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Applied Soil Ecology 61 (2012) 76– 84

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Applied Soil Ecology

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Electromagnetic induction (EMI) measurements as a proxy of earthwormpresence in Southern French vineyards

Egidio Lardoa, Patrice Collb, Edith Le Cadreb, Assunta Maria Palesea,∗, Cécile Villenavec,d,Cristos Xiloyannisa, Giuseppe Celanoa

a Dipartimento di Scienze dei Sistemi Colturali, Forestali e dell’Ambiente, Università degli Studi della Basilicata, Viale dell’Ateneo Lucano, 10 I-85100 Potenza, Italyb Montpellier SupAgro, UMR Eco&Sols, 2 place Pierre Viala, 34060 Montpellier, Francec IRD, UMR Eco&Sols, 2 place Viala, 34060 Montpellier, Franced ELISOL environnement, 2 place Viala, 34060 Montpellier, France

a r t i c l e i n f o

Article history:Received 7 March 2012Received in revised form 8 June 2012Accepted 11 June 2012

Keywords:Soil apparent electrical conductivityEndogeic earthwormsAnecic earthwormsVolumetric water contentSoil managementSoil quality

a b s t r a c t

Electromagnetic induction (EMI) is a non-invasive geophysical technique for studying the physical andchemical features of soil distribution. This paper focuses on the relations of the apparent electrical con-ductivity (ECa) of soil, measured using a Profiler GSSI EMP-400, with soil abiotic and biotic parameters:volumetric water content, pH, electrical conductivity, coarse elements, density and biomass of differentearthworm ecological categories (anecics and endogeics). Trials were carried out in nine commercial vine-yards in Languedoc-Roussillon (France) cultivated according to different soil management technologies:grass cover, chemical weeding and tillage. Hand sorting and a mustard method were used to measurethe earthworms. Strong relationships between the abundance and biomass of anecic and endogeic earth-worms and ECa values were found, particularly in grass covered and chemically weeded plots. This resultis supported by the high determination coefficients found (R2 from 0.53 to 0.94). Our results suggest thatthe EMI signal, as an integrated value of the physical–chemical conditions of soil, represents a real possi-bility for reducing the number of laborious earthworm samplings, by guaranteeing the reliability of theestimates of mean real values of earthworm parameters and their surface spatial distribution. EMI couldtherefore be a useful tool to spatialize and to predict the presence of earthworms in fields in perennialcrops.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

In agro-ecosystems, earthworms play a key role in promotingsoil functioning and then in ecosystems services (Brown et al.,1999; Cenci and Jones, 2009). Some soil properties can be signif-icantly affected by earthworm activities (Cannavacciuolo et al.,1998; Pérès et al., 1998; Eijsackers et al., 2005; Rombke et al.,2005). Earthworms influence soil structure by forming macropores(Lavelle, 1988). They can then decrease the bulk density andavailable moisture content (Stockdill and Cossens, 1966; Lavelleet al., 2004; Eijsackers et al., 2005). In addition, earthworms havebeen shown to increase soil aggregate stability (Li and Ghodrati,1995). However, they are sensitive to both chemical and physicalsoil modifications (Paoletti, 1999; Coll et al., 2011). In fact, asthe soil is their natural habitat, earthworm communities areprofoundly affected by agricultural practices, such as soil tillage,crop residue restitution, the use of fertilizers, organic and mineral

∗ Corresponding author. Tel.: +39 971205274.E-mail addresses: assunta.palese@unibas.it, dinapalese@hotmail.it (A.M. Palese).

pesticides (Chan and Munro, 2001; Eijsackers et al., 2005). Vrsic(2011) found that soil managed with mulching increased theabundance of earthworms. In other cases, chemical weedingreduced earthworm density and soil tillage had negative effects onearthworms, precisely in the soil layers affected by tillage passages(Paoletti et al., 1998; Pérès et al., 2010). Therefore, earthwormabundance and diversity, are often considered as indicators of soilquality (Franzle, 2006; Bispo et al., 2011) and recently, Coll et al.(2011) demonstrated the usefulness of these bioindicators.

Earthworms can be sampled using physical techniques(Bouché, 1969), by chemical techniques using formalin or potas-sium permanganate (Raw, 1959) or by electroshocking procedures(Osterholz, 2006). These methods present logistical difficultiesand several of them can be toxic for the operators (Eichinger et al.,2007). Innovative and harmless earthworm extracting substances,such as mustard, have been used (Chan and Munro, 2001) withencouraging and positive results (Lawrence and Bowers, 2002;Pelosi et al., 2009). Mustard suspension has no phytotoxic effectson plants, as shown by potassium permanganate and formalin, it isnot prohibited by organic practices and it is safe for human beingsand the environment (Valckx et al., 2009). If the expellant method is

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E. Lardo et al. / Applied Soil Ecology 61 (2012) 76– 84 77

Table 1Physical and chemical characteristics of the studied soil (mean ± �). For each parameter, the analytical method used is specified.

Soil parameters Method of analyses Mean value ± SD

Physical parametersSand (%, m/m) NF X 31-107 51.7 ± 6.9Clay (%, m/m) NF X 31-107 15.1 ± 2.6Silt (%, m/m) NF X 31-10 33.2 ± 5.3Coarse particles than >2 mm (g kg−1) MUAFS 55.9 ± 36.6Soil water holding capacity (%, w/w) ISO 10693 (1995) 12.6 ± 0.4Total porosity (%, w/w) ISO 11274 (1998) 0.4 ± 0.0Bulk density (kg dm−3) Musy and Soutter (1991) 1.4 ± 0.1

Chemical parameterspH In water 1:2.5 8.3 ± 0.2Total organic C (g kg−1) ISO 10694 (1995) 14.2 ± 4.7Total N (g kg−1) ISO 13878 (1998) 1.1 ± 0.3P (�g kg−1) Ohno and Zibilske (1991) 2.6 ± 0.9K (mg kg−1) Atomic absorption spectrometry 45.6 ± 17.9Cu (mg kg−1) Atomic absorption spectrometry 0.4 ± 0.2Total calcareous (g of CaCO3 kg−1) NF ISO 11272 (1998) 220.2 ± 71.3CEC (cmol+/kg) NF ISO 23470 (2007) 9.9 ± 1.6

completed by an hand sorting soil monolith in order to have acomplete earthworm sampling, the whole sampling is laborintensive and time consuming, especially in wet, stony and heavysoils (Bouché and Aliaga, 1986).

Non-invasive geophysical techniques, such as electromagneticinduction (EMI), can be useful tools to study the spatial and tem-poral variation of the physical and chemical characteristics of soil(Doolittle et al., 2001; Corwin et al., 2003; Davies, 2004; Morariet al., 2009; Tromp-van Meerveld and McDonnell, 2009). Soil elec-trical conductivity (ECa), as an EMI measurement output, is affectedby the water content in soil as well as water-soluble salt content,soil texture, coarse elements, temperature, clay content, mineral-ogy, cation exchange capacity, organic matter content, and bulkdensity (Rhoades et al., 1999; Corwin and Lesch, 2003; Chen et al.,2004; Domsch and Giebel, 2004; Bronson et al., 2005; Friedman,2005; Samouëlian et al., 2005). Among all above cited soil proper-ties affecting the ECa, water content, soil organic matter and bulkdensity are those which are mostly affected by earthworm activi-ties.

ECa measurements have been successfully applied in soil micro-biological studies within agricultural systems. Johnson et al. (2004)found that zones of ECa provided a point of reference for comparingthe data collected (soil properties, vesicular–arbuscular mycor-rhizal fungi and wheat yields) at different scales.

More recently, Valckx et al. (2009) underlined that ECa map-ping can be a useful tool to represent earthworm distribution andactivity. They demonstrated that the spatial distribution of theearthworm species according to age can be adequately evaluatedby ECa measurements (Valckx et al., 2009). Therefore, the potentialhabitats for biota in soil may be identified and characterizedthrough rapid and time-saving geophysical methods, which couldreduce soil manipulation. Thus, ECa could be used as an estimator(proxy) of earthworm parameter values and of their horizontaldistribution within the field. However, according to Valckx et al.(2009), this pioneer study performed on a single field has to beconfirmed in other different agro-ecosystems as perennial crops.In the line with Valckx et al. (2009), we hypothesized that ECa

could be a valuable sensor or proxy of earthworm abundance andbiomass able to facilitate the monitoring of soil quality affectedby agricultural practices. To achieve our objective, we evaluatedthe relationships between ECa and soil abiotic characteristics in avery short time frame to avoid any changes of the latter. Then, wedetermined earthworm parameters and their relationships withgeophysical measurements. To reach this objective, EMI surveysand soil measurements were performed concurrently within ninecommercial vineyards managed according to different soilmanagements named hereafter as treatments: soil with a

permanent grass cover (GC); grass cover managed with chemicalweeding (CW) and grass cover managed with tillage (T), eachreplicated three times. We studied the vine because it is the mostimportant crop of the experimental area and is usually subjectedto intensive agricultural practices which strongly affect soil quality(Coll et al., 2011).

These treatments are thought to give different earthworm abun-dances (Pérès et al., 1998; Rothwell et al., 2011). First, we evaluatedthe relationships between ECa and soil abiotic characteristics. Then,we determined the earthworm parameters and their relationshipswith geophysical measurements.

2. Materials and methods

2.1. Site description and soil management systems in vineyards

This study was conducted in February 2011 in Saint-Victor la Coste, which is located in the south of France, inthe Languedoc-Roussillon region (44◦04′44.65′′N, 4◦38′47.12′′E;85–100 m elevation). The climate is typically Mediterranean witha 14.7 ◦C mean annual temperature, 776 mm of annual rainfall and1418 mm of annual ETP Penman–Monteith (average values basedon data collected from 2000 to 2010 by Météo-France). The dif-ferent plots exhibited the same soil type, classified as Calcisols(IUSS WRB, 2006), which was sandy clay loam. Soil sampling wasperformed at a 0–15 cm depth. For each plot, ten representativesub-samples were collected from the middle of the inter-row witha gouge auger to make one composite sample to be analyzed inthe laboratory (Morlat and Chaussod, 2008). Mean physical andchemical characteristics of the studied soil are reported in Table 1.

The study was conducted in nine wine grape commercial plots,whose mean area was around 1 ha. Plot mean slope was slight (<2%).They presented different varieties of grape (Vitis vinifera L.) such asCabernet-Sauvignon, Carignan N, Grenache N, Merlot, Mourvèdreand Syrah. The rootstocks were mainly R110 and SO4. The year ofplantation varied from 1950 to 1999, and the vine density rangedfrom 4000 to 5000 vines per hectare. Details of soil managementsystems are reported in Table 2. All field measurements were car-ried out in the span of one week.

2.2. Electromagnetic induction measurements

Electromagnetic induction (EMI) measurements were carriedout on 15th and 16th February 2011, under similar meteorologi-cal conditions, using a multi-frequency EMI sensor (GSSI ProfilerEMP-400) that is able to simultaneously measure up to threefrequencies between 1 kHz and 16 kHz, with an intercoil spacing of

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Table 2Soil management characteristics in the different vineyards studied.

Treatments Grass cover (GC) Chemical weeding (CW) Tillage (T)

Type of viticulture Conventional Conventional Organic (since 2006)Soil management

Rows Chemical weeding(glyphosate at 700 g ha−1

with flazasulfuron at150 g ha−1, 1 y−1)

Chemical weeding(glyphosate, 700 g ha−1, 1 y−1)

Tillage(5–15 cm of depth, 4 y−1)

Interrows Grass-cutting (3–4 y−1)and chemical weeding(glyphosate at 700 g ha−1, 1each 3 years)

Chemical weeding (glyphosate,700 g ha−1, 2 y−1)

Mouldboard ploughing(5–15 cm depth, 7 y−1)

Cover crops Spontaneous permanent Spontaneous temporary Spontaneous temporaryFertilization No No Organic

(Guano, 1.5 t y−1)Plant residues

managementMulching Mulching Burying

Vine phytosanitaryprotectiona

Synthesis and natural(13 treatments y−1)

Synthesis and natural(13 treatments y−1)

Natural(10 treatments y−1)

Tractor frequency(times y−1)

17–18 14–15 17

a Phytosanitary treatments are combined.

1.2 m. In order to obtain variable ECa signals and test the suitabilityof the EMI technique under different conditions, several plots man-aged according to diverse soil management systems were selected.To gain information regarding the soil layers explored by earth-worms, frequencies at 3, 8 and 15 kHz were used and instrumentswere set in vertical dipole mode (VDP). The estimated depth of themagnetic field penetration was about 1.5 m for VDP modes (GeonicsLimited, 1998; Allen et al., 2007). In fact, instrument sensitivityvaried as a non-linear function of depth (McNeil, 1990). ECa valueoutputs, expressed as mS m−1, were used. The data were acquiredevery 0.75 s in continuous mode. ECa measurements were taken bywalking along each inter-row at a speed of about 4–5 km h−1. AllECa acquisition points were georeferenced using the Tripod DataSystem Recon Personal Digital Assistant (PDA) with an integratedBluetooth service and HoluxTM Wide Area Augmentation SystemGlobal Position System (WAAS-GPS) with a differential correctionhorizontal dilution of precision (HDOP). On the basis of these pointsmaps were constructed. To have the highest georeferencing preci-sion, coordinates of ECa values were determined using at least 10satellites. Anomalous ECa data acquired near the vineyard’s metaltrainers were removed (about 8% of total data) to generate cor-rect ECa maps by the kriging method (singular cell 1 × 1 m) usingMagMap2000© and Surfer Golden©.

2.3. Earthworm sampling and analyses

Two days after the EMI surveys, under similar weather condi-tions, earthworms were sampled in the center of the inter-row, inall nine plots (three per soil management system) with four repli-cates per plot according to what suggested by Cluzeau et al. (1999)and Pelosi et al. (2009).

In all the vineyards, the average distance between the earth-worm sampling areas was 56 ± 29 m (mean ± �), ranging from 20to 172 m. First, ten liters of mustard solution, used as expellant,were applied three times at 10-min intervals, on a frame of 1 m2

(1 m × 1 m). The mustard solution was obtained by thoroughly mix-ing commercial mustard, Amora® “fine et forte”, with water ata concentration of 15 g L−1. Emerging earthworms were sampledmanually after every mustard solution application. One monolithof soil 25 cm × 25 cm at a depth of 15 cm was then extracted persubplot. Earthworms were sampled using a hand-sorting method(adapted from Pelosi et al., 2009). Earthworms were then placedin an alcohol solution at 75% to avoid any dehydration during the

field survey and then transferred into a 4% formaldehyde solutionfor storage prior to laboratory analysis.

In the laboratory, earthworms were gently dried before beingweighed and counted after being separated into two ecologicalcategories: endogeics and anecics (Bouché, 1972).

2.4. Soil sampling and physical and chemical analyses

Two days after the earthworm sampling, on 19th February 2011,soil samples were taken in one representative plot of each soil man-agement system showing soil measured parameters (Table 1) nextto mean values of each group in order to reduce sampling time andexpenses. Hereafter, these parameters will be cited as 1GC (grasscover), 1CW (chemical weeding) and 1T (tillage). Soil was sampledwith a systematic procedure adapting both a regular grid size andnumber of soil subsamples to the plot surfaces: for 1GC, 18 bore-holes in a 25 m × 15 m grid; for 1CW, 24 in a 15 m × 10 m grid, andfor 1 T, 20 in a 20 m × 15 m grid.

In each borehole, soil was sampled in the middle of the inter-row at 0–0.15, 0.15–0.30 and 0.30–0.45 m soil depths with a gaugeauger. A total of 186 soil samples were thus collected. For each soilsampling, the soil water content (�w, g g−1) was determined by agravimetric method. Soil volumetric water content (�v, mm mm−1)was obtained by multiplying the gravimetric water content by soilbulk density (Gardner, 1986). Electrical conductivity, as an estima-tor of soil soluble salt content, was measured in the laboratory bya conductivimeter (Crison 525 – Barcelona) according to Rhoades(1982) (dry soil:distilled water ratio 1:2.5).

2.5. Statistical analyses

A descriptive analysis of the measured parameters was carriedout. An ANOVA test was performed on the mean values of the physi-cal and chemical parameters of the soil, earthworm abundance andbiomass in order to highlight any statistical differences. Maps ofsoil water content and ECa data were constructed using a krigingmethod with 1 m × 1 m cell dimensions (Surfer Golden© Software).In order to identify plot specific univariate relationships among ECa,�v, earthworm abundance and biomass, and other soil parameters,linear regressions were calculated using data relative to the specificplot.

Statistical regression analyses, showing a minimization of thesum of square residuals, a normal distribution of the data residues,

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Fig. 1. Apparent electrical conductivity (ECa) maps, at 15 kHz frequency, of the representative plot of each soil management system: grass cover (A), chemical weeding (B)and tillage (C). X and Y axes represent the longitude and latitude of the maps, respectively.

and the highest statistical significant relationship were selected.A Wilcoxon signed-rank test was applied in order to evaluate theerror inherent of the obtained relationship between ECa values and�v data. To perform the test, two datasets were randomly extracted:a training dataset (n = 40) and a validation dataset (n = 22).

The statistical analysis was performed using STATISTICA® 6.0(Stat-Soft, Inc.; www.statsoft.com).

3. Results

3.1. ECa surveys

ECa at 15 kHz showed stronger relationships with soil watercontent and earthworm parameters than those calculated at 8 and3 kHz. Therefore, maps and statistical elaborations were reportedmostly for ECa at 15 kHz (Fig. 1). ECa values (mean ± �), measuredat 15 kHz, were 18.3 ± 12.8, 14.5 ± 6.4 and 14.4 ± 10.8 mS m−1 forCW, GC and T plots, respectively, and they were not significantlydifferent between the soil management systems (P < 0.05). Con-versely, differences in the spatial distribution of ECa values wereobserved at the applied frequencies (data not shown). ECa data,recorded in the representative plots, were 20.5 ± 8.3, 16.5 ± 3.4and 21.5 ± 5.1 mS m−1 in 1CW, 1GC and 1T, respectively. Also inthis case, there were no differences in ECa mean values betweenthe selected plots. The spatial distribution of ECa values recordedat a 15 kHz frequency in these commercial vineyards is shown in

Fig. 1. A more conductive area was found in the middle of the 1GCplot (Fig. 1A). This was around 30% of the total surface and fac-ing southwest and northeast along transverse axis of the field. Ahigh resistivity area was found along the east margin in plot 1CW(Fig. 1B). ECa values rose steadily along the transverse axis of thefield following the increase in �v. A relatively resistive zone wasobserved in the tilled plot (1T) from the outside part towards theinternal part of the vineyard. On the other hand, along the drainagechannels, ECa was typical of a conductive soil (Fig. 1C). Strips withhigh ECa values corresponded to inter-rows temporarily coveredby vegetation.

3.2. Physical and chemical parameters of the soil

Parameters of soil sampled from 1GC, 1CW and 1T plots at dif-ferent depths are reported in Table 3. The ANOVA test for pH valuesdid not show significant differences along the profile of the studiedsoil samples (0–45 cm). In particular, pH-H2O was 8.4, 8.0 and 7.9in 1GC, 1CW and 1T, respectively. The soil electrical conductivity,measured in the laboratory, reached mean values ranging from13.5 to 18.8 mS m−1. Soil electrical conductivity showed a signifi-cant reduction along the profile only in 1CW. The �v significantlyincreased along the soil profile in all plots. Specifically, 1CW showeda high water depletion in the shallow layer compared to the otherplots (Table 3). The soil particles coarser than 5 mm did not showstatistical differences between the various depths studied and

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Table 3Soil physical and chemical parameters measured in the representative plot of each soil management system: grass cover (GC), chemical weeding (CW) and tillage (T). Samplingwas performed at 0–15, 15–30, 30–45 cm depth.

Plots pH Electrical conductivity (mS m−1) Volumetric soil water content (% v/v) Coarse elements (% w/w)

Depth (cm)

0–15 15–30 30–45 0–15 15–30 30–45 0–15 15–30 30–45 0–15 15–30 30–45

1GC 8.41 a 8.42 a 8.40 a 18.01 a 17.82 a 18.85 a 17.02 c 19.42 b 21.41 a 0.39 a 0.51 a 0.71 a1CW 8.03 a 8.01 a 8.01 a 18.24 a 15.45 b 13.53 c 9.38 c 14.18 b 18.78 a 0.52 a 0.51 a 1.01 a1T 7.92 a 7.94 a 7.93 a 18.02 a 17.51 a 17.81 a 16.71 c 19.33 b 23.52 a 0.32 a 0.58 a 0.82 a

For each parameter, means in row with different lowercase letters are statistical different according to Duncan’s test (*P < 0.05).

represented less than 1% (w/w) of the total soil mass in the threeplots.

3.3. Abundance and biomass of earthworms

Data referring to mean earthworm abundance and biomass arereported in Table 4. Within each soil management system, the plotsdid not show significant differences in terms of the abundance andbiomass of earthworms. In the sampled plots, earthworm abun-dance ranged between 0 and 283 individuals per m2 (data notshown), while earthworm biomass varied from 0 to 105 g m−2.The total, anecic and endogeic earthworm abundances were sig-nificantly different between the soil management systems. Meanvalues for anecic, endogeic and total earthworm abundances in CWwere higher than in T and GC plots (Table 4). The lowest values wereobserved for the tilled plots. The earthworm biomass exhibited thelowest values in T plots, while GC and CW were not different froma statistical point of view.

Significant differences between earthworm categories wereobserved within the soil management systems, except for thebiomass values in the T plots (Table 4).

3.4. Relationships between ECa and soil abiotic characteristics

Univariate relationships between ECa and pH, conductivity, �v

and coarse elements were tested.The pH, electrical conductivity and coarse soil fraction values

showed only slight linear relations with ECa measured at 8 kHz (R2

equal to −0.18, 0.16 and 0.41, respectively) (data not shown).With respect to the measured volumetric water contents (�vm),

the best statistical model describing their relationship with ECa

was the linear one (�vm = ˇ1 × ECa + ˇ2). Errors in the relation-ships between ECa values and �v data, evaluated according to theWilcoxon signed-rank test, were low (Fig. 2). An estimated volu-metric soil water content (�ve) was then obtained using the linearmodel equation calculated with all ECa and measured volumet-ric soil water content (�vm) data (�vm = 0.13 × ECa + 6.62; R2 = 0.79;n = 62). Fig. 3A shows a map of soil water contents measured by thegravimetric method for 1CW in a 0–45 cm soil layer. Fig. 3B showsan example of the derivate map of soil water content obtained usingthe relationship between ECa and �vm.

Fig. 2. Scatterplot of measured versus estimated volumetric soil water content (�vm

versus �ve).

3.5. Relationships between ECa and earthworm parameters

Relationships between ECa (15 kHz) and total biomass, totalabundance and categories of earthworms measured, were per-formed in each vineyard. For each plot, earthworm parameterswere estimated using plot-specific relationships with ECa data.Plot-specific relations cannot be applied outside of the investi-gated plot. Estimated values, pooled by soil management, were thenplotted against the measured values (n = 36). Data were stronglydistributed around the bisecting line (Fig. 4). The coefficient ofdetermination (R2) was higher for total abundance than for biomassvalues (83 and 57, means of the three soil management types). Ofthe earthworm categories, endogeics showed higher R2 values thananecics ranging from 66 to 94 (Table 5). In CW plots, the R2 value ofanecic abundance was not significant. In T plots, both anecics abun-dance and biomass did not show statistically significant coefficientsof determination (P < 0.05).

4. Discussion

4.1. Earthworms and agricultural practices

Grass cover and applications of organic matter generally havepositive effects on earthworms in vineyards (Pérès et al., 1998;

Table 4Abundance and biomass of anecic, endogeic and total earthworms measured in the plots belonging to the different soil management system: grass cover (GC), chemicalweeding (CW) and tillage (T).

Abundance (ind m−2) Biomass (g m−2)

Anecics Endogeics Total Anecics Endogeics Total

GC 17.6 ab B 53.7 ab A 71.3 b 31.8 a A 6.8 a B 38.6 aCW 41.4 a B 88.5 a A 129.9 a 42.7 a A 5.7 ab B 48.4 aT 2.7 b B 21.5 b A 24.2 c 1.3 b A 2.3 b A 3.6 b

Means (n = 3 plots × 4 replicates) in columns with different lowercase letters are statistically different according to Duncan’s test (*P < 0.05). Means in lines with uppercaseletters are statistically different according to Duncan’s test (*P < 0.05).

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Fig. 3. Measured (A) and estimated (B) soil water content (�v) maps of the representative plot belonging to chemical weeding (CW) at 0–45 cm soil depth. The estimated �v

was obtained from the relationship between the apparent electrical conductivity (ECa) and the measured �v.

Table 5Linear regressions, forced to intercept zero, between measured and estimated earthworm parameters: abundance and biomass of anecic, endogeic and total. The estimatedparameters were obtained by plot-specific relationships with ECa values.

Grass cover Chemical weeding Tillage

Slope R2 Slope R2 Slope R2

Anecic abundance 0.88 0.67 n.s. n.s.Endogeic abundance 0.90 0.66 0.94 0.78 0.71 0.54Total abundance 0.95 0.67 0.97 0.85 0.97 0.97

Anecic biomass 0.94 0.54 1.00 0.67 n.s.Endogeic biomass 0.90 0.76 0.96 0.80 0.81 0.94Total biomass 0.94 0.59 0.99 0.64 0.76 0.49

Paoletti, 1999; Eisenhauer et al., 2009). Under our experimentalconditions, the abundance and biomass of earthworms showedlower values in the GC than CW plots, although significant differ-ences between the two systems were observed only for the totalearthworm abundance (Table 4). This unexpected result was prob-ably due to not very marked differences in organic matter inputbetween the two systems, GC and CW (Table 2).

The frequent soil tillage for weed control in the T plots couldexplain the low values of the earthworm parameters measured,compared to the other soil management systems, and significantdifferences in abundance between earthworm ecological cate-gories (Table 4). This evidence confirms the results of severalauthors who highlighted the negative effects of tillage on soil com-paction and earthworm density and biomass, and a selective tillageaction towards the earthworm ecological categories (Hansen, 1996;Hansen and Engelstad, 1999; Paoletti, 1999; Rasmussen, 1999;Chan and Munro, 2001; Emmerling, 2001; Pommeresche and Loes,2009).

4.2. ECa and soil abiotic properties

The vineyards studied are cultivated on young and not evolvedsandy soils, with a scarce content of soluble salts attested by thelow electrical conductivity measured in the laboratory. Low ECa

values, measured on the sandy soils studied (Fig. 1), were typicalof a resistive soil (al Hagrey et al., 2004). In fact, the electrical cur-rent in soil depends both on the amount of water in the pores and

on soluble salts (Gupta and Hanks, 1972; Rhoades and Ingvalson,1971; Rhoades et al., 1989; Kurtulus et al., 2009; Celano et al., 2011).Interestingly, the strong relationship found between ECa and �v,validated by the Wilcoxon rank-test (Fig. 2), was strong in all theconsidered treatments (1GC, 1CW, 1T). This relationship enabledthe estimated soil water content to be spatialized at a field scale(Fig. 3B). Under our pedoclimatic conditions, soil water content maybe the principal factor affecting ECa (Brevik and Fenton, 2002; alHagrey et al., 2004; Brevik et al., 2006) and thus its spatial variabilitycould be explained by soil moisture distribution (Kachanoski et al.,1988; Hezarjaribi and Sourell, 2007; Bozkurt et al., 2009). Thus, thehigh similarity found in our experiment between ECa and �v maps(Fig. 3A and B) confirms that ECa can be a good indicator of field�v distribution. Although many authors have shown strong rela-tions between soil moisture and earthworm parameters (Reineckeand Venter, 1987; Dominguez and Edwards, 1997; Gunadi et al.,2003), under our experimental conditions, only the ECa signal wasstrongly correlated to earthworm abundance and biomass.

Finally, the weak relationships between ECa values, pH and soilelectrical conductivity (measured in the laboratory) observed inall the soil management treatments (1GC, 1CW and 1T) are prob-ably due to a partial obfuscation effect induced by the importantwater content recorded in the studied soils and caused by the rainyseason. In fact, of all the soil parameters affecting ECa, soil watercontent has the highest effect on signal. Therefore, under wet soilconditions, it is difficult to study the effect of the other soil charac-teristics on ECa (Pozdyakov et al., 2006).

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Fig. 4. Relationships between measured and estimated abundance and biomass of anecic (A and B), endogeic (C and D) and total earthworms (E and F) in plots managedwith the three soil management systems: grass cover (GC), chemical weeding (CW) and tillage (T).

4.3. ECa as a proxy for earthworm presence

Our study found strong relationships between ECa values andabundance and biomass of anecic and endogeic earthworms asalso observed by Valckx et al. (2009) and Joschko et al. (2010) forarable fields. However, these relationships differed depending onthe soil management system. The linear relationship between ECa

and earthworm abundances was higher in GC and CW plots than inT plots (Table 5). The strong relations found in GC and CW plotssuggest that the studied earthworm parameters should be spa-tialized at a field level especially in undisturbed soil managementsystems. The estimated values of the anecic, endogeic, and total

biomass and abundance of earthworms aligned very well with therespective values measured (Fig. 4). This result is supported aboveall by the high coefficients of determination between the earth-worm parameters measured and estimated, found in GC and CWas an effect of the soil management. Taking the different categoriesof earthworms into account, generally speaking the strongest rela-tionships were found for the endogeic groups (Table 5). This couldbe due to the lower sensitivity of this earthworm category tosoil management practices (tillage depth equal to 15 cm; seventillages per year) with respect to anecics (Edwards and Lofty, 1975;House and Parmelee, 1985; Wyss and Glasstetter, 1992; Kladivko,2001). The EMI signal is an integrated value of the physical and

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chemical conditions of the soil that are influenced by earthwormactivities.

5. Conclusions

Although, ECa depends on soil water content, under our experi-mental conditions, it showed a slight relationship with earthwormparameters. On the other hand, strong relations between ECa dataand earthworm parameters were found in the studied vineyardsmanaged by different soil systems. Taking into account our find-ings, it may possible to define an integrated method (earthwormmeasurements combined with geophysical techniques) for thereasonable evaluation of earthworm populations to be used asbioindicators of soil quality in agricultural systems under variouspedoclimatic areas.

The EMI technique could therefore be an effective tool to studythe spatial variability of these biological indicators, by reducing theamount of expensive measures that are needed for environmentcharacterization.

This integrated process could reduce the number of soil sam-plings and their costs (manpower and sampling material, such aswater and mustard) and return a map of the distribution of theearthworm population affected by soil managements. The proce-dure entails using ECa measurements to identify homogeneouszones where earthworm measurements are needed. Afterwards,once the relationship between ECa and earthworm data has beenestablished and its significance evaluated, it may be possible tospatialize the earthworm parameters to the whole field. Once thisprocedure has been weighted to ECa values, it could be a proxy ofearthworm abundance and biomass. In the future, the efficiency ofthis integrated procedure needs testing in different pedoclimaticconditions by comparing the obtained data with values from theapplication of classical assessment methods characterized by a highstatistical input.

Acknowledgments

We thank the “Victor Contis” Association for allowing us to carryout our research in their vineyards in Saint-Victor-la-Coste. Wealso thank Abid Hussain, Yulia Kiryakova and Zeeshan Majeed fortheir help with the soil samplings, and 23 students of MontpellierSupAgro (viticulture and enology 2011) and UMR Eco&Sols stafffor their help with the earthworm samplings. This work was finan-cially supported by the INRA AIDY project ‘Analyse Intégrée de laDYnamique des systèmes biophysiques, techniques et de décisionlors de la conversion à la viticulture biologique’, by ADEME ‘Agencede l’Environnement et de la Maîtrise de l’Energie’ and by MIUR PRIN2008 project ‘Ciclo del carbonio in ecosistemi produttivi arborei’.

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