Contents lists available atScienceDirect
Industrial
Crops
and
Products
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 / i n d c r o p
Assessing
and
modelling
water
use
and
the
partition
of
evapotranspiration
of
irrigated
hop
(Humulus
Lupulus),
and
relations
of
transpiration
with
hops
yield
and
alpha-acids
M.
Fandi ˜
no
a,
J.L.
Olmedo
b,
E.M.
Martínez
a,
J.
Valladares
c,
P.
Paredes
d,
B.J.
Rey
a,
M.
Mota
d,
J.J.
Cancela
a,∗,
L.S.
Pereira
daGI-1716,DepartmentofAgroforestryEngineering,UniversityofSantiagodeCompostela,CampusUniversitario,27002Lugo,Spain
bHijosdeRiveraInversionesCorporativas,ACoru˜na,Spain
cCentrodeInvestigaciónsAgrariasdeMabegondo(INGACAL-CIAM),XuntadeGalicia,Spain
dLEAF—Landscape,Environment,AgricultureandFood,InstituteofAgronomy,UniversityofLisbon,Lisbon,Portugal
a
r
t
i
c
l
e
i
n
f
o
Articlehistory:
Received3March2015
Receivedinrevisedform7July2015
Accepted20August2015
Keywords:
SIMDualKcmodel
Hoptranspiration
Dryweightcone
Alpha-acidscontent
Irrigationscheduling
a
b
s
t
r
a
c
t
Thisstudywasconductedduringthreeseasons(2012–2014)inanexperimentalhopyardatMabegondo, Galicia,NWSpain.Theresearchaimedatcalibratingandvalidatingthesoilwaterbalancemodel SIMD-ualKcforHumuluslupulusL.cv.‘Nugget’.Themodelcomputesthesoilwaterbalanceusingthedual Kc approach,thuspartitioning cropevapotranspiration (ETc)intocrop transpiration,ground cover
transpirationandsoilevaporation.CalibrationandvalidationwereperformedusingTDRsoilwater contentmeasurements,whichproducedsmallrootmeansquareerrors(RMSE)rangingfrom0.012to 0.015cm3cm−3.Theinitial,mid-seasonandend-seasonbasalcropcoefficients(Kcb)thatallow
com-putinghoptranspirationwererespectively0.16,0.97and0.83.ThesingleKcforthesamecropgrowth
stages,whichreferstotranspirationandsoilevaporationtogether,wererespectively0.69,1.02and0.85. SIMDualKcprovidedtoestimatewaterusebythehopyardandthecomponentsofthesoilwater bal-ance,particularlyhoptranspiration(THop),groundcoveredtranspiration(Tcover)andsoilevaporation(Es).
THoprepresented92%ofactualevapotranspiration(ETcact)duringthemid-season,andEsaveraged69%of
ETcactduringtheinitialstage.ItwasobservedthatTcoverwasstronglyinfluencedbysoilandgroundcover
management.TheimpactsofwateruseandTHoponhopyieldquantityandqualitywereassessed.A
lin-earregressionbetweenhopconeyieldandTHophasbeenfound,withahighcoefficientofdetermination
r2=0.92,whilethelinearregressionsofT
Hopwithalphaandbeta-acidshadregressioncoefficientsnot
significantlydifferentfromzero.Theseresultsdenoteappropriateirrigationmanagementwithabsence ofstressesthatcouldaffectyieldsortheconcentrationofbitteracids.
©2015ElsevierB.V.Allrightsreserved.
1. Introduction
Hop(HumuluslupulusL.)isanherbaceousperennialplantwhich femaleflower,alsonamedhopconeorhops,isusedindifferent industries.Currentlythebeerbrewingindustryaccountsfor98% oftheworlduseofhops(ZanoliandZavatti,2008).Hopis culti-vatedforitssecondarymetabolites,mainlybitteracids:alphaand beta-acids(Verzele,1986).Alpha-acidsaredirectprecursorsofthe mainbitteringprinciplesofbeer,contributingaswelltothe over-allmicrobialstability(asnaturalpreservatives),andanenhanced foamstability(Moir,2000;Steenackersetal.,2015).Theyarehighly
∗ Correspondingauthor.
E-mailaddress:javierjose.cancela@usc.es(J.J.Cancela).
cultivar-dependentalthoughhopscontain,onaverage,9–10wt%of alpha-acids;however,recentlydevelopedhopcultivarsmay con-tainupto19wt%(YakimaChief,2013).Thebeta-acidsarelessacidic andhaveamorepronouncedbacteriostaticactionthanthe alpha-acids.Duetotheirinsolubilityin thebeer matrix (Briggs etal., 2000),thebeta-acidswereusuallyconsideredtobelargely irrel-evantforthebrewingindustry,besidesthattheymayoxidizeinto compoundsthatcangivebeeroff-flavors.However,theycontribute tobeer’saroma,andhighbeta-acidhopvarietiesareoftenusedat theendofthebrewingprocessforaroma,alsobecausebetaextract canbeusedasafeedfortheproductionofbitteringcomponents, eitherbythetransformationofthebeta-acidsintothesynthetic iso-alpha-acidsorbytheiroxidationintothehulupones(Steenackers etal., 2015).Alpha and beta-acidscouldbecomplementaryfor
http://dx.doi.org/10.1016/j.indcrop.2015.08.042
bittertasteandpreservativevalue(SchönbergerandKostelecky, 2011)inbrewingindustry.
Hopconeshavealsobeenusedformedicinalpurposesbecause beta-acidsareapotentialcancerchemopreventiveorare antibac-terialagents(ZanoliandZavatti,2008)duetoitsrelativelyhigh content of polyphenolic substances, with beneficial effects on healthandmetabolism(Cehˇ etal.,2007).Recently,Abrametal. (2015)reportedthathopsnotonlyplayamainroleinthe brew-ingindustrybut leavesand stemscouldbeused asasourceof antioxidantcompoundsforthefoodindustry.
Severalauthorshaveshowntherelevanceofprecipitationand temperature for an adequate growth of hop and for yield and quality,e.g.,Sreˇcecet al.(2004, 2008) inCroatia; Koˇren(2007) andMoznyetal.(2009)inCzechRepublic;Engelhard(2004)in Germany;Bavecetal.(2003)andPavlovicetal.(2012,2013)in Slovenia;Benítezetal.(1998)inSpain;andWampleandFarrar (1983)inUnitedStates.Moznyetal.(2009)reportedonclimate changeeffectsconcludingthathopmaybeaparticularly vulnera-blecrop.Inaddition,variousstudiestackledtheeffectsofirrigation onproduction atthefarmscale(e.g.,Wampleand Farrar1983; Svoboda et al., 2008; Delahunty et al., 2011; Nakawuka 2013) oratplantscale(Keukeleireetal.,2007;Hniliˇckováetal.,2009; Gloseretal.,2013).Thesestudiesreportedthatirrigationincreases yieldanddonotnegativelyaffectthealpha-acidcontentalthough impactsofwaterandtemperaturestressrelatewiththe phenolog-icalstageswhenoccurring.ThisiswelldescribedbyPotop(2014) whoreportedalowyieldwhendrought/drynessconditions pre-vailedinMaythroughAugustandhigheryieldsundernormalwet conditionsandbyKuˇceraandKrofta(2009)thatreportedthatthe strongestinfluenceonthealpha-acidcontentwasexertedbyair temperaturesinJulyandthatrainfallhadsignificanteffectsduring theperiodfromMaytoJuly.However,fewstudieshavefocused anindepthdeterminationofhopwaterrequirements(Báreketal., 2009;Kroftaetal.,2013)andtherearenostudiesprovidingforan in-depthanalysisofevapotranspirationorthewaterbalance.Urban etal.(2012)appliedthesapflowtechniqueandBREBwiththehop cv.‘Agnus’inCzechRepublic,andKroftaetal.(2013)measured thesapflowwiththehopcv.‘Premiant’.Bothstudiesestimated hoptranspirationbutdidnotquantifysoilevaporation,theactual evapotranspiration,orthecomponentsofthesoilwaterbalance.
Cropevapotranspiration(ETc)iscommonlyestimatedusingthe
cropcoefficientapproach,i.e.,ETc=Kc ETo,whereKc isthecrop
coefficientandEToisthereferenceevapotranspiration(Allenetal.,
1998).AdoptingthedualKc approach(Kc=Kcb+Ke),whereKcbis
thebasalcropcoefficientrelativetocroptranspirationandKeis
thesoilevaporationcoefficient,itispossibletopartitionETcinto
hoptranspiration(THop=KcbETo)andsoilevaporation(Es=KeETo)
asdescribedbyAllenetal.(1998,2005)andRosaetal.(2012a,b). Initial,mid-seasonandend-seasonsingleandbasalKcforhopare
tabledbyAllenetal.(1998,2007),whichallowtodrawtheKc(and
Kcb)curvesrelativetothefullcropseason.ThedualKc approach
requiresadailysoilwaterbalancefordeterminationoftheactual evapotranspirationandadailywaterbalanceoftheuppersoillayer fromwheresoilevaporationoriginatesfordeterminationofKe.This
requirementgiveshigherprecisiontocomputationsusingthedual KcapproachrelativetousingthesingleKcbutthecomputational
proceduresarequitedemandingandamodellikeSIMDualKc(Rosa etal.,2012a,b)isthenneeded.
Severalmethodsand techniquesmaybeappliedtomeasure andestimatecropevapotranspiration,e.g.,lysimeters,heatpulse, heatbalance,Bowenratioenergybalance(BREB),surfacerenewal energybalance,eddycovarianceandthesoilwaterbalance(Allen etal.,2011).Theselectionofthemethodstobeusedrelatewith theobjectivesofthestudyandthecomplexityoftheecosystem tobe observed.Relative to theintended hop croppedareathe objectivesofthestudyweretodeterminehoptranspirationand
soilevaporationinanirrigatedplantationwithactivegroundcover intheinter-rowandwherecapillaryrisefromashallowwatertable couldcontributetoevapotranspiration.Therewasnointentionof analysingthevarioushydrologicprocessesinvolvedbuttoestimate wateruseandconsumptionaimingatsupportingimproved irriga-tionmanagementwithconsiderationofwater-yieldinteractions. Thusitwasselectedasimplebutaccurateapproachthatallowed thecalibrationanduseofthereferredwaterbalancemodel SIM-DualKc.Otherwiseasoilwaterfluxmodelwouldberequiredthat wouldbeabletocomputetheup-anddownwardfluxesthrough therootzonebottom,whichcouldalsoconsideramultidomainET referringtothecrop,thesoilandtheunderstoryvegetation,and thatwouldbeabletoaccuratelyseparatecroptranspirationand soilevaporation.Thisapproach,inadditiontomeasurementsofET (Allenetal.,2011),wouldrequireobservationofsoilwater poten-tialinadditiontosoilwatercontenttodescribethedynamicsof waterintherootzone(e.g.,Cameiraetal.,2005;Liuetal.,2006;Ma etal.,2013),ortheuseoftracerstodetectthewateroriginatedfrom thewatertable(Grünbergeretal.,2011),ortheuseofwatertable lysimeters(LuoandSophocleous,2010).Theapproachwouldneed theuseofminiand/ormicro-lysimeterstoestimatethe evapotran-spirationfromtheunderstoryvegetationandtheevaporationfrom thesoilwithandwithoutmulching(Yunusaetal.,2004;Centinari etal.,2012),theuseofsapflowdevicestoestimatethetranspiration fluxesfromasampleofhopplantsand/ortheuseofsomeisotopic approachestoidentifytheoriginofwatervapour(Williamsetal., 2004;Rothfussetal.,2010;Sutantoetal.,2012).Thatcomplex, costlyandlabourdemandingapproachcannotbejustifiedfora studyaimedatsimplyassessingwaterusedynamicsofahop plan-tationaimedatimprovedirrigation.Thealternativewastoadopta simplerfieldresearchapproachthatwouldallowtocalibrateand furtherusetheSIMDualKcmodelthathasprovedabletosimulate thedynamicsofthesoilwatercontentofaplantationtakinginto considerationtheimpactsofashallowwatertable(e.g.,Rosaetal., 2012b;Wuetal.,2015),topartitionETintocroptranspirationand soilevaporationusingthedualKcapproach(e.g.,Pac¸oetal.,2012,
2014;Zhaoetal.,2013;Zhangetal.,2013;Weietal.,2015),and toconsidertheeffects of understoryvegetation(Fandi ˜noetal., 2012;Cancelaetal.,2015).Moreover,theapplicationofthemodel respondstorequisitesofcalibrationandvalidation,includingthe determinationofrelatederrorsandothergoodness-of-fit indica-tors(Pereiraetal.,2015)andresultsareassessedcomparatively tootherstudiesaimingatbetterbasingrelatedanalysis.
Considering the good results obtained with the SIMDualKc modelinformerapplicationsanditsabilitytosimulatewateruseby acropunderconditionsofvariablewatertabledepthandvariable inter-rowsoilcover,SIMDualKcwasselectedtofilltheknowledge gaponwaterusebyhopplantations,onhopevapotranspiration anditspartitionintoTHopandEs.Thus,themainobjectivesofthe
presentstudyare:(i)tocalibrateandvalidatethesoilwater bal-ancemodelSIMDualKcforthehopcv‘Nugget’inGalicia;(ii)to determinehoptranspirationandsoilevaporationduringthecrop seasonsof2012–2014;(iii)tocomputethewaterbalance compo-nentsofhopfields,includinggroundwatercontributionandactive groundcovertranspiration;and(iv)toassesstheimpactsofcrop transpirationonhopyieldquantityandquality,mainlythealpha acids.
2. Materialsandmethods
2.1. Sitedescriptionandmeteorologicaldata
Thestudywasconductedduringthreeseasons(2012–2014)in anexperimentalhopyardatthe‘CentrodeInvestigaciónsAgrarias deMabegondo’(CIAM),locatedinMabegondo,Galicia,NWSpain (43◦143.7N,8◦1512.9Wand60melevationa.s.l.).Thefield
isabout1haandisnearlyflat.Thecropwasestablishedin2007 andwasplantedwithspacing3.0m×2.0m;theplantdensitywas 1667plantsha−1andthetrelliswere6.0mhigh.Plantationhasthe East–Westorientation(Olmedo,2011).Thestudyfieldwasdivided intotwo plotscorrespondingtodifferentdripirrigationsectors. Plot1referstothelowerpartofthefield,closetoasmallriver,and Plot2isintheupperpartofthefield.
Soils are silt loam with an average of 33.7% sand, 46.7% silt and 19.6% clay; the organic matter content averages 6.7%. The total available soil water (TAW) down to 0.8m depth was 168 and 160mm in Plot 1 and Plot 2, respectively, which was calculated from the difference between the field capacity (FC=0.38cm3cm−3)and the permanent wilting point
(WP=0.18cm3cm−3forPlot1and0.17cm3cm−3forPlot2).Soil
watercontentatsaturation(Sat)was0.42cm3cm−3,forbothplots.
TheclimateisofAtlantictype.TheKöppen–Geigerclassification isCsb,i.e.,awarmtemperateclimatewithdryandwarmsummer (Kotteketal.,2006).Agrometeorologicaldatawereobtainedfrom theCIAMstation,locatedafewhundredmetersawayofthefield. Dailydataconsistedofmaximumandminimumtemperature(Tmax
andTmin,◦C),windspeedat2mheight(u2,ms−1)andrainfallfor
theperiod2012–2014(Fig.1).DuringtheperiodMarch–September of2012themonthlyaverageTmaxandTminrangedbetween13.9
and25.2◦Cand3.6and13.2◦C,respectively.In2013thosemonthly temperatureswereslightlyhigher,withTmaxranging14.5–27.2◦C
and Tmin ranging 5.9–13.7◦C, while in 2014 the monthly Tmax
andTmin variedrespectively 17.3–26.1◦C and 5.2–13.4◦C. Thus,
monthlytemperatureswerequitesimilarinthethreecrop sea-sonsand nohotwaveswereobserved.ETo wascomputedwith
theFAOPenman–Monteithequationusinglimitedweatherdata, i.e.,estimatingtheactualvapourpressurefromthedailyminimum temperatureandsolarradiationfromdailymaximumand mini-mumtemperature(Allenetal.,1998).Thismethodwastestedfor similarclimatesbyCancelaetal.(2015)inGalicia.
DailyprecipitationandEToforMarchtoSeptemberduringthe
threeyearsofstudyarepresentedinFig.1,whichshowsthat pre-cipitationin2012wasconcentratedinAprilandMaywhileJuly toSeptemberwasadryperiod.Differently,rainfallin2013was concentratedinMarch–AprilbutJulytoSeptemberwerealsodry months.In 2014precipitationwasmoreevenlydistributed.ETo
showstobehigherinthesummermonthswhere itmayattain 6mmd−1.However,itsdailyvariabilityishighfollowingthe nat-uralvariability of radiation, temperature, and wind speed. ETo
wasquitesimilarduringthethreecropseasons,higherin2014 (603mm)andlowerin2013(543mm).
2.2. Modellingapproach—theSIMDualKcmodel
SIMDualKc(Rosaetal.,2012a)isa soilwaterbalancemodel aimedatassessingcropwateruseandrequirementsandtosupport irrigationscheduling andmanagementatfield scale.Themodel adoptsthedualKcapproach(Allenetal.,1998,2005)for
partition-ningcropevapotranspirationintosoilevaporation(Es)andcrop
transpiration(Tc)
ETc= (Kcb+Ke) ETo (1)
whereETciscropevapotranspiration[mmd−1],Kcbisthebasalcrop
coefficient[],Keisthesoilevaporationcoefficient[]andEToisthe
grassreferencecropevapotranspiration[mmd−1],thusresulting Tc=KcbEToandEs=KeETo.ThedualKcapproachisoftenuseddue
toitsgoodpracticalperformanceasanalyzedbyKooletal.(2014). Applicationsrefernotonlytosoilwaterbalancemodelsbutalso tomechanisticmodels(Forkutsaetal.,2009;Ramosetal.,2012; Gonzálezetal.,2015).Theapproachhasbeenverifiedfrom obser-vationsofTcand/orEsbyPac¸oetal.(2012,2014),Cammallerietal.
(2013),Dingetal.(2013),Zhaoetal.(2013)andWeietal.(2015).
ToconsidertheinfluenceofcropdensityandheightonKcbthe
modelusestheeffectivefractionofgroundcoveredorshadedbythe vegetation(fceff)andthecropheigh(h)consideringacropdensity
coefficient(Kd)(Allenetal.,2007;AllenandPereira,2009):
Kd=min(1,MLfcefff
1 1+h
ceff) (2)
wheretheMLparameter[]isamultiplieroffceffrepresentingthe
maximumratioofETperfractionofgroundshaded.fceff differs
fromthefractionofsoilsurfacecoveredbyvegetation(fc)duetothe
combinedeffectofthecanopyshape,plantheightandsolarangle abovethehorizonontheshadedarea(AllenandPereira,2009;Rosa etal.,2012a).Inincompletecovercrops,suchasorchards,vineyards andhopplantations,anactivegroundcovermayoccurthat com-peteswiththecropfortheavailablesoilwaterand contributes tothetotal evapotranspiration.Thus, thefollowingapproach is adoptedinSIMDualKc(AllenandPereira,2009;Rosaetal.,2012a) forestimatingKcb: Kcb=Kcbcover+Kd
max Kcbfull−Kcbcover, Kcbfull−Kcbcover 2 (3) whereKcbcoveristheKcboftheactivegroundcoverintheabsenceoftreefoliage,Kdisthecropdensityfactor(Eq.(2)),andKcbfull
isthebasalKcbforthecropunderfullcover(orLAI>3)conditions
andcorrectedforclimate,withallcoefficientsnon-dimensional.To takeintoaccounttheeffectsofshadingoftheactivegroundcoverby thetallerplantsthesecondtermofthemaxfunctionreducesthe estimatedKcbbyhalfthedifferencebetweenKcbfullandKcbcover.
Thisapproachisalreadytestedforvineyards(Fandi ˜noetal.,2012; Cancelaetal.,2015),peachorchards(Pac¸oetal.,2012)andolives (Pac¸oetal.,2014).TheKcbcoverreflectsthedensityandvigorof
thesurfacecover;itchangeswithsoilmanagementpractices,e.g., tillageoperationsandapplicationof herbicides.Themodel esti-matesKcbcoverusingfieldobserveddata,suchasthefractionofthe
activegroundcover,itsdensityanditsheight.Detailedinformation onthegroundcovermanagementonhopfieldsalongtheseasons isprovidedinAppendix1.
InSIMDualKc(Rosaetal.,2012a)Esiscomputedperforming
adailywaterbalanceoftheuppersoillayerfromwheremostof waterevaporates. Theevaporationlayer,ofthickness Ze (m),is
characterizedbythetotalevaporable water(TEW, mm),that is themaximum depthofwaterthat canbeevaporatedfromthat layerwhenithasbeencompletelywetted,andthereadily evap-orablewater(REW,mm),whichisthedepthofwaterthatcanbe evaporatedwithoutwaterrestrictions(Allenetal.,1998,2005). TEWandREWareestimatedfromthesoiltexturalandhydraulic characteristics,respectively.Esislimitedbytheamountofenergy
availableatthesoilsurfacecombinedwiththeenergyconsumedby transpiration(Allenetal.,1998,2007)anditsvaluecannotexceed thedifferenceKcmax−Kcb,withKcmax=max(Kcb+Ke).Asthe
top-soildriesandlesswaterisavailableforevaporationareductionof Esoccursinproportiontotheamountofwaterremaininginthe
surfacesoillayer;thus,Kebecomes
Ke=Kr(Kcmax−Kcb) withKe≤fewKcmax (4)
whereKristheevaporationreductioncoefficient(≤1.0),Kcmaxis
themaximumvalueofKc followingarainoranirrigationevent,
andfewisthefractionofthesoilthatisbothexposedand
wet-ted.SIMDualKccomputesKrusingthe2-stagedryingcycle(Ritchie,
1972;Allenetal.,1998),wherethefirststageisanenergylimited stage,andthesecondisa waterlimitedstagewithevaporation decreasingasevaporablewaterisdepletedintheevaporationsoil layerbeyondREW,thus
Fig.1.Totaldailyprecipitation( )andreferenceevapotranspiration( )intheexperimentalarea:(a)2012,(b)2013and(c)2014.
Kr=
TEW−De,i−1
TEW−REW forDe,i−1>REW (5b)
SIMDualKcadjustsKetothefractionofgroundcoverbyboththe
cropandactivevegetationtoestimatethefractionofsoilfrom whichmostevaporationoccursandthatisbothexposedto radi-ationandwettedbyrainandirrigation.Whenthegroundcover isnotactiveduetoherbicidesapplication(seeAppendix1),the vegetationresiduesonthesoilactasamulch,hencereducingsoil evaporation,whichisconsideredbythemodelasdescribedbyRosa
etal.(2012a).ImpactsofmulchonEsweretestedbyMartinsetal.
(2013)usingSIMDualKc.Themodelperformsadailysoilwater bal-ancetothedepthoftherootzoneexpressedintermsofdepletion attheendofthedayi(Di,mm)asfollows:
Di=Di−1(P−RO)i−Ii−CRi+ETcact,i+DPi (6)
hereDi-1isthedepletionattheprecedingday,Pisprecipitation,
Iisirrigation,CRiscapillaryrisefromashallowwatertable,DP isdeeppercolationoutoftherootzoneandROisrunoff,withall termsexpressedinmm.
TheactualcropET(ETcact,mm)iscomputedasafunctionofthe
availablesoilwaterintherootzoneusingawaterstresscoefficient (Ks,0−1).Ksisexpressedasalinearfunctionofthecumulative
depletionintheeffectiverootzone(Dr)followingAllenetal.(1998,
2005): Ks=
TAW−Dr
TAW−RAW=
TAW−Dr
(1−p)TAWforDr>RAW (7a)
Ks=1forDr≤RAW (7b)
whereTAWandRAWare,respectively,thetotalandreadily avail-ablesoilwater(mm)relativetotherootzonedepth,Zr,andpisthe
soilwaterdepletionfractionfornostress(dimensionless),which correspondstothedepletionthresholdbeyonditthecropiswater stressed.Thepvaluevarieswiththecrop,varietyand environmen-talconditions,mainlytheevaporativedemandoftheatmosphere (Allenetal.,1998).RAWisthereforedefinedas
RAW=pTAW (8)
Considering what explained above, themodel computes actual evapotranspirationas:
ETcact=THopact+Tcover+Es (9)
where THopact=Ks KcbHop ETo is actual hop crop transpiration,
Tcover=KcbcoverEToisthetranspirationfromanactivegroundcover,
andEs=KeEToissoilevaporation.
Thedailycapillary riseand deeppercolation fluxesare esti-matedwiththeparametricequationsdevelopedbyLiuetal.(2006). TheactualCRfluxes (mmd−1)areestimated usingdataonthe actualwatertabledepth(Dw,m),actualsoilwaterstorage(Wa,
mm),potentialcropevapotranspiration(ETc,mmd−1)and
max-imumcapillaryrise(CRmax,mmd−1).TheactualdailyCRfluxis
computedproportionallytoCRmaxdependinguponETc,Dw and
Wa.CRreducesrelativetoCRmaxwhentherootzonestorageWa
ishigh,orthedepthDwofthewatertableincreases,and/orETc
decreases.Theparametersrelativetowaterstorage(a1,b1,a2,b2),
totherelationshipsbetweenDwandETc(a3,b3),andtotheCRmax
calculation(a4,b4)needtobecalibratedtakingintoconsideration
theactualsoilandwatertablecharacteristics(Rosaetal.,2012b; Wuetal.,2015).Liuetal.(2006)proposedsetsofparametervalues forsilty,siltloamandsandyloamsoilsthatmaybeusedasdefault values.TheDPflux(mmd−1)iscomputedusingatimedecay func-tionrelatingthesoilwaterstoragenearsaturationwiththetime aftertheoccurrenceofaheavyrainorirrigation(Liuetal.,2006). Thecorrespondingparameters(aD,bD)alsoneedtobecalibrated.
Thesurfacerunoffgeneratedwhenrainfallexceedsinfiltration issimulatedinSIMDualKcusingthecurvenumber(CN)method (USDA-SCS,1972).TheparameterCNrelatestosoil,vegetation,and actualsoilwaterstorageandmaybeestimatedusingtabledvalues (Allenetal.,2007).Thismethodiscommonlyusedinsoilwater balancemodeling(e.g.,Raesetal.,2006;Luoetal.,2008).
TheinputdatarequirementsoftheSIMDualKcmodelinclude: i.Dailyclimaticdata:referenceevapotranspiration(ETo,mm),
precipitation(P,mm),maximumandminimumtemperature (TmaxandTmin,◦C),minimumrelativehumidity(RHmin,%),and
windspeedat2mheight(u2,ms−1).
ii. Soildataforamulti-layeredsoil:numberoflayersandlayer depths,d(m),therespectivesoilwatercontentatfieldcapacity andthewiltingpoint(FCandWP,cm3cm−3)orthetotal
avail-ablewaterintherootzone(TAW,mm);thecharacteristicsof thesoilevaporationlayer(Ze,REWandTEW);andthesoilwater
contentatplantinginboththerootzoneandtheevaporation layerexpressedasa%ofTAWandTEW,respectively.
iii.Cropdata:datesoftheinitial,cropdevelopment,mid-season, andlateseasoncropstages;thebasalcropcoefficientsforthe
initial,mid-seasonandendseason(Kcbini,Kcbmid,andKcbend);
thesoil water depletionfractionsfor nostressat thesame stages(рini,pmidandpend);rootdepths(Zr,m),plantheight
(h,m), and thefractionof groundcover by thecrop(fc, %)
throughoutthecropseason.
iv.Irrigationschedulingdata:datesanddepthsofobserved irriga-tioneventsorthesoilwaterthresholdsandpreferredirrigation depthsandfrequencywhenthemodelisusedtogenerate irri-gationschedulesforthefieldpractice.
v.Irrigationparametersinfluencingsoilevaporation:thefraction ofsoilwettedbyirrigation(fw),andthefractionofsoilwetted
andexposedtoradiation(few).Inthecurrentstudy,few=1−fc
and,becausethesoilwaspartiallywettedbydripirrigation,fw
was0.05.
vi.Parametersrequiredbytheparametricequationsusedto com-putecapillaryriseanddeeppercolationasreferredabove. vii.Basedatatoestimateimpactsofactivegroundcover,effectsof
soilmulchesandtoassesssurfacerunoffwiththeCNmethod. 2.3. Calibrationandvalidationproceduresandaccuracy
indicators
Inthepresentstudy,modelcalibrationwasassumedasthe pro-cessof“adjustinginfluentialmodelparametersandinputswithin their reasonable ranges so that the model resultsare realistic and/orconsistentwithavailableobserveddata”;modelvalidation wasconsideredastheprocessofevaluatingtheaccuracyofthe modelestimationsusingthecalibratedparameterswith indepen-dentobserveddatasets(Moriasietal.,2007;Wangetal.,2012; Pereiraetal.,2015).
The calibration of the model was performed by adjusting thecropparameters(basal cropcoefficients,Kcb,and depletion
fractions for no stress, p), the soil evaporationparameters (Ze,
TEW,REW), and theparameters relative tothe DP,CR and RO throughminimizingtheresidualdeviationsbetweensimulatedand observedsoilwatercontent.Atrialanderrorprocedurewasused asdetailedbyPereiraetal.(2015).Theinitialvaluesof parame-terswere:KcbandpastabledbyAllenetal.(1998),Ze,TEWand
REWestimatedaccordingtoAllenetal.(1998,2005),DPandCR parametersasproposedbyLiuetal.(2006)forsandy-loamsoils, andCNastabledbyAllenetal.(2007).Thefirstiterationsaimed atimprovingtheKcbandpvaluesandthefollowingiterationsalso
consideredthesoilevaporationparameters;later,theDP,CRand CNparameterswereoptimized,andfinallyallparameterswere adjustedagain.Thetrialanderrorprocedurewasendedwhen dif-ferencesbetweensimulatedandobservedsoilwatercontent(SWC) valueswereminimizedanddidnotchangefromaniterationtothe next.ThecalibrationwasperformedusingdatafromPlot1in2013. Validationofthemodelwasperformedforalltheremainingdata setsusingtheparametersobtainedfromthecalibration.
Theevaluationanddiscussionofthemodelapplicationwas per-formedusinggoodness-of-fitindicators(Moriasietal.,2007;Wang etal.,2012;Pereiraetal.,2015).Toassesstheaccuracyofmodel pre-dictions,severalapproacheswereusedinadditiontoanalysingthe graphicaltimedependentrepresentationsofmodel-simulatedvs. observedSWCthatwasusedduringthetrialanderrorprocedure. Thefirstapproachwasthelinearregressionbetweenobservedand model-predictedSWCforcedtotheorigin,whichregression coef-ficientis b0=
⎡
⎢
⎢
⎢
⎢
⎣
ni=1 OiPi n
i=1 O2 i
⎤
⎥
⎥
⎥
⎥
⎦
(10)and the ordinary least squares regression which coefficient of determinationis: r2=
⎧
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎩
ni=1 Oi− ¯O
Pi− ¯P n
i=1 Oi− ¯O 2 0.5 n
i=1 Pi− ¯P 2 0.5
⎫
⎪
⎪
⎪
⎪
⎪
⎬
⎪
⎪
⎪
⎪
⎪
⎭
2 (11)whereOiandPi(i=1,2,...,n)representpairsofobservedand
pre-dictedvaluesforagivenvariable,and ¯Oand ¯P aretherespective meanvalues. Whenb0 is closeto1.0 thecovarianceis closeto
thevarianceoftheobservedvalues,indicatingthatpredictedand observedvaluesarestatisticallysimilar.Ar2closeto1.0indicates
thatmostofthetotalvarianceoftheobservedvalueswasexplained bythemodel.
Inaddition,asetofindicatorsconsideringtheresidual estima-tionerrorswasalsoused(Moriasietal.,2007;Pereiraetal.,2015): a.)The rootmeansquare error(RMSE),which characterizesthe varianceoftheerrorsandisexpressedinthesameunitsasthe observedvaluesOi: RMSE=
n i=1(Oi−Pi) 2 n 0.5 (12) b.)TheratioRSRoftheRMSEtothestandarddeviationofobserved data(sd)thatstandardizesRMSEusingthesdofobservations, withRSRvaluescloseto0.0indicatingagoodsimulation per-formance; RSR= n i=1(Oi−Pi)2 0.5 n i=1 Oi− ¯P 20.5 (13)c.)Theaverageabsoluteerror(AAE),whichexpressesthesizeof theestimationerrors,inthesameunitsasOi:
AAE= 1n n
i=1 Oi−Pi (14)
d.)The averagerelative error(ARE), whichindicatesthe sizeof errorsinrelativetermsandisexpressedasapercentage: ARE=100 n n
i=1 Oi−Pi Oi (15)
e.)Thepercentbias(PBIAS)thatmeasurestheaveragetendencyof thesimulateddatatobelargerorsmallerthantheir correspond-ingobservations,withlowvaluesindicatinganaccuratemodel simulation;positiveornegativevaluesrefertotheoccurrence ofanunder-orover-estimationbias.
PBIAS=100
n i=1(Oi−Pi) n i=1Oi (16) f.)Themodelingefficiency,EF,anormalizedstatisticdevelopedby NashandSutcliffe(1970)thatdeterminestherelative magni-tudeoftheresidualvariancecomparedtothemeasureddata variance: EF=1.0− n i=1(Oi−Pi)2 n i=1 Oi− ¯O 2 (17)EFcloseto1.0indicatesthattheresidualvarianceismuchsmaller thanthemeasureddatavariance,thusanexcellentsimulation per-formance.Contrarily,anullornegativevalueforEFindicatesthat
thesimulationispoorandthatusingthemeanofobservationsis asgoodasthemodel.
3. Resultsanddiscussion 3.1. Fieldobservationresults
In2012twotreatmentswereconsidered:Plot1wasrain-fed andPlot2wasirrigated.In2013and2014bothplotswereirrigated withdifferentirrigationdepths.Theseasonalirrigationdepthsare showninTable2.IrrigationstartedbyearlyJulyandendedafew daysbeforeharvest,inSeptember.Generally,theintervalbetween irrigationeventswasfourdays.Surfacedripirrigationwasused. Thelateralpipeswereequippedwithin-linenon-compensating emittersspacedby50cmalongthecroprow,thusresultinginfour emittersperplant;theemittersflowratewas2Lh−1.Toassessthe variabilityofdischargesalongthelaterals,fieldevaluationswere performed.Netapplicationdepthsvaried3.9–9.9mm.
Duringthethreecropseasonsstudied,thedatesofthe phe-nologicalstagesweredeterminedaccordingtotheBBCHsystem (Rossbaueretal.,1995).TheseBBCHphenologicaldateswerelater convertedintotheFAOstandardcropgrowthstages(Allenetal., 1998)forbuildingthecropcoefficient(KcandKcb)curves:the
ini-tialstage(thatreferstoKciniorKcbini)startsatplantinitiationand
endsatformationofsideshoots(BBCHstage2);thecrop develop-mentstageisfromthereuntilflowering(stage6);themid-season stage(KcmidorKcbmid)includesfloweringandthedevelopmentof
cones(stage7);thelateseasonisfromthenuntilmaturity/harvest (stage8);theendseason(KcendorKcbend)isatharvesting.Thedates
limitingeachcropgrowthstagearegiveninTable1forthethree cropseasons;theyvariedmuchduringtheinitialandcrop develop-mentperiodsduetothevariabilityofrelatedweatherconditions, butthatvariabilitydecreasedafterthestartofthemid-season.No differenceswereobservedbetweenplots.
Cropdatawascollected/observedinsixplantsperplot,within eightrowsperplotandtworeplicationsofthreeplantsineachplot. Cropheightswereobservedthroughouttheseasonsandtheir val-uesatthedatesofthecropgrowthstagesarepresentedinTable1. Thecropattained amaximumheightof6.0m,whichrelated to theadoptedtrellissystem.Theeffectiverootdepthwas0.80mas observedinthefield.Thefractionofgroundcovered/shadedby thecrop(fc),thatwasusedtocomputethecroptranspirationand
evaporationfromfcandheightasproposedbyAllenandPereira
(2009),wasmeasuredduringthethreeseasonswithhelpof pho-tostakennearthesolarnooninsunshinedaystowellobservethe shadowproducedbythecrop.Themaximumhopcovernearsolar noonranged8–9%ofthecroppedareadependingonthemaximum vegetativedevelopmentachieved.
Observationsoftheactivegroundcover,mainlynatural herba-ceous vegetation and rapeseed in the inter-row area, were performedthroughoutallcropseasonsandincludedthe respec-tivedensity,heightandfractionofsurfacecover.Thesedataare giveninAppendix1referringtothedatesofrelatedgroundcover managementoperationsandwereusedasmodelinputtoproperly performingthepartitionofactualevapotranspiration.
Thesoilwatercontent(SWC)wasmonitoredwithaTDR100 (CampbellScientific,USA),whichoperatesinthefieldusingthe softwarePCTDR(Soutoetal.,2008).TDRusedwaveguidestainless steelof0.80mlengthintegratingSWCtotherootzone.The equa-tionofToppetal.(1980)relatingthevolumetricwatercontentwith themeasuredbulkdielectric constantwasusedsince itproved appropriateforsoilsthatdonot containsubstantialamountsof boundwater,e.g., mostsandyandloamysoils(Robinson etal., 2003).TheSWCwasmeasuredin12stationsperplot; observa-tionswereperformedtoadepthof0.80m.Measurementswere
Table1
Hopgrowthstagesdates,andrespectivecropheight(h)andfractionofgroundcoverbythecrop(fc),2012–2014seasons.
Cropgrowthstages 2012 2013 2014
Dates h(m) fc Dates h(m) fc Dates h(m) fc
Initiation 22March 0.01 0.01 15April 0.01 0.01 11March 0.05 0.01
Startrapidgrowth 10May 0.10 0.04 19May 0.15 0.05 20April 0.15 0.04
Startmid-season 15July 5.00 0.08 30July 5.50 0.09 10July 6.00 0.08
Startmaturity 15August 6.00 0.08 25August 6.00 0.09 10August 6.00 0.08
Harvesting 11September 6.00 0.08 13September 6.00 0.09 04September 6.00 0.08
Table2
Treatmentsappliedinallseasonsandlocations.Irrigationandrainfalldepths.
Year Location Treatment Numberof
irrigations Irrigation depth(mm) SeasonETo (mm) 2012 Plot1 Rainfed 0 0 578 Plot2 Drip 14 128 2013 Plot1 Drip 15 69 546 Plot2 58 2014 Plot1 Drip 15 148 603 Plot2 141
performedtwodaysafterirrigationtoavoidmeasurementbiasas advisedbyAllenetal.(2011),withatotalof9,11and10 measure-mentsdays,in2012,2013and2014,respectively.SWCdatawere usedforcalibrationandvalidationoftheSIMDualKcmodel.
Duringthe2012season,yieldproductionandqualitywere eval-uatedusingallplantsofthreerowspertreatment.Differently,at harvestsof2013and2014seasonssixplantspertreatmentwere cutoffatgroundlevelandthenumberofbinesandconesperplant wereaccounted.Freshweightofhopconesharvestedinthefull experimentalareawasdetermined,andthenfoursamplesperplant (about250grofhopspersample)weretakenanddriedat55±5◦C untilconstantweightwasobserved.Samplesofdryconeswere ana-lysedbythe‘SociedadAnónimaEspa ˜noladeFomentodeLúpulo´ı’ (SAEFL),inLeón,todeterminethepercentageofalphaand beta-acidsusingthemethod7.7(EBC-EuropeanBreweryConvention, 2010).Adescriptiveanalysisofresultswasapplied,forthedifferent yearsandtreatments,inrelationtoyieldandqualityparameters. 3.2. Modelcalibrationandvalidation
Aspreviously discussed,modelcalibrationallowedadjusting modelparameterstominimizedeviationsbetweensimulatedand observedSWC.SIMDualKcwascalibratedwithdataofPlot 1in 2013,whichwasselectedbecauseithadhighervariabilityofSWC thentheotherdatasets.TheremainingseasonsanddatafromPlot 2in2013wereusedforvalidation.Table3presentsalltheinitial andcalibratedparametervalues.
Thepotential basal cropcoefficients obtainedare similarto thosetabled by Allenetal. (1998); however, Kcbend values are
higher,likelydue tothefact that,inthis study,harvestingwas performed beforesenescence was much developed. Kcb values
reportedby Krofta et al. (2013) are much smaller but authors referredtheoccurrenceof waterstressduringlong dryperiods withinthecropseason;thus,thosevaluesrefertoKcb.actandnot
topotentialKcb.Thesoilwaterdepletionfractionfornostress(p)
valueswerehigherthanthoseproposedbyAllenetal.(1998).This factrelatestocropmanagement,tothevarietyandtotheclimatic demand,whichwasnotveryhighintheGalicianconditions, par-ticularlyatendseason,whenthecalibratedpendwasquitelarger
thanthetabledvalues.Unfortunately,literaturelacksother infor-mationonhopKcborKc,orrelativetothepfraction.Thecalibrated
parametersrelativetosoilevaporation,deeppercolation, ground-watercontributionandrunoffresultedclosetotheinitial/default ones(Table3).
Fig.2.Observedvs.simulatedsoilwatercontentrelativetoallhopexperimental
data(2012–2014).
The“goodnessoffit”indicatorsrelativetomodelcalibrationand validationforallcropseasonsareshowninTable4.Resultsforthe regressioncoefficientb0varyfrom0.99to1.03,thusverycloseto
1.0,indicatingthatpredictedandobservedvalueswerestatistically similarforallcropseasons.Valuesofr2rangedfrom0.72to0.97,
thusindicatingthatmostofthetotalvarianceoftheobserved val-ueswasexplainedbythemodel.Fig.2showstheregressionforced totheoriginbetweenSWCobservationsandsimulations;itrefers toalldatasetsandconfirmsthegoodnessofvaluesobtainedforb0
andr2.
RMSE values are quite low, ranging from 0.012 to 0.015cm3cm−3, thus indicating that errors of estimation were
small,representingonly1.2–1.6%ofTAW.Thesevaluescombined withlowRSRratios(ranging0.10–0.14),thusindicatinglow resid-ualerrors.AAEvaluesrangedbetween0.009and0.011cm3cm−3,
thus being also quite small. ARE values were also very small, rangingfrom3.1to5.0%.ThePBIASwereverylow, indicatinga slightover-estimationbiasinthecalibrationplot(PBIAS=1.5)and aslightlyunder-estimationbiasinplot2duringthesameyear.For 2012,PBIASresultsshowa slightunder-estimationbiasandfor 2014averylowover-estimationbias.Thus,themodeldoesnot showatrendforunderorover-estimationbias.TheNash–Sutcliffe
Table3
Initialandcalibratedmodelparameters.
Parameter Initialvaluesc Calibratedvalues
Crop KcbHopini 0.15 0.16
KcbHopmid 1.00 1.00
KcbHopend 0.80 0.85
pini 0.50 0.60
pmed 0.50 0.60
pend 0.50 0.70
Soilevaporation TEW(mm) 25 29
REW(mm) 10 9 Ze(m) 0.15 0.12 Deeppercolation aD 400 380 bD −0.0173 −0.0173 Runoff CN 75 72 Groundwater contribution a1 380 380 b1 −0.17 −0.17 a2 305 303 b2 −0.27 −0.27 a3 −1.3 −1.3 b3 6.3 6.0a/6.6b a4 4.2 4.0a/4.4b b4 −0.65 −1.00a/−0.85b
KcbHopini,midandend:basalhopcoefficientforinitial,midandendseason.pini,midandend:soilwaterdepletionfractionfornostressforinitial,midandendseason.TEW:total
evaporablewater;REW:readilyevaporablewater;Ze:depthoftheevaporablelayer;aD:soilwaterstoragevaluecomprisedbetweenFCandSat;bD:<−0.0173forsoils
drainingquicklyandb>−0.0173forsoilswithslowdrainage;CN:curvenumber;a1:FC;a2:1.1(FC+WP)/2.
aPlot1. bPlot2.
c AdaptedfromAllenetal.(1998,2007)andLiuetal.(2006).
Table4
Indicatorsof“goodness-of-fit”relativetomodelcalibrationandvalidation.
b0 r2 RMSE
(cm3cm−3) RSR AAE(cm3cm−3) ARE(%) PBIAS(%) EF
Calibration 2013,plot1 0.99 0.90 0.015 0.14 0.011 4.0 1.5 0.81
Validation 2012,bothplots 1.01 0.73 0.012 0.10 0.010 4.4 -1.9 0.84
2013,plot2 1.03 0.97 0.015 0.12 0.011 5.0 -3.5 0.86
2014,bothplots 0.99 0.72 0.012 0.10 0.009 3.1 0.4 0.81
b0:regressioncoefficient,r2:determinationcoefficient,RMSE:rootmeansquareerror,RSR:RMSE-observationsstandarddeviationratio;AAE:averageabsoluteerror,ARE: absoluterelativeerror,PBIAS:Percentbias;EF:modellingefficiency.
modelefficiencywashighforboththecalibration(EF=0.85)and validation(EF>0.81),whichmeansthattheresidualvariancewas muchsmallerthanthemeasureddatavariance,thusindicatinga goodmodelsimulationperformance.Overall,thegoodnessoffit indicatorsdemonstratetheabilityofthemodeltopredictthesoil watercontentoverawiderangeofobservedvaluesaswellasto predictthetermsofthesoilwaterbalance,includingtheactual evapotranspiration.
3.3. Cropcoefficientcurves
Thehoppotentialbasalcropcoefficientcurves(KcbHoppot)and
actualKcbcurves(KcbHopact),withKcbHopact=KsKcbHoppot,are
pre-sentedinFig.3togetherwiththeevaporationcoefficient(Ke)and
theactivegroundcoverbasalcropcoefficient(Kcbgcover).To
bet-terfollowtheirdynamics,irrigationandprecipitationeventsare alsodepicted.Becauseresultsforbothirrigatedplotsin2013and 2014wereverymuchsimilar,onlytheresultsforPlot1(usedfor calibration)in2013andforPlot2in2014arepresented.
Resultsshowthataslightwaterstressoccurredduringtheinitial cropgrowthstagein2012,withtheKcbHopactcurvelayingbelow
thatofKcbHoppotbutforashortperiodoftimeonly(Fig.3aand
b).Differently,waterstressdidnotoccurin2013and2014inthe initialstage.Resultsfortherain-fedandirrigatedplotsin2012are contrasting.Aheavywaterstresswasobservedintherain-fedplot (Fig.3a)fromtheendofthecropdevelopmentstageuntilharvest, thuswithKcbHopactbelowtheKcbHoppot duringtheentire mid-andlate-seasonstages.Contrastingly,theirrigatedplot(Fig.3b) hasshownonlyamildstressduringthemid-seasonstage.
Com-paringtheirrigatedplotsrelativetothethreecropseasons,itcan beobservedthatonlymildwaterstressoccurred:in2012during themid-season,whichrelatetoaseveredrysummerwhere irri-gationwasinsufficient;in2013,duringthelateseason,duetoa dryautumnperiod;andin2014,duringaveryshortperiodofthe cropdevelopmentstage.Theseresultsindicatethatirrigationwas performedwithparsimony.
HighKevalueswereobservedduringtheinitialandthecrop
developmentstageinresponsetoprecipitationthatoccurred dur-ingtheseperiods.Thisbehaviouris similarforallcropseasons analysed,includingwhencomparingtherain-fedwiththeirrigated plotsin2012(Fig.3).Nevertheless,differencesbetweentheseplots arenoticeableinFig.3aandbduringthemid-andlate-seasonin relationtotheoccurrenceofirrigationevents.Forallcropseasons, it canbeobserved(Fig.3)thattheKepeaksdecrease fromthe
cropdevelopmentstagetothemid-season duetotheincreased groundcover/shadowbyboththehopcropandtheactiveground covervegetationortherespectiveresidueswhenanherbicideis applied.TheKcbgcovervariesamongthethreecropseasonsdueto
differencesinmanagement:in2012(Fig.3aandb)groundcover vegetationwasactiveuntilendJulyandKcbgcoverwasrelatively
high;in2013(Fig.3c)asmallKcbgcoverwasestimatedfrom
mid-June toharvest;in2014(Fig.3d),Kcbgcover wasquitesmallbut
effectsofrelatedresiduesinlimitingKeareobservable.Forall
sea-sons,anon-negligibleandsteadilygrowingKcbgcovercanbenoticed
(Fig.3),thatrelatestothedevelopmentoftherapeseedcropinthe inter-row.
The curves of the potential KcHop, i.e., the sumKcbHop+Ke,
Fig.3.VariationofKcbHoppot( ),KcbHopact( ),Kcbgcover( ),Ke( ),irrigation( )andprecipitation( )relativeto:(a)Plot1,rain-fed,2012,(b)Plot2,
irrigated,2012,(c)Plot1,irrigated,2013,and(d)Plot2,irrigated,2014.
KcHopmid=1.02 and KcHopend=0.85. These values are similarto
thoseproposedbyAllenetal.(1998)exceptthetabledKcHopinithat
ismuchsmaller.Howeverthoseauthorsrecommendedthattabled Kcinivaluesshouldbereplacedbyvaluescomputedaccordingtothe
soilwettingamounts,frequencyandatmosphericdemand. There-foretheproposedvaluetoKcHopiniisappropriatetotheclimate
conditionsofthestudyarea.Ifactivegroundcoverisadoptedthan theKcvalueshavetobeincreasedby0.05–0.20intheinitialstage,
0.05–0.10duringmid-seasonandupto0.20atend-season depend-ingupontheinter-rowmanagementselected.
The potential basal crop coefficients resulting from this study are the following: KcbHopini=0.16; KcbHopmid=0.97 and
KcbHopend=0.83,whicharesimilartothosetabledbyAllenetal. (1998).These Kc and Kcb values should beusedwithirrigation
schedulingmodels tosupportirrigation management, henceto supporthopgrowerstomanageirrigationwithprecisionandto maximizeyieldandqualityofproduces.Examplesrelativeto appli-cationstocottonandmaizewerereportedrespectivelybyPereira etal.(2009)andParedesetal.(2014).ItisexpectedthatKcHopand
KcbHopvaluesproposedhereinmayalsobeusedasdefault
poten-tialvaluesforotherareas,mainlyinnorthernSpainwhereclimate isnotverydifferent.Nevertheless,KcHop andKcbHopneedtobe
usedwiththeappropriateinformationontheactualdatesofthe phenologicalstages.
3.4. ETpartitioningandsoilwaterbalancecomponents
Theresultsofpartitioning ETcact intoEs, THop and Tcover are
showninFig.4.Resultsshowthattranspirationslightlyincreases fromcropinitiationtothemid-season,whenthemaximum tran-spirationrate,near5mmd−1,wasattainedintheirrigatedplots (Fig.4b–d).Intherain-fedplot,THop valuesaresimilartothose
oftheirrigated plotinthesameyearof 2012but weresmaller fromtheendofthecropdevelopmentstageuntilharvesting(cf. Fig.4aandb).ThemaximumvalueforTHopoftherain-fedcrop
wascloseto4mmd−1andwasachievedearlier,duringthecrop developmentstage,i.e.,beforewaterstressaffectedthecrop.This
mainlyrelateswithlowerwater availabilitywhich caused crop waterstress(Fig.3a).
Groundcovertranspirationvariedwiththemanagementofthe groundcovervegetation(Appendix1).In2012Tcoverwas
increas-ingfromcropinitiation,atendofthewinter,untilthecompetition by the crop reduced its photosynthetic activity when the hop cropdeveloped.AtreatmentbyJulyalsoreduceditsactivityand Tcover becamenullduringlatemid-season untilrapeseed
devel-opedduringlateseason(Fig.4).Differencesbetweentherain-fed andirrigatedplotswereonlynoticeablebythelateseasonwhen waterwasfullyavailablefortheirrigatedplotbutinsufficientfor therain-fed.Differently,in2013thegroundcoverwasactivefrom thecropdevelopmentstageuntilharvestingbutwithasteadily smallTcoverwhilein2014groundcoverwasactivefromthe
mid-seasontoharvest,alsowithsmallTcover.Bytheendoftheseason
andforallyearsTcoversteadilyincreasedduetosowingrapeseedby
theendofmid-season.Thus,Tcoverinfluencedthewateravailability
forthehopcropandthereforedecreasedTHopandEs.
Soilevaporationishighduringtheinitialandcropdevelopment stages,whenmoreenergywasavailableatthesoilsurfacefor evap-oration.Esishighlyinfluencedbythegroundcovervegetationasit
canbenoticedcomparing2012(Fig.4aandb)withthefollowing cropseasonsof2013and2014(Fig.4candd):Eswashigherduring
thereferredperiodsduringtheseyears,whengroundcoverwas notactive(Tcoverwasnull)whileEswassmallerin2012whenan
activegroundcoverwaspresent.Thisinfluenceisalsoevidenced in2013(Fig.4c),fromthecropdevelopmentstageuntilharvesting, whereEswasmuchsmallerthanfor2012and2014.Inaddition,soil
evaporationwasalsoaffectedwhenvegetationresidualswerenot removedfromthefield,thusactingasanorganicmulchtoreduce Es,Thismaybeobservedwheninterpretingthedynamicsofthe
evaporationcoefficientKeobservingtogetherFigs.3and4.
Theseasonalsoilwaterbalancecomponentsofthehop plan-tationforallthreeseasonsarepresentedinTable5.ETcactvaried
between398and568mmwhilethevariationofTHopwassmaller,
from273to371mm.THopwasthemaincomponentofETcact
Fig.4.Variationofsoilevaporation(Es, ),hoptranspiration(THop, )andgroundcovertranspiration(Tcover, )throughoutthecropseasonfor;(a)Plot1,
rain-fed,2012;(b)Plot2,irrigated,2012;(c)Plot1,irrigated,2013(calibration)and(d)Plot2,irrigated,2014.
Table5
Partitionevapotranspirationandsoilwaterbalancecomponentsforallexperimentalplots(2012–2014).
Treatment P (mm) I (mm) ETo (mm) ETcact (mm) THop (mm) Tcover (mm) Es (mm) SWC (mm) GWC (mm) RO (mm) 2012 Plot1 335 0 578 449 273 79 97 5 125 17 Plot2 128 537 352 84 102 -20 110 17 2013 Plot1 172 69 546 461 324 27 110 86 133 3 Plot2 58 398 273 23 102 58 109 3 2014 Plot1 311 148 603 576 375 15 187 13 101 0 Plot2 141 568 371 15 182 58 56 0
P:precipitation;I:irrigation;ETo:referenceevapotranspiration;ETcact:actualevapotranspiration;THop:croptranspiration;Tcover:covercroptranspiration;Es:soil evapora-tion;SWC:variationinstoredsoilwater;GWC:groundwatercontribution;RO:runoff.
theirrigatedplots,butalowervaluewasobservedintherain-fed plotduetoahightranspirationoftheactivegroundcoverin2012. AmuchlowerTHoppercentage(29%)wasreferredbyUrbanetal.
(2012)likelyduetoabundantrainfallandbaresoilinthe inter-row.Tcoverwashighin2012butduetodifferentmanagementof
thevegetationintheinter-rowdecreasedinthefollowingyears becomingnegligiblein2014whenitwaslessthan3%ofETcact.Soil
evaporationwasanimportantcomponentofETcact,rangingfrom
19to32%,withlowpercentageswhenTcoverwashighin2012and
higherpercentagewhenTcoverwasthelowestin2014.Itcanbe
concludedthatgroundcovermanagementpracticesallowedthat moreenergybecameavailableatsoilsurfaceforevaporationwhen Tcoverwasreduced.
Thelowestprecipitationwasobservedin2013butduetoits gooddistributionalongthecropseasonandtothecapillaryrise froma shallowwatertable,thetotalirrigationdepthwassmall withoutgreatlyaffectingcroptranspirationbutproducingonlya mildstress.Resultsshowthatgroundwatercontribution(GWC) duetocapillaryriseplaysanimportantroleinsupportingETcact
withtheratioGWC/ETcactrangingfrom10%inthePlot2in2014
to29%inPlot1inthedriestseasonof2013.Thatratiowasalso
veryhigh(28%)intherain-fedplotin2012.Thecontributionof thesoilwaterstoredinthesoilbytheendofwinterwasnegligible in2012becausewinterrainsweresmall,andranged13–86mm inthefollowingyears.HigherSWCwasobservedin2013when rainfallduringthecropseasonwaslowerandwinterrainfallwas notlow.ROwasnegligible,representing5and1%ofthe precipi-tationin2012and2013,respectively;thehighervalueobserved in 2012wasdue toa heavy rainof approximately40mmthat occurredwhensoilwaterstoragewashigh.Deeppercolationwas notobserved/simulatedlikelybecauseheavyrainsdidnotoccur duringthestudyseasonsandsurfacedripirrigationmanagement wasappropriate.
3.5. Impactsofhoptranspirationonhopsyieldandquality
Mean of hop cone yield were calculated for all treatments (Table6).In2012,yieldobtainedintherain-fedplot1was37% lowerthanintheirrigatedplot2.In2013and2014allplotswere irrigatedandresultsofplot1and2werecomputedtogether,sothe trendobservedin2012couldnotbefollowed.Toassesstheimpacts ofwateruseonhopconeyieldalinearregressionbetweenyield
Table6
Yieldandqualityparametersofhopscv.‘Nugget’.
Treatment Drymatterhops (kgha−1) Alpha-acids (%) Beta-acids (%) 2012 Plot1 932.7 13.06 4.11 Plot2 1486.0 12.66 4.06 2013 Plot1 869.3 12.83 4.05 Plot2 2014 Plot1 1639.1 12.70 4.60 Plot2
Fig.5. Linearregressionbetweenyield(drymatterhops)andhoptranspiration (THop).Filledpointsrepresentedirrigatedexperiment;emptypointrepresented rain-feddata.Errorbarsrepresentedstandarddeviation.
andhoptranspirationwasapplied.Itresultedapositiveregression coefficientandahighcoefficientofdeterminationr2=0.92(Fig.5).
Theseresultsshowthattranspirationfavourshopsyield.Svoboda etal.(2008)andDelahuntyetal.(2011)reportedanincreaseabout yielddue toirrigation.Mozny et al.(2009) referred thatyields increasedunder rainfedconditions when summerprecipitation washigher,andNakawuka(2013)referredpositivetrendsrelative toevapotranspiration.However,norelationswithtranspirationare reportedintheliterature.
Averageobservedvaluesofthealphaandbeta-acidscontent aregivenin Table6.Alpha-acidsobservedin thecurrentstudy weresimilartoaveragevaluesreportedbyOlmedoetal.(2009) atthesamelocation.LinearregressionsrelatingTHopand
alpha-acids,beta-acidsandtheratiobetweenalphaandbeta-acidshave shownregressioncoefficientsnotsignificantlydifferentfromzero. Thisbehaviourcouldbeexpectedknowingthattheacidscontents relatemostlywiththecultivarunlessthatahighdifferentiationof temperatureandwateravailabilitycouldhavebeenimposed,what wasnotthecase.Nakawuka(2013)reportedthatnosignificant differenceswerefoundrelativetocontentofalpha-acidsbetween irrigationtreatments.Avariationofalpha-acidscouldbeexpected inrelationtothetemperaturepatternattheendofthecycle,when alpha-acidssynthesisismoreintense;however,temperatureby thenwasnotverydifferentforthethreeyearsofstudyand irri-gationapplieduntiltheendoftheseasonwasenoughtodonot producestressbythen.Wangetal.(2008)describedthat accumula-tionofalpha-acidsasmostintensiveinthirdandfourthweekafter
hopflowering.Sreˇcecetal.(2008)foundasignificantdecreaseof alpha-acidswhentheclimaticdemandforwaterattheflowering andconeformation stagewasveryhighandrainfallwas insuf-ficient.Differently,Benítezetal.(1998)reportedanon-response toclimaticdata.Thereporteddifferentialeffectsoftemperature andwateravailabilitymightberelatedtothetimepointsofthe differentphenologicalstagesasthedescribedeffectsof tempera-ture(negativeeffect)andwatersupply(positiveeffect)aremost relevantduringflowering,coneformationandearlyripening,i.e., beforethestrongestaccumulationofalpha-acids.Ingeneral,results ontrendsofalpha-acidsshoweddifferencesinrelationwith culti-var,locationandclimateconditions,mainlyfromvegetativestages untilearlyripeningstages(Sreˇcecetal.,2008,2013;Pavlovicetal., 2012,2013;Potop,2014).
Severalmetabolicpathwaysareinvolvedinthebiosynthesisof alphaandbeta-acids(ZanoliandZavatti,2008).Itisoften consid-eredthat,duringthebiosynthesisofthebittersubstancesincones, firstasurplusofthebeta-acidscomparedtothealpha-acidsmay occur(Goeseetal.,1999).Subsequently,beta-acidsconvertinto alpha-acids,oraredegradedbyoxidation,andtheratioalpha-beta acidsthenincreases.Ifincreaseoftranspirationdoesnotpromote thesynthesisofalpha-acids,itmayhappenthatbeta-acids accu-mulateandthenapositivecorrelationappears.However,excluding therain-fedtreatment,sincenowaterorheatstressoccurred,a bal-ancebetweenalphaandbeta-acidswasobservedthatresultedina stableconcentrationofbitteracidsinthethreeyearsofstudysince thatconcentrationismainlydependentonthecultivar,inabsence ofextremeclimateconditions(Cehˇ etal.,2007).Nakawuka(2013) alsoreportedthatnosignificantdifferenceswerefoundrelativeto contentofbeta-acids.
4. Conclusions
TheSIMDualKcmodelperformedwellindetermininghop tran-spirationandthesoilwaterbalancecomponentsofH.lupulusL. cv.‘Nugget’croppedinGalicia,namelysoilevaporation, ground-watercontribution,deeppercolation andrunoff. THop represent
92%ofETcactduringthemid-season,andEswas69%ofETcact
dur-ingtheinitialstage.Calibratedparameters,particularlybasalcrop coefficients,depletionfractionfornostressandsoilevaporation parametersmaywellbeusedasdefaultparametersforoperational applicationsofthemodelwhentobeusedtosupportirrigation managementorforplanningpurposes.
AlinearregressionbetweenTHopanddryyieldcone(r2=0.92)
hasshownthatyieldincreaseswhencropandirrigation manage-mentfavourcroptranspiration.Differently,thelinearregressions betweenTHopandtheconcentrationsofalpha-acidsandbeta-acids
hadregression coefficients notsignificantlydifferentfromzero. Thisresultmeansthattheappliedirrigationmanagementdidnot influencethesynthesisofthebitteracids,whichiscultivar depen-dentbutcouldbemodifiedifstresshadoccurred.Thus,itcould beconcludedthatirrigationmanagementappliedwasappropriate intermsofthequalityofthehopconesproduced.Nevertheless, it isdesirable todevelopfurtherresearchusing different treat-mentswhichcouldinducedifferentresponsesofthecropinterms ofqualityandthatcouldconfirmthehopyieldresponsestowater. Acknowledgements
Thisresearchwas funded bytheenterprise ‘Hijosde Rivera InversionesCorporativas,S.L.’(EstrelladeGaliciabeer)(CN-12-045), EuropeanAgriculturalFundforRuralDevelopment(EAFRD),No. 2012/34,throughConselleríadoMedioRuraledoMar—Xuntade Galicia.Firstauthorthanksto‘LEAF-Landscape,Environment, Agri-cultureandFood(ISA-Univ.Lisbon)fortheinvitationtodevelopa stayresearch.
Appendix1.
Fraction,densityandheightoftheactivegroundcover,before andaftereachgroundcovermanagementoperationforallseasons.
Date Location fgcover Density hgcover
Before After Before After Before After
2012
InitialConditions Row 40 40 15 15 0.05 0.05
(22-March) Inter-row 37 37 25 25 0.05 0.05 10-May Row 80 80 30 30 0.10 0.10 10-May Inter-row 75 75 50 50 0.10 0.10 27-June Row 65 65 50 50 0.20 0.20 24-July Inter-row-sowing 0 0 0 0 0.00 0.00 26-July Row 65 65 30 30 0.25 0.25 03-August Row-herbicide* 65 0 30 0 0.30 0.00 23-August Inter-row 85 85 1 1 0.01 0.01 02-September Inter-row 85 85 15 15 0.03 0.03 06-September Inter-row 85 85 20 20 0.05 0.05
Finalconditions Row 0 0 0 0 0.00 0.00
(11-September) Inter-row 85 85 35 35 0.10 0.10
2013
Initialconditions Row 0 0 0 0 0.00 0.00
(15-April) Inter-row 30 30 30 30 0.02 0.02 24-April Inter-row 30 0 60 0 0.05 0.00 31-May Row 0 10 0 10 0.00 0.01 07-June Row 10 10 10 10 0.02 0.02 14-June Row 20 20 20 20 0.05 0.05 28-June Row 85 85 70 70 0.25 0.25 31-July Inter-row-sowing 0 0 0 0 0.00 0.00 31-July Row 85 85 70 70 0.40 0.40 18-August Row-herbicide* 40 0 35 0 0.40 0.00 22-August Inter-row 85 85 5 5 0.01 0.01 27-August Inter-row 85 85 10 10 0.02 0.02
Finalconditions Row 0 0 0 0 0.00 0.00
(13-September) Inter-row 85 85 35 35 0.15 0.15
2014
Initialconditions Row 0 0 0 0 0.00 0.00
(11-March) Inter-row 0 0 0 0 0.00 0.00 10-June Row 0 10 0 10 0.00 0.00 17-June Row 15 15 15 15 0.02 0.02 26-June Row 15 15 20 20 0.02 0.02 02-July Row 20 20 20 20 0.03 0.03 09-July Row 33 33 20 20 0.05 0.05 09-July Inter-row 0 0 0 0 0.00 0.00 17-July Inter-row 40 40 20 20 0.05 0.05 17-July Row 33 33 40 40 0.10 0.10 21-July Row 40 40 50 50 0.10 0.10
21-July Inter-row-ploughed 50 0 40 0 0.15 0.00
29-July Row-herbicide* 40 0 50 0 0.15 0.00
29-July Inter-row-sowing 0 0 0 0 0.00 0.00
15-August Row* 40 0 30 0 0.15 0.00
26-August Inter-row 85 85 15 15 0.05 0.05
Finalconditions Row 0 0 0 0 0.00 0.00
(04-September) Inter-row 85 85 25 25 0.10 0.10
fgcover:fractionofthegreenactivegroundcover;hgcover:heightofthegreenactivegroundcover *Turnstomulch.
AppendixA. Supplementarydata
Supplementarydataassociatedwiththisarticlecanbefound,in theonlineversion,athttp://dx.doi.org/10.1016/j.indcrop.2015.08. 042.
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