Geoderma 192 (2013) 430–436

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Persistent subsoil compaction and its effects on preferential flow patterns in a loamytill soil

A. Etana a,⁎, M. Larsbo a, T. Keller a,b, J. Arvidsson a, P. Schjønning c, J. Forkman d, N. Jarvis a

a Swedish University of Agricultural Sciences, Department of Soil & Environment, P.O. Box 7014, SE-75007 Uppsala, Swedenb Agroscope Research Station ART, Department of Natural Resources & Agriculture, Reckenholzstrasse 191, CH-8046 Zürich, Switzerlandc Aarhus University, Department of Agroecology, Research Centre Foulum, P.O. Box 50, DK-8830 Tjele, Denmarkd Swedish University of Agricultural Sciences, Department of Crop Production Ecology, Box 7043, SE-75007 Uppsala, Sweden

Abbreviations: BB, Brilliant Blue FCF; DC, dye coveragerated hydraulic conductivity; ME, metric entropy; NF, num⁎ Corresponding author. Tel.: +46 18671259.

E-mail address: ararso.etana@slu.se (A. Etana).

0016-7061/$ – see front matter © 2012 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.geoderma.2012.08.015

a b s t r a c t

a r t i c l e i n f o

Article history:Received 31 January 2012Received in revised form 17 July 2012Accepted 13 August 2012Available online 20 November 2012

Keywords:Dye coverageDye tracingFlowpath widthHydraulic conductivityMetric entropySoil compaction

Persistence of subsoil compaction was investigated in a field experiment in southern Sweden. The investiga-tion compared two treatments (control and compaction by four passes track-by-track), 14 years after the ex-perimental traffic. The compaction experiment was carried out in 1995 with a 6-row sugar beet harvesterwith a wheel load of c. 10.4 Mg. Investigations included penetration resistance, bulk density, water retention,saturated hydraulic conductivity, in situ near-saturated hydraulic conductivity, and dye tracing experiments.The measurements of penetration resistance and bulk density clearly showed the persistence of subsoil com-paction. In addition, both macroporosity and saturated and near-saturated hydraulic conductivity weresmaller in the compacted plots, although these differences were not statistically significant. Dye tracingallowed us to visualize flow patterns in the soil and to quantitatively distinguish compacted and non-compacted subsoil profiles. Despite significant soil textural heterogeneity across the experimental field, thedye tracing data showed that persistent compaction may enhance preferential flow.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

In addition to supporting crop growth, subsoil serves as a filter forremoving surface-applied contaminants. Its filtering efficiency how-ever depends, among other factors, on the pore structure of the soil.In a soil with a wide pore-size distribution, which allows a relativelyuniform water flow, the probability of attenuation of solutes is high.In contrast, in a soil with irregular and especially bimodal pore-sizedistribution, for example when dense regions alternate with cracksor biopores, preferential or bypass flow can reduce the filtering capac-ity of the soil (Jarvis, 2007).

The ever-increasing demand for food and biofuel production hasled to an increase in the weight of agricultural vehicles, which causessubsoil compaction (Hadas, 1994; Jones et al., 2003; Smith andDickson, 1990). The key natural processes that are important for therestoration of compacted soils are the swelling–shrinkage behaviorof soil governed by drying and wetting, root proliferation, and earth-worm bioturbation (Dexter 1991Dexter 1991). Also, freezing andthawing cycles may play a positive role in soil structure evolution.

; FW, flowpath width; Ks, satu-ber of flowpaths.

rights reserved.

However, unlike in the topsoil, the impact of these natural processeson amelioration of soil compaction seems limited in the subsoil(Alakukku and Elonen, 1994; Blake, et al., 1976; Etana andHåkansson, 1994; Schjønning and Rasmussen, 1994; Voorhees,2000). Mechanical loosening of the subsoil may not be economicallysustainable, and in some conditions may lead to severe re-compactionof the loosened soil layer (Munkholm et al., 2005a). Subsoiling hasbeen observed to even reduce root intensity at depth (Munkholm etal., 2005b) and induce yield reduction (Olesen and Munkholm, 2008;Soane et al., 1987). Research on biological subsoil amelioration has notproved promising. For example, Cresswell and Kirkegaard (1995)found that roots of Brassica napus L. were unable to create new pores,relying instead on pre-existing ones in very dense subsoil.

Subsoil compaction is known to restrict rooting and affect cropyield (Hammel, 1994; Kirkegaard et al., 1992; Raper, 2005), butlong-term effects on other soil functions and processes are not welldocumented. In particular, the environmental impacts of subsoilcompaction are less well investigated. It is well known that compac-tion may limit infiltration capacity and therefore exacerbate waterquality problems associated with surface runoff, but it has alsobeen suggested that it may promote preferential flow and may in-crease the risk of agrochemical leaching to groundwater and surfacewaters via drainage systems (Jarvis, 2007). Few studies have beencarried out to test this hypothesis, but the results of Kulli et al.(2003b) and Alaoui and Goetz (2008) would seem to support it.

431A. Etana et al. / Geoderma 192 (2013) 430–436

Subsoil compaction with heavy machinery, and its persistence inSwedish arable soils have been investigated since the 1970s (Etanaand Håkansson, 1994; Håkansson, 1985). Arvidsson (2001) conducteda series of subsoil compaction experiments using modern heavy sugarbeet harvesters. This paper presents investigations of soil mechanicaland hydraulic properties and dye tracing made 14 years after the com-paction was carried out in one of these experiments in 1995 in order toinvestigate the persistence of subsoil compaction, and its effects onpreferential flow patterns in the subsoil.

2. Materials and methods

2.1. Site and compaction experiment

The subsoil compaction experiment was conducted in 1995 atBrahmehem farm (55°49′N, 13°11′E). The soil has developed inglacial till and is classified as a Mollic Endogleyic Luvisol accordingto the WRB soil classification system (fao.org). The experiment in-cluded five treatments but in the present investigation we com-pared two treatments, control or no experimental traffic, andcompaction by four passes track-by-track. The compaction wasdone with a 6-row sugar beet harvester with a wheel load of c.10.4 Mg. The experiment was carried out in autumn, at the usualharvest time for sugar beets. Volumetric water content at thetime of compaction was 30% at 30–35 cm depth and 29% at50–55 cm depth. Each treatment was replicated four times in ablock design. More details on the experiments and measurementsof various physical, hydraulic and mechanical properties thatwere made 1 and 4 years after compaction are given in Arvidsson(2001). Since 1999, the field has been managed according to localfarming practice. The field is usually ploughed annually to a depthof 25 cm but reduced tillage has been practiced occasionally.More details are given in Berisso et al. (2012). After the compactionexperiment, sugar beet was grown four times, the latest in 2006,three years prior to our measurements.

Table 1Particle size distribution (g 100 g−1) in the subsoil. Data for the topsoil is found in Arvidss

Control

Depth (cm) Block b2 μm 2–50 μm 0.5–2 mm

30–40 I 24 23 47II 26 27 46III 18 29 47IV 19 25 49

40–50 I 26 25 39II 23 27 48III 18 26 47IV 17 28 49

50–60 I 21 28 44II 21 26 46III 19 25 45IV 20 28 46

60–70 I 21 24 49II 22 27 48III 9 26 60IV 19 30 43

70–80 I 24 28 41II 26 36 33III 14 28 53IV 19 25 48

80–90 I 20 24 47II 28 40 28III 17 26 45IV 25 31 35

90–100 I 11 14 70II 34 47 19III 15 28 50IV 21 35 39

2.2. Soil physical and hydraulic properties

Particle size distributionwas determined in all eight plots at 10‐cm in-tervals down to 1 mby the pipette and sievingmethods (Gee and Bauder,1986; see Table 1). Particle density was determined by the submersionmethod (Blake and Hartge, 1986). Soil penetration resistance was mea-sured in April 2009, when the soil was approximately at field capacity,using a hand-driven Eijkelkamp penetrologger 06.15 (www.eijkelkamp.com) equipped with a cone of 1.0 cm2 base area and a 60o top angle(according to NEN 5140). Ten measurements per plot were made to48 cm depth. All other measurements and sampling were done in May2009, when the soil was slightly drier than field capacity (Table 2).

For measurement of dry bulk density, saturated hydraulic conduc-tivity (Ks) and water retention characteristics six core samples perplot and treatment were collected in stainless steel cylinders (50 mmin height, 72 mm in diameter) at 30–35, 50–55, 70–75 and 90–95 cmdepths. Water retention was measured on sand boxes at a pressure po-tential of−100 cm,while Ks wasmeasured by a constant-headmethod(Andersson, 1955). Steady state infiltrationwasmeasured in the field at30 cm depth at pressure potentials of −10,−30,−60 and −100 mmusing a tension infiltrometerwith a basal porous plate 200 mmin diam-eter (www.soilmeasurement.com; Casey and Derby, 2002). Two mea-surements were made per plot. Hydraulic conductivities in the range−100 mm to −10 mm were obtained using Wooding's equation(Ankeny et al., 1991; Messing and Jarvis, 1993).

2.3. Dye tracing

Dye tracing experiments with Brilliant Blue FCF (Flury and Flühler,1995) were performed in May 2009 with a rainfall simulator on plots1.6×1.6 m in size. We applied 47 mm of a solution containing 2 gBrilliant Blue FCF (BB) per L water, at an intensity of 34 mm h−1.After c. 24 h, a soil pit was excavated and vertical profiles (>1 mwide and >1 m deep) were prepared. The profiles were thenphotographed with a digital camera (Olympus E500). Three profiles

on (2001).

Compacted

2–20 mm b2 μm 2–50 μm 0.5–2 mm 2–20 mm

6 22 24 41 131 19 30 40 116 15 22 47 167 19 23 43 15

10 21 26 42 112 24 28 41 79 16 17 62 56 17 21 55 77 12 22 51 157 18 25 48 9

11 16 21 55 86 21 25 48 66 21 25 45 93 18 24 44 145 16 25 53 68 18 25 42 157 20 24 40 165 22 26 45 75 13 23 62 28 14 21 54 119 20 28 45 74 20 27 39 14

12 11 19 61 99 11 26 51 125 18 28 40 140 21 23 39 177 19 25 48 85 6 10 77 7

-50

-40

-30

-20

-10

00 1 2 3

Dep

th (

cm)

Penetration resistance (MPa)

ControlCompacted Depth, cm P value

0-2627-34

36-4142-46

ns< 0.050

35 0.077< 0.050< 0.005

47 0.01348 ns

Fig. 1. Penetration resistance to 48 cm depth.

432 A. Etana et al. / Geoderma 192 (2013) 430–436

per pit at 10 cm separation were photographed. Due to light varia-tions during the day, only two of the pictures were used for furtherimage analysis.

Image analysis was carried out using the image analysis toolbox inMATLAB Version 7.11.0.504 (R2010b). The depth from the soil surfacedown to 80 cm was used for the analysis. The brightness of the im-ages was linearly adjusted so that all images had the same overallmean brightness. The adjusted images were then converted to binary(black and white) images using a logistic regression approach. Tenpixels considered representative for dyed soil and 10 pixels consid-ered representative for undyed soil were sampled from one profilefrom each pit. A logistic function was then fitted to the sampleddata using the mnrfit function in MATLAB. The resulting logistic func-tion was used to calculate the probability of a pixel being dyed. If thisprobability exceeded 0.5, the pixel was classified as dyed. Finally, amedian filter was used to reduce the noise in the images.

The resulting binary images are influenced by the subjective sam-pling of dyed and undyed pixels. This means that all quantifications ofdye patterns are also to some extent subjective. However, because thesame logistic functions were used for all images it is still possible toevaluate differences between treatments.

We used four measures to characterise the flow patterns, i) dyecoverage (DC), ii) the average number of flowpaths (NF), iii) the av-erage width of flowpaths (FW), and iv) the metric entropy (ME).The first three measures were calculated for 2.5 cm thick layerswhile ME was calculated for one horizontal binary sequence in themiddle of 2.5 cm thick layers. Dye coverage is simply given by thenumber of dyed pixels divided by the total number of pixels in alayer. The average number of flowpaths is estimated from the averagenumber of changes from undyed to dyed soil in the horizontal binarysequences within a layer divided by the width of the profile. It pro-vides a simple measure which reflects the heterogeneity of the dyedistribution. For example a large number of small vertically orientedbiopores would result in a large NF. The average width of flowpathsis given by:

FW ¼ DC=NF

The metric entropy (Pachepsky et al., 2006) defined as theShannon entropy (Shannon, 1948) normalized by the word length,L, is given by:

Hμ Lð Þ ¼ −X2L

i¼1

pL;i log2pL;iL

where p is the probability of the i:th state estimated by the corre-sponding relative frequency. For L=2, which was used in this study,there are 2L possible states (i.e. 00, 01, 10, and 11 where 0 and 1 de-note undyed and dyed pixels, respectively). The metric entropy is ameasure of the information content of a binary sequence of symbols.It vanishes to zero for constant sequences and is equal to 1 for uni-formly distributed random sequences. The Shannon entropy has pre-viously been successfully used to analyze dye patterns in soil (Wanget al., 2009). Mean values for the two profiles in a pit were used inthe statistical analysis for all four measures.

2.4. Statistical analysis

A linear least squares model (JMP, Version 9. SAS Institute Inc.) wasused for variance analysis of physical, mechanical and hydraulic proper-ties. In the fitted model, subsamples (repeated measurements) were in-cluded in the interactions with treatment and block. Soil texture variedacross the experimental field, especially in the lower subsoil (Table 1).For example, the sand fraction, which ranged from 19 to 77 g 100 g−1,was lowest in block 2 at 0.9–1 m in the control treatment and greatestin block 4 at the same depth in the compacted treatment. Thus, soil

texture was included as a covariate (Gomez and Gomez, 1984). Thesand fraction (dominant in almost all layers and across the experimentalfield) was used as a covariate because it reduced the standard error andimproved the significance of the treatment effect. Prior to statistical anal-ysis, measured values of saturated and near-saturated hydraulic conduc-tivity were log-transformed, because hydraulic conductivity is assumedto be a log-normally distributed variable (Bathke and Cassel, 1991; Tiejeand Hennings, 1996). In all cases, the Kenward and Rogermethod (Littellet al., 2006) was used for the calculation of degrees of freedom.

The four measures used to characterize flow patterns (DC, NF, FWand ME) were analyzed using the mixed procedure in SAS version 9.2(Littell et al., 2006). The square root transformation was applied inthe analysis of DC in order to produce homoscedastic residuals. Themodel comprised the factors treatment, block and depth, as well asthe treatment-by-depth and block-by-depth interactions. The sandfraction was included in the model as a covariate. Since the upperlayer in the dyed soil profile influences the lower layers, the correla-tion between observations from the same plot should be consideredin the statistical analysis (Kulli et al., 2003a). Hence, we modeledthe correlation within plots in vertically adjacent layers at 25 mmsteps using the first order autoregressive structure. The Kenwardand Roger method (Littell et al., 2006) was used for the calculationof degrees of freedom.

3. Results and discussion

Penetration resistance in the topsoil was similar for both treat-ments (Fig. 1) showing the alleviation of compaction by annual tillageand natural processes. In the Swedish climate, compaction effects onpenetration resistance in the plough layer normally disappear withinfive years (Arvidsson and Håkansson, 1996). From 28 cm to 48 cmdepth (the maximum depth of the measurements), the penetrationresistance was greater (pb0.05) in the compacted treatment than inthe control. Bulk density was also significantly greater in the subsoilof compacted plots (Table 3), except at 50–55 cm depth. Thus, thecompaction of the subsoil resulting from 4 passes of a sugar beet har-vester 14 years previously proved persistent.

Macroporosity, defined as pores >30 μm in diameter, was greaterin the control than in compacted treatment at 30–35 cm and70–75 cm depths (Table 2). Although not shown here, air-filled po-rosity measured at pressure heads of −6 and −30 cm on cores sam-pled from 30 to 35 cm depth was also significantly lower incompacted soil than in the control treatment (Berisso et al., 2012).Thus, the water retention data also confirms the persistence of thesubsoil compaction. The volume of pores b30 μm, which correspondsto 1m, was similar between the treatments (Table 2). This result is inline with previous studies which show that compaction affects mainlylarge pores (Gupta, et al., 1989; Horn et al., 1995; Schäffer et al.,2007).

Table 3Mean dry bulk density and saturated hydraulic conductivity (geometric mean) at se-lected depths.

Depth Dry bulk density (Mg m−3) Saturated hydraulicconductivity (mm h−1)

Control Compacted P-values Control Compacted P-values

0.30–0.35 m 1.66 1.76 0.003 75.9 10.0 0.3170.50–0.55 m 1.66 1.69 0.515 147 36.0 0.1410.70–0.75 m 1.67 1.72 0.050 96.0 30.0 0.2900.90–0.95 m 1.71 1.78 0.017 8.5 7.8 0.762

Table 2Water content at the time of soil sampling (⊖samp.) and at 1 m suction (⊖1 m) and estimated macroporosity (m3 100 m−3). Macroporosity was computed for equivalent porediameters>30 μm. More data on soil moisture characteristics is given in Berisso et al. (2012).

Parameter 0.30–0.35 m 0.50–0.55 m 0.70–0.75 m 0.90–0.95 m

Cont. Comp. P-value Cont. Comp. P-value Cont. Comp. P-value Cont. Comp. P-value

⊖samp. 28.2 26.6 0.044 27.0 27.4 0.466 28.2 26.4 0.024 28.2 26.5 0.147⊖1 m 31.1 29.8 0.049 29.6 29.3 0.506 29.0 27.6 0.134 28.1 26.8 0.479Macroporosity 6.4 4.0 b0.001 8.9 7.0 0.238 8.9 7.8 0.036 8.0 5.9 0.164

433A. Etana et al. / Geoderma 192 (2013) 430–436

Geometric mean saturated hydraulic conductivity, Ks, was larger inthe control than in the compacted treatment for all measured depths,but the differences were not statistically significant (Table 3), due tothe large within-treatment variability in measured Ks (see Fig. 2).Fig. 2 shows that some very large Ks values (>300 mm h−1) werefound to 70 cm depth in both treatments, presumably reflecting thepresence of macropores that are continuous through the core sampleswhich were only 5 cm in height. The mean values of Ks at 30–35 cmand 50–55 cm depth were much larger than those reported byArvidsson (2001), whichweremeasured c. 1 and 4 years after the com-paction. This suggests that some changes in subsoilmacropore structuremay have occurred, for example through biological processes such asroot or earthworm activity and/or by swell-shrink processes (Löfkvist,2005; Messing and Jarvis, 1990), even if significant treatment differ-ences in physical, mechanical and hydraulic properties still persist14 years after compaction. It should be noted, however, that it is diffi-cult to interpret the differences in Ks between sampling occasions interms of long-term trends, because saturated hydraulic conductivity ishighly sensitive tomacroporosity (Bouma, 1982), which can vary great-ly on a short-term (seasonal) time scale.

Fig. 2. Saturated hydraulic conductivity at selected depths. The bottom and top of thebox are the lower and upper quartiles, respectively, and the line in the box is the me-dian. The box includes 50% of the observations. The length of the box is theinterquartile range (IQR). The ends of the whiskers (dashed lines) indicate the lowestobservation that lies within 1.5 IQR of the lower quartile, and the highest observationstill within 1.5 IQR of the upper quartile.

Near-saturated hydraulic conductivity measured at a 30‐cmdepth (Fig. 3) was smaller in the compacted plots at pressureheads>c. −5 cm, reflecting the smaller macroporosity, but the dif-ferences were not statistically significant. As Fig. 3 shows, atsteady-state, flow in large macropores (>3 mm in equivalent cylin-drical diameter, equivalent to a pressure head of −10 mm) will beinduced by precipitation rates larger than about 2 and 8 mm h−1

in the compacted and control treatments respectively. Thus, prefer-ential flow should in principle be triggered more frequently in thesubsoil of the compacted plots.

Fig. 4 shows the dyed vertical soil profiles for blocks I–IV. To agreater or lesser extent, strong preferential flow was observed inthe subsoil of all plots and in both treatments. The dyed flow path-ways comprised both aggregate surfaces and biopores such as rootand earthworm channels. The soil textures of blocks I and II were sim-ilar in most layers (Table 1) and so were the dye patterns. Apart fromthis, Fig. 5 illustrates that considerable variation in the dye patternwas found across the experimental field. Therefore, we show dye cov-erage (DC) values for individual plots (Fig. 5) as well as mean valuesper treatment (Fig. 6). The DC of the upper subsoil in both treatmentsin block IV was very low but some larger patches of dyed soilappeared in the lower part of the profiles at c. 70 cm depth, especiallyin the compacted treatment (Fig. 5). The large dyed patches observedin block IV seemed correlated with the locations of sandy lenses,which seem to have dispersed the concentrated preferential flow inmacropores that penetrated the compacted upper subsoil, resultingin a more homogeneous matrix flow within these sand lenses. Wesuspect that this flow occurred laterally, as the sand lenses followedthe slight slope at this pit. Compared with the other three blocks,block III had a coarser texture (less clay) at 30–80 cm depth and DCwas also considerably larger in the subsoil, with a peak dyed area ofc. 50%.

Despite large variations inDCbetween plots (Fig. 5) and a significantinteraction between blocks and depths (p=0.012), mean values of DCwere significantly different between the treatments (Fig. 6). The differ-ence between the treatments varied with depth (p=0.006). No statis-tical difference between treatments was observed in the upper part ofthe recently tilled topsoil, where the flow was uniform and dye cover-age was very high (Fig. 6). Dye coverage decreased towards the lowest

0

1

10

0 25 50 75 100

Hyd

raul

ic c

ondu

ctiv

ity (

mm

h -1

)

Pressure head (-mm)

Control

Compacted

Fig. 3. Near-saturated hydraulic conductivity (geometric mean) in the field at 30 cmdepth (bars show standard errors of the mean).

Fig. 4. Soil profiles showing dye patterns in blocks I–IV (From left to right). Upper and lower pictures are from the control and compacted plots, respectively.

434 A. Etana et al. / Geoderma 192 (2013) 430–436

part of the topsoil and at 20–25 cm depth it was statistically larger inthe compacted treatment. We suggest that this was due to a sharp de-crease in hydraulic conductivity in the underlying compacted layer.Temporary accumulation of ‘perched’ water above the compactedlayer would give more time for lateral re-distribution and adsorption

Block I

Block III

Fig. 5. Dye coverage (mean fractions of dyed area) in 2.5 cm thick layers. Data for each

of BB to the soil (Flury and Flühler, 1995; Ketelsen and Meyer-Windel,1999). No statistical difference was observed at 27.5–32.5 cm depth,but at 35–40 cm the DC was significantly larger in the control than inthe compacted treatment, reflecting a confinement ofwater flow and sol-ute transport to a smaller part of the pore space in the compacted

Block II

Block IV

block and treatment is presented separately to illustrate variations between blocks.

-80

-60

-40

-20

0

0,0 0,5 1,0

Dep

th (

cm)

Mean dye coverage (fraction)

Control

Compacted

0-20 ns

20-25 <0.001

25-35 ns35-40 <0.05

40-60 ns

60-80 <0.023

Depth, cm P value

Fig. 6. Average dye coverage in 2.5 cm thick layers.

-80

-60

-40

-20

00 20 40 60 80

Dep

th (

cm)

Number of flowpaths(m-1)

Control

Compacted

Depth, cm P value

0-55.0 ns

55.0-57.5 <0.049

57.5-60.0 ns

60.0-62.5 <0.020

62.5-80.0 ns

Fig. 7. Average number of flow paths in 2.5 cm thick layers.

-80

-60

-40

-20

0

0,0 0,5 1,0

Dep

th (

cm)

Metric entropy(fraction)

Control

Compacted

Depth, cm P value

0-20.0 ns

20.0-27.5 <0.046

27.5-37.5 ns

37.5-42.5 <0.05

42.5-62.5 ns

62.5-80.0 <0.023

Fig. 9. Metric entropy calculated for one binary sequence in the middle of 2.5 cm thicklayers.

435A. Etana et al. / Geoderma 192 (2013) 430–436

treatment. The difference between the treatmentswas again insignificantat 42.5–57.5 cm depth, beneath which the DC was larger in thecompacted treatment than in the control.

-80

-60

-40

-20

0

1 10 100

Dep

th (

cm)

Mean flowpath width (mm)

Control

Compacted

Depth, cm P value

0-20.0 ns

20.0-27.5 <0.015

27.5-37.5 ns

37.5-42.5 <0.030

42.5-67.5 ns

67.5-70.0 <0.002

70.0-72.5

72.5-77.5 <0.022

77.5-80.0 ns

ns

Fig. 8. Average width of flowpaths in 2.5 cm thick layers.

The number of flowpaths (NF) is shown in Fig. 7. The differencesbetween the depths varied with blocks (p=0.015). There were nosignificant differences between the treatments in the topsoil andthe upper subsoil for NF, whereas the number of flowpaths was sig-nificantly larger in the compacted treatment at 55–57.5 and 0.60–62.5 cm depths. Thus, the trends in treatment differences were simi-lar for NF and DC, with similar values in topsoil, smaller values in thecompacted treatment in the upper subsoil, and the opposite trend inthe deeper subsoil. The average width of flowpaths (FW) is shown inFig. 8. FW plotted on a logarithmic scale also shows a similar patternto DC (Fig. 6), with significantly larger FW at the 20–30 cm depth andsignificantly smaller FW at 37.5–42.5 cm depth for the compactedtreatment. These results (Figs. 6–8) indicate that the smaller DC inthe compacted treatment in the upper subsoil was due both to small-er flowpath widths and a smaller number of flowpaths and thatpersistent subsoil compaction has resulted in a generally deeper pen-etration of the dye.

The metric entropy, averaged over blocks, is shown in Fig. 9. Therewere significant interactions between treatments and depths (p=0.013), as well as between blocks and depths (p=0.002). The metricentropy distribution with depth was similar to the DC distribution(Fig. 6) except close to the soil surface where ME decreased withdepth while DC increased. The differences between treatments werealso similar and significant differences between treatments occurredat more or less the same depths. The reason for this similarity is prob-ably that the horizontal sequences were dominated by the states 00(undyed) and 11 (dyed) (not shown). For DC below 0.5 an increasein coverage, therefore, resulted in an increase in the 01 and 10 statesand, hence, an increase in ME. For our case, the analysis of ME did notprovide additional information to the DC analysis.

4. Conclusions

Measurements of physical, hydraulic and mechanical propertiesshowed that significant effects of subsoil compaction had persistedfor 14 years. Penetration resistance was greater in the compactedplots and macroporosity was smaller. Saturated and near-saturatedhydraulic conductivity were also generally smaller, although the dif-ferences were not significant due to large within-treatment variabili-ty. In particular, some extremely large values of saturated hydraulicconductivity were measured in both compacted and control treat-ments, presumably reflecting the presence of continuous macroporesthrough the small core samples. Dye tracing was used to visualizeflow patterns in the soil and to quantitatively distinguish betweencompacted and non-compacted subsoil. Despite soil textural

436 A. Etana et al. / Geoderma 192 (2013) 430–436

heterogeneity across the experimental field, our results suggest thatpersistent subsoil compaction may enhance preferential flow.

Acknowledgments

We are highly indebted to Gert Persson of the Brahmehem farm forhis interest and support that made this research possible. We thankChristina Öhman, Per Björklund, Aron Westlin, Liselott Evasdotter andEmma Petersson (SLU Uppsala, Sweden), Mikael Koppelgaard, Stig T.Rasmussen and Søren Torp (Aarhus University, Research CentreFoulum, Denmark), and Lars Börjesson for help with the field and labo-ratorywork. This work is part of the Scandinavian project ‘Persistent ef-fects of subsoil compaction on soil ecological services and functions,POSEIDON’ (www.poseidon-nordic.dk) financed by the Danish Minis-try of Food, Agriculture and Fisheries, and the Swedish Research Councilfor Environment, Agricultural Sciences and Spatial Planning (Formas),which is gratefully acknowledged.

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