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On-line measurement of soil properties without direct spectral response in near infrared spectral range

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On-line

measurement

of

soil

properties

without

direct

spectral

response

in

near

infrared

spectral

range

Omar

Marı´n-Gonza´lez

a,

*

,

Boyan

Kuang

b

,

Mohammed

Z.

Quraishi

b

,

Miguel

A´ngel

Muno´z-Garcı´a

a

,

Abdul

Mounem

Mouazen

b

a

RuralEngineeringDepartment,ElectrotechnicalSection,EUITAgrı´cola,UPM,AvenidaComplutense,s/n,28040Madrid,Spain

b

EnvironmentalScienceandTechnologyDepartment,CranfieldUniversity,BedfordshireMK430AL,UnitedKingdom

1. Introduction

Duringthelastdecadevisiblenearinfraredspectroscopy(vis– NIRS) is being increasingly used todetectsoil properties, with variableaccuracydependingonseveralfactors(Kuangetal.,2012). The vis–NIRS is a simple, non-destructive and rapidtechnique, needsnosamplepreparationforfieldapplications,andcanbeused forthelaboratory,insitu(ViscarraRosseletal.,2006a),andon-line measurements(Mouazenetal.,2005).Furthermore,insomecases accuracyobtainedishighandverysimilartothatofconventional procedures(ViscarraRosseletal.,2001).Thetechniqueallowsthe assessmentofprimarypropertieswithdirectspectralresponses, which are directly affected by combinations and overtones of

fundamentalvibrationsfororganicfunctionalgroupsandwater, particlesize,andsurfaceproperties(Changetal.,2001).Inareview paper,Stenberget al.(2010) definedorganic carbon (OC), total carbon (TC),moisture content(MC) and clay minerals to have directspectralresponseintheNIRspectroscopy.Inthiscontext,it ispossibletodetectclaytypeandcontent,MC,OCandTCwithhigh accuracy(Volkanetal.,2010).Othersoilpropertieswithoutdirect spectral absorption features in the vis–NIR range (secondary properties)canbealsomeasuredwithgoodtomoderateaccuracy duetoco-variationwithoneormoreprimaryproperties(Stenberg etal.,2010).Forbothsoilcategories,colourplaysanimportantrole in enhancing measurement accuracy. Good results have been reported for cation exchange capacity (CEC), pH, extractable calcium (Caex) and extractable magnesium (Mgex) under

non-mobilelaboratoryandinsituconditionsbutunderperformedthose forpropertieswithdirectspectralresponsesintheNIRrange,e.g. OC,TC,MCandclaycontent(Kuangetal.,2012).Accordingtothe

ARTICLE INFO Articlehistory:

Received5December2012 Receivedinrevisedform17April2013 Accepted24April2013 Keywords: On-linemeasurement Fertility Vis–NIRspectroscopy Accuracy Independentvalidation ABSTRACT

Sofar,themajorityofreportsonon-linemeasurementconsideredsoilpropertieswithdirectspectral

responsesinnearinfraredspectroscopy(NIRS).Thisworkreportsontheresultsofon-linemeasurement

ofsoilpropertieswithindirectspectralresponses,e.g.pH,cationexchangecapacity(CEC),exchangeable

calcium(Caex)andexchangeablemagnesium(Mgex)inonefieldinBedfordshireintheUK.Theon-line

sensorconsistedofasubsoilercoupledwithanAgroSpecmobile,fibretype,visibleandnearinfrared

(vis–NIR)spectrophotometer(tec5TechnologyforSpectroscopy,Germany),withameasurementrange

305–2200nmtoacquiresoilspectraindiffusereflectancemode.Generalcalibrationmodelsforthe

studiedsoilpropertiesweredevelopedwithapartialleastsquaresregression(PLSR)withone-leave-out

crossvalidation,usingspectrameasuredundernon-mobilelaboratoryconditionsof160soilsamples

collectedfromdifferentfieldsinfourfarmsinEurope,namely,CzechRepublic,Denmark,Netherlandand

UK.Agroupof25samplesindependentfromthecalibrationsetwasusedasindependentvalidationset.

Higheraccuracy was obtainedforlaboratoryscanning ascomparedto on-linescanning ofthe25

independent samples. The prediction accuracy for thelaboratory and on-line measurements was

classifiedasexcellent/verygoodforpH(RPD=2.69and2.14andr2=0.86and0.78,respectively),and

moderatelygoodforCEC(RPD=1.77and1.61andr2=0.68and0.62,respectively)andMg

ex(RPD=1.72

and1.49andr2=0.66and0.67,respectively).ForCa

ex,verygoodaccuracywascalculatedforlaboratory

method(RPD=2.19andr2=0.86),ascomparedtothepooraccuracyreportedfortheon-linemethod

(RPD=1.30andr2=0.61).Theabilityofcollectinglargenumberofdatapointsperfieldarea(about

12,800pointper21ha)andthesimultaneousanalysisofseveralsoilpropertieswithoutdirectspectral

responseintheNIRrangeatrelativelyhighoperationalspeedandappreciableaccuracy,encouragethe

recommendationoftheon-linemeasurementsystemforsitespecificfertilisation.

ß2013ElsevierB.V.Allrightsreserved.

* Correspondingauthor.Tel.:+34689616130;fax:+34915449983.

E-mailaddresses:o.marin@upm.es,o.marin.gon@gmail.com(O.Marı´n-Gonza´lez).

ContentslistsavailableatSciVerseScienceDirect

Soil

&

Tillage

Research

j o urn a l hom e pa g e : ww w . e l se v i e r. c om / l oca t e / st i l l

0167-1987/$–seefrontmatterß2013ElsevierB.V.Allrightsreserved.

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literature,sodium(Naex)andpotassium(Kex)areamongthemost

difficult properties to be measured with the NIR spectroscopy (Malleyetal.,1999;Changetal.,2001;Zornozaetal.,2008;Pirie etal.,2005;Dunnetal.,2002;Shepherd&Walsh,2002;Islametal., 2003;Volkanetal.,2010;Mouazenetal.,2006).Forthesamesoil property,laboratoryvis–NIRmethodsachievedhigheraccuracyas comparedtomeasurementunderfieldsoilconditions,particularly withon-linevis–NIRsensors(Stenbergetal.,2010;Kuangetal., 2012).

Thepredictionaccuracyachievedsofarwiththeon-linevis– NIRSsensorsavailabletoday(Shibusawaetal.,2001;Mouazenand Ramon, 2006; Christy, 2008) might be sufficient for many applicationsin precisionagriculture,since spatialand temporal variation of soil properties is large relative to the precision of measurement (Shepherd andWalsh,2002).However,similarto laboratory and in situ measurements, Kuang et al. (2012)

concludedthat thebestaccuracyis achievedforsoilproperties withdirectspectralresponse,whichisprobablythereasonwhy researchersusingon-linevis–NIRSsensorshavefocusedmainlyon soilpropertieswithdirectspectralresponses(Shonketal.,1991; Mouazenetal.,2005;BricklemyerandBrown,2010;Munozand Kravchenko, 2011, Knadel et al., 2011). Mouazen et al. (2007)

showedpotentialsuccessfortheon-linevis–NIRSmeasurementof extractableandavailablePandpH,withoutprovingtheaccuracy tobe ofquantitative meaning.AlthoughMouazen etal. (2007)

demonstratedspatialsimilaritiesbetweenmeasuredandon-line predictedpH,norobustconclusionsonaccuracyofmeasurement couldbedrawn.LaterMouazenet al.(2009)conductedon-line measurementofavailablePwithremarkableaccuracy(RPD=1.42; r2=0.62). To our knowledge none of theprevious studies has

reported ontheon-linemeasurementofCEC,CaexandMgex.In

addition,nocomparisonwasmadebetweenlaboratoryandto on-linemeasurementofthenamedsoilproperties.

The aim of this paper is to evaluate the performance and accuracyofon-linemeasurementofsoilpropertieswithoutdirect spectralresponsesintheNIRspectroscopyrange,namely,CEC,pH, CaexandMgex.Italsoaimstocomparethepredictionaccuracyof

these properties based on soil spectra collected under on-line measurement conditions with those collected under laboratory non-mobileconditions.Calibrationmodelsdevelopedforseveral farmsinEuropewillbeusedtovalidatetheon-linemeasurement ofthesepropertiesinoneselectedfieldintheUK.

2. Materialsandmethods 2.1. Soilsamples

A total of 140 soil samples were used to develop general calibrationmodelsforthepredictionofpH,CEC,Caex,andMgex.

Thesesoilsampleswerecollectedfromdifferentfieldsinsixfarms infourdifferentEuropeancountries(Fig.1andTable1).Atotalof 25sampleswerecollectedfromonefieldinMespolMedlov,A.S. farm(CzechRepublic),20samplesfromtwofieldsinBramstrup

Estatefarm(Denmark),23samplesfromonefieldinWageningen University(theNetherland),25samplesfromonefieldinElyfarm (Cambridgeshire, UK), 17 samples from five different fields in Silsoeexperimentalfarm(Bedfordshire,UK)and30samplesfrom fields2and3inDuckEndfarm(Bedfordshire,theUK)(Table2). Bulkedsampleswerecollectedfromtheuppersoillayer(0–30cm) inthespringof2008intheMespolMedlov,A.S.farmandinthe springof 2009in BramstrupEstatefarm (Kuangand Mouazen, 2011).Fortheremainingfarms,soilsampleswerecollectedfrom thebottomof15cmdeeptrenchesopenedbyasubsoilerduring the on-line vis–NIR measurement in the autumn of 2010 in WageningenUniversity,summerof2009in Silsoeexperimental farm,springof2011inElyfarm,andsummerof2011inDuckEnd farm(MouazenandRamon,2006).Thesefieldscorrespond toa largediversityofsoiltextures,cropsandlandscapes(Table1).

Inordertovalidatethecalibrationmodelsdeveloped,on-line measurementwascarriedoutinfield1inDuckEndFarm(Table2). Duringthismeasurementatotalof45sampleswerecollectedfor modeldevelopmentandvalidation.About44%ofthesesamples (20 sample) were added to the general calibration set (140+20=160samples)todevelopthegeneralcalibrationmodels oftheselectedfourproperties,whereastheremaining25samples were used as validation set for the validation of the general calibrationmodelsbasedonnon-mobile(laboratory)andon-line measuredspectra.

Approximately200gofeachsoilsamplewaskeptdeepfrozen ( 188C)untiltesting.Beforeanalysis,eachsamplewasdefrosted, carefullymixedanddividedintotwoportions,withoneusedfor chemical analysis,andtheotherusedfor opticalmeasurement. Sampleswerestoredinplasticbagsat48Cduringtheanalysisto avoidlosinghumidity.

Fig.1.Locationsofthefieldswheresoilsampleswerecollectedtoestablishgeneral calibrationmodelsforsoilpH,cationexchangecapacity(CEC),extractablecalcium (Caex)andmagnesium(Mgex)werecollected.

Table1

InformationaboutthefieldsinthefarmsinMespolMedlov,A.S.inCzechRepublic,BramstrupEstateinDenmark,WageningenUniversityintheNetherlandandSilsoe,Elyand DuckEndintheUK,wheresoilsampleswerecollected.

Farm Area,ha Crop Texturetype Sand,% Silt,% Clay,%

CzechRepublic 3 Wheat Siltclayloam 4.86 70.58 24.56

Denmark 2 Wheat Sandyloam 68.57 21.96 9.48

Netherland 1 Maize Siltyclayloam 6.17 66.22 27.61

UK(Silsoe) 16 Wheat Clayloam 40.11 27.38 32.51

UK(DuckEnd) 37.1 WinterBarley Clayloam 42.46 29.45 28.09

UK(Ely) 1.5 Lettuce Siltyclay 2.25 48.03 49.72

O.Marı´n-Gonza´lezetal./Soil&TillageResearch132(2013)21–29 22

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2.2. Laboratorymeasurementofsoilproperties

Laboratory analyses of pH, CEC and base saturation, were performed at the soil laboratoryof the National Soil Resource Institute, School of Applied Sciences (NR-SAS) of Cranfield University(Bedfordshire,theUK)usingtheirstandardprocedures. SoilpHwasmeasuredina1:5soil:H2Osuspensionfollowingthe

BS ISO 10390 (2005) for the determination of pH. In order to determine the CEC and exchangeable cations (Naex, Kex, Caex,

Mgex),theair-driedsoilsampleswerefirstsaturatedwithrespect

tobariumbyadding30mlofbariumchloridesolution(Reagent ProductionUnit(RPU)10),afterwhich30mlexcessof0.02mol/l magnesiumsulphate(RPU11)wasadded.Thismakesallbarium presentinthesolutionaswellasadsorbedinexchangeablesitesto precipitateintheformofhighlyinsolublebariumsulphate.Asa result, theexchangeablesites areoccupied by magnesium.The surplus of magnesium was determined by atomic absorption following the NR-SASSOP 42/Version 1,based onthe BS7755 Section3.12(1996),forthedeterminationofthepotentialcation exchange capacity and exchangeable cations using barium chloride buffered at pH=8.1, which is identical to ISO 13536 (1995)(Table3).Basesaturationwascalculatedastheequivalent sumofmajorbasecations(Caex,Mgex,KexandNaex)percentageof

CEC(Chodaketal.,2004).

2.3. Opticalmeasurement

2.3.1. Opticalmeasurementinlaboratory

Foreachsoilsampleacertainamountofsoilwasmixedupina glassbowl.Stonesandplantresidueswereremovedatthispoint. Threesmallcupsof1cmdeepand3.6cmindiameterwerefilled upwiththesamesoilsample.Thesurfaceofthesampleswassoftly pressedandsmootheddownwithaspatula,simulatingtheeffect

ofthesubsoilersmoothingofsoilbeneaththechiselduringon-line measurement,whichincreasesthesignaltonoiseratio(Mouazen etal.,2005).

An AgroSpec mobile, fibre type, vis–NIR spectrophotometer (Tec5 Technology for Spectroscopy,Germany) with a measure-mentrangeof305–2200nmwasusedtomeasuresoilspectrain diffusereflectancemode.A100%ceramicwasusedasthewhite reference,whichwasscannedonceevery30min.Opticalscanning wasconductedonnon-treated,freshsoilsamplestosimulatefield measurementconditions.Atotaloftenscanswereperformedfor each of the three bowels prepared for each soil sample. The resultedthirtyspectrawereaveragedintoonespectrumforeach sample(KuangandMouazen,2011).Thisaveragedspectrumwas used for spectra pre-treatment and model development. All calibration (160) and validation (25) sampleswere scanned in thelaboratory.

2.3.2. On-linevis–NIRmeasurement

Theon-linespectrawerecollectedalongparallelmeasurement linesinthefield1intheDuckEndFarmintheUK(Fig.2).Detailed informationaboutthisfieldisshowninTable4.Thesamevis–NIR spectrophotometerusedforlaboratorymeasurement(AgroSpec) was used for the on-line measurement. The on-line sensor developedbyMouazen(2006)wasusedtocarryouttheon-line measurement. The subsoilermakes a trench in thesoil, whose

Table2

Soil samples used to establish and validate general calibration models for measurement of soilpH,cation exchange capacity(CEC), extractablecalcium (Caex),andmagnesium(Mgex)(KuangandMouazen,2011).

Country Numberof samples Numberof Fields Sampling time CzechRepublic 25 1 2008 Denmark 20 2 2009 Netherland 23 1 2010 UK(Silsoe) 17 5 2009

UK(DuckEnd,field1) 45 1 2011

UK(DuckEnd,field2) 10 1 2011

UK(DuckEnd,field3) 20 1 2011

UK(Ely) 25 1 2011

Table3

Laboratorymethodsofsoilanalyses.

Soilproperty na

Technique References

pHw 185 1:5soil:H2Oextract BSISO10390(2005)

CECcmolc/kg 171 BaCl2bufferedatpH=8.1 BS7755Section3.12(1996)

ExchangeableNacmolc/kg 171 BaCl2bufferedatpH=8.1 BS7755Section3.12(1996)

Exchangeablekcmolc/kg 171 BaCl2bufferedatpH=8.1 BS7755Section3.12(1996)

ExchangeableCacmolc/kg 171 BaCl2bufferedatpH=8.1 BS7755Section3.12(1996)

ExchangeableMgcmolc/kg 171 BaCl2bufferedatpH=8.1 BS7755Section3.12(1996)

a

Numberofsamplesusedfortheanalysis.

Fig.2.On-linemeasuredlinesinthefield1intheDuckEndFarm(Bedfordshire,the UK).

Table4

Informationaboutthefield1intheDuckEndfarm,Bedfordshire(UK),whereon-linesoilmeasurementtookplaceinsummer,2011.

Field Area,ha Crop NumberofSamplesNr Texture Sand,% Silt,% Clay,%

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bottom is smoothened by the subsoiler itself, due to the downwardsverticalforces.Theopticalprobeprotectedinasteel lensholderwasappendedtothebacksideofthesubsoilerchiselto measure soil spectra in diffuse reflectance mode from the smoothened bottom of the 15cm deep trench created by the subsoilerchisel.Thesubsoilerretrofittedwiththeopticalprobe wasattachedtoaframe,whichwasmountedontothethreepoint linkage of a tractor (Fig. 3). The spectrophotometer was IP 64 protected forharsh workingenvironments.A deferentialglobal positioningsystem(DGPS)(EZ-Guide250,Trimble,USA)wasused torecordthepositionofon-linemeasuredspectrawithsub-metre accuracy. A Panasonic semi-rugged laptop was used for data loggingandcommunication.Thespectrophotometer,laptopand DGPSwerepoweredbythetractorbattery.

2.4. Pre-processingofspectra

Several spectra pre-processing were tested and the best performing one was kept. The selection criteria of any pre-processingwerethelargestcoefficientofmultipledetermination (r2)andresidualpredictiondeviation(RPD),whichistheratioof

standarddeviation(S.D.)ofthepredictiondatasettorootmean square error of prediction (RMSEP) and the smallest RMSEP. Spectrapre-processingandestablishmentofcalibrationmodelsof differentpropertiesweredonebyUnscrambler7.8software(Camo Inc., Oslo, Norway). To remove noise at edges of spectra, soil spectrum was first abridged to 400–2100nm for each sample. Afternoisewasremoved,spectrawerereducedbyaveragingthree successivewavelengthsinthevisiblerange(400–1000nm)andsix successive wavelengths for the NIR region (1000–2100nm). Averaging over wavelengthswasused to decreasethe number ofwavelengthsandtosmooththespectrum(Nicolaetal.,2007). Maximumnormalisationwasfollowed,whichistypicallyusedto getalldatatoapproximatelythesamescale,ortogetamoreeven distributionofthevariancesandtheaveragevalues.Thismethod attemptedtoremovetheeffectsofscatteringbylinearisingeach spectrum to some ‘ideal’ spectrum of the sample, which, in practice,correspondstotheaveragespectrum(Nicolaetal.,2007). Themaximumnormalisation‘polarizes’thespectra.Thepeaksof allspectrawithpositivevaluesarescaledto+1,whilespectrawith negativevaluesarescaledto 1.Thepeaksofthesespectrawere scaledto+1,sinceallsoilspectrainthisstudyhadpositivevalues (Mouazenetal.,2005).Themaximumnormalisationwasselected because it provided better resultsfor all properties considered comparedwithotherpre-processingtested.Spectrawere subse-quentlysubjectedtoSavitzky–Golayfirstderivation(Martensand

Naes,1989).Thistransformationproceduregenerallyintensifies theabsorptioncharacteristicsindicative of soilsproperties,and diminishes variation among spectra (Volkan et al., 2010). This method enabled the computation of the first or higher-order derivatives,includingasmoothingfactor,whichdetermineshow many adjacent variables should be used to estimate the polynomial approximationused for derivatives.A second-order polynomial approximation was selected with a 2:2 smoothing factor.A2:2Savitzky–Golaysmoothingwascarriedoutafterthe firstderivativetoremoverandomnoisefromspectra(Kuangand Mouazen,2011).

2.5. Establishmentofcalibrationmodels

The pre-processed spectra and the results of laboratory chemical analyseswere usedtodevelop calibrationmodels for all studied properties. Before partial least squares regression (PLSR)analysis,the160calibrationsamples(Tables1and2)were dividedintocalibration(85%ofsamples)andpredictionsets(15% of samples). Furthervalidation of developed models was done usingthe25samplescollectedinthefield1 inDuckEndfarm, whichwasdesignatedasindependentvalidationset.

To develop calibration models, especial attentionshould be paid in the selection of calibration and validation sets. The distributionand sizeof thecalibrationdatasetmust coverthe entirerangeofconcentrationofasoilpropertyexamined.Tobuild arobustcalibrationmodel,validationofmodeldevelopedhastobe carried out using validation samples,which were not used for model development.This meansthat validationsamples ofthe independentvalidationset(25samplescollectedfromthefield1 intheDuckEndfarm)shouldnotbeusedforthedevelopmentof calibration models in cross-validation, avoiding this way any influenceinthepredictioncapacityofthemodelselectedandthe consequentoverestimation(Brownetal.,2005).Thesamesamples ofcalibrationand validationwereselectedforeach property.A previousthreshingofsampleswasexecutedtogetthewiderrange ofvaluesandspectravariationinthecalibrationset.Calibration andvalidationsampleswerethenselectedrandomly.Performing thiswayacalibrationdatasetwassettocoverthewholerangeof concentrationforthedifferentsoilpropertiesexamined.

The most used multivariate methods to develop calibration modelsarebasedonlinearregressions.Mainly,stepwisemultiple linearregression (SMLR),principalcomponentregression (PCR), andPLSR(Stenbergetal.,2010)areused.ThePLSRwith leave-one-out cross-validation was carried out using Unscrambler 7.8 software (Camo Inc., Oslo, Norway) togenerate thecalibration modelsrelatingsoilindependentvariables(wavelengths)ofthe diffusereflectancespectratoeachsoilparameter.PLSRperforms particularly well, compared with other multivariate statistical methods,whenthereisa highdimensionalcorrelationbetween variables, whichis thecase forsoilspectral data(Volkanetal., 2010).Also,PLSRisfavouredbecauseitrequiresfewercomponents toexplainthevarianceintheresponse,duetotherelationthatthis methodestablishesbetweenresponseandpredictorvariables,and itsresultsaremoreinterpretable(Stenbergetal.,2010).

Thenumberoflatentvariablesforamodelwasdeterminedby examining a plot of leave-one-out cross-validation residual variance against the number of latent variables obtainedfrom PLSR.Thelatentvariableofthefirstminimumvalueofresidual variancewasselected(Brownetal.,2005).Theresidualsample variance and predicted vs. measured plots were assessed for outliersdetermination after runningthePLSR. Outliers maybe induced by typing errors, file transfer, interface errors, sensor malfunctionsandfouling,poorsensorcalibration,badsamplingor sample presentation, etc. (Nicola et al., 2007). Sampleslocated individuallyfarfromthezerolineofresidualvariancetogether

Fig.3.Theon-linevisibleandnearinfrared(vis–NIR)spectroscopy-basedsensor, attachedtothetractorforon-linemeasurement(Mouazen,2006).

O.Marı´n-Gonza´lezetal./Soil&TillageResearch132(2013)21–29 24

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with a far position from the trend line in the predicted vs. measuredplotwereconsideredasoutliersandexcludedfromthe analysis.Amaximumof5%oftheentiresampleswasacceptedas the maximum number of outliers to be removed (Kuang and Mouazen,2011).

2.6. Performanceassessmentofcalibrationmodels

Predictionaccuracy ofa PLSRmodel wasdeterminedby the RMSEP, r2 and RPD.The criteria adopted for RPDclassification

(ViscarraRosseletal.,2006b)wasasfollows:RPD<1.0indicates verypoormodel/predictionsandtheiruseisnotrecommended; RPDbetween1.0and1.4indicatespoormodel/predictionswhere onlyhighandlowvaluesaredistinguishable;RPDbetween1.4and 1.8 indicates fair or moderately good model/predictions which maybeusedforassessmentandcorrelation;RPDvaluesbetween 1.8and2.0indicatesgoodmodel/predictionswherequantitative predictionsarepossible;RPDbetween2.0and2.5indicatesvery good, quantitative model/predictions, and RPD>2.5 indicates excellent model/predictions. This classification system was adoptedinthisstudy.

3. Resultsanddiscussion

Forrobustmodellingofthevis–NIRspectraldata,theselection of calibration set should be carefully done so that to be representativeofthesamplesusedfor validation.Therangefor sampleconcentrationsintheprediction(Table5)andindependent validation(Table6)setsissmallerandwithinthecorresponding range for the calibration set (Table 5) for all properties. This confirms that variation in the prediction and independent validationsetsareaccountedforinthecalibrationset.

SincesamplesofElyfieldwereofexcessivelyhigh exchange-able Caexand Mgexvalues, the25samplesfrom Elyfield were

disposed duringthedevelopment of the calibrationmodelsfor these two properties, as these might negatively affect the predictionaccuracyofthecorrespondingmodels.

3.1. Evaluationofgeneralcalibrationmodels

Examiningtheresultsofthepredictionset(Table7),reveals thatbestresultsareachievedforMgex(r2=0.88;RPD=2.55)and

pH(r2=0.86;RPD=2.37).Lessaccurateestimationswereobtained

for CEC (r2=0.72; RPD=1.70) and Ca

ex (r2=0.76; RPD=1.87).

AccordingtotheclassificationbasedonRPDproposedbyViscarra Rosseletal.(2006b),thepredictionaccuracyinthepredictionsetis excellentandverygoodforMgexandpH,respectively,goodfor

CaexandmoderatelygoodforCEC.Theseresultsareinlinewith

those found in the literature. Comparing with other studies reportedintheliteratureunderlaboratorymeasurementcondition (ShepherdandWalsh,2002;Cohenetal.,2005;Mouazenetal., 2006;ViscarraRosselandBehrens,2010),themodelperformance forpHinthepredictionsetisamongthebestmodels(r2=0.50–

0.97;RMSEP=0.04–1.43;RPD=0.57–2.39).Similarconclusioncan bedrawnforthepredictionofMgex,withoverallresultsreported

in the literature for r2 of 0.53–0.91; RMSEP of 0.03–

38.36cmolkg 1 and RPD of 0.48–2.54 (Cozzolino and Moron,

2003;Groenigenetal.,2003;Udelhovenetal.,2003;Wetterlind et al.,2010).Almostthesameresultsasthose achievedin this studywerereportedbyDunnetal.(2002),whenanalysingpHfor thetopsoilof0–10cm(r2=0.80,RPD=2.3)andMgex(r2=0.85,

RPD=2.7). The models for the other two properties are less accurate,ascomparedwithothervaluesreportedintheliterature for Caex (r2=0.07–0.95; RMSEP=0.66–52.90cmolkg 1; Table5

Statisticsoflaboratoryanalysesofsoilsamplesofcalibration(85%ofsamples)andprediction(15%ofsamples)sets.

Property Samples Calibrationset Predictionset

Min Max Mean SDa Min Max Mean SDa

pH 160 4.87 8.43 7.28 0.89 5.36 8.19 7.08 0.79 CEC(cmolc/kg) 146 7.30 21.10 16.97 3.27 10.70 20.40 17.23 2.66 Caex(cmolc/kg)b 122 9.91 135.13 40.05 21.53 17.33 72.68 34.23 13.43 Mgex(cmolc/kg)c 122 0.43 7.69 1.51 0.95 0.57 3.93 1.41 0.79 a Standarddeviation. b Ca

exafterexcludingElyfarmsamples. c

MgexafterexcludingElyfarmsamples.

Table7

PerformanceofthegeneralcalibrationmodelsofsoilpH,cationexchangecapacity (CEC),extractablecalcium(Caex)andmagnesium(Mgex),validatedwithprediction

set,laboratoryscannedandon-linescannedindependentvalidationset(25sample) inthefield1intheDuckEndfarmintheUK.

Property r2a SD RMSEPb RPDc

Fullcrossvalidation

pH 0.79 0.86 0.40 2.16 CEC 0.58 2.72 1.77 1.54 Caex 0.89 18.38 22.05 0.83 Mgex 0.69 4.22 0.38 2.44 Prediction pH 0.86 0.79 0.33 2.37 CEC 0.72 2.50 1.47 1.70 Caex 0.76 13.77 7.34 1.87 Mgex 0.88 0.78 0.31 2.55

Laboratoryindependentvalidation

pH 0.86 0.77 0.28 2.69

CEC 0.68 1.74 0.99 1.77

Caex 0.86 9.70 4.43 2.19

Mgex 0.66 0.50 0.29 1.72

On-lineindependentvalidation

pH 0.78 0.76 0.36 2.14 CEC 0.62 1.56 0.97 1.61 Caex 0.61 9.29 7.11 1.30 Mgex 0.67 0.51 0.34 1.49 a Coefficientofdetermination.

b Rootmeansquareerrorofpredictionincmolkg1forCEC,Ca

exandMgex. cResidualpredictiondeviation(standarddeviation/rootmeansquareerrorof

prediction). Table6

Statisticsoflaboratoryanalysesoftheindependentvalidationsetsamplescollected inthefield1inDuckEndfarm(UK).

Property Samples Min Max Mean SDa

pH 25 5.34 8.41 6.66 0.77 CEC(cmolc/kg) 25 10.40 18.00 14.62 1.74 Caex(cmolc/kg)b 25 5.91 44.91 24.16 9.69 Mgex(cmolc/kg)c 25 0.57 2.08 1.08 0.49 a Standarddeviation. b Ca

exexcludingElyfarmsamples. c

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RPD=0.60–2.75)(e.g.CozzolinoandMoron,2003; Cohenetal., 2005; Mouazen et al., 2006; Zornoza et al., 2008) and CEC (r2=0.13–0.90; RMSEP=1.22–10.43cmolkg 1; RPD=0.60–2.7)

(e.g. Ben-Dor and Banin, 1995; Chang et al., 2001; Mouazen etal.,2006;Awitietal.,2008).Islametal.(2003),reportedsimilar resultsforCEC(r2=0.64,RPD=1.6),usingaseparatevalidation

set.However,theseaccuraciescanstillbeconsideredasgoodand usefulforquantitativepredictions.

3.2. Comparisonoflaboratoryandon-linemeasurementaccuracy Inordertocomparetheperformanceofthegeneralcalibration models for the prediction of studied soil properties between laboratoryand on-linescannedspectra,theindependent valida-tionset(25samples)collectedfromthefield1inDuckEndfarm wasused.Generally,smalleraccuraciesareobservedwhenusing soilspectracollectedunderon-linemeasurementconditions,as compared to that for spectra collected under laboratory non-mobile scanning conditions (Table 7).Results confirm that the predictions of the laboratory and on-line measurements were classifiedasexcellent/verygoodforpH(RPD=2.69and2.14and r2=0.86 and 0.78, respectively), and moderately good for CEC

(RPD=1.77and1.61andr2=0.68and0.62,respectively)andMg ex

(RPD=1.72and1.49andr2=0.66and0.67,respectively).ForCaex,

very good accuracy was calculated for the laboratory method (RPD=2.19andr2=0.86),ascomparedtothepooraccuracyforthe

on-linemethod(RPD=1.30andr2=0.61).

There is little literature about the on-line prediction of secondary soilpropertiese.g. propertieswithoutdirect spectral responses in the NIR spectroscopy. Soil pH was the most successfully measured property with on-line vis–NIR sensors, withallreportsshowless accurateon-linepredictionsthanthe onesachievedinthecurrentstudy(r2=0.78)withr2valuesof0.62

(Christy,2008)and0.61(Shibusawaetal.,2001).Onlycomparison between measured and predicted pH maps was provided by

Mouazenetal.(2007),showingsimilartrendsofspatial distribu-tion.Thelowpredictionaccuracyobtainedinthisstudyforon-line Caex might be attributed to theuneven distribution of sample

concentrationovertheentireconcentrationrange.Inspiteofthe wide rangeof concentration, themajorityof sampleshave low valuesforCaexwhilethereisasmallgroupofsampleswithvery

highvalues(Fig.4).Thus,thereisagapofvaluesbetween both groups, hindering the creation of a robust calibration model. Similarcommentcanbemadeforlaboratoryscannedspectra(data notshown).SimilartrendisobservedforMgexwithalowerimpact

on the accuracy for both laboratory non-mobile and on-line measurement.ToreducethiseffectsamplesfromElyfield(UK), withthehighestvaluesforCaexandMgex,wereremovedduring

thedevelopmentofcalibrationmodels,whichledtotheresults showninTable7andFig.4.Inthefuture,itisrecommendedto considernewsampleswithCaexandMgexvaluescoveringthegap

in theconcentrationrange forthedevelopmentof morerobust calibrationmodels.Thelow accuracy in predictionsof theCaex

modelcouldalsoberelatedwiththeextractionmethodselected. This method might be inappropriate for Caex detection in

predominantlycalcareoussamples.

3.3. Analysisoferror 3.3.1. Histogramoferror

The histogram of normal distribution plots of error was calculated by subtracting predicted from measured values for eachpropertyusing25samplesoftheindependentvalidationset (Table8).Itcanbeclearlyobservedthatthemean,varianceandSD valuesaremuchlargerwithon-linescannedthanwithlaboratory scannedspectra.Thisisaclearsigntosupporttheconclusionthat accuracy decreases during on-line measurement due to the ambientconditions(e.g.noise,vibration,stones,plantroots,and aspectrumpositionwithacorrespondingsoilsample)(Mouazen et al., 2007; Stenberget al., 2010). However,under laboratory

Fig.4.Scatterplotsbetweenlaboratoryandon-linevisibleandnearinfrared(vis–NIR)predictedvaluesforextractablecalcium(Caex)and(Mgex)beforeremovingElyfield

samples(a)andafterremovingElyfieldsamples(b).

O.Marı´n-Gonza´lezetal./Soil&TillageResearch132(2013)21–29 26

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scanning conditions all these ambient conditions affecting accuracyareeleminated.

3.3.1.1. HistogramsoferrorforpH. Thehistogramplotofnormal error distribution for pH prediction using laboratory scanned spectra is normally distributed around 0 (Fig.5 a), withslight skewness (0.392) towards the positive side, indicating slight

overestimation. However, for on-line prediction, a smaller skewness (0.024) can be observed (Table 8). Around 76% and 64%oflaboratoryandon-linepredictions,respectively,arewitha smallererrorthan0.3inabsolutevalues,whichissmallerthan12% ofthenormalpHrangeforagriculturalsoils(4–9)(USDA,1998). Furthermore, therange of predictionerror is largerfor on-line prediction( 1.003to1.122),ascomparedtolaboratoryprediction.

Fig.5.Histogramofnormaldistributionoferrorforlaboratory(left)andon-line(right)predictionsofsoilpH(a),cationexchangecapacity(CEC)(b),extractablecalcium(Caex)

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The smaller range of prediction errorin additionto the larger portionofsmallerrorthan0.3(76%)oflaboratoryscannedspectra suggests the better performance of the general pH model for predictionbasedonlaboratorythanon-linescannedspectra.

3.3.1.2. HistogramsoferrorforCEC. SimilartopH,thehistogram plotsofCECerrorforbothlaboratoryandon-linepredictionsshow normal distributed around0 (Fig. 5b),withskewnessvaluesof 0.004and 1.201,respectively(Table8).Around64%and76%of laboratoryandon-linepredictions,respectively,arewithsmaller errorthan1.1cmol+/kg,whicharesmallerthan4.4%ofthenormal CEC rangeforagriculturalsoilsCECranges from0cmol+/kgfor sandysoilstoabout50cmol+/kgforclaysoils(MengelandKirkby, 1982).Thissurprisinglyshowsabiggerproportionoferroraround 0 foron-lineprediction,ascompared thelaboratoryprediction. However, the range of error was larger for on-line than for laboratory prediction (Table 8). This might be attributed to difficulties associated with matching position of a soil sample collectedforlaboratorychemicalanalysisandthecorresponding spectrumcollectedduringon-linemeasurement(Mouazenetal., 2007).

3.3.1.3. HistogramsoferrorforCaex. Thenormaldistributionplotof

errorforCaexpredictionusinglaboratoryscannedspectra(Fig.5c)

is clearly skewed towards the negative side of the plot. This skewness is larger for on-line (skewness= 0.523) than for laboratory (skewness= 0.010) predictions (Fig. 5c, Table 8). About 80% and 52% of laboratory and on-line predictions, respectively,arewithsmallererrorthan5.5cmol+/kginabsolute values,whicharesmallerthanabout9%oftheCaexrangepresent

inthesamples.ThisconfirmsthatlaboratorypredictionofCaexis

moreaccuratethantheon-lineprediction,whichissupportedbya largerrangeoferrorfortheon-line,ascomparedtothelaboratory prediction(Table8).

3.3.1.4. HistogramsoferrorforMgex. Thenormaldistributionplot

oferrorofMgexoflaboratorypredictionspresentsarathernormal

distribution of erroraround 0.However, thisis clearly skewed towardsthenegativerangewhenon-linespectraareused(Fig.5d,

Table8).About56%and48%oflaboratoryandon-linepredictions, respectively,arewithsmallererrorthan0.17cmol+/kginabsolute values,whichareasmallererrorthanabout4.7%oftheMgexrange

presentinthesamples.ThisindicatesthatasforpHandCaex,the

predictionofMgexismoreaccurateandstablewhenlaboratory

scannedspectraareused,ascomparedtoon-linemeasurement. ThisdidnotproveclearlyforCEC,althoughthetrendissimilarto theotherthreeproperties.

The above discussion about histogram plots of error and accuracyanalysisconfirmsthatcalibrationmodelsdevelopedwith laboratoryscannedspectraperformbetterwhenvalidated with laboratorymeasured spectra, ascompared toon-line measured

spectra.However, in comparison withlaboratorychemical and vis–NIR analyses, the on-line measurement of soil properties enables the collection of high number of data points (around 12.800readingsfor21haofthefield1),withaverageofaround2 pointspermetretraveldistance.Thislargeamountofdataallows thespatialinterpolationtoestimatesvaluesforun-sampledpoints inthefield.Then,thepossibilitytopredictseveralsoilproperties fromthesamespectrum,theopportunitytocreatemapsfromthis largeamountofinformationanditsutilisationforsitespecificland managementwithinthefield,suggesttheon-linemeasurementof soilpropertieswithoutdirectspectralresponsesintheNIRrange asavaluablemeasuringtechnique.

4. Conclusions

Thispaperreportsontheperformanceofavis–NIR spectrosco-py-based on-line sensor for the prediction of soil properties withoutdirectspectralresponseintheNIRrange,namely,pH,CEC, Caex and Mgex. It also compares prediction accuracy of these

propertiesbetweenon-lineandnon-mobilelaboratoryscanning. Theresultsobtainedinthisstudyallowthefollowingconclusions tobedrawn:

1.The on-line measurement system enabled the simultaneous measurementofseveralsoilpropertieswithoutdirectspectral responsesintheNIRspectroscopyacrossafield.

2.Generalcalibrationmodels,developedwithsamplescollected from fields in different European counties are a successful procedureforthecalibrationoftheon-linevis–NIRsensorfor thepredictionofpH,CECandCaexandMgex.

3.Higheraccuracywasobtainedforpredictionsusinglaboratory scanned,ascomparedtoon-linescannedspectra.Thelaboratory andon-linepredictionswereclassifiedasexcellent/verygood forpH(RPD=2.69and2.14andr2=0.86and0.78,respectively),

andmoderatelygoodforCEC(RPD=1.77and1.61andr2=0.68

and 0.62, respectively) and Mgex (RPD=1.72 and 1.49 and

r2=0.66and0.67,respectively).For Ca

ex,very goodaccuracy

was calculated for the laboratory method (RPD=2.19 and r2=0.86), as compared to the poor accuracy for the on-line

method(RPD=1.30andr2=0.61).

4.The histogram plots of error proved the general calibration modelsdevelopedwithlaboratoryscannedspectratoperform betterwhenusedtopredictthestudiedsoilpropertiesusing laboratoryscanned spectra, as compared to on-line scanned spectra.

5.Theabilityofcontinuousdatagatheringwiththeon-linesoil sensor at a relatively highoperationalspeed (about 3km/h) withverygoodtomoderateaccuracyobtainedforsomeofthe properties investigated (e.g. pH,CEC and Mgex), suggest the

recommendation of the on-line soil sensor for site specific fertilisation.

Table8

Summaryofstatisticsforthehistogramplotsoferrorforon-lineandlaboratoryscannedspectra.

Elements Skewness Kurtosis Mean Variance SD Errorrange

Laboratoryindependentvalidation

pH 0.392 1.649 0.034 0.083 0.289 0.674to0.762

CEC 0.004 1.098 0.079 1.004 1.002 1.840to1.611

Caex 0.010 0.738 1.858 16.859 4.106 9.349to5.022

Mgex 0.491 1.489 0.053 0.084 0.289 0.655to0.733

On-lineindependentvalidation

pH 0.023 2.949 0.124 0.164 0.405 1.003to1.122

CEC 1.201 2.324 0.176 1.761 1.327 3.908to2.483

Caex 0.523 0.439 5.511 50.620 7.115 21.911to9.025

Mgex 0.016 0.744 0.242 0.115 0.338 1.010to0.608

O.Marı´n-Gonza´lezetal./Soil&TillageResearch132(2013)21–29 28

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Furtherresearchisneededtoupgradethecalibrationmodelsof pH,CEC, Caexand Mgex, developedin thisstudyusing samples

collectedfromalargernumberoffieldsandcountrieswitheven distributionofconcentrationsalongtheentireconcentrationrange encounteredinagriculturalsoils.Thisisrecommendedtoimprove thepredictionaccuracyandrobustnessofmodelsdevelopedfor studiedsoilproperties.

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