Integrating biological indicators in a Soil Monitoring ... · soil quality in Wallonia in a SMN. Six biological indicators as well as two eco-physiological quotients have been selected

BASE Biotechnol. Agron. Soc. Environ.201721(S1),

IntegratingbiologicalindicatorsinaSoilMonitoringNetwork(SMN)toimprovesoilqualitydiagnosis–astudycaseinSouthernBelgium(Wallonia)InkenKrüger(1),CarolineChartin(2),BasvanWesemael(2),SandrineMalchair(1),MoniqueCarnol(1)(1)UniversitédeLiège.Laboratoired’Écologievégétaleetmicrobienne.BotaniqueB22.QuartierVallée,1.ChemindelaVallée,4.BE-4000Liège(Belgique).E-mail:[email protected](2)UniversitéCatholiquedeLouvain.GeorgesLemaîtreCentreforEarthandClimateResearch-EarthandLifeInstitute.PlaceLouisPasteur,3bteL4.03.08.BE-1348Louvain-la-Neuve(Belgique).

Reçule13janvier2016,acceptéle18octobre2016.

Description of the subject.Soilorganismsandtheiractivitiesareessentialforsoilecosystemfunctioningandtheycanthusbeusedaspertinentindicatorsofsoilquality.Recenteffortshavebeenundertakentoincludebiologicalindicatorsofsoilqualityintoregional/nationalmonitoringnetworks.Objectives.Theaimofthisstudywastoprovideafirstdatasetofsixbiologicalindicatorsandtwoeco-physiologicalquotientsfor two landscape units inWallonia.These spatial units are characterized by homogeneous climate conditions, soil type,land-useandmanagement(here,grasslandsintheArdennes,andcroplandsintheLoamRegion).Method.Respirationpotential,microbialbiomasscarbonandnitrogen,netnitrogenmineralization,metabolicpotentialofsoilbacteriaandearthwormabundanceweremeasuredatatotalof60sitesintwodifferentlandscapeunits(LSU).VariabilitywithineachLSUwasstudied.Datawassynthesizedthroughcalculationofacomprehensivescoreandpresentationasradarplots.Results.Allselectedbiologicalindicatorsweresignificantlyhigherundergrasslandthanundercroplandsoils,highlightingthe biological indicators’ power of discrimination betweenmain land use types.VariabilitywithinLSUdepended on thebiologicalindicatorandwasgenerallyhigheringrasslandthanincroplandsoils.Eachsitecouldunambiguouslybeassignedtoitslandscapeunitbasedonitscalculatedcomprehensivescore.Radarplotsallowedanassessmentofthedistributionofvalueswithinalandscapeunitataglance.Conclusions.Thepilot-studydefinedthefirstbaselinevaluesforagriculturalsoilsinWalloniaandlaidthefoundationforamonitoringnetworkofbiologicalsoilquality.Keywords.Biologicalindicators,networks,monitoring,soilbiology,Belgium.

L’intégration d’indicateurs biologiques dans un réseau de surveillance des sols afin d’améliorer le diagnostic de la qualité du sol – une étude de cas dans le sud de la Belgique (Wallonie)Description du sujet.Lesorganismesdusolainsiqueleursactivitéssontessentielspourlefonctionnementdel’écosystèmedusoletpeuventêtreemployéscommeindicateurspertinentsdelaqualitédusol.Deseffortsontrécemmentétémenéspourintégrerlesindicateursbiologiquesdelaqualitédusoldanslesréseauxrégionaux/nationauxdesurveillance.Objectifs.Lebutdecetteétudeétaitd’acquérirunpremiersetdedonnéespoursixindicateursbiologiquesetdeuxquotientséco-physiologiquespourdeuxunitéspaysagèresenWallonie.Cesunitésspatialessontcaractériséespardesconditionsdeclimat,detypedesoletd’utilisationdeterrainsimilaires(prairiesenArdenneetculturesenRégionLimoneuse).Méthode.Larespirationpotentielle, labiomassemicrobienne(lecarboneet l’azotemicrobien), laminéralisationnettedel’azote,lepotentielmétaboliquedesbactériesdusoletl’abondancedeversdeterreontétémesuréspouruntotalde60sitesrépartisdanslesdeuxunitéspaysagères.Lavariabilitédesmesuresdanslesunitésdepaysageestcalculée.Lesdonnéessontsynthétiséesvialecalculdescorescompréhensifsetlaprésentationsousformedeplotsradar.Résultats.Tous les indicateursbiologiquessélectionnésprésententdesvaleurs significativement supérieuresdans les solsdeprairieparrapportàceuxdeculture,soulignantlepouvoirdiscriminantdesindicateursbiologiquespourlesprincipalesutilisationsdesol.Lavariabilitédanslesunitéspaysagèresdiffèreselonl’indicateurbiologiqueconsidéréetestgénéralementplusélevée

2 Biotechnol. Agron. Soc. Environ. 201721(S1), KrügerI.,ChartinC.,vanWesemaelB.etal.

1. INTRODUCTION

Soil quality has been defined as “the capacity of aspecific kind of soil to function, within natural ormanaged ecosystem boundaries, to sustain plant andanimal productivity, maintain or enhance water andairquality,andsupporthumanhealthandhabitation”(Karlenetal.,1997).Theawarenessfortheextentandimpactofhumanactivityonsoilshasgrownsincethe1990sandinterest insoilqualityhasbeenincreasingamong scientists andpolicymakers (Arrouays et al.,2012). Soils are considered to be a non-renewableresource within human lifetime (Lal, 2009). For anearly detection of soil degradation at landscape orregional scale and to counteract the decline of soilfunctions, Soil Monitoring Networks (SMN) arerequired(Arrouaysetal.,2012).

ASMNisasetofsitesorareaswherechangesinsoil characteristics are documented through periodicassessment of an extended set of soil properties(Morvan et al., 2008). Most member states of theEuropeanUnion(EU),includingBelgium,haveSMNwith varying goals, duration andmethods (Goidts&vanWesemael, 2007;Rutgers et al., 2009;Arrouayset al., 2012). Soil characteristics and functions aremeasured with physical, chemical and biologicalindicators. Physical and chemical indicators, such assoil pH, particle size distribution, element contents,and bulk density, have been recorded in monitoringnetworks for decades. Soil Organic Carbon (SOC)is widely accepted as the most important chemicalindicatorofsoilquality.Itislinkedtoprovisionoffood,fiber andwater (Koch et al., 2013). Furthermore, anincreaseinSOCstocksisconsideredtobeapotentialmechanismforclimatechangemitigation(Smithetal.,2008).However,atleastadecadeisrequiredtodetectchangesinSOCstocks(Smith,2004;Sabyetal.,2008;Schrumpfetal.,2011).

Biologicalindicatorshavebeendefinedasorganismsorbiologicalprocessesprovidinganearlydiagnosisofchangesintheenvironment(Ramade,2008;Malchairetal.,2010).IncontrasttoSOC,biologicalindicatorsof soilquality, suchasbasal respirationormicrobialbiomass,havebeenshowntoreacttochangeswithindays or months (Bending et al., 2004; Bünemannetal.,2006;Dillyetal.,2011).Suchindicatorsmightthereforebemorerelevantfordetectingchangesofsoilquality at an early stage.Recent efforts to introducebiological indicators into soil monitoring networks

havebeenmadeattheregional,nationalandEU-widescale(Bispoetal.,2009;Gardietal.,2009).DespiteEuropeaninitiativessuchasENVASSO(Bispoetal.,2009) and ECOFINDERS (Stone et al., 2016a),currentSMNandthestageofinclusionintoaregularmeasurement network reflect national or regionalchoices of measured indicators, methodologies andsite selection (Malchair et al., 2010). The DutchbiologicalSMNisconsideredasthemostcompleteinEurope(Havlicek,2012).Twenty-fivesoilparameters,including 12biological indicators, are measured at300locationsinasix-yearcycle(Rutgersetal.,2009).

TheinclusionofbiologicalindicatorsofsoilqualityintoaSMNrequiresseveralsteps:–definitionofagoal,–selectionofsuitablebiologicalindicators,–choiceofstudysites,–definitionofbaselinevalues,–the compilation of data into synthesized forms fordecisionmakers.

Complex patterns of soil microbial propertiesacross landscapes (Cavigelli et al., 2005) need to beconsideredwhenestablishingasoilqualitymonitoringnetwork.Variability,thechangeoverspaceortimeinthevalueofagivenproperty(Ettema&Wardle,2002),impactsthemagnitudeofdefinedbaselinevaluesandthepower todetect long-termtrends.Thisvariabilityrestrains the possibility of extrapolating baselinevaluesfrompublisheddata.Afterthefirstthreesteps,arepresentativenumberofsitesthusneedtobesampledtodefinebaselinevalues,essentialfortheinterpretationofmeasurements(Nielsen&Winding,2002).

The concept “soil quality” comprises measuresof several indicators, which need to be synthesizedfor comparison across sites and for assessment ofchanges in the environment. Different mathematicalandgraphicalmethodsexisttotransformthedatasetsintomoreeasilyunderstandableformsthatcanbeusedto answer specific questions (Andrews et al., 2003).Synthesis of biological indicators into standardizedscores presents a flexible, simple and inclusive toolleadingtoaholisticoverviewofbiologicalsoilquality(Liebigetal.,2001).Suchscoresareobtainedbythetransformationofrawdatatoacommonunitlessscale,allowing mathematical operations to derive a single“comprehensive score” from several measurements.Scores are used in SMN of many countries, suchas Spain, Poland and Italy (Bastida et al., 2008).

pourlessolsdeprairiequeceuxdeculture.Chaquesitepeutsanséquivoqueêtreattribuéàsonunitédepaysagesurbasedesonscorecompréhensif.Lesplotsradarillustrentladistributiondesdonnées.Conclusions.L’étudepilotedéfinitlespremièresgammesdevaleurspourlesindicateursbiologiquesdanslessolsagricolesenWallonieetprésenteunebasesolidepourunréseaudesurveillancedelaqualitébiologiquedusol.Mots-clés.Indicateurbiologique,réseau,surveillance,biologiedusol,Belgique.

MonitoringofbiologicalsoilqualityinWallonia 3

Graphicalapproaches, likeradarplots,sunraygraphsoramoebadiagrams,areusedtosummarizesoildatainothercountriesliketheCzechRepublic,FranceandTheNetherlands(Hofmanetal.,2003;Cluzeauetal.,2012;Rutgersetal.,2012),andpresentanappealingwaytopresentthedatainavisualform.

InWallonia (SouthernBelgium),changes inSOCcontenthavebeenevaluatedwithin15homogeneous“landscape units” (LSU), characterized by similarland use, soil type, climate and agricultural region(CARBOSOL network; Goidts et al., 2009a).Whilemeasurementsof somebiological indicatorsexist forindividualsites(Buysseetal.,2013),norepresentativedata on biological soil quality exist for agriculturalsitesinWallonia.Thispilot-study,CARBIOSOL,aimsat the assessment of the current state of biologicalsoil quality in Wallonia in a SMN. Six biologicalindicators aswell as twoeco-physiologicalquotientshavebeenselectedfortheassessment,accordingtoaliterature review and the use of biological indicatorsinothercountries(Malchairetal.,2010).Theselectedbiological indicators are related tomicrobial activity(respiration potential, net nitrogen mineralization,microbial biomass [Cmic, Nmic]), microbial diversity(metabolic potential of soil bacteria) and soil fauna(earthwormabundance).

Thegeneralobjectiveofthisstudywastoprovidefirstvaluesforbiological indicatorsofsoilquality inWallonia at sites selected from two contrastingLSUwithintheCARBOSOLnetwork.Specificaimswere:

–toprovideafirstdatasetforsixbiologicalindicatorsintwoLSUandcomparevaluestotheliterature,

–calculate the variability and required sample sizewithinLSU,

–compare two methods to synthesize the datasets:comprehensivescoresandradarplots.

2. MATERIALS AND METHODS

2.1. Study site selections

TwoLSU(grasslands in theArdennesandcroplandsin the Loam Region) with a total of 60sites wereselected from the CARBOSOL network (Figure 1).Shallow and stony soils are found in theArdennes;theLoamRegionencompassesfertilesoilsonniveo-eolian silt loess (Goidts & Westmael, 2007). Thesiteswerepreviouslysampledin2005forSOCstockassessment (Goidts &Westmael, 2007) and markedwith belowground electromagnetic markers (BallMarker, 3M,Austin, Texas) to accurately locate thesitesinfuturesamplingcampaigns.

2.2. Soil sampling

At each site, a composite soil sample (0-10cm soildepth)wastakeninautumn2013.Itwasmixedfromfiveindividualsamplestakenbetweenoneandfourmetersfrom thecenterpointwithamanualauger following

Sampling points

Croplands

ForestsGrasslandsother

30 15 0 30 km

N

Figure 1.Mapofsitessampledinautumn2013—Carte des sites échantillonnés en automne 2013.


Goidtsetal.(2009b).Freshsoilsamplesweresieved(4mm)andstoredat4°Cuntilanalysis(Bloemetal.,2005).Allmeasurementsweremadewithin amonthof soil sampling.Soilmoisturewasdetermined afterdryingat105°Cfor3h(Allen,1989).Waterholdingcapacity (WHC) of sieved soil was measured usinga Haines-funnel system, where 50ml of water wasadded to 50g of fresh soil for 30min.Excesswaterwas collected and its volume measured (Jenkinson& Powlson, 1976). Before biological analysis, soilsamples were adjusted to 50-60% water holdingcapacity.ThepHwasmeasuredinasuspension(1:1;m:v) with distilled water with a pH meter (HI2550HANNA instruments). Carbon and nitrogen weremeasured by element analysis (VarioMax CN drycombustionAnalyzer,ElementarGmbH,Germany).

2.3. Earthworm abundance

Earthwormsweresampledinproximityofthemarkedsites,butoutsideof thesampling radiusaroundeachmarker.Earthwormswereextractedwithtwotimes4lofmustardsolution(3and6gofmustard[SinapisAlbaeSeminis Pulvis, Pharmaflore].l-1 respectively) (Gunn,1992; Lawrence & Bowers, 2002) on a 30x60cmsurfacemarkedwithawoodenframe.Allearthwormsleavingthesoilwerecollectedandconservedin70%ethanol. Earthworms were counted and their massmeasured(gbiomasssaturatedwithethanol).

2.4. Net nitrogen mineralization

Net nitrogenmineralizationwas studied in a 29-dayaerobic laboratoryincubationatconstant temperature(25°C)inthedark(Hartetal.,1994).Atthebeginningandattheendoftheincubation,inorganicnitrogenwasextractedwith1MKClsolution(1:5;w:v)(Allen,1989)andanalyzedcolorimetricallyusingacontinuousflowanalyzer (AutoAnalyser3, BranLuebbe, Germany).The net nitrogen mineralization rate was calculatedby dividing the net increase in inorganic nitrogen(N-NH4

+andN-NO3-)duringtheincubationperiodby

thenumberofincubationdays.

2.5. Microbial biomass

SoilmicrobialbiomassCandNweredeterminedbythe chloroform fumigation extractionmethod (Vanceet al., 1987), followedby0.5MK2SO4extractionofboth fumigated and unfumigated soils. Fumigationswerecarriedoutfor3daysinavacuumdesiccatorwithalcohol-freechloroform.Fumigatedandunfumigatedextractswerefiltered(WhatmanFilterPapers42,CATNo.1442-150). In both extracts, dissolved organiccarbon was measured with a Total Organic Carbonanalyzer (Labtoc, Pollution and Process Monitoring

limited,UK)andtotalNwasmeasuredcolorimetricallyusingacontinuousflowanalyzerequippedwithaUVdigestionunit(Autoanalyser3,BranLuebbe,Germany).Soil microbial biomass C and N were calculatedby dividing the difference of total extract betweenfumigatedandunfumigatedsampleswithaconversionfactorof0.45 forbiomassC(Jenkinsonetal.,2004)and0.54forbiomassN(Joergensen,1996).

2.6. Respiration potential

Respiration potential (Robertson et al., 1999) wasmeasured as CO2 accumulation in the headspace(250ml)ofanamberbottle(Supelco,USA)from20gfreshsoil,at15°Cinthedarkafteranovernightpre-incubation.Gassamples(4ml)were takenat0,120,150and180minwith an air-tight syringe (HamiltonModel1005)andanalyzedwithaninfraredabsorptiongasanalyzer(EGM-4,Ppsystem,UK).TherespirationpotentialwasestimatedbylinearregressionofCO2-Cagainsttime(μg.g-1.h-1).

2.7. Metabolic potential of soil bacteria

BIOLOG ECOplates (BIOLOG™, California) with32wells each containing one of 31different carbonsubstratesandonecontrolwellwithwaterwereusedtoassessmetabolicpotentialofsoilbacteria.Eachwellcontainedanoxidizedtetrazoliumdye,changingfromcolorlesstopurplewhenbacterialrespirationoxidizedthe carbon source provided.Oneg of fresh soil wasextractedwith9mlof0.1%sodiumcholateanddilutedtothreedilutions(10-2,10-3,10-4)with0.85%NaCltodeterminethenumberofCFU(colonyformingunits)on R2A agar. An aliquot of 100µl of the dilutioncorrespondingto1,000-2,000CFUwasincubatedfor72hat20°C.Numberofsubstratesusedbybacteriawere detected through visual observation of coloredwellsafterincubation(Buysseetal.,2013).

2.8. Eco-physiological quotients

Themetabolicquotient(qCO2)representsthequantityof respiredCO2-Cperunitof soilmicrobialbiomassand was calculated by dividing respiration potentialby soil microbial biomass C (Anderson & Domsch,1990). The microbial quotient (qmic) represents theavailabilityof soilC andwas calculatedbydividingmicrobialbiomassCbytotalorganiccarbon(Anderson&Domsch,1990).

2.9. Literature search

ThesearchenginesGoogleScholarandScopuswereusedtoretrievescientificpublicationsusingtheselectedkeywords“basalrespiration”,“earthwormabundance”,


“microbial biomass”, “nitrogen mineralization” or“ecoplatesbiolog”.Studiesreportingvaluesforatleastone of the selected biological indicators as well asvaluesoforganiccarbon,informationonsoilsamplingandtheappliedmethodologieswereselected.Studiesusing different methodologies were used as long astheresultsweregiveninunitsthatcouldbeconvertedinto the ones used in this study without additionalinformation. Further, studies were selected based onsampling depth (overlap with a depth of 0-10cm)and site conditions similar to those studied (locatedin temperate zone, under cropland or grassland, nocontaminationwithheavymetalsororganicsolvents).

2.10. Data synthesis and analysis

Comprehensive scores and radar plots were usedto summarize and organize the individual values ofdifferentbiologicalindicators.Acomprehensivescoreofbiologicalsoilqualitywascalculatedbyconvertingmeasurements for each biological indicator into ascorewithoutunits,whichcan thenbeunified intoasinglescore(Karlen&Stott,1994;Rahmanipouretal.,2014).Inthisstudy,scoresateachsitewerecalculatedby scaling individual biological indicators to valuesbetween0and1with

SIndi =Indi −min(Ind)

max(Ind)−min(Ind)

where SInd is the individual score of the biologicalindicator, Ind the unscaled value of the biological

indicator and min/max(Ind) the minimum ormaximum unscaled value of the biological indicatorof the whole dataset (van der Plas et al., 2016).Comprehensive scores for each site were calculatedby adding the individual scores ofmicrobial carbon,microbialnitrogen, respirationpotential,netnitrogenmineralization, metabolic potential of soil bacteriaandearthwormabundance.Thecomprehensivescoresrangedbetween0and6.

Radar plots present each biological indicator ononeofsixaxes.Themedian,firstandthirdquartilesaswellastheminimumandmaximumindividualscoresarepresented foreachLSU.RadarplotsweredrawnusingthefunctionradarchartfromthefsmbpackageinR3.2.2.

Differences between regions were tested with aWelch’st.test.Relationsbetweenbiologicalindicatorswere tested with Spearman’s correlation analysis.ApoweranalysisofWelch’s t-testwithapowerof0.8andalphaof0.05wasused todetermine thenumberof samples needed to detect significant differencesbetween regions. Data was analyzed withMicrosoftExcel2013andR3.2.2.

3. RESULTS

Organic carbon content was between 7.9 and 49.4gC.kg-1with an averageof 21gC.kg-1.Total nitrogencontentrangedfrom0.9to5.5gN.kg-1withanaverageof2.2gN.kg-1.ThepHH2O

valuesvariedbetween4.03and8.31withanaverageof6.6(Table 1).

Table 1. Minimum,maximum,mean andmedian values under grasslands in theArdennes and croplands in the LoamRegion—Valeurs minimales, maximales, moyennes et médianes pour prairies en Ardenne et cultures en Région Limoneuse. Biological indicator Grasslands Croplands p

Min Mean Med. Max Min Mean Med. MaxOrganiccarbon(g.kg-1) 17.1 38.4 40.4 49.4 7.9 12.2 11.5 19.8 <0.001Totalnitrogen(g.kg-1) 2.3 4.2 4.4 5.5 0.9 1.2 1.2 2.1 <0.001pHH2O

4.03 5.47 4.81 6.53 6.36 7.31 7.43 8.31 <0.001Earthworms(Ind.m-2) 78 174 183 272 0 54 33 217 <0.001Nitrogenmineralization(mg.kg-1.d-1) 0.052 3.60 3.08 13.85 0.23 0.49 0.40 1.01 <0.001Microbialcarbon(mg.kg-1) 356 1,225 1,156 2,185 127 244 224 419 <0.001Microbialnitrogen(mg.kg-1) 113 278 259 551 15 45 43 71 <0.001Metabolicpotentialofsoilbacteria(%) 25 51 52 68 5 39 42 55 <0.01Respirationpotential(mg.kg-1.h-1) 0.61 1.31 1.08 2.30 0.13 0.65 0.56 1.59 <0.001Microbialquotient 1.25 3.43 2.87 11.9 1.25 2.01 1.87 4.66 0.02Metabolicquotient(mg.kg-1.h-1) 0.49 1.20 0.95 2.77 0.45 2.81 2.27 8.02 <0.001DifferencesbetweenLSUweretestedwithaWelch’st-test—Les différences entre unités paysagères ont été testées par un t-test de Welch.


3.1. Biological indicators

Earthworm abundances varied between 0 and272 individuals per m², with an average of86individuals per m². This corresponded toearthworm masses between 0 and 240g.m-2 withanaverageof69g.m-2.Earthwormabundances andmasseswerecorrelated(rS=0.81,p<0.001).Valuesfor microbial biomass ranged between 127 and2,185mgC.kg-1withanaverageof546mgC.kg-1formicrobialcarbonandbetween15and551mgN.kg-1 with an average of 118mg N.kg-1 for microbialnitrogen.Cmic/Nmicratios(g/g)variedbetween3.1and8.3withanaverageof5.2.Nitrogenmineralizationwasbetween0.052and13.85mgN.kg-1.d-1,withanaverage of 1.5mg N.kg-1.d-1. Respiration potentialranged between 0.13 and 2.30mg C-CO2

.kg-1.h-1withanaverageof0.86mgC-CO2

.kg-1.h-1.Bacteriaused between 5 and 68% of Ecoplate’s the carbonsubstrateswithamedianof52%.Metabolicquotientranged from 0.45 to 8.02 with an average of 2.32mg.kg-1.h-1 and microbial quotient was between1.25 and 11.9 with an average of 2.45. Rangesof values found in literature were wider for allbiological indicators than ranges identified in thisstudy (Table 2). Ingeneral, the rangesmeasured inWallonia were within the range found in literature(Chartinetal.,2015).

All biological indicators differentiated betweenthetwoLSU(Table 1).Meanvaluesofnetnitrogenmineralization, microbial biomass nitrogen (Nmic)andmicrobialbiomasscarbon(Cmic)weremorethanfive times higher in soils under grasslands in theArdennesthaninsoilsundercroplandsintheLoamRegion.Earthwormabundance(3times),respirationpotential (2times) and metabolic potential of soilbacteria (1.3time) showed smaller differences inmeanvaluesbetweenthetwosites.Ingrasslandsin

theArdennes,correlationsweresignificantbetweenmetabolic potential and microbial carbon andnitrogen(rS=0.52,p=0.03andrS=0.47,p=0.04respectively),earthwormabundanceandnetnitrogenmineralization(rS=0.47,p=0.04).Nostatisticallysignificant correlations were found betweenindividual biological indicatorswithin croplands intheLoamRegion.SoilpHH20

waspositivelycorrelatedwith respiration potential (rS= 0.49, p= 0.03) andmicrobialnitrogen(rS=0.47,p=0.04)ingrasslandsintheArdennesandmetabolicpotential(rS=0.40,p=0.02)incroplandsintheLoamRegion.Soilorganiccarbon was positively correlated with microbialcarbon(rS=0.39,p=0.02)andmetabolicpotential(rS=0.40,p=0.02)incroplandsintheLoamRegion.Noothersignificantcorrelationsbetweenbiologicalindicatorsandchemicalindicatorswerefound.

VariabilitywithinLSUdependedonLSUandthebiological indicator observed. Standard deviations(SD)weregenerallyhigherundergrasslands in theArdennes (Table 3) than under croplands in theLoam Region. For net nitrogen mineralization andmicrobial biomass (Cmic andNmic), SDswere aboutone order of magnitude higher under grasslandsin theArdennes than under croplands in the LoamRegion(Table 3).Earthwormabundance,metabolicpotential of soil bacteria and respiration potential(Table 3) had SDs in the same order ofmagnitudeunder grasslands in theArdennes and croplands inthe Loam Region.As SDs and true differences inmeansdifferedbetweenbiologicalindicators,sodidthe number of necessary sample sites to calculatesignificant differences between agricultural regionswith aWelch’s t-test. The total number of samplesites needed to discriminate between grasslands inthe Ardennes and croplands in the Loam Regionranged from 4 (Cmic and Nmic) to 30 (net nitrogenmineralization)(Table 3).

Table 2.Minimum,maximum,mean andmedian values as well as number of data points from published data undergrasslandsandcroplands in the temperatezone—Valeurs minimales, maximales, moyennes et médianes et nombres de données d’études publiées pour les prairies et les cultures en zone tempérée.Biological indicator Grasslands Croplands

Min Mean Med. Max n Min Mean Med. Max nEarthworms(Ind.m-2) 2 152 51 1,347 10 0 190 92 1,060 192Netnitrogenmineralization(mg.kg-1.d-1) -3.44 22.7 17.4 93.8 16 1.14 9.62 9.45 34.3 22Microbialcarbon(mg.kg-1) 26 505 398 5,028 30 14.5 460 259 9,760 100Microbialnitrogen(mg.kg-1) 5.8 76 72 444 16 0.01 35 30 123 22Metabolicpotentialofsoilbacteria(%) 32 61 58 93 34 16 61 71 97 14Respirationpotential(mg.kg-1.h-1) 0.001 4.5 2.0 5.6 102 0.00003 2.3 0.6 32 23Metabolicquotient(mg.kg-1.h-1) 0.24 2.63 1.18 30 23 0.001 2.75 2.28 7.7 102Microbialquotient 0.19 6.57 2.35 12.6 30 0.20 2.43 1.76 8 116


3.2. Data synthesis

The combination of biological indicators to acomprehensivescoresumsuptheresultsforeachsite.GrasslandsintheArdenneshadcomprehensivescoresbetween2.0and5.4(meanvalueof3.2)andcroplandsintheLoamRegioncomprehensivescoresbetween0.5and1.7(meanvalueof1.1)(Figure 2).Theradarplots(Figure 3) showed the distribution of the individualscoresforeachbiologicalindicatorwithinthetwoLSU.Biological soil quality in each LSU is characterizedthroughaspecificpattern.TheradarplotshighlightthehigherscoresandgreaterrangeundergrasslandsintheArdennesthanundercroplandsintheLoamRegion.

4. DISCUSSION

Thispilotstudysetsthefounda-tion for the establishment of amonitoringnetworkofbiologicalsoilqualityinWallonia.Choicesin the establishment of a newmonitoring network are guidedbyamultitudeoffactorsspecificto each country including thepolitical will and legislations,availablefunding,aswellaspre-existing knowledge, resourcesand information on potentialstudysitesandmethodologiestomeasurebiologicalindicators.

Approximately 100potentialbiological indicators have beenidentifiedintheliterature(Bispoet al., 2009). To select suitableindicators, iterative numerical

“logical sieve” approaches are commonly used torankpotentiallymeasuredbiological indicatorsbasedonrelevance,applicabilityandtechnicalcriteria(Ritzetal.,2009).InScotland,thisapproachwascombinedwithworkshops to ensure stakeholders’ participationin the assessment process (Aalders et al., 2009). InWallonia,pertinentbiologicalindicatorswereselectedthrougha logical sieveapproach taking into accountthe relevance and applicability of the biologicalindicators, theiruse inexistingmonitoringnetworks,as well as methodological criteria (Malchair et al.,2010).

Selectionofsitescaneitherbebasedonsystematicgridsoronastratificationfollowingexpertjudgement(Morvan et al., 2008). The French network RMQS

Table 3. Totalmean,standarddeviation(SD),coefficientofvariation(CV)andnecessarysamplesizeforWelch’st-test(n)todetectasignificantdifferencebetweengrasslandsintheArdennesandcroplandsintheLoamRegion—Moyenne absolue, écart-type, coefficient de variation et nombre d’échantillons nécessaires pour un t-test de Welch pour les prairies en Ardenne et les cultures en Région Limoneuse.Biological indicator Mean Grasslands Croplands n

SD CV SD CVEarthworms(Ind.m-2) 86 66 77 51 59 12Nitrogenmineralization(mg.kg-1.d-1) 1.5 2.84 189 0.23 15 30Microbialcarbon(mg.kg-1) 546 516 95 71 13 4Microbialnitrogen(mg.kg-1) 118 119 101 13 11 4Metabolicpotentialofsoilbacteria(%) 52 10 19 16 31 24Respirationpotential(mg.kg-1.h-1) 0.86 0.49 57 0.33 38 20Metabolicquotient(mg.kg-1.h-1) 2.44 0.64 26 1.70 70 36Microbialquotient 2.32 2.33 100 0.63 2 88

Figure 2.Mapofcomprehensivescores(0-6)ofbiologicalsoilquality—Carte des scores compréhensifs (0-6) de la qualité biologique du sol.

6

3

00 20 40 60 km


(Réseau de Mesures de la Qualité des Sols) is anexample for the use of a systematic grid (16km *16km) to select representative sites of the mainsoil systems and landuses across thewhole country(Ranjardetal.,2010).Incontrast,theDutchnetworkNSMN (Netherlands Soil Quality MonitoringNetwork)usesinformationavailableatthefarmscaletoselectsamplingsites indifferent landuseandsoiltype categories (Rutgers et al., 2009). In Wallonia,measurementsofbiological indicatorsare introducedin selected sites of theSMNCARBOSOLdedicatedtoSOCcontentsandstocksevaluation(Goidtsetal.,2009a). This network consists of 592sites and usesa stratification based on land use, soil type, climateandagriculturalregiondefininghencetheLandscapeUnits, i.e. LSU (CARBOSOL network) (Goidts &vanWesemael,2007).CARBOSOLisadensenetworkofgeoreferencedsoilprofileswhoseSOCcontentswerefirstmeasuredbetween1950and1970(DeLeenheeretal.,1968)andre-sampledbetween2005and2014(Goidts&vanWesemael,2007;Chartinetal.,2015).The stratification does not take into account currentmanagement(e.g.crops,conservationorconventionalagriculture,organicinputs,etc.),thoughmanagementis thought to explain thevariability in carbon stocksandimpactsbiologicalindicators.

Baselinevaluesarenecessaryfortheinterpretationof data (Nielsen &Winding, 2002) as they providea background against which measurements can beevaluated. Baseline values can be defined throughdescriptive statistics: the minimum and maximumvalues or, as for example in France, the 1st and 3rd

quartilesareusedaslowerandupperbaselines(Cluzeauet al., 2012). In The Netherlands, multidimensionalellipsoidsarecalculatedfromaminimumdatasetofsoilparameterstodescribethenormaloperatingrange(NOR)takingseasonalvariabilityintoaccount(PereiraeSilvaetal.,2013;Semenovetal.,2014).Inthispilotstudy,rangesofvaluesthatmightbeusedasbaselinesagainstwhichfuturemeasurementscanbecomparedweredefinedforsixbiologicalindicatorsandtwoeco-physiologicalquotientsintwoLSUinWallonia.

Rangesofvaluesfoundintheliteraturearelarge.Methodological differences between laboratoriesare likely to account for some of this variability.The selected biological indicators are all measuredwith widely used techniques (Malchair et al., 2010)and, with the exception of metabolic potential ofsoil bacteria, ISO norms are available. However theISOnormsareoftenvagueconcerning thedefinitionof measurement parameters such as pre-treatmentof soil, incubation time and temperature (see ISO14238:1997 fornitrogenmineralization), resulting inahugediversityofexperimentalconditionspublishedin the literature (Creamer et al., 2014). Samples arecollectedindifferentseasons,underdifferentweatherconditions, causing further variability of up to twoorders of magnitude depending on the geographiclocationofthesiteandtheindicatorobserved(Wardle,1998; Gil-Sotres et al., 2005). This often hindersmeaningful comparisons between studies performedin different laboratories across Europeanmonitoringnetworks.Furthermore,therangeofbaselinevaluesisgreatlyaffectedbythespatialscaleatwhichtheyare

Figure 3. RadarplotsforgrasslandsintheArdennes(a)andcroplandsintheLoamRegion(b)—Plot radar pour les prairies en Ardennes (a) et les cultures en Région Limoneuse (b).

Net nitrogen mineralizationNet nitrogen mineralization

Respiration potential Respiration potential

a bMinimum-maximum1st-3rd quartilesMedian

EarthwormsEarthworms

Metabolicpotential

Metabolicpotential Microbial

nitrogenMicrobial nitrogen

Microbial carbon Microbialcarbon


measured.The literaturesearch in thisstudycovereddifferent countries, soil types,management practicesand climatic conditions in the temperate zone.Therefore, rangesaremuchwider than thosedefinedfor the two studied LSU inWallonia. For example,ranges for basal respiration from literature coveredsix orders of magnitude, while our measurementsof respiration potential, an analogous measurementwith a shorter pre-incubation and incubation timethat might include some flush of microbial activityafter sample preparation, defined ranges within twoordersofmagnitudeandacoefficientofvariationof60% across the twoLSU.This emphasizes the needtodefinebaselinevaluesatameaningfulspatialscale,inaccordancewiththeobjectiveof thenetwork.Thedefinition of different baseline values for each LSUin Wallonia, rather than one baseline value for allagricultural sites, is thus a promising approach forestablishingamonitoringnetwork.Theusefulnessofastratifiedapproachforbiologicalsoilmonitoringhasbeen shown in other countries, likeTheNetherlands(Rutgersetal.,2009).Allbiologicalindicatorsenablediscriminationbetweenthesoilunderneathgrasslandsin theArdennes and croplands in the LoamRegion.Power to discriminate is a mandatory trait of anybiological indicator that is to be used to identifymeaningfulchangestotheecosystem(Cluzeauetal.,2012;Stoneetal.,2016b).ThesignificantdifferencesfurtherunderlinetheneedtodefinedifferentbaselinevaluesfordifferentLSU.Thedatasetdoesnotenableto quantify howmuch each divergence between theselectedLSU(grasslandvscropland,temperatureandprecipitationgradient,geology,andhistoricallanduse)contributestothemeasureddifferences.

Despite relatively homogenous conditions withinagricultural units, spatial variability in each studiedLSU resulted in coefficients of variation of up to189%. Variability in soils under grassland in theArdennes was higher than variability in soils undercropland in the Loam Region. This is partially dueto the higher geomorphological variability in theArdennes in comparison to the flatterLoamRegion,resulting in differences in altitude, exposure andvegetation. Additionally, cropland soils generallyhave lower variability than grasslands due to tilling(Gubler et al., 2015).Variabilitywas highest for netnitrogen mineralization. Net nitrogen mineralizationis a measure integrating nitrogen producing andconsuming soil processes which are controlled bydifferentmechanisms(Wangetal.,2001).Ithasbeenfoundtobemorevariablethanmicrobialbiomassandbasalrespirationandhasbeenreportedtobeabetterdiscriminator to separate soilsby their suitability fordifferent agricultural uses (Benintende et al., 2015).Thepowerofadataset todiscriminatebetweentwosubsets, in this case two agricultural regions with

contrasting characteristics depends on the numberofobservations, the truedifference inmeansand thestandard deviation (Welch, 1947). The number ofsamples necessary to describe a region is valuableinformationwhen settingupaSMN. In this study, asinglesamplingcampaignwasperformedinautumn,butideally,seasonalvariabilityofindicatorsshouldbeconsidered.Forexample,thesoilfaunaandmicrobialcommunity are sensitive to weather conditions, likesoil moisture and temperature (Edwards & Bohlen,1996;Waldrop&Firestone,2006),causingsignificantseasonal variability in biological indicators (Gil-Sotresetal.,2005). In temperateclimate, springandautumn are generally considered the best seasonsfor sampling for biological indicators (Bloem et al.,2005). Furthermore, analyses need to be performedrapidly after soil sampling, as storage can affectmicrobialactivityandfunctions(Zellesetal.,1991);the number of sites that can be sampled in a singlebatchisthuslimited.Wefoundthatwiththeequipmentand resources available, one experienced person caneffectively analyze about 40samples in parallel.Abouttwoweeksarerequiredforsoilsamplingandafurtherthreeweeksforlaboratoryanalysis.Twofieldcampaignsarefeasibleperseason(lateApriltoJuneorSeptember to earlyNovember).The time constraintsneed to be taken into consideration when decidingwhetherresearchgoalsforaSMNcanbemet.

Scores and radar plots are two frequently usedmethods to synthesize the data. They presentcomplementary tools that help to answer specificquestions.Comprehensivescoreswereusedtocreateamap, providing a quick overviewof the biologicalsoil quality at each site (Figure 2). Calculationof comprehensive scores enhances the power ofdiscrimination between the two LSU. All sites canunambiguously be attributed to either of the twoagricultural regions. Sites from croplands in theLoamRegion had a comprehensive score from 0 to1.8 and comprehensive scores for grasslands in theArdennes ranged from 1.8 to 6. Indicator scoringis an established method to facilitate assessmentof soil quality by providing a summary of values ofdifferentmeasurements and their combination into asingle comprehensive score (Wienhold et al., 2009).However the calculation of a comprehensive scoreresults in a loss of informationon individual scores,whichcanbeaboveorbelowbaselinevalues.Radarplots visualize the distribution of values within theLSUforallbiologicalindicatorsthatcanbeassessedinaglance.Theradarplotsshowthegenerallyhighervalues for all biological indicators under grasslandsin theArdennes compared to croplands in theLoamRegion,aswellasthehighervariabilitybetweensitesundergrasslandsintheArdennesthanundercroplandsin theLoamRegion.Both toolsmightbeadapted to


specificquestions,byincludingorexcludingdifferentbiological indicators or attributing a differentweightto individual biological indicators. In combination,both tools are useful for communicating results tostakeholders through thecreationofmapsandeasilyunderstandablegraphicsofsoilquality.

5. CONCLUSIONS

Thispilot-studysetsthefoundationfortheestablishmentof amonitoringnetworkof biological soil quality inWallonia.Thisfirstdatasetforsixbiologicalindicators,respirationpotential,microbialbiomass(CmicandNmic),netnitrogenmineralization,metabolicpotentialofsoilbacteriaandearthwormabundance,demonstratestheirpowerofdiscriminationbetweentwomainLSU.WiderrangeswerefoundforgrasslandsintheArdennesthanforcroplandsintheLoamRegion.Thisstudyhighlightstheneedforestablishingbaselinevaluesofbiologicalindicatorsofsoilqualityatmeaningfulspatialscales,i.e. LSU for Wallonia. Comprehensive scores andradarplotsareusefulastoolsforsynthesizingthedata.Comprehensivescorestransformthebiologicalqualityinto one number that enables a quick assessment ofthesoilqualityatsitelevelandmightbeusedforthecreationofmapsofsoilquality.Radarplotsgivemoredetailed informationon thedistribution and rangeofindividual biological indicators at the scale of LSU.In combination, they enable a fast assessment ofbiologicalsoilqualityattheregionalscale.

Acknowledgements

Thisworkwas fundedby theServicepublicdeWallonie-DGARNE. The authors would like to thank B. Bosman,M.Bravin,A.Degueldre,A. Piret andM.-C. Requier fortechnicalsupportandadvice.

Abbreviations

CFU:colonyformingunitCmic:microbialbiomasscarbonEU:EuropeanunionLSU:landscapeunitNmic:microbialbiomassnitrogenNOR:normaloperatingrangeNSMN:NetherlandsSoilQualityMonitoringNetworkqCO2:metabolicquotientqmic:microbialquotientRMQS:RéseaudeMesuresdelaQualitédesSolsSD:standarddeviationSMN:soilmonitoringnetworkSOC:soilorganiccarbon

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Integrating biological indicators in a Soil Monitoring ... · soil quality in Wallonia in a SMN. Six biological indicators as well as two eco-physiological quotients have been selected