Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index

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Contents lists available atScienceDirect

Agricultural

Water

Management

j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / a g w a t

Evaluation

of

neural

network

modeling

to

predict

non-water-stressed

leaf

temperature

in

wine

grape

for

calculation

of

crop

water

stress

index

B.A.

King

a,∗

_,

_K.C.

_Shellie

b

a_USDA-ARS,_Northwest_Irrigation_and_Soils_Research_Laboratory,₃₇₉₃_N₃₆₀₀_E,_Kimberly,_83341-5076_ID,_USA b_USDA-ARS,_{Horticultural}_Crops_Research_{Unit-worksite,}₂₉₆₀₃_University_of_Idaho_Lane,_Parma,₈₃₆₆₀_ID,_USA

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received30July2014

Receivedinrevisedform2December2015 Accepted12December2015

Keywords:

Canopytemperature Irrigationmanagement Waterstress

a

b

s

t

r

a

c

t

Precisionirrigationmanagementofwinegraperequiresareliablemethodtoeasilyquantifyandmonitor

vinewaterstatustoalloweffectivemanipulationofplantwaterstressinresponsetowaterdemand,

cultivarmanagementandproducerobjective.Mildtomoderatewaterstressisdesirableinwinegrapein

determinedphenologicalperiodsforcontrollingvinevigorandoptimizingfruityieldandquality

accord-ingtoproducerpreferencesandobjectives.Thetraditionalleaftemperaturebasedcropwaterstress

index(CWSI)formonitoringplantwaterstatushasnotbeenwidelyusedforirrigatedcropsingeneral

partlybecauseoftheneedtoknowwell-wateredandnon-transpiringleaftemperaturesunderidentical

environmentalconditions.Inthisstudy,leaftemperatureofvinesirrigatedatratesof35,70or100%

ofestimatedevapotranspirationdemand(ETc)underwarm,semiaridﬁeldconditionsinsouthwestern

IdahoUSAwasmonitoredfromberrydevelopmentthroughfruitharvestin2013and2014.Neural

net-work(NN)modelsweredevelopedbasedonmeteorologicalmeasurementstopredictwell-wateredleaf

temperatureofwinegrapecultivars‘Syrah’and‘Malbec’(VitisviniferaL.).Inputvariablesforthecultivar

speciﬁcNNmodelswithlowestmeansquarederrorwere15-minaveragevaluesforairtemperature,

relativehumidity,solarradiationandwindspeedcollectedwithin±90minofsolarnoon(13:00and

15:00MDT).CorrelationcoefﬁcientsbetweenNNpredictedandmeasuredwell-wateredleaf

tempera-turewere0.93and0.89for‘Syrah’and‘Malbec’,respectively.Meansquarederrorandmeanaverage

errorfortheNNmodelswere1.07and0.82◦_C_for_‘Syrah’_and_1.30,_and_0.98◦_C_for_{‘Malbec’,}_{respectively.}

TheNNmodelspredictedwell-wateredleaftemperaturewithsigniﬁcantlylessvariabilitythan

tradi-tionalmultiplelinearregressionusingthesameinputvariables.Non-transpiringleaftemperaturewas

estimatedasairtemperatureplus15◦_C_based_on_maximum_temperatures_measured_for_vines_irrigated

at35%(ETc).DailymeanCWSIcalculatedusingNNestimatedwell-wateredleaftemperaturesbetween

13:00and15:00MDTandairtemperatureplus15◦Cfornon-transpiringleaftemperatureconsistently

differentiatedbetweendeﬁcitirrigationamounts,irrigationevents,andrainfall.Themethodologyused

tocalculateadailyCWSIforwinegrapeinthisstudyprovidedadailyindicatorofvinewaterstatusthat

couldbeautomatedforuseasadecision-supporttoolinaprecisionirrigationsystem.

PublishedbyElsevierB.V.

1. Introduction

Inmanyaridwinegrapeproductionareasirrigationiswidely usedtomanagevinevigorandyieldtoinducedesirablechanges in berrycomposition for wine production (Chaves et al., 2010; Lovisoloetal.,2010).Inred-skinnedwinegrapecultivars,amild tomoderatewaterstressindeterminedphenologicalperiodshas

∗Correspondingauthor.

E-mailaddresses:[email protected](B.A.King),

[email protected](K.C.Shellie).

beenfoundtoincrease water productivityand toimprovefruit quality(Romeroetal.,2010;Shellie,2014).Theoptimum sever-ityandphenologicaltimingofimposedwaterdeﬁcitisinﬂuenced bycultivar, climaticand edaphicgrowingconditions, and wine grapeculturalpractices.Applicationofprecisionirrigation tech-niquesrequiresaccurate, reliablemethodsfor determiningvine waterdemandandformonitoringvinewaterstatuscoupledwith anirrigationsystemcapableofapplyingwateron-demand,in pre-ciseamounts(Jones,2004).Thelackofarapid,reliablemethodfor monitoringvinewaterstatuswithhighspatialandtemporal res-olutionhashinderedtheadoptionofprecisionirrigationpractices inwinegrapeproduction.

http://dx.doi.org/10.1016/j.agwat.2015.12.009

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Manyofthemethodscurrentlyavailablefordeterminingwater demandandmonitoringvinewaterstatusareeithertoolaborious forautomationorhavepoorspatialandtemporalresolution.The Penman–Monteithequationiscommonlyusedtoestimate evap-otranspirationdemand(ETc)and calculateanirrigationamount (Allenetal.,1998);however,periodicmeasurementsofplantor soilwaterstatusarerequiredtoverifythatthesuppliedamount actuallyinducedthedesiredseverityofwaterstress.Soil volumet-ricwatercontentisnotasuitableindicatorofvinewaterstatus becauseithaslowspatialresolutionandisinfluencedbyspatially heterogeneoussoilattributes,suchastextureanddepth.Williams andTrout(2005)foundthatmeasurementofsoilwatercontent toadepthof3matninelocationswithinone-quarterofan indi-vidualvinerootzonewasnecessarytoaccuratelydeterminethe amountofwaterwithinthesoilprofileavailabletodrip-irrigated vines.Also,agivensoilvolumetricwatercontentmayinduce dif-feringseveritiesofwaterstressindifferentgrapevinecultivarsdue tointrinsicdifferencesamongcultivarsintheirhydraulicbehavior rangingfromisohydrictoanisohydric(Schultz,2003;Shellieand Bowen2014;Williamsetal.,2012;Bellvertetal.,2015a).Thus,bulk changesinsoilwatercontentorsoilwaterpotentialmaynot cor-respondwithchangesinvinewaterstatus(Jones,2004;Williams andTrout,2005;Ortega-Fariasetal.,2012).Measurementsofleaf or stemwater potentialoffer theadvantage ofintegrating soil, plantandenvironmentalfactors;however,theirpoortemporaland spatialresolutionandhighlaborrequirementlimittheir poten-tialforautomationintoaprecisionirrigationsystem.Plantwater potentialhaspoortemporalresolutionduetoitshighsensitivity toenvironmentalconditions(Rodriguesetal.,2012;Williamsand Baeza,2007;Jones,2004).Therealsoisnogeneralagreementas towhich measurement ofplantwater potential(pre-dawnleaf ormiddaystemorleaf)mostreliablyindicatesvinewaterstatus (Williams and Araujo,2002; Williamsand Trout,2005; Ortega-Fariasetal.,2012).WilliamsandTrout(2005)foundthatpre-dawn leafwaterpotentialwasunsatisfactoryforaccuratelydetermining vinewaterstatuswhilemiddayleafandstemwaterpotentialwere linearlycorrelatedandequallysuitablefordeterminingvinewater status.Middayleafwaterpotentialisthemostcommonmethod usedinCalifornia toindicatevinewater status(Williams etal., 2012)perhapsbecauseitislesstimeconsumingthaneither pre-dawnleafwaterpotentialormiddaystemwaterpotentialallowing more acreageto becovered during midday climaticconditions (WilliamsandAraujo,2002).Middaystemandleafwater poten-tialofgrapevinesarehighlycorrelatedwithvaporpressuredeficit (VPD)undersemi-aridconditionsbutthecorrelationsdifferforleaf waterpotentialslessthanorgreaterthan1.2MPa(Williamsand Baeza,2007;Williamsetal.,2012)Ingeneral,amiddayvalueofleaf waterpotentiallessnegativethan−1.0MPaunderhighevaporative demandhasgenerallybeenacceptedasindicativeofwell-watered vines(Shellie, 2006;Williams and Trout, 2005; Williamset al., 2012;ShellieandBowen,2014;Bellvertetal.,2014).

Thermalremote sensing hasrecentlybeen usedto estimate evapotranspirationand droughtstressin manycrops,including grapevine(MaesandSteppe,2012).Waterstresspromotes stoma-talclosure,reducingtranspirationandevaporativecoolingwhile increasingleaftemperature.Infraredradiometershavebeenused underﬁeldconditionstomeasuretheincreaseinwinegrapeleaf surfacetemperatureunderdifferingseveritiesofdeﬁcitirrigation (Cohen et al., 2005; Glenn et al.,2010; Shellie and King 2013; Bellvertetal.,2014,2015a).Changesinleaftemperaturehavebeen correlated withratesof stomatalconductance andleafor stem waterpotentialingrapevineandresponsivenesshasbeenshown tovarybycultivar(Cohenetal.,2005;Glennetal.,2010;Pouetal., 2014;Bellvertetal.,2015a,b).Thedifferenceinleaftemperature betweenstressedandnon-waterstressedplantsrelativeto ambi-entairtemperaturehasbeenusedtodevelopanormalizedcrop

waterstressindex(CWSI)(Idsoetal.,1981;Jacksonetal.,1981)for quantifyingplantwaterstatus.TheCWSIisdeﬁnedas:

CWSI=

Tcanopy−Tnws

Tdry−Tnws

(1)

whereTcanopyisthetemperatureoffullysunlitcanopyleaves(◦C), Tnwsisthetemperatureoffullysunlitcanopyleaves(◦C)whenthe cropisnon-water-stressed(well-watered)andTdryisthe temper-atureoffullysunlitcanopyleaves(◦C)whenthecropisseverely water stresseddue tolow soilwater availability. Temperatures TnwsandTdryarethelowerandupperbaselinesusedtonormalize CWSIfortheeffectsofenvironmentalconditions(airtemperature, relativehumidity,radiation,windspeed,etc.)onTcanopy.Ideally, CWSIrangesfrom0to1where0representsawell-watered condi-tionand1representsanon-transpiring,water-stressedcondition. PracticalapplicationoftheCWSIhasbeenlimitedbythedifficulty ofestimatingwithoutactuallymeasuringTnwsandTdry(Maesand Steppe,2012).Experimentaldeterminationofacropspecific con-stantforTnwsandTdryrelativetoambientairtemperaturehasnot beenfruitfulduetothepoorlyunderstoodandcomplexinfluences ofenvironmentalconditionsonthesoil–plant–aircontinuum(Idso etal.,1981;Jones, 1999,2004;PayeroandIrmak,2006).In the originaldevelopmentandapplicationoftheCWSI,TnwsandTdry wereexperimentallydeterminedwithTnws correlatedwithVPD toaccountforclimaticeffectsonTcanopymeasurements.Canopy temperature measurements and application of the CWSI were restrictedtotimesnearsolarnoononcloudlessdaystoaccount fortheeffectofsolarradiationonstomatalconductance.Artificial wetanddryreferencesurfaceshavebeenusedsuccessfullyto esti-mateTnwsandTdryunderthesameenvironmentalconditionsas

Tcanopy.(Jones,1999;O’shaughnessyetal.,2011;Jonesetal.,2002;

LeinonenandJones,2004;Cohenetal.,2005;Grantetal.,2007; Mölleretal.,2007;Alchanatisetal.,2010;Pouetal.,2014); how-ever,therequiredmaintenanceoftheartiﬁcialreferenceslimits potentialuseforautomationinaprecisionirrigationsystem.

Physicalandempiricalmodelshavebeendevelopedtoestimate TnwsandTdrywithvaryingdegreesofsuccess.Aleafenergybalance (Jones,1992)approachwasusedbyJones(1999)todevelopthe followingequationsforcalculatingTwetandTdry:

Twet=Tair−

rHRraWRni

cp[raW+srHR]−

rHRıe

raW+srHR

(2)

Tdry=Tair+

rHRRni

cp (3)

whereTwetisthetemperature(◦C)ofanartiﬁciallywetleaf,Tairisair temperature(◦C),raWisboundarylayerresistancetowatervapor (sm−1_),_R

niisthenetisothermalradiation(Wm−2),ıeiswaterair vaporpressuredeﬁcit(Pa),rHRistheparallelresistancetoheatand radiativetransfer(sm−1_),_is_the_{psychrometric}_constant_(Pa◦_C−1_),

isthedensityofair(kgm−3_),_c

pisthespeciﬁcheatcapacityof air(Jkg−1◦_C−1₎_and_s_is_the_slope_of_the_curve_relating_saturation vaporpressuretotemperature(Pa◦C−1_)._Sensible_heat_loss_for_a_dry surfacewiththesameradiativeandaerodynamicpropertiesofa leafwasassumedtobeequaltonetabsorbedradiation(Eq(3)).Heat transferresistanceinleaves(rHR)wasestimatedtobeafunctionof characteristicdimension(d)andwindspeed()(Jones,1992)and, assumingisothermalradiation,canbeestimatedas(Jones,1999):

rHR=100

_d

(4)

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measure-Growing Deg

ree

Days aft

er Growth Stage

29 (

o

C)

0 200 400 600 800 1000 1200 1400

Cr

op

C

oef

fic

ien

t,

K

c

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Fig.1.GeneralizedgrowingdegreedayalfalfabasedwinegrapecropcoefﬁcientusedtoestimatedailyevapotranspirationofallwinegrapevarietiesatParma,ID.Grapevine growthstage29correspondstoberrysizeof4mm(Coombe,1995).

Table1

Minimumand maximum15-minaveragevaluesmeasured at1-minintervals between13:00and15:00MDTfromJuly11throughSeptember22,2013andJuly 3throughSeptember28,2014atParma,IDusedtolinearlyscaleneuralnetwork inputparameters.

Minimum Maximum

Airtemperature,(◦_C) _14.0 _38.3

Relativehumidity,(%) 8 84

Windspeed,(ms−1) 0.4 5.7

Solarradiation,(Wm−2) 17 1139

Well-watered‘Malbec’leaftemperature,(◦_C) _13.9 _36.6

Well-watered‘Syrah’leaftemperature,(◦_C) _13.7 _38.1

mentsunderminimal windconditions.Numericalestimation of

TnwsandTdryeliminatestheneedforartiﬁcialreferencesurfaces

orwell-wateredandwaterstressedreferenceplots;howeverEqs.

(2)through (4)requireancillary measurementstoreliably esti-mateequationparameters. Multiplelinearregression equations havealsobeenusedtoestimateTnwsandTdry asafunctionofair temperature,solarradiation,cropheight,windspeed,andvapor pressuredeficit(orrelativehumidity),withcorrelationcoefficients of0.69–0.84betweenthepredictedandmeasuredleaftemperature ofwell-wateredcornandsoybean(PayeroandIrmak,2006).Irmak etal.(2000)determinedincornthatTdrywas4.6–5.1◦Caboveair temperatureand,inseveralsubsequentstudieswithcropsother thancorn(includinggrapevines),avalueofairtemperatureplus 5.0◦ChasbeenusedforTdryinEq.(1)(Cohenetal.,2005;Möller etal.,2007;Alchanatisetal.,2010).O’shaughnessyetal.(2011) usedmaximumdailyairtemperatureplus5.0◦CforTdryofsoybean andcotton.Regressionequationshavebeenthemostpromising, practicalapproachusedtoestimateTnwsand Tdry for theCWSI. However,regression,bynecessity,simplifiescomplex,unknown interactionsintoaprioriorassumedmultiplelinearornonlinear relationships(PayeroandIrmak,2006).

ArtiﬁcialNeuralNetworks(NN)havebeenusedsuccessfullyto modelcomplex,unknownrelationshipsandpredictphysical con-ditions,suchasevapotranspiration (Kumaret al.,2002; Bhakar etal.,2006;Trajkovicetal.,2003)andmanyotherwaterresource applications(ASCE,2000).AcommonNNarchitectureconsistsof

multiplelayersofsimplecomputingnodesthatoperateas nonlin-earsummingdevicesinterconnectedbetweenlayersbyweighted links.Eachweightisadjustedwhenmeasureddataarepresented tothenetworkduringatrainingprocesswithanobjectivefunction suchasminimizingmeansquareerror.SuccessfultrainingofaNN resultsinanumericalmodelthatcanpredictanoutcomevaluefor conditionsthataresimilartothetrainingdataset.Tothebestofour knowledge,aNNhasnoteverbeenusedtopredictTnwsorTdryfor calculationofaCWSI.ANNisparticularlywell-suitedfor predict-ingTnwsandTdrybecausetherelationshipsbetweenenvironmental factorsandtheirinteractionwithvineresponsearecomplex,poorly understood,anddifﬁculttorepresentmathematically.Also,a train-ingdatabaseforNNmodeldevelopmentcanberapidlyandreliably generated.

TheCWSIcanpotentiallybeusedtocontinuouslymonitorwater stressseverityinwinegrapeHowever,theneedtomeasureTnws andTdryunderthesameenvironmentalconditionsasTcanopyposes logisticalproblemsincommercialvineyardswhere neitherTnws norTdry aredesirablesoilmoistureconditions.The objectiveof thisstudywastoinvestigatethefeasibilityofusingaNNto esti-mateleaftemperatureofwell-wateredgrapevineanddemonstrate applicabilityin calculating aCWSI forwine grape underdeﬁcit irrigation.PerformanceofthedevelopedNNwasevaluatedby com-paringNNpredictedwell-wateredleaftemperaturewithmeasured well-wateredleaftemperatureand estimatedwell-wateredleaf temperatureusingtraditionalmultiplelinearregressionmodeling. ApplicabilityoftheNNmodelwasdemonstratedbycalculatinga dailyCWSIforvinesdeﬁcitirrigatedatfractionalamountsof evap-otranspirationdemand(ETc).

2. Materialsandmethods

2.1. Vineyardandexperimentdescription

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Air Temperature (oC)

18 2022 2426 2830 3234 3638 40

N

u

m

b

er

of

O

b

s

e

rv

a

ti

o

ns

0 50 100 150 200 250

Solar Radiation (W m-2)

50 ₁₅0 ₂₅0 ₃50 ₄₅0 ₅₅0 ₆₅0 ₇₅0 ₈₅0 ₉₅0 10

50 11

50

Nu

m

be

r o

f Ob

s

e

rv

a

ti

o

n

s

0 100 200 300 400

Relative Humidity (%)

5 15 25 35 45 55 65 75 85

N

u

m

b

e

r of

O

b

s

e

rv

at

io

n

s

0 100 200 300 400 500

Wind Speed (m sec-1

) 0.7

5

1.251.752.252.753.253.754.254.7 5

5.255.75

Nu

m

b

e

r o

f O

b

s

e

rv

at

ion

s

0 100 200 300 400 500 600

Fig.2.Histogramsshowingthefrequencyofrecorded15-minaveragedclimaticvaluesmeasuredat1-minintervalsbetween13:00and15:00MDTfromJuly11through September22,2013andJuly3throughSeptember28,2014atParma,ID.

conditions(semi-arid,drysteppewithwarmestmonthlyaverage temperatureof32◦C),andirrigationwatersupply(wellwaterwith sandmediafilter)atthislocationwerewell-suitedforconducting deficitirrigationfieldresearch.Vineswereplantedasun-grafted, dormant-rootedcuttings in 2007 and werewell-watered using abovegrounddripthroughthe2010growingseason.Onetrialwas plantedwiththewinegrapecultivar‘Malbec’andtheotherwas

plantedwiththecultivar‘Syrah’.Rowbyvinespacing(1.8×2.4m), trainingandtrellissystem(double-trunked,bilateralcordon, spur-prunedannuallyto16buds/mofcordon,verticalshootpositioned onatwowiretrelliswithmoveablewindwires)followed local commercialpractices.Diseaseandpestcontrolwerealsomanaged accordingtolocalcommercialpractices.Alleyandvinerowswere maintainedfreeofvegetation.

Table2

Historical,2013and2014climatedata(±standarddeviation)forApril1throughOctober31collectedfromBureauofReclamationAgriMetsystem[(www.usbr.gov/pn/ agrimet/),latitude43◦₄₈₀₀_,_longitude₁₁₆◦₅₆₀₀_,_elevation₇₀₂_m]_for_Parma,_Idaho_weather_station_and_seasonal_irrigation_amounts_applied_to_irrigation_treatments.

Accumulatedgrowingdegreedayswerecalculatedfromdailymaximumandminimumtemperaturewithnoupperlimitandabasetemperatureof10◦_C.

2013 2014 1994–2012average

Precipitation(mm) 101 88 99.6±35

Dailyaveragetotaldirectsolarradiation(MJm−2₎ _22.3 _22.3 _22.1_±_0.9

Daysdailymaximumtemperatureexceeded35◦_C ₃₅ ₂₇ ₂₈_±₁₂

Accumulatedgrowingdegreedays(◦_C) ₁₇₉₈ ₁₇₅₉ ₁₇₀₈_±₁₁₅

Alfalfa-basedreferenceevapotranspiration,ETr(mm) 1307 1314 1212±55

Well-wateredSyrah(mm) 603 776

‘Syrah’70%ETcwith1irrigation/week(mm) 432 491

‘Syrah’70%ETcwith3irrigations/week(mm) 423 482

‘Syrah’30%ETcwith1irrigation/week(mm) 214 285

‘Syrah’30%ETcwith3irrigations/week(mm) 215 287

Well-wateredMalbec(mm) 603 554

‘Malbec’70%ETcwith1irrigation/week(mm) 456 399

‘Malbec’70%ETcwith3irrigations/week(mm) 413 397

‘Malbec’30%ETcwith1irrigation/week(mm) 220 219

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T

nw

s

- T

ai

r

(D

eg

C)

-10 -8 -6 -4 -2 0 2 4 6 8

Vap

or Pressure Deficit (kPa)

0 1 2 3 4 5 6 7

T

nw

s

-

T

ai

r

(D

eg

C)

-8 -6 -4 -2 0 2 4

Syrah

Mallbec

y = 2.1 - 0.92

*VP

D

R2 = 0.29

y = 2.2 - 1.17*VPD

R2 = 0.35

Fig.3. Inﬂuenceofvaporpressuredeﬁcitonthedifferencebetweensurfacetemperatureofansunlit,leaves(Tnws)ofthewell-wateredwinegrapecultivars‘Syrah’(a)and

‘Malbec’(b)andambientairtemperature(Tair)measuredat1-minintervalsandrecordedas15-minaveragedvaluesfromJuly11untilSeptember22,2013andJuly3through

September28,2014atParma,ID.

Irrigationdesign ineach trialwasidenticalandprovided for theapplication of four,independentirrigation treatmentlevels inarandomizedblockdesignwithsix(‘Malbec’)orfour(‘Syrah’) replicateblocksandindependentirrigationwatersupplyto bor-dervinesinthetrialperimeter.Eachwatersupplymanifoldwas equippedwithaprogrammablesolenoid,aﬂowmeter(tomeasure deliveredirrigationamount),apressureregulatorandapressure gauge(tomonitordeliveryuniformity).Treatmentplotsconsisted ofthreevinerowswithsixvinesperrow(18vinesperplot).The vinesinouterplotrows wereconsideredbuffersanddatawere

Table3

Multiplelinearregressionequationsforpredictingwell-wateredsunlitleaf temper-ature(Tnws,◦C)asafunctionofairtemperature(Tair,◦C),solarradiation(SR,Wm−2),

relativehumidity(RH,%)andwindspeed(WS,ms−1_).

Cultivar Equation R2

Syrah Tnws=0.696Tair+0.005RS+0.265RH+0.019WS+2.760 0.90

Malbec Tnws=0.638Tair+0.005RS+0.387RH+0.010WS+4.357 0.84

collectedoninteriorvinesinthecenterrowofeachplot.Thetrial wasborderedbyatwo-vinedeepperimeter.Bordervinesinthe trialperimeterwereirrigatedfrequentlywithanamountofwater thatmetorexceededETc throughoutcanopyandberry

develop-ment.Bordervines wereusedtomeasure Tnws.Treatmentplot

replicatesreceivedoneoffourirrigationtreatments:deﬁcit irri-gationamountssupplyingeither70or35%ETcatafrequencyof

oneorthreetimesperweek.The70%ETc amountwasintended

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Pr

ed

ic

ted

Leaf

Te

m

pe

rat

u

re (

o

C)

13 15 17 19 21 23 25 27 29 31 33 35 37 39

Leaf Temperature

1:1

Y = 2.11 + 0.92*X

R2 = 0.93

Mea

sured

Lea

f Tempe

rature (Deg

C)

13 15 17 19 21 23 25 27 29 31 33 35 37 39

Pr

ed

ic

ted

Leaf

Te

m

pe

rat

u

re (

o

C)

13 15 17 19 21 23 25 27 29 31 33 35 37

Leaf Temperature

1:1

Y = 3.17 + 0.88*X

R2_{= 0.89}

Syrah

Malbec

Fig.4.Performanceofneuralnetworkmodelsforpredictingsunlitleaftemperaturerelativetothemeasuredsunlitleaftemperatureofwell-watered‘Syrah’and‘Malbec’ grapevinesrecordedbetween13:00and15:00MDTas15-minaveragedvaluesmeasuredat1-minintervalsfromJuly22untilSeptember22,2013andJuly3through September28,2014atParma,ID.

measuredthedaypriortoscheduledirrigationtomaintaina con-sistentrangeinvinewaterstress.Theirrigationamounttodeficit irrigated treatments wasdelivered weekly in a singleevent or dividedintothreeportionsdeliveredthreetimesperweek, depend-ingupontreatment.Deficitirrigationinallplotswasfirstinitiated inthe2011growingseason.

2.2. Plantwaterstatusmeasurements

Vine water status was monitored weekly throughout berry developmentonthedayprecedinganirrigationeventby measur-ingthemiddayleafwaterpotentialoftwo,fullyexpanded,sunlit leavespertreatmentplotusingapressurechamber(model6101_; PMSInstruments,Corvallis,OR)asdescribedbyShellie(2006).

Mid-1_Mention_of_trade_names_or_commercial_products_in_this_article_is_solely_for_the

purposeofprovidingspeciﬁcinformationanddoesnotimplyrecommendationor endorsementbytheauthorsortheU.S.DepartmentofAgriculture.

dayleafwaterpotentialofbordervinesthedaypriortoirrigation wasmonitoredevery14 daysin2013andweeklyin 2014.The irrigationamount,equal tocumulativeprecedingweekETc was increasedifleafwaterpotentialwasmorenegativethan−1.0MPa orlessthanthepreviousmeasurement.

2.3. Yieldmeasurements

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Mea

sured Le

af Tempe

rature (Deg

C)

13 15 17 19 21 23 25 27 29 31 33 35 37 39

Pr

ed

ic

ted

Leaf

Tem

pe

rat

u

re (

o

C)

13 15 17 19 21 23 25 27 29 31 33 35 37

Leaf Temperature

1:1

Y = 4.25 + 0.84*X

R2 = 0.84

Pr

ed

ic

ted

Leaf

Tem

pe

ra

tu

re

(

o

C)

13 15 17 19 21 23 25 27 29 31 33 35 37 39

Leaf Temperature

1:1

Y = 2.74 + 0.90*X

R2_{= 0.90}

Syrah

Malbec

Fig.5.Performanceofmultiplelinearregressionmodelsforpredictingsunlitleaftemperaturerelativetothemeasuredsunlitleaftemperatureofwell-watered‘Syrah’and ‘Malbec’grapevinesrecordedbetween13:00and15:00MDTas15-minaveragedvaluesmeasuredat1-minintervalsfromJuly22untilSeptember22,20132013andJuly3 throughSeptember28,2014atParma,ID.

2.4. Leaftemperatureandclimaticmonitoring

Canopy temperature was measured with infrared tempera-ture sensors (SI-121 Infrared radiometer; Apogee Instruments, Logan,UT)positionedapproximately15–30cmaboverecentfully expandedsunlitleaveslocatedatthetopofthevinecanopyand pointednortherlyatapproximately45◦fromnadirwiththecenter ofﬁeldofviewaimedatthecenterofsunlitleaves.Thetemperature sensorswerefactorycalibratedwithin18monthsofinstallation. Themeasuredcanopyareareceivedfullsunlightexposure dur-ing midday. The temperature sensing area was approximately 15–30cmindiametertoensureonlysunlitleaveswereinviewof theinfraredtemperaturesensor.Thepossibilityofbaresoil visibil-ityinthebackgroundwaslimitedbyleaflayerswithinthecanopy belowthemeasuredlocation.Temperaturesensorviewwas peri-odicallycheckedandadjustedasnecessarytoensuretheﬁeldof viewconcentratedonrecentlyfullyexpandedsunlitleavesonthe topofthecanopy.Infraredtemperaturesensorswereinstalledin

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Well

-watered

Lea

f Tempe

rature Pred

iction

Err

or (

o

C)

-5 -4 -3 -2 -1 0 1 2 3 4 5

Nu

m

b

er o

f V

a

lue

s

0 20 40 60 80 100 120 140

Neural Network Model

Regression Model

Num

be

r o

f

Va

lue

s

0 20 40 60 80 100 120 140 160

Neural Network Model

Regression Model

Syrah

Malbec

Fig.6. Histogramofsunlitwell-wateredleaftemperaturepredictionerrorfor‘Syrah’and‘Malbec’grapevinesrecordedbetween13:00and15:00MDTas15-minaveraged valuesmeasuredat1-minintervalsfromJuly22untilSeptember22,20132013andJuly3throughSeptember28,2014atParma,ID.

2.5. Neuralnetworkmodeling

Neuralnetworksoftware(NeuroIntellligence,AlyudaResearch Inc.,Cupertino,CA)wasusedtodevelopandtestNNmodels.The recordedtemperatureandenvironmentaldataset,2013and2014 combined,wasfilteredtoincludeonlyvaluescollectedbetween 13:00and15:00MDT(±90minofsolarnoon)basedonprevious experiencewithgrapevinecanopytemperaturemeasurementat thestudysite(ShellieandKing,2013).Thefiltereddatasetwas ran-domlysubdividedintooneofthreedatasetsusedtotrain,validate andtesttheNNmodels.Sixty-eightpercentofthefiltereddataset wasusedfortraining,16%forvalidation,and16%fortesting.Input parameterswerelinearlyscaledtoarangeof−1to1whichisa normalprocedureforNNmodeling.Themaximumandminimum valuesofmeasuredparametersinthecombined,filtereddataset (Table1)wereusedforlinearscaling.Amultilayerperceptronfeed forwardNNarchitecturewasusedtoestimatecanopytemperature ofwell-wateredgrapevines.Hiddenlayerneuronsuseda hyper-bolictangentactivationfunctionandthesingleoutputneuronused

alogisticactivationfunction.Neuralnetworkarchitectureswere evaluatedwithoneandtwohiddenlayersanduptotenneuronsper hiddenlayer.TheConjugate-GradientandQuasi–Newtonmethods (Haykin,2009)wereusedtotrainthenetwork usingthe train-ingdataset.Thebestarchitecturewasselectedbytrialanderror basedonmaximizingcorrelation(R2₎_with_the_training_and_test datasetswhileusingaminimumnumberofneuronstoreducerisk ofover-trainingtheNNtothedata.

2.6. Statisticalanalysis

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Leaf Tempe

rature - Air Tempe

rature (

o

C)

-4 -2 0 2 4 6 8 10 12 14 16 18 20 22

Cum

u

la

ti

ve P

roba

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lity

0.0 0.2 0.4 0.6 0.8 1.0

Syrah 1 Irrigation/week

Syrah 3 Irrigations/week

Malbec 1 Irrigation/week

Malbec 3 Irrigations/week

Zero Transpiration model

Fig.7. Cumulativeprobabilityfordifferencebetweencanopyandambientairtemperaturein‘Syrah’and‘Malbec’grapevinesdeﬁcit-irrigatedat35%ofETcwithirrigation

eventsoccurringoneorthreetimesperweek.Temperatureswere15-minaveragedvaluesmeasuredat1-minintervalsbetween13:00and15:00MDTfromJuly11until September22,2013andJuly3throughSeptember28,2014atParma,ID.Zerotranspirationrepresentstheleafenergybalancemodelestimateddifferencebetweena non-transpiringleafandairtemperaturefortheenvironmentalconditions.

datawerenotnormallydistributed.NormalQ–Qplots,histograms, andShapiro–Wilk’stest(p≤0.05)wereusedtoassessifdatawere approximatelynormallydistributed.

3. Results

3.1. Climaticandcanopytemperaturecharacteristics

Growingseasonprecipitationin2013wasnearlyequaltothe 19-yearaverage(Table2)andslightlylessin2014.Averagetotal directsolarradiationin2013and2014wereequalandnearlyequal tothe19-yearsiteaverage,respectively.Thenumberofdaysthat dailymaximumtemperatureexceeded35◦Cwasmorefrequent thanthe19-yearsiteaveragein2013,butwaswithinone stan-darddeviationofaverageandnearlyequaltothe19-yearaverage in2014. Accumulatedgrowingdegree days(GDD) in2013 and 2014weregreater,butwithinonestandarddeviationofthe 19-yearsiteaverage.Thegrapeproductionclimateclassificationfor thestudysite,basedoncumulativegrowingdegreedaysinthe Winklersystem(Winkleretal.,1974),wasregionIII(1666–1944 GDD),which issuitable forproduction ofthewine grape culti-vars‘Malbec’and ‘Syrah’.Referenceevapotranspiration(ETr)for thestudysitein both yearswasmore thanone standard devi-ationgreaterthanthe19-yearsiteaverage. Seasonalamountof waterprovidedtowell-wateredvineswas46%ofETrin2013.In 2014,seasonalamountsof waterprovided well-watered‘Syrah’ and‘Malbec’vineswas60and42%ofETr,respectively.Seasonal irrigationamountssuppliedtovinesdeficit-irrigatedwith35or 70%ETcin2013were∼36and70%oftheirrigatedamountapplied towell-watered vines.In2014,theseasonalirrigationamounts supplied‘Syrah’vinesdeficit-irrigatedat35or70%ETcwere∼37 and63%oftheamountappliedtowell-watered‘Syrah’vines. ‘Mal-bec’vinesdeficitirrigated at35 or 70%ETc were supplied∼40 and72% oftheamountappliedtowell-watered‘Malbec’ vines, respectively.

Histograms of 15-min averaged values for environmental parameters(Tair,SR, RHandWS)measuredbetween13:00and 15:00MDTfromJuly11throughSeptember22,2013andJuly3, throughSeptember 28, 2014combined arepresented in Fig.2. At least70% or more of measuredvalues for Tair and SR were

≥26◦Cand750Wm−2_,_{respectively,}_and_RH_and_WS_were_≤_30%_and 2ms−1_,_{respectively.}_The_frequent_incidence_of_calm,_dry,_warm, sunnydaysrecordedatthisstudysiteischaracteristicofasemi-arid steppeclimate.Infrequentoccurrenceofeventswithhigh humid-ityandlowsolarradiationatthestudysitelimitstheapplicability oftheNNmodelsderivedfromthedatasetobtainedinthisstudyto regionswithsimilarwarm,arid,sunnyconditions.Themaximum andminimumvalues ofinputparametersusedtolinearlyscale NN datasetvaluesand leaftemperaturesof well-wateredvines arepresentedinTable1.Themeasuredmaximumandminimum temperaturesofwell-wateredgrape leaveswerecooler, respec-tively,thanmaximumand minimumambientair temperatures. Well-wateredvinesof‘Syrah’hadagreaterrangebetween max-imumandminimumleaftemperaturesthanwell-wateredvinesof ‘Malbec’.

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Day of

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CWSI 35% ET CWSI 70% ET Irrigation 35% ET Irrigation 70% ET

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tions/week

Fig.8. ActualirrigationandprecipitationamountsandcalculatedCWSIof‘Syrah’grapevinesintreatmentplotsdeﬁcit-irrigatedat30or70%ofETcatafrequencyofoneor

threetimesperweek.Non-waterstressedandnon-transpiringleaftemperatureswereestimated,respectively,fromtheneuralnetworkmodelandairtemperature+15◦C. Ambientairanddeﬁcit-irrigatedleaftemperaturewererecordedas15-minaveragedvaluesmeasuredat1-minintervalsbetween13:00and15:00MDTfromJuly11until September22,2013atParma,ID.

3.2. Neuralnetworkmodelperformance

TheNNmodelsdevelopedindividuallyfor’syrah’and‘Malbec’ vinesprovidedexcellentestimationofwell-wateredleaf temper-atureforbothcultivars(Fig.4).LinearregressionofNNpredicted versusmeasuredwell-wateredleaftemperatureresultedin cor-relation coefﬁcients of 0.93 and 0.89 for ‘Syrah’ and ‘Malbec’, respectively.Rootmeansquareerrors(RMSE)andmeanabsolute errors(MAE)oftheNNmodelswere1.07and0.82◦Cfor‘Syrah’and 1.30,and0.98◦C‘Malbec’.Theslopeoftheregressionlineswere sig-niﬁcantlydifferent(p<0.001)from1.0forbothcultivarsindicating abiasinestimatedNNwell-wateredleaftemperature.The feed-forwardNNmodelarchitectureselectedtoestimatewell-watered grapevineleaftemperature(Tnws,Eq.(1))usedfourinput parame-ters,onehiddenlayerwithsixnodes,andoneoutputnode(4-6-1). ThefourinputswerethemeasuredenvironmentalparametersTair, SR,RHandWS.Increasingthenumber ofhiddennodesbeyond sixprovidedminimaldecreaseinNNmodelstandarderror.Using VPDratherthanRHdidnotaffectperformanceoftheNNmodels, whichwasexpectedsinceRHandVPDarehighlycorrelatedfor agivenairtemperature.Theslopeoftheregressionlinebetween NNpredictedandmeasuredTnwsforbothcultivarswaslessthan

oneindicatinganegativebiasinpredictedvalueswhenmeasured leaftemperaturewas>32◦Candapositivebiaswhenmeasuredleaf temperaturewas<22◦C.Thiswasmorepronouncedforthecultivar ‘Malbec’.Thisbiasmaybetheresultofthelimitednumberof train-ingdatasetvalueswithaleaftemperature>32and<22◦C.Alarger datasetoverseveralyearsandlocationswithagreaterproportion ofhighandlowleaftemperaturemeasurementsmayimproveNN modelperformanceattheupperandlowertemperatures.

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CWSI 35% ET CWSI 70% ET Irrigation 35% ET Irrigation 70% ET

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igations/week

Fig.9.ActualirrigationandprecipitationamountsandcalculatedCWSIof‘Malbec’grapevinesintreatmentplotsdeﬁcit-irrigatedat30or70%ofETcatafrequencyofoneor

threetimesperweek.Non-waterstressedandnon-transpiringleaftemperatureswereestimated,respectively,fromtheneuralnetworkmodelandairtemperature+15◦_C.

Ambientairanddeﬁcit-irrigatedleaftemperaturewererecordedas15-minaveragedvaluesmeasuredat1-minintervalsbetween13:00and15:00MDTfromJuly11until September22,2013atParma,ID.

models(Fig.6),indicatingthatNNmodelsprovidedlessvariability inestimatedwell-wateredleaftemperaturethanmultiplelinear regression.Theslopesoftheregressionlinesforpredictedversus measuredwell-wateredleaftemperaturefor‘Malbec’were signif-icantlydifferent(p≤0.05) betweentheNN model andmultiple linearregression model indicatingthat theNN model provided significantly better estimates of well-watered leaf temperature withless errorvariance. Slopesof theregression lines for pre-dictedversusmeasuredwell-wateredleaftemperaturefor‘Syrah’ betweenNN model and multiplelinear regression model were not significantlydifferent even thoughthe NN model provided significantlylesserrorvarianceinestimatedwell-wateredleaf tem-perature.

3.3. Estimationofnon-transpiringleaftemperature

Weusedcumulativeprobabilitydistributionsofmeasured tem-peraturedifferencesbetweenthecanopyofdeﬁcit-irrigatedvines suppliedwith35%ETcandambientairtoestimateavalueforTdry (Fig.7).WhenweusedT_air+5◦Ctoestimatenon-transpiringleaf temperature(Tdry),asproposedbyIrmaketal.(2000)forcornand usedbyMölleretal.(2007)forgrape,weobtainedCWSIvalues

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0 20 40 60 80 100 120 140

CWSI 35% ET Irrigation 35% ET

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igations/week

Fig.10.ActualirrigationandprecipitationamountsandcalculatedCWSIof‘Syrah’grapevinesintreatmentplotsdeﬁcit-irrigatedat30or70%ofETcatafrequencyofoneor

threetimesperweek.Non-waterstressedandnon-transpiringleaftemperatureswereestimated,respectively,fromtheneuralnetworkmodelandairtemperature+15◦_C.

Ambientairanddeﬁcit-irrigatedleaftemperaturewererecordedas15-minaveragedvaluesmeasuredat1-minintervalsbetween13:00and15:00MDTfromJuly3through September28,2014atParma,ID.

differencebetweencanopyandambientairof19◦C(Fig.7).This valueservesasanupperlimitformeasuredvaluesofTnws−Tair for deﬁcit-irrigatedvines in this study.Based onthese results, weelectedtoestimateTdry forcalculationoftheCWSI(Eq.(1)) asTair+15◦Cforbothcultivars,whichisunlikelytobeexceeded (Fig.7)forgrapevinesirrigatedatapproximately70%ETc,which isacommonregionalpractice.Inpracticalterms,anytemperature forTdrygreaterthanthatdesiredforviableberryproductioncould beusedforTdryinEq.(1)toobtainaCWSIvaluebetween0and 1forirrigationschedulingpurposes.Referencetemperaturesdo notnecessarilyneedtobeanabsolutecanopytemperaturelimitor evenatrueTdryvalue,butserveratherasindicatortemperaturesto scalemeasuredcanopytemperaturetotheenvironmentfor calcu-latingrelativewaterstress(Grantetal.,2007).Theoverallgoalof theCWSIvalueobtainedinthisstudywasaconsistentvalue repre-sentingplantwaterstresstoaidirrigationschedulingratherthan serveasanabsolutemeasureofplantwaterstress.

3.4. CWSIcharacteristics

We calculated seasonal daily CWSI values for vines deﬁcit-irrigatedat70%or35%ETcusingNNestimatedvaluesforTnwsand

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CWSI 35% ET Irrigation 35% ET

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iga

tions/week

Fig.11.ActualirrigationandprecipitationamountsandcalculatedCWSIof‘Malbec’grapevinesintreatmentplotsdeﬁcit-irrigatedat30or70%ofETcatafrequencyofoneor

threetimesperweek.Non-waterstressedandnon-transpiringleaftemperatureswereestimated,respectively,fromtheneuralnetworkmodelandairtemperature+15◦C. Ambientairanddeﬁcit-irrigatedleaftemperaturewererecordedas15-minaveragedvaluesmeasuredat1-minintervalsbetween13:00and15:00MDTfromJuly3through September28,2014atParma,ID.

proposedbyIdsoetal.(1981)andJacksonetal.(1981),wherea nearinstantaneousmeasureofcanopytemperaturewasusedto calculatetheCWSI.Amajoradvantageoftheaveragingapproach usedinthisstudyisthatitminimizestheinﬂuenceofrapid ﬂuctu-ationsinleaftemperatureduetovariabilityincloudinessorwind speedresultinginaconsistentrepresentativevalueoftheCWSI. OurmethodofcalculatingTdryfortheCWSIsupportstheconcept usedbyothersofestimatingTdryasthesumofmeasuredTairand aconstant(Cohenetal.,2005;Mölleretal.,2007;Alchanatisetal., 2010;O’shaughnessyetal.,2011);howeverestimatingtheconstant valuefromthecumulativeprobabilityofmeasuredleaf temper-aturesunder35%ETc irrigationtreatmentgeneratedanestimate ofTdrythatlimitedCWSIvaluestotherangeof0–1forthestudy conditions.

3.5. SeasonalCWSIandyield

SeasonalmeandailyCWSIvaluesbetween70and35%ETc irriga-tiontreatmentsweresigniﬁcantlydifferent(p≤0.001)regardless ofcultivarorirrigationfrequency(Table4).Onaveragetherewas a0.14and0.22differenceinseasonalmeanCWSIvaluebetween 70and35%ETcacrossyearsandirrigationfrequenciesfor‘Syrah’

and ‘Malbec’, respectively. For ‘Syrah’, mean vine yields were significantlydifferent(p_≤0.05)betweenthe35%and70%ETc irri-gationtreatmentsacrossirrigationfrequencytreatmentsandyears (Table4).For‘Malbec’,meanvineyieldsweresignificantly differ-ent(p≤0.05)betweenthe35%and70%ETcirrigationtreatmentsin 2013foronlythethreeirrigationsperweektreatmentandonlythe oneirrigationperweektreatmentin2014.Clusternumberpervine weresignificantlydifferent(p≤0.05)betweenthe35%and70%ETc irrigationtreatmentsfor‘Syrah’in2013fortheoneirrigationper weektreatmentandfor‘Malbec’in2013forthethreeirrigationper weektreatment(Table4).Therewerenosignificantdifferencesin clusternumberpervinebetweenthe35%and70%ETcirrigation treatmentsin2014foreithercultivarorirrigationfrequency.

4. Discussion

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Table4

MeancomparisonsofCWSI,yieldpervine,andclusternumberpervinebyyear, cultivar,irrigationtreatmentandweeklyirrigationfrequency.

Irrigationtreatment CWSI Yield/vine(kg) Cluster number/vine

2013 2014 2013 2014 2013 2014

Syrahoneirrigationperweek

70%ET 0.33 0.29 7.1 13.0 34.6 53.1

35%ET 0.50 0.44 4.6 7.5 22.3 47.6

<0.001** <0.001** 0.0017** <0.001** 0.008**0.321

Syrahthreeirrigationsperweek

70%ET 0.30 7.3 12.1 34.2 54.0

35%ET 0.52 0.45 4.6 9.2 27.0 48.3

<0.001** – 0.005** 0.040* 0.204 0.381

Malbeconeirrigationperweek

70%ET 0.41 0.40 5.2 6.9 42.3 50.1

35%ET 0.59 0.67 4.4 4.7 33.6 47.8

<0.001** <0.001** 0.278 0.027* 0.09 0.648

Malbecthreeirrigationsperweek

70%ET 0.37 5.7 5.9 41.7 47.6

35%ET 0.52 0.67 3.2 4.8 30.7 45.3

<0.001** – <0.001** 0.239 0.03* 0.723

*_and**_denote_statistical_{signiﬁcance}_at_p_≤_0.05_and_p_≤_0.01,_respectively_by_paired

t-test.

fruitmaturity)in2013and2014.SeparateNNmodelswereused toestimateTnws for each cultivarbased ondifferentresponses

between(Tnws−Tair)andVPDforthecultivars.Theuseof

culti-varspeciﬁcNNmodelstoestimateTnws maybea disadvantage

fromanimplementationviewpointbutresultedinCWSIvaluesof sufficientaccuracytoeasilydifferentiatebetweenirrigation treat-mentsunderclimaticconditionsvaryingfromhotandsunnywith lowhumiditytocoolandcloudybetweenrainfallevents.The abil-itytocalculateareliableandconsistentCWSIvalueunderwidely varyingvineyard conditions is necessaryfor implementationof cultivarandsite-specificirrigationmanagementstrategies.The pri-marydisadvantageofusingNNmodelstoestimatewell-watered leaftemperatureforcalculationoftheCWSIistheneedtogenerate adatasetofwell-wateredcanopytemperaturestotrain,validate andtraintheNNmodel.TheNNmodelsdevelopedinthisstudy areregionspecificatbestastheyweredevelopedusingdatafrom onespecificlocation.AdatasetofmeasuredTnwsacrossarange

ofclimaticregionsmayallowdevelopmentofNNmodelscapable ofestimatingTnwsforaspeciﬁccultivarunderagreaterrangeof

climaticconditions.

Thedifferencebetweencanopyand airtemperatureofvines receiving35%oftheirrigationwaterappliedtowell-wateredvines wasfoundtobeasgreatas17◦C,muchgreaterthanthevalueof

Tdry assumedinotherresearchstudiesandslightlylessthanthe

energybalancemodelbasedvalue(Eqs.(3)and(4))forthestudy conditions.UsingavalueofTdry=Tair+15◦CtocalculateCWSIover twogrowingseasonsprovidedconsistentdifferentiationbetween deﬁcitirrigationamountsthatsupplied∼70and35%ofthe irri-gationamountsuppliedtowell-wateredvinesinbothcultivars. Asmallerconstant(e.g.13◦C)couldpotentiallyhavebeenusedto calculateTdryfor‘Syrah’basedonthemaximummeasured temper-aturedifferencebetweencanopyandTairinvinesdeﬁcit-irrigated with 35% ETc (Fig. 7). Reference values Tnws and Tdry used to calculateCWSIneednotbeabsoluteminimumandmaximum tem-peratures(Grantetal.,2007),althoughdoingsorestrictstheCWSI valuetobetween0and1.SeasonalaveragemeandailyCWSIvalues for35%ETandweeklyirrigationwasabout0.63and0.47for ‘Mal-bec’and‘Syrah’(Table4),respectively.IfTdry=Tair+13◦Chadbeen usedfor‘Syrah’,seasonalaveragemeandailyCWSIvaluewould havebeennearlythesameas‘Malbec’duetoa2◦Cdecreaseinthe denominatorofEq.(1).Theuseofreferencetemperaturescloseto

cultivarspeciﬁcabsoluteminimumandmaximumtemperatures fortheenvironmentmaymakethresholdCWSIvalues transfer-ablebetweenclimaticregions,butneedstobefurtherinvestigated. TransferabilityofthresholdCWSIwouldlikelyrequiremodiﬁcation ofreferencetemperaturesforclimaticdifferencesbetweenregions. Eq.(3)couldbeusedtoadjustTdryforclimaticregiondifferencesbut wouldnotaccountforcultivardifferencesinrelationshipsbetween waterstressandleaftemperature.ModifyingTnwsforclimaticand cultivardifferenceswouldneedtobeaccomplishedexperimentally andtheNNmodelretrainedusingthenewdata.

TheoriginalapplicationoftheCWSImethodologyforirrigation schedulingwaslimitedbyarequirementtomeasureTnwsonclear cloudlessdays.Inthisstudy,thincirruscloudswereoftenpresent duringthetwohourtimespanfrom13:00to15:00MDTresulting inrapidlyvaryinglevelsofsolarradiation,whichhavealargeeffect onTnws(Eq.(2)).Thevariablepresenceofthin,cirruscloudswas thereasonwhyamajorityofmeasuredvaluesforsolarradiation werelessthan900Wm−2_(Fig._2)._This_variability_contributed sub-stantialuncertaintyinthelinearrelationshipsbetweenTnws−Tair andVPD(Fig.3).Thisuncertaintyleadstosubstantialuncertainty intheneedtoirrigate,whichisonereasonwhytheCWSIapproach hasseenlimitedadoptionforirrigationscheduling.Fromthe pro-ducer’sperspective,uncertaintyisminimizedbysimplyirrigating andforegoinguseoftheCWSI.UseoftheNNmodeltoestimate TnwsallowedreliablecalculationofCWSIinthisstudydespitethe limitednumberofclearcloudlessdays.

5. Conclusionsandfuturedirections

The feasibility of using NN modeling to estimate thelower thresholdtemperature(Tnws)neededtocalculatethetraditional CWSI was demonstrated for wine grape. Wine grape cultivars ‘Syrah’ and ‘Malbec’ werefound tohave differing canopy tem-peratureresponsetoclimaticconditionswhenwell-wateredand water stressed. Neural network models developed for estimat-ingTnwsofeachcultivarperformedexceptionallywell.UseofNN modelestimatedTnwsforcalculatingadailymeanCWSIovertwo growingseasonssuccessfullydifferentiatedbetweentwolevelsof waterstressforeachcultivar.Themaximumdifferencein temper-aturebetweentheleafandambientairforthe35%ETc irrigation treatmentswasfoundtobeabout13◦Cand17◦Cfor‘Syrah’and ‘Malbec’,respectively,muchgreaterthan5◦Cusedinother stud-ies as anestimate for Tdry.Air temperature plus 15◦C used to estimateTdryforbothcultivarsincalculationofCWSIprovideda suitablepracticalupperreferencetemperatureforbothcultivars. A2-haveragedCWSIvaluebasedon15-minaveragemeasured canopytemperature,Tair,SR,RH,andWSvaluesprovideda consis-tentdailyCWSIvalueundervariableclimaticconditions.Additional researchshouldfocusondevelopmentofNNmodelstoestimate Tnwsforothergrapecultivarsovermultipleyearsandacross cli-maticregionstoextendapplicabilityofNNmodelsdevelopedin thisstudytocalculateTnws.

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