi-arid regions maylead to inaccurate assessment of K (Refahi, 1996),which limits the application of the USLE and its revisedversions to calcareous soils. Accordingly, the presentstudy examines data from field erosion plots with cal-careous soils under natural rainfall events in northwest-ern Iran to: (i) determine the value of K; (ii) recognizesoil physicochemical properties that affect K; and(iii) develop a model for estimating K based on easilymeasurable physicochemical properties of calcareoussoils.

416 A.R. Vaezi et al. / Geomorphology 97 (2008) 414–423

2. Study area and methods

2.1. Study area

The study area is located in Hashtrood City, in asouthern part of East Azarbyjan Province, northwesternIran (Fig. 1). It encompasses an area of 900 km2 between37°18′49′'E–37°35′0′'E, and 46°46′5′'N–47°6′5′'N,and was divided into 36 grids of 5×5 km squares(Fig. 1). The climate is semi-arid with a mean annualtemperature of 13 °C and an average annual precipitationof 322 mm. Soils are generally deep to moderately deepwith clayey texture, ∼10% CaCO3 and 1% organicmatter. They are moderately permeable and usually used

Fig. 1. Geographical location (above) and g

for wheat dry farming (Hakimi, 1986). Slope in the studysite is 5–15%.

2.2. Plot installation and equipping

In order to measure K, the unit plots (Wischmeier andSmith, 1978) were installed in agricultural lands withuniform slopes of 9%, at three replicates with 1.2 mspacing for each 5×5 km grid square (Fig. 2). To avoidthe effects of crop cover on soil erodibility, plots wereinstalled on fallows (Hussein et al., 2007) and weremaintained in a bare condition during the study periodusing herbicide treatment (Rejman et al., 1998). The studyplots were ploughed in the slope direction inMarch 2005.

eneral view (below) of the study site.

Fig. 2. Experimental set up of the measurement system.


The plots were besieged using earthen bermswith a heightof 30 cm to control surfacewatermovement from inside tooutside the area, and vice versa. Runoff collecting systemswith gutters, pipes and 70-liter tanks were installed at thelower part of each plot. A plastic cover was placed onthe ridge of the lower part of the plot and was driven intothe soil with a depth of∼10 cm to transport runoff into thecollection system (Rejman et al., 1998).

2.3. Soil property measurements

Soil samples were taken randomly from three points ineach plot from 0–30 cm depth, and then mixed together.

The mixed samples were air-dried and sieved through a2 mm sieve. The samples were then analyzed in thelaboratory for evaluating physicochemical properties. Theparticle size distribution including sand (0.05–2 mm),very fine sand (0.1–0.05 mm), silt (0.002–0.05 mm) andclay (b0.002 mm) was determined by applying thehydrometer method, based on displacement of a hydrom-eter as a result of change in relative density. The organicmatter content was analyzed with the Walkly–Blackmethod (Nelson and Sommer, 1982). The lime contentas the total neutralizing value (TNV) was determinedbased on the neutralizing rate of carbonates with aceticacid buffered at pH5 (Goh et al., 1993). The available


potassium content was also determined with the ammo-nium acetate extraction method (Knudsen et al., 1982).The aggregate stability was determined using the wet-sieving method based on mean weight diameter (MWD),as proposed by Angers and Mehuys (1993). Water-stableaggregates were determined by placing 100 g aggregateswith diameter N8 mm on the top of the sieve set, andmoved in water for 1 minute. Soil permeability for eachstudy plot was determined based on the final infiltrationrate bymeasuring the one-dimensional water flow into thesoil per unit time using double-ring infiltrometers(Scholten, 1997) at four to six replications in the field.The infiltration measurements were carried out at the endof the dry season (July 2005) in order to minimize theinfluence of antecedent moisture on infiltration rates, asdescribed by Turner and Summer (1978).

2.4. Rainfall property measurements

Rainfall was measured at five locations in theexperimental site (Fig. 1). Four standard rainfall gaugeslocated in the grids 2, 10, 27 and 30 were used tomanually measure rain depth after each event. Homo-geneity of rainfall amounts in the four gauges wasevaluated for 23 storm events using the ANOVAparametric test. An automatic rain gauge belonging tothe Irrigation Office of Hashtrood, located in grid 17,was also used to determine rainfall intensities. Kineticenergy was then computed using the following equation(Wischmeier and Smith, 1978):

E ¼ 210:3þ 87log10I ð3Þ

where E is the kinetic energy per unit area in unit of rainheight (J m2 cm−1) and I is the rainfall intensity (cm h−1).

The kinetic energy resulted from each storm event wasthen obtained by multiplying E by rain depth (cm). Therainfall erosivity index (EI30) for each event was thencomputed by multiplying E by I30 (maximum 30-minuterainfall intensity in cm h−1). The annual rainfall erosivityindex or R (MJ mm ha−1 h−1) was obtained by summingup EI30 for all storm events during the study year.

Table 1Descriptive statistics of physicochemical properties of studied soils

Descriptive statistics SA(%) VFS(%) SI(%) CC(%) GR(%)

Mean 36.7 16.8 31.6 32.0 9.9Minimum 24.8 8.9 20.2 20.8 5.3Maximum 48.3 24.8 44.8 42.2 14.8Standard deviation 6.7 3.8 7.1 5.7 2.4

SA: sand; VFS: very fine sand; SI: silt; CC: clay; GR: gravel; PO: potassiuaggregate stability; PE: permeability.

2.5. Soil loss measurement

The entire runoff volume including eroded sedi-ment was measured on storm basis using the col-lection tanks. The collected runoff from each plot wasthen mixed thoroughly and a sample was taken fordetermining sediment concentration by the weigh (Guy,1975). The per-storm and annual soil losses were thenobtained.

2.6. Determination of soil erodibility factor

The soil erodibility factor (K) was determined basedon annual soil loss per the unit annual rainfall erosivityindex. The K value for each grid was obtained as themean of the measured K values in the three plots.

2.7. Development of an estimation model

Soil predictor variables were tested for normalityusing the Kolmogorov–Smirnov test before modeldevelopment. An attempt was then made to developan empirical model for evaluating calcareous soilerodibility in the study area with the help of principalcomponent analysis (PCA) and multivariate regressionmodels. The PCA mathematically transformed severalcorrelated variables (soil properties) into fewer uncor-related variables (Jollife, 1986). Multivariable analysiswas also carried out to quantitatively elucidate theerodibility-determining factors. The stepwise multipleregression was performed using the scores of theextracted principal components and the soil properties.The statistical software SPSS13 was used for facilitatingthe entire analyses.

3. Results and discussion

3.1. Soil properties

The physicochemical properties of the sampled soilsare reported in Table 1. The results of soil analysesshowed that they had low organic matter content and

PO(mg kg−1) OM(%) TNV(%) MWD(mm) PE(cm h−1)

314.7 1.08 12.7 1.13 3.5237.4 0.70 4.15 0.27 1.4390.5 2.09 23.7 1.91 5.825.4 0.25 5.2 0.44 1.2

m; OM: organic matter; TNV: total neutralizing value; MWD: water-

Table 2Characteristics of 23 storm events during the study period (April2005–March 2006)

No. Date Duration(h)

Depth(mm)

30-minuteintensity(mm h−1)

Meanrunoff(lit. perplot)

EI30(MJ mmha−1 hr−1)

1 2 April 1.15 2.55 3.0 2.15 1.182 3 April 1.36 3.65 3.2 4.31 1.883 12 April 3.40 13.70 15.2 32.49 36.644 13 April 1.00 2.70 3.0 4.18 1.305 16 April 1.30 4.80 4.8 9.36 3.986 17 April 1.10 3.70 5.4 7.33 3.387 26 April 6.98 17.85 7.6 25.08 21.558 27 April 0.70 2.80 5.4 12.69 2.669 3 May 1.50 8.35 8.4 16.66 13.2010 4 May 0.71 2.00 3.8 3.43 1.2411 7 May 0.73 2.50 4.8 9.46 2.0412 9 May 1.15 4.20 5.0 12.23 3.6213 14 May 1.18 11.90 21.8 39.72 54.6314 15 May 0.90 12.40 22.8 49.38 62.8815 16 May 1.60 8.10 25.0 28.24 37.3716 19 May 2.10 12.50 13.0 22.53 30.9917 20 May 1.30 10.40 12.2 28.99 25.6118 31 May 0.50 3.50 7.0 14.76 4.8219 2 June 0.77 1.90 3.6 2.48 1.0820 28 September 1.38 15.30 22.4 36.99 73.4021 4 February 0.65 4.00 6.8 23.59 5.2222 9 February 0.58 2.40 4.6 8.91 1.9523 9 March 4.00 9.30 4.4 24.20 6.35


high potassium content. The soils were limey with a lowaggregate stability and a moderate permeability. The soiltextures were mainly clay loam and loam. The entire soilsample collection can be grouped as Inceptisols.

3.2. Rainfall characteristics

The annual precipitation in the study site was 447 mmand the rainfall intensity varied from 0.1 to 13.8 mm h−1

Table 3Mean soil loss and soil erodibility of the unit plots located in each grid

Grid no. Soil erodibility(t h MJ−1 mm−1)

Soil loss(t ha−1year−1)

Grid no. Soil erodibility(t h MJ−1 mm−

1 0.0066 2.968 13 0.00732 0.0064 2.887 14 0.00513 0.0065 2.926 15 0.00424 0.0036 1.633 16 0.00085 0.0057 2.563 17 0.00246 0.0013 0.578 18 0.00497 0.0013 0.600 19 0.00618 0.0028 1.270 20 0.00339 0.0043 1.931 21 0.002410 0.0056 2.513 22 0.002911 0.0050 2.264 23 0.005012 0.0021 0.921 24 0.0041

with a mean of 2.8 mm h−1 and a mean duration time of3.9 h. The maximum and minimum rainfall occurredduring May (105 mm) and July (0.1 mm), respectively.Some 60 rainfall events occurred during the study period,and 23 events generated runoff and therefore caused soilloss at the plots. The insignificant difference in rainfallamounts on a storm basis among the rain gauges wasconfirmed using ANOVA (pb0.198), allowing thetransformation of data from the rain gauges to the entirestudy area. The detailed characteristics of the 23 eventshave been summarized in Table 2. The mean amount ofrainfall which yielded sediment in grids 2, 10, 17 and 30were 7.22, 6.59, 6.98 and 6.84 mm, respectively. Theannual rainfall erosivity index (R) was also obtained as448.7 MJ mm (ha h)−1 based on data from grid 17.

3.3. Soil properties–K factor relationships

Annual values of soil loss in the study plots (Fig. 1)varied from 0.364 to 3.289 t ha−1 with a mean of 1.869 tha−1 and a standard deviation of 0.793 t ha−1 (Table 3).Mean K varied from 0.0008 to 0.0073 t h MJ−1 mm−1,with an average and a standard deviation of 0.0043 thMJ−1 mm−1 and 0.0018 t hMJ−1 mm−1, respectively. AT-test indicated that themeanK is significantly ( pb0.001)lower than that estimated using the USLE monogram(0.0359 t h MJ−1 mm−1) by a factor of 8.35. Thecorrelation between the measuredK andK estimated fromthe monogram is also low (R2=0.16, p=0.014, n=36).

The relationship between the measured K and soilproperties was initially evaluated using a correlationmatrix (Table 4).K significantly correlates with the contentof sand ( pb0.01, n=36), silt ( pb0.001), silt+very finesand (pb0.01), organic matter ( pb0.01), and lime( pb0.01), as well as water-aggregate stability ( pb0.05)and permeability ( pb0.001). K positively correlates with

1)Soil loss(t ha−1year−1)

Grid no. Soil erodibility(t h MJ−1 mm−1)

Soil loss(t ha−1year−1)

3.289 25 0.0070 3.1482.282 26 0.0029 1.2921.902 27 0.0021 0.9580.364 28 0.0066 2.9641.067 29 0.0044 1.9552.203 30 0.0054 2.4272.728 31 0.0034 1.5441.482 32 0.0017 0.7681.060 33 0.0042 1.9051.279 34 0.0036 1.6192.253 35 0.0045 2.0211.839 36 0.0042 1.880

Table 4Correlation matrix of soil properties and soil erodibility (K) at the 108 plots in the 36 grids

Variable SA SI SI+VFS CC GR PO OM TNV MWD PE K

SA 1SI −0.670⁎⁎⁎ 1SI+VFS −0.234 0.853⁎⁎⁎ 1CC −0.379⁎ −0.400⁎⁎ −0.733⁎⁎⁎ 1GR 0.018 0.024 0.000 −0.058 1PO −0.078 −0.177 −0.221 0.309⁎ 0.093 1OM 0.061 −0.228 −0.323⁎ 0.208 0.165 0.059 1TNV −0.269 0.174 0.046 0.028 −0.030 −0.092 0.046 1MWD −0.457⁎⁎ −0.123 −0.419⁎⁎ 0.705⁎⁎⁎ −0.091 0.217 0.293⁎ 0.481⁎⁎ 1PE 0.569⁎⁎⁎ −0.553⁎⁎⁎ −0.392⁎⁎ −0.069 0.091 0.080 0.541⁎⁎⁎ 0.295⁎ 0.134 1K −0.470⁎⁎ 0.613⁎⁎⁎ 0.514⁎⁎ −0.133 −0.157 −0.125 −0.478⁎⁎ −0.410⁎⁎ −0.350⁎ −0.882⁎⁎⁎ 1

SA: sand; SI: silt; VFS: very fine sand; CC: clay; GR: gravel; PO: potassium; OM: organic matter; TNV: total neutralizing value; MWD: water-aggregate stability; PE: permeability; K: erodibility factor of the USLE, ⁎⁎⁎. Correlation significant at pb0.001, ⁎⁎. Correlation significant atpb0.01, ⁎. Correlation significant at pb0.05.

Table 5Principle components related to soil properties

Variable Component

1 2 3 4

SA 0.837 -0.365 -0.337 -0.047SI −0.828 −0.365 0.197 0.130CC 0.005 0.925 0.103 −0.113OM 0.400 0.236 0.358 0.455TNV −0.051 −0.052 0.889 −0.037GR −0.032 −0.034 −0.085 0.899PO 0.047 0.607 −0.251 0.256MWD −0.037 0.715 0.616 −0.069PE 0.849 −0.054 0.379 0.210

SA: sand; SI: silt; CC: clay; OM: organic matter; TNV: total neutralizingvalue; GR: gravel; PO: potassium; MWD: water-aggregate stability; PE:permeability.


silt and silt+very fine sand, but negatively with the water-aggregate stability (MWD). This result accords withCharman and Murphy (2000), who found that surfaceaggregates can reduce erodibility because their stablemassincreases resistance to raindrop detachment. The water-aggregate stability was also significantly correlated withthe content of sand ( pb0.01), clay ( pb0.001), organicmatter ( pb0.05) and lime ( pb0.01). In contrast, sanddecrease the water-aggregate stability, which agrees withVeihe (2002). The role of the clay and organic matter as abinding agent in improving the aggregation of soil colloidswas also reported by Skidmore and Layton (1992) andBartoli et al. (1992).

The negative correlation betweenK and soil permeabil-ity accords with Yu et al. (2006). Table 4 also indicated thatsoil permeability significantly correlates with the content ofsand ( pb0.001), silt ( pb0.001), silt+very fine sand( pb0.001), organic matter ( pb0.001) and lime ( pb0.05).In contrast to sand, organic matter and lime, an increase insilt content decreases soil permeability. The effects of sandand organic matter to increase infiltration and decrease Khave been reported by Santos et al. (2003), Evrendliek et al.(2004),Tejada and Gonzalez (2006), and Rodríguez et al.(2006). In fact organic matter significantly influences theform and stability of soil structure and correspondingly thesoil infiltration rate (Scholten, 1997; Zhang et al., 2007).The effect of aggregate stability and permeability onK alsoaccords with Barthès and Roose, (2002),Gupta (2002) andHoyos (2005). The contents of fine sand and silt positivelycorrelate with K because of their high susceptibility to soildetachment and transport (Wischmeier and Mannering,1969; Duiker et al., 2001). The negative correlationbetween clay content and K agrees with Dimoyianniset al. (1998),Zhang et al. (2004) and Rodríguez et al.

(2006). The negative influence of lime content on Kaccords with Castro and Logan (1991),Orts et al. (2000),Charman andMurphy (2000), andDuiker et al. (2001)whofound that Ca2+ affects flocculation and aggregate stability,and hence decreases erodibility.

To develop an empirical model for estimating K forcalcareous soils, soil properties were categorized into fourmain groups using PCA (Table 5). A soil property with thehighest explanation coefficient in each component (i.e.clay, lime and gravel content and permeability) was thenselected as a representative factor affecting K. Therelationship betweenK and the selectedmain soil propertieswas established using multiple regression, whose detailsare shown in Table 6. The results indicate that thecombination of the four properties significantly affects K(R2=0.85, pb0.001, n=36). The decreasing effect of clay

Table 8Regression coefficients, standard error and significant levels ofrelationship between the erodibility and the three easily-measurablesoil properties (n=36)

Model Unstandardizedcoefficients

Standardizedcoefficients

T Significancelevel (p)

B Std.error

Beta

Constant 0.01852 0.00195 9.472 b0.001SA −0.00021 0.00003 −0.794 −7.008 b0.001CC −0.00013 0.00003 −0.415 −3.817 0.001TNV −0.00021 0.00004 −0.611 −5.844 b0.001

SA: sand; CC: clay; TNV: total neutralizing value.

Table 6Regression coefficients, standard error and significance levels of themultiple regression between calcareous soil erodibility and four mainsoil properties at the 36 grids (n=36)



T Significancelevel ( p)

B Std.error

Beta

Constant 0.01197 0.00099 12.073 b0.001CC −5.9×10−5 0.00002 −0.192 −2.716 0.011TNV −5.4×10−5 0.00002 −0.160 −2.170 0.038PE −0.00127 0.00011 −0.840 −11.375 b0.001GR −7.3×10−5 0.00005 −0.097 −1.376 0.179

CC: clay; TNV: total neutralizing value; PR: permeability; GR: gravel.


content, lime content and permeability was significant atlevels of b0.05, 0.05 and 0.001, respectively. The effect ofgravel content on K is not significant indicating that itsinfluence on the final determination coefficient (R2) is verylow and it could be omitted from the analysis. The revisedrelationship with the other three soil properties was thendeveloped (Table 7), which confirms that the combinationof the three properties significantly affect K (R2=0.84,pb0.001, n=36). Based on the results shown in Table 7,the following relationship was developed:

K ¼ 0:0123� 5:7� 10�5 CC� 5:2� 10�5TNV�0:00129PE; R2 ¼ 0:84

ð4Þ

where CC is clay content in %, TNV is total neutralizingvalue in %, PE is permeability in cm h−1, and K is in t h(MJ mm)−1.

Tables 6 and 7 show that soil permeability is the mostimportant factor for estimating K in the studiedcalcareous soils. The correlation matrix (Table 4)suggests that even if soil permeability is difficult tomeasure, it may be estimated using the content of sand,silt or organic matter with 56.9%, 55.3% and 54.1%

Table 7Regression coefficients, standard error and significance levels of themultiple regression between calcareous soil erodibility and three mainsoil properties at the 36 grids (n=36)



T Significancelevel (p)

B Std.error

Beta

Constant 0.0123 0.00085 13.267 b0.001CC −5.7×10−5 0.00002 −0.187 −2.616 0.013TNV −5.2×10−5 0.00003 −0.154 −2.066 0.047PE −0.00129 0.00011 −0.850 −11.374 b0.001

CC: clay; TNV: total neutralizing value; PE: permeability.

explanation variance, respectively. It is also revealedthat K could also be estimated from sand, clay and limecontents (Table 8; pb0.001, R2 =0.68):

K ¼ 0:0185� 2:1� 10�5SA� 1:3� 10�5CC�2:1� 10�5PE: R2 ¼ 0:68

ð5Þ

where SA is sand content in %.

4. Conclusions

A study was conducted in Hashtrood City, Iran, toevaluate and model calcareous soil erodibility (K). Thestudy took place in standard erosion plots and the resultswere used to develop a new and easily applicableprediction model of K. The contents of sand, silt, organicmatter, and lime as well as water-aggregate stability andpermeability significantly affect K, although they havenot directly been taken into account by traditional soilerodibility estimation methods. Aggregate stability andsoil permeability, which were strongly influenced by thesoil texture, as well as organic matter content and limecontent considerably control K. Soil permeability isparticularly important because of its major role ingenerating runoff. In the study area, soil permeabilitymarkedly increases with the contents of sand, organicmatter, and limes. Aggregate stability also increasessignificantly with organic matter and lime contents.Consequently, organic matter and lime contents are twomajor factors affecting K. In calcareous soils, the effect oflime is highly important, although it has not beenconsidered in the USLE nomograph. Indeed, measuredK values are significantly lower than those estimatedfrom the USLE monogram, and their correlation coeffi-cient is low. Statistically acceptable and easily applicablemodels were established betweenK and the selected mainsoil properties based on principle component analysis and


multiple regression techniques. Further studies on otherscalcareous soils in arid and semi-arid regions is needed todevelop more accurate models for estimating K.

Acknowledgments

We thank the Institute of Agriculture Research and theUniversity of Zanjan for analyzing physical and chemicalsoil properties. We also acknowledge Hassan Fayyazi forproviding rainfall data from the automatic rain gaugebelonging to the Irrigation Office of Hashtrood. We areindebted to Reza Akhond for his help in statisticalanalysis, Majid Vaezi for providing transportationservices during field surveys, and local farmers forsupplying farming land for establishing the erosion plots.

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modeling the usle k-factor for calcareous soils in northwestern iran

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