Comparing sprinkler and drip irrigation systems for full and deficit irrigated maize using multicriteria analysis and simulation modelling: ranking for water savings vs. farm economic returns

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ContentslistsavailableatSciVerseScienceDirect

Agricultural

Water

Management

jo u r n al ho 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

Comparing

sprinkler

and

drip

irrigation

systems

for

full

and

deﬁcit

irrigated

maize

using

multicriteria

analysis

and

simulation

modelling:

Ranking

for

water

saving

vs.

farm

economic

returns

Gonc¸

alo

C. Rodrigues,

Paula

Paredes,

José

M. Gonc¸

alves,

Isabel

Alves,

Luis

S. Pereira

∗

CEER–BiosystemsEngineering,InstitutoSuperiordeAgronomia,UniversidadeTécnicadeLisboa,TapadadaAjuda,1349-017Lisboa,Portugal

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received25January2013 Accepted2May2013 Available online 29 May 2013 Keywords:

Economicwaterproductivity Irrigationandproductioncosts Deﬁcitirrigation

Multicriteriaanalysis Alternativeirrigationsystems

a

b

s

t

r

a

c

t

Thisstudyaimstoassesstheeconomicfeasibilityoffullanddeficitirrigatedmaizeusingcenterpivot,set sprinklersystemsanddriptapesystemsthroughmulticriteriaanalysis.Differentirrigationtreatments wereevaluatedandcomparedintermsofbeneficialwateruseandphysicalandeconomicalwater pro-ductivityfortwocommoditypricesandthreeirrigationsystemsscenariosappliedtoamediumanda largefieldof5and32harespectively.Resultsshowthatdeficittreatmentsmayleadtobetterwater productivityindicatorsbutdeficitirrigation(DI)feasibilityishighlydependentonthecommodityprices. Variouswell-designedandmanagedpressurizedirrigationsystems’scenarios–center-pivot,set sprin-klersystemsanddriptapesystems–werecomparedandrankedusingmulticriteriaanalysis.Forthis, threedifferentprioritizationschemeswereconsidered,onereferringtowatersavings,anotherrelativeto economicresults,andathirdonerepresentingabalancedsituationbetweenthefirsttwo.Therankingsof alternativesolutionswereverysensitivetothedecision-makerpriorities,mainlywhencomparingwater savingandeconomicresultsbecausetheselectedalternativesweregenerallynotcommontoboth pri-orityschemes.However,someofthebestalternativesforthebalancedprioritiesschemearecommonto theothertwo,thussuggestingapossibletrade-offwhenselectingthebestalternatives.Deficitirrigation strategiesalsorankdifferentlyforthevariousscenariosconsidered.Thestudyshowsthatdeficitirrigation withexceptionofmildDIisgenerallynoteconomicallyfeasible.Theadoptionofwelldesignedand man-agedirrigationsystemsrequiresconsiderationofprioritiesoffarmmanagementintermsofwatersaving andeconomicresultssincethatsomewatersavingsolutionsdonotallowappropriaterecoverofthe investmentcosts,particularlywithDI.Basingdecisionsuponmulticriteriaanalysisallowsfarmersand decision-makerstobetterselectirrigationsystemsandrelatedmanagementdecisions.Resultsalso indi-catethatappropriatesupportmustbegiventofarmerswhenadoptinghighperformancebutexpensive irrigationsystemsaimedatsustainablecropprofitability.

1. Introduction

MaizeisoneofthemaincropsinPortugal.Itisthefourthmost produced commodityin the country, averaging more than 760 thousandtonnesfrom1992to2010(FAO,2012a).Thepercentageof thecultivatedareaequippedforirrigationincreasedfrom28.87to 30.75%from1990to2007(FAO,2012b)andtheagriculturalsector isresponsibleformorethan73%ofthecountrytotalwater with-drawal.Withtheincreasingwaterscarcity,there istheneedto optimizewateruse,mainlyforirrigationpurposes(Pereiraetal., 2009).Thus,farmersareforcedtoadoptimprovedirrigation mana-gementsinordertooptimizewateruse,includingtheadoptionof

∗ Correspondingauthor.

E-mailaddresses:lspereira@isa.utl.pt,luis.santospereira@gmail.com

(L.S.Pereira).

deﬁcitirrigationandenhancingirrigationperformance,thus lead-ingtohigherwaterproductivities(WP).Thepathwaytoachieve anefﬁcientirrigationwateruseimposestheneedto systemati-callyoptimizethesoilandwatermanagementpracticesandthe irrigationequipment(Knoxetal.,2012).

Theoptimizationofwateruseandproductivity,whose indica-torsaredeﬁnedbyPereiraetal.(2012),maybeachievedthrough theadoptionofdeﬁcitirrigation(DI).DIconsistsofdeliberately applyingirrigationdepthssmallerthanthoserequiredtofully sat-isfythecropwaterrequirementsbutkeepingapositiveeconomic return.ManyauthorsassessedtheimpactsofDIonmaizeyields (Cabelguenneetal.,1999;FarréandFaci,2009;PopovaandPereira, 2011; Maet al.,2012), water productivity(Payeroet al.,2009; Katerjietal.,2010)andeconomicreturns(RodriguesandPereira, 2009;AbdEl-WahedandAli,2012;Domínguezetal.,2012). Conse-quently,authorssearchedirrigationschedulesthatcouldachieve thefeasibilityofDIbecausethistechniquehighlydependsuponthe

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Listofacronymsandnomenclature DI deﬁcitirrigation

MCA multicriteriaanalysis S1 singleStewarts’model S2 multiphasicStewarts’model

D euro

a exponentoftheKostiakovinﬁltrationratecurve Ainv investmentannuity(Dyear−1)

ASWD allowedsoilwaterdepletion BWUF beneﬁcialwaterusefraction

Ca investment annuity per unit of irrigated area

(Dha−1year−1)

Cd energydemandtax(DkW−1)

Cen sumoftheannualenergycosts(D)

Cinv investmentcosts(D)

Cm annualmaintenancecosts(D)

CRF capitalrecoveryfactor D netapplieddepth(mm) DU distributionuniformity(%) Er energyrate(DkWh−1)

ETcadj adjustedcropevapotranspiration(mm)

ETc maximumcropevapotranspiration(mm)

ETo referenceevapotranspiration(mm)

EWP economicwaterproductivity(Dm−3) EWPR economicwaterproductivityratio fc fractionsofsoilcoverbyvegetation

fw fractionwettedbyrainandirrigation

h cropheights(m)

I inﬁltrationrate(mmh−1) i interestrate(%)

Kcb basalcropcoefﬁcients

kp parameteroftheKostiakovinﬁltrationratecurve

(h−a)

Ky yieldresponsefactorfortheentirecropgrowth

sea-son

P isthepowerofthepumpingstation(kW) p soilwaterdepletionfractionsfornostress pa percentageofareaadequatelyirrigated(%) PELQ potentialapplicationefﬁciencyrelativetothelower

quarter(%)

REW readilyevaporablewater(mm) RMSE rootmeansquareerror(mm)

t time(h)

Tadj adjustedtranspiration(mm)

TAW totalavailablesoilwater(mm) Td transpirationdeﬁcit(mm)

Td,f transpirationdeﬁcitforﬂoweringperiod(mm)

Td,m transpirationdeﬁcitformaturationperiod(mm)

Td,v transpirationdeﬁcitforvegetativeperiod(mm)

TEW totalevaporablewater(mm) Ti totaloperationtime(h)ofthepump

Tm maximumcroptranspiration(mm)

TWU totalwateruse(m3₎

Uj utilitiesrelatingtocriterionj[0–1]

WP waterproductivity(kgm−3) xj attribute

Ya actualyield(kgha−1)

Ym maximumyield(expected)yield(kgha−1)

Ze thicknessoftheevaporationsoillayer(m)

Zr rootdepths(m)

˛ graphslope ˇ utilityvalue

ˇf yieldresponsefactorforﬂoweringperiod

ˇm yieldresponsefactormaturationperiod

ˇv yieldresponsefactorforvegetativeperiod

FC ﬁeldcapacity(m3m−3)

WP wiltingpoint(m3m−3)

j weightsforcriterionj

adoptedmanagement,i.e.,whenthosedeﬁcitsareapplied(Bergez etal.,2004),aswellasonirrigationandwatercosts(Kampasetal., 2012;Monteroetal.,2012).Modellingcanplayamainrolein deter-miningrationaldeﬁcitirrigationschedules(Mailholetal.,2011; DeJongeetal.,2012;Maetal.,2012).

Higher WPmay be achieved by adoptinghigh performance irrigation systems, havinghigh distributionuniformity (Pereira et al., 2002,2009).Numerous studies showthat there is great potentialtoachieve amore efficientwater use,mainlythrough an enhanced distribution uniformity when improving surface irrigation(RaghuwanshiandWallender,1998;Horstetal.,2007; Gonçalvesetal.,2011)orpressurizedsprinkleranddripirrigation (Namaraetal.,2007;Pedrasetal.,2009;López-Mataetal.,2010; Ørumet al., 2010;Mailholet al., 2011;Abd El-Wahedand Ali, 2012;vanDonketal.,2012).Choosingthemostsuitableirrigation systeminvolvesnumerousfactors,suchasirrigationscheduling, soils,systemperformance,irrigationcosts,andtheperformance of the off farm systems. The latter are particularly important becauseadoptinganoptimizedirrigationschedulingincollective irrigationsystemsrequiresthatofffarmsystemsaredependable andreliableintermsofdischargesandtimeofdeliveriesinsurface irrigationsystems(Gonçalvesetal.,2007;Zaccariaetal.,2010), andin termsof timing,dischargeand pressureincase of pres-surized systems (Lamaddalena and Pereira, 2007; Lamaddalena etal., 2007;Calejoet al.,2008).Theadoption ofmore uniform systems involves a trade off between increased capital expen-diture onequipment and the benefitsassociated with reduced water application when it is uniformly distributed (Brennan, 2008).

When modelling to rank the best irrigation management alternatives,simulation outputs may bedifﬁcult tohandle and the selection of the most feasible alternativesmay be hard to achieve.However,avarietyofdesignandmanagementalternatives canbecreatedandthenrankedbyadoptingmulticriteria analy-sis(MCA)(RoyandBouyssou,1993;PomerolandRomero,2000), multi-attributemodelling(Bartolinietal.,2007),ormulti-objective optimization(Grootetal.,2012).Whenaimingatcombining dif-ferentactorsindecision-making, e.g.,farmersandstakeholders, insteadofrankingsolutions,fuzzycognitivemappingmaybeused; however,few studieshavebeenappliedtoirrigation(Giordano etal.,2007;MouratiadouaandMoranb,2007;Kafetzisetal.,2010). MCAprovestobeausefulapproachthatcanincorporatea mix-ture of quantitative and qualitative information and take into accountthepreferencesofusers.VariousapplicationsofMCAto irrigationarereportedintheliterature(TecleandYitayew,1990; Bazzani,2005;Manosetal.,2006;RiesgoandGómez-Limón,2006; Bartolinietal.,2010)andareappliedtoirrigationsystemsdesign (Gonc¸alvesetal.,2007,2011;Pedrasetal.,2009;Darouichetal., 2012).

Considering theaspectsanalyzed aboveand previous devel-opmentsbyRodriguesandPereira (2009),themaingoalofthis studyistoassesstheeconomic impactsofwater deﬁcits, com-moditypricesandenhancedirrigationsystemsperformanceonthe physicalandeconomicwaterproductivityofirrigatedmaizeinthe VigiaIrrigationDistrict,SouthernPortugal.Multicriteriaanalysis isadoptedtorankalternativesolutionsandhelp understanding contradictoryresultsduetoassigningprioritiestowatersavingvs. farmeconomicresults.

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2. Materialsandmethods 2.1. Yieldresponsestoirrigation

Themaizeyieldresponsetowaterwasderivedusingseveral ﬁeldtreatmentsthatweredesignedtodeterminetheimpactsof deﬁcitirrigationindifferentstagesofthemaizecropseasonon yield.TheseexperimentswereperformedattheAntónioTeixeira ExperimentalStation,locatedintheSorraiaValley,nearCoruche, Portugal.AdescriptionoftheexperimentsisgivenbyAlvesetal. (1991). The SIMDualKcmodel adopted in this study was cali-brated/validatedformaizeinthesamearea,withthedescriptionof theexperimentalarea,soilsandclimatebeinggivenbyRosaetal. (2012b).

Fieldexperimentswereperformedwithmaize(ZeamaysL.)var. LG18(FAO300)withaplantdensityofaround90,000plantsha−1 during1989(Alvesetal.,1991).Maizewassownby10Mayand maturation,dependingontheirrigationtreatment,wasreached duringtheperiod 29 Augustto5 September.Harvest was per-formedfor alltreatments by 5 September. Using a line-source system,sevendifferentirrigationschedules,withvarious replica-tions,wereadopted,includingfullanddeﬁcitirrigationtreatments andconsideringthreecropdevelopmentstages:vegetative, ﬂow-eringandmaturation(Alvesetal.,1991).

Duetoheavyyieldlossesassociatedwithstressatthemid sea-sonstage,fromﬂoweringtomaturation,imposing stressduring that period have beenshown to beeconomically unfeasibleas observedbyseveralauthors(StewartandHagan, 1973;Stewart etal.,1977;NeSmithandRitchie,1992;Karametal.,2003;Farré andFaci,2009);thusthecorrespondingtreatmentsarenot con-sideredinthepresentstudy.Thefourtreatmentsanalyzedherein differinthetimingthatthestresswasimplemented:

A.fullirrigationwithapplicationoftherequiredirrigationwater depthinalltheselectedcropdevelopmentstages;

B. stressimposedduringthevegetativestage; C. stressimposedduringmaturationand;

D.stressimposedduringthevegetativeandmaturationstages.

Theirrigationtimingwasassessedusinginfra-red thermome-ters (Jackson, 1982; Alves and Pereira, 2000). This experiment allowed verifying that the transpiration rate did not decrease during the 5–6 days following an irrigation event, with this irrigationinterval beingadoptedthereaftertomeet evapotrans-pirationdemand(Alvesetal.,1991).

Theactualyieldwasassessedbyharvesting7plantsforeach replicationtreatment.Theyieldwasevaluatedat13%grain mois-ture(Popovaetal.,2006;PopovaandPereira,2011).

Toestimatetheimpactsofwater onyieldtheStewartetal. (1977)single (S1)and multiphasic(S2)models wereused.The modelS1givesanaverageyieldresponsefactorfortheentirecrop growthseason(Ky),with

Ya=Ym−YmKy

Td Tm

, (1)

whereYmandYaare,respectively,themaximum(expected)yield

of thecropin absence of environmentalor water stresses and theactualyieldobtainedunderstressconditions,bothexpressed in kgha−1; Td is thetranspiration deﬁcit deﬁned as the

differ-encebetweenmaximal(Tm)andadjustedtranspiration(Tadj),all

expressedinmm.Intheﬁeldstudiesdescribedabove,Alvesetal. (1991)obtainedKy=1.35whenYmwasthehighestyieldachieved

inthefullirrigationTreatmentA,wherenostresswasobserved.

Since themaize cropexhibitsdifferentsensitivitiestowater stressthroughoutthegrowingcycle,theexperimentsallowedto useandparameterizetheS2model

Ya= Ym−Ym(ˇ

vTd,v+ˇfTd,f+ˇmTd,m)

Tm , (2)

whereˇv,ˇfandˇmaretheyieldresponsefactorsforeachcrop

growthstage(vegetative,ﬂoweringandmaturation)andTd,v,Td,f

andTd,marethetranspirationdeﬁcitsforthesamecropstages.The

yieldresponsefactorsfortheS2modelwereˇv=1.2andˇm=2.1;

ˇf,wasnotconsideredinthecurrentstudy.

2.2. Waterproductivityandwateruseindicators

The water productivity concepts used are those deﬁned by Pereiraetal.(2012).Thetotalwaterproductivity(WP,kgm−3)is: WP= Ya

TWU (3)

whereYaistheadjusted(actual)yieldachieved(kg)andTWUisthe

totalwateruse(m3_)._Y

avariedwiththeDImanagementadopted

andTWUvariedwiththeperformanceoftheirrigationsystems (referredinthenextsection)andwiththeDImanagement con-sidered.ReplacingthenumeratorofEq.(3)bythemonetaryvalue oftheachievedyield,itresultstheeconomicwaterproductivity (EWP,Dm−3):

EWP= Value(Y_TWUa) (4)

Inaddition,tobetterconsidertheeconomicsofproduction,a ratioexpressingboththenumeratorandthedenominatorin mone-tarytermsisused.Thisratioisnamedeconomicwaterproductivity ratio(EWPR)andrelatestheyieldvaluewiththefullfarmingcosts whenTWUistheamountofwaterusedtoachieveYa,i.e.,also

dependingonthefarmirrigationsystemconsidered: EWPRfull-cost= Value(Ya

)

Cost(TWU) (5)

Pereira etal.(2012)alsoproposednewwater useindicators aimed at distinguishing between beneficial and non-beneficial wateruse,whichisimportantfromthewatereconomyperspective. Thebeneficialwaterusefraction(BWUF)isdefinedasthefraction oftotalwaterusethatisusedtoproducetheactualyield,i.e.,it correspondstotheratiobetweentheactualcropETandtheTWU ascomputedwiththeSIMDualKcmodelasdescribedinSection2.4. 2.3. Scenariocharacterization

Twodifferentapproacheswereconductedinthisstudytoassess thefeasibilityoffullanddeficitirrigatedmaize.Thefirstconsists incomparingtheresultsrelativetoselectedirrigationtreatments whenconsideringdifferentscenariosonDImanagementand com-modityprices.Thesecondcomparesdifferentwell-designedand managed pressurizedirrigation systems – center-pivot and set sprinklersystemsanddriptapesystems–whenusedwithbase datarelativetovariousfullanddeficitirrigationtreatments.Two field sizes are considered, 5 and 32ha, representing small to mediumandlargetomediumsizefarmsatVigiaIrrigation Sys-tem,southernPortugal.Vigiahasbeenobjectofpreviousstudies (Calejo,2003;RodriguesandPereira,2009;Rodriguesetal.,2010a). Twocommoditypricesscenarioswereconsideredtoassessthe feasibilityoffarmingmaizeunderdifferentdeficitirrigation man-agement. The commodity prices refer to thegrain yield prices of154and264Dt−1,referredhereinas“low”and“high”prices, respectively.Thelowpricecorrespondstoapessimistscenariothat occurredin2008.Contrarily,thesecondpricerefersto2011,which isthereferenceyearforallcostsandprices.

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Table1

Monthlyaverageclimaticdata,Évora,maizeseasonof2011andaveragefor2002–2012.

May June July August September

2002–12 2011 2002–12 2011 2002–12 2011 2002–12 2011 2002–12 2011

Maxairtemperature,◦C 25.4 27.7 30.5 30.2 32.7 32.0 33.9 32.3 30.4 30.9

Minairtemperature,◦_C _10.1 _12.7 _13.3 _12.3 _14.4 _13.7 _15.0 _14.7 _13.4 _12.9

Minimumrelativehumidity,% 37.2 40.1 32.9 32.5 28.5 29.6 27.8 30.6 33.5 31.9

Maximumrelativehumidity,% 93.7 94.6 91.4 91.2 89.1 89.7 88.3 90.5 90.6 91.8

Windspeed,ms−1 _2.2 _1.5 _2.2 _2.1 _2.5 _2.9 _2.2 _2.2 _1.9 _1.7

Solarradiation,MJm−2_d−1 _22.0 _21.7 _24.6 _25.7 _25.9 _26.2 _22.5 _21.7 _17.3 _17.9

ETo,mmd−1 4.2 4.1 5.3 5.3 5.9 6.0 5.4 5.1 3.8 3.8

Precipitation,mm 38.2 26.8 15.1 15.8 0.9 0.6 3.1 7.6 40.6 33.2

Note:Allclimaticvariableswereobtainedaveragingdailydataexceptforprecipitationthatrepresentsthemonthlyaccumulation.

Table2

Soilphysicalandhydraulicpropertiesandtotalavailablewater(TAW).

Soillayerdepth(m) Coarsesand(%) Finesand(%) Loam(%) Clay(%) FC(m3m−3) WP(m3m−3) TAW(mm)

0.00–0.20 38.4 39.4 12.2 10.0 0.33 0.11 44.0

0.20–0.50 23.0 33.0 15.8 28.2 0.34 0.18 48.0

0.50–1.00 34.4 40.9 10.3 14.4 0.33 0.15 90.0

FCisforﬁeldcapacity,WPforwiltingpointandTAWfortotalavailablesoilwater.

2.3.1. Weatherandsoilsdata

MeteorologicaldataforVigiaarethoserelativetothenearby

stationofÉvora(38.77◦N,7.71◦W,and472melevation),whichare

reportedinTable1,bothforthelastdecadeandthemaizeseason

of2011usedforsimulation.Datareferstothereference evapo-transpiration(ETo),theclimaticvariablesusedtocomputeETo,and

rainfall.ETowascomputeddailywiththeFAO-PMmethod(Allen

etal.,1998).

SoildataaresummarizedinTable2.Theyconsistoftexturaland basicsoilhydraulicpropertiesoftheVigiaﬁelds.Thetotal avail-ablesoilwater(TAW,mm)wascomputedfromﬁeldcapacityFC

(m3_m−3₎_and_wilting_point_WP_(m3_m−3₎_as_deﬁned_by_Allen_et_al.

(1998).Followingthemodeltest byRosaetal.(2012b)andthe soilpropertiesinTable2,thefollowingsoilevaporation character-isticswereadopted:totalevaporablewaterTEW=38mm,readily evaporablewaterREW=9mm,andthicknessoftheevaporation soillayerZe=0.15m.ThedeﬁnitionsproposedbyAllenetal.(1998)

wereadoptedforallsoilvariables. 2.3.2. Cropdata

AFAO600maizevariety(NKFamoso)withaplantingdensity ofapproximately90,000plantsha−1wasusedinthesimulations. Ymwasobtainedusingthemodiﬁedapproachofthe‘Wageningen’

method(DoorenbosandKassam, 1979)andtakingintoaccount theaverageyieldvalues observedintheVigiaarea;Ym wasset

at 16,860kgha−1. The dates of cropgrowth stages, basal crop coefﬁcients(Kcb),soilwaterdepletionfractionsfornostress(p),

rootdepths(Zr,m),cropheights(h,m),andfractionsofsoilcover

byvegetation(fc)aregiveninTable3.handfc varywith

treat-mentsandmanagement.Thefractionwettedbyrainandsprinkler irrigationwasfw=1.0;fordripirrigationfwwas0.6.Kcbvalueswere

obtainedfromtheSIMDualKcmodelwhenusingobservationsof thesoilwaterbalance(Alvesetal.,1991),whoseglobalresultsare showninSection3.1.Theadjustedcropevapotranspiration(ETcadj)

wasthenobtainedfromSIMDualKcsimulations. 2.4. Models

Thesimulationscenariosrelativetothevariousfarmirrigation systemsweredevelopedconsideringtheactualcharacteristicsof systemsoperatinginVigia.Theconsideredfarmirrigationsystems weredesignedwiththesupportofthreedifferentmodels:DEPIVOT (Valínetal.,2012)forcenter-pivotirrigation,MIRRIG(Pedrasetal., 2009)fordripirrigation,andPROASPER(Rodriguesetal.,2010b)for setsprinklersystems.Theirrigationmanagementscenarioswere simulatedwiththeSIMDualKcmodel(Rosaetal.,2012a).

Table3

Cropgrowthstagesandrelatedcropparametersformaize.

Treatments Initial Cropdevelopment Midseason Endseason

Periodlengths A 10May–16June 17June–15July 16July–28August 28August–20September

B 10May–16June 17June–22July 23July–30August 01September–20September C 10May–16June 17June–16July 17July–28August 29August–20September D 10May–16June 17June–19July 20July–02September 03September–20September

Kcb 0.15 0.15–1.15 1.15 1.15–0.40 p 0.65 0.65 0.65 0.65 Zr(m) 0.20 0.40 1.00 1.00 h(m) A 0.10 0.50–1.00 2.85 2.85 B 0.10 0.50–1.00 2.50 2.50 C 0.10 0.50–1.00 2.50 2.50 D 0.10 0.50–1.00 2.00 2.00 fc A 0.1 0.59 0.97 0.92 B 0.1 0.45 0.91 0.88 C 0.1 0.45 0.95 0.80 D 0.1 0.45 0.91 0.79

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Table4

Maincharacteristicsandcostsoffarmirrigationsystems. Systemscenario Irrigatedarea

(ha) Discharge(lh−1) System pressure (kPa) Emitter spacing(m) DU(%) Potential application efﬁciency(%) Investment annuity (Dha−1₎ Maintenance annualcost (Dha−1₎ Center-pivot 5 80–270 150 Variable 83.5 83.5 345 35 32 80–750 290 90.8 87.3 152 21 Setsprinkler 5 890 214 14×14 84.1 84.1 289 75 32 270 70 Drip 5 1.10 118 0.2×1.4 93.8 93.8 867 120 32 815 112

DEPIVOTconsistsofasimulationmodelallowingthe

develop-mentandevaluationofsprinklerpackagesforcenter-pivots.The

modelperformsvariouscomputationsincluding:(1)sizingofthe

lateralpipespans;(2)selectionofthesprinklerspackage;(3)

esti-mation of potentialrunoff; and (4) estimation of theexpected

performanceindicatorswheninoperation,mainlythedistribution

uniformity.Tosizethelateralpipes,boththefrictionlossesand

theeffects oftopographyareconsidered.Thisallowsestimating

thepressureanddischargeateachoutlet,recognizingwhen

pres-sureregulatorsarerequired.Oncethesprinklerpackageisknown,

themodelcomparestheapplicationandinﬁltrationratesatvarious

locationsalongthelateraltoestimatetherunoffpotential.The

com-putationscanbereinitiatedasmanytimesasnecessaryuntilthe

userveriﬁesthattheexpectedperformanceiswithintargetvalues

(Valínetal.,2012).Maininputdataconsistedof:netapplieddepth, D=12mm;percentageofareaadequatelyirrigated,pa=95%; sys-tempressurenotexceeding150and300kPaforthe5haand32ha ﬁelds,respectively;sprayersondroptolimitwindandinterception losses.Theinﬁltrationratecurveappliedwas

I=kpta (6)

whereIistheinﬁltrationrate(mmh−1),t istime (h),kp and˛

areempiricalparameters.FortheVigiasoilandafteraﬁeld exper-iment,kp=6.070mmh−a anda=−0.891.Main characteristicsof

thecenter-pivotsystemsare includedinTable4.The terrainis nearlyflatwithaslope rangingfrom0.5to2%;runoffwasnull forthesmallfieldandabout9%ofDforthe32hafield.Asdiscussed byPereiraetal.(2002)andpreviouslyadoptedbyRodriguesand Pereira(2009),itwasassumedthatthepotentialapplication effi-ciencyrelativetothelowerquarter(PELQ)couldbeestimatedby thedistributionuniformity(DU);actualefficiencymaybelower dependinguponfarmer’smanagement.

The PROASPER model was developed to support farmers in decision-makingonsetsprinklersystemsdesignandevaluation. Themodel includesmodules fordesign, simulation and perfor-manceanalysis.Designisperformedeitherthroughindirectcontrol bytheuser(optimizedsimulation)ordirectinteractivecalculations asselectedbytheuser.Optingfor indirectcontrol,the simula-tionisperformedtooptimizethedesign,withautomaticsearch inthedatabaseofthecharacteristicsofthepipesandsprinklers thatmeettheuser’spreviouschoicesintermsofspacing,length andperformance.Whentheuserdirectlycontrolsthesimulation, messagesaredisplayedthatindicateifdesignconditionsarenot

beingmetpromptingtheusertosearchforappropriatesolutions. Themodelallowsobtainingasetofresultsrelatedtopipes’ sys-temsizes,hydraulicpressureanddischargeofeachsprinklerand theirvariationacrossthesystem,aswellasperformanceindicators (Rodriguesetal.,2010b).MaininputdatawereD=12mm;pa=95%; systempressurelimitedto250kPa;inﬁltrationrategivenbyEq. (6).PELQwasassumedequaltoDUasinaformerapplicationto Vigia(RodriguesandPereira,2009).Maincharacteristicsoftheset systemarealsoinTable4.

MIRRIG is aimed at designing microirrigation systems, i.e., drip and microsprinkling set systems. MIRRIG is composedby designandsimulationmodels,amulticriteriaanalysismodeland a database.Variousalternativedesignsolutionsarecreatedand thenrankedbaseduponanintegrationoftechnical,economicand environmentalcriteria.Designalternativesrefertothelayoutof thepipesystem,thepipecharacteristicsandtheemitters,either drippersormicrosprinklers.Themodelcomponentsinclude:(1)a designmoduletoiterativelysizethepipeandemitterssystem;and (2)aperformanceanalysismodulethatsimulatesthefunctioning ofthesystemandcomputesvariousindicatorsusedasattributes ofthealternativesrelativetothedesigncriteriaadoptedforMCA (Pedrasetal.,2009).Maininputdataconsistedof:driptapeonthe surfaceanddoublerowirrigation;D=8mm;fw=0.6;pressurenot

exceeding120kPa;targetDU>90%;inﬁltrationasforothercases. RelevantsystemcharacteristicsareincludedinTable4withPELQ alsoassumed equal toDUfollowingﬁeld observations(Pereira, 2007).

ThemodelSIMDualKcadoptsthedualcropcoefﬁcientapproach asproposedbyAllenetal.(1998,2005)tocalculateETcconsidering

theEandTcomponentsseparately.Themodelisdescribedindetail byRosaetal.(2012a)anditstestwithfielddataonmaizeis pre-sentedbyRosaetal.(2012b).Weather,soils,cropandirrigation data usedin this application are described above (Tables 1–3). SimulationswithSIMDualKcwereperformedforvarious scenar-iosrelativetotheallowedsoilwaterdepletion(ASWD)thresholds asdescribedinTable5,whicharedefinedinrelationtothesoil waterdepletionfractionsfornostress(p).Treatmentsarethose definedinSection2.1.

2.5. Investment,operationandproductioncosts

Data for labour, machinery, seeds, fertilizers and irrigation costswereobtainedfromregionaldatafor2008.Thesedatawere

Table5

Allowedsoilwaterdepletionfractions(ASWD)relativetoeachtreatmentandcropstage. Treatments Imposedstressduringmaizedevelopmentstages

Initial Development Mid End

A ASWD=p×TAW ASWD=p×TAW ASWD=p×TAW ASWD=p×TAW

B ASWD=1.2p×TAW ASWD=p×TAW ASWD=p×TAW ASWD=p×TAW

C ASWD=p×TAW ASWD=p×TAW ASWD=p×TAW ASWD=1.2p×TAW

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Table6 Productioncosts. Category Cost Seeds(Dha−1₎ _243.50 Labour(Dha−1₎ Farm 101.00 Irrigation 25.80 Fertilizers(Dha−1₎ _1013.70 Machinery(Dha−1) 527.20 Graindrying(Dt−1) 15.60 Electricity(DkWh−1₎ _0.13 Watercost Fixed(Dha−1₎ _52.00 Variable(Dm−3₎ _0.03

adjustedto 2011consideringthe averageannualinﬂation rate,

resultinginthevaluespresentedinTable6.

Investment costs (Cinv, D) were computed for each system

scenario.Theycomprisethepump,thepipesystem,andthe cho-senemitterpackageforallirrigationsystemscenariosdeﬁnedin Table4. The investmentannuity Ainv (Dyear−1) relative tothe

investmentcostCinvis

Ainv=CRFCinv, (7)

whereCRFisthecapitalrecoveryfactor.Ainvwascomputedfor

center-pivotequipment(includingpump,pumppipe,distribution pipeandcenter-pivot)consideringalife-timeofn=24yearsand n=12yearsforthesprinklers.Forthesetsprinklerirrigation sys-tem,thelife-timeforallsystemcomponentswasn=15years.For thedripirrigation system,differentlife-times wereconsidered: n=15yearsforthePVCpipes,n=10yearsforthePEpipes,and n=2yearsforthedriptape.Computationswereperformed assum-inganinterestratei=5%.CRFwasthencalculatedfromthelife-time n(years)andtheinterestrateias:

CRF= i(1+i)n

(1+i)n−1 (8)

The investment annuity per unit of irrigated area is Ca

(Dha−1year−1),whichistheratioofAinvbytheirrigatedarea.

Theoperationcostswereobtainedfromthesumoftheannual energycosts(Cen), theenergydemandtax(Cd),and theannual

maintenancecosts(Cm).Ceniscalculatedas:

Cen=PErTi (9)

where P is the power of the pumping station (kW), Er is the

energyrate(DkWh−1),andTiisthetotaloperationtime(h)ofthe

pumprequiredannually.Theenergycostperunitirrigatedarea (Dyear−1ha−1)iscalculatedbydividingCenbytheirrigatedarea.

CalculationsarebasedinenergypricespresentedinTable6.The annualmaintenancecosts(Cm)areconsideredtobeanadditional

1%,2.5%and5%oftheinvestmentcostforcenter-pivot,setsprinkler anddripirrigationsystems,respectively.

Thescenariosconsideredfor theirrigationsystems designed throughapplicationoftheabovereferredmodelsarecharacterized inTable4,whichincludesthechosenemitterpackage-discharge, systemworkingpressure,spacing,distributionuniformity(DU)and seasonalapplication efﬁciency,aswellastheinvestment annu-ityandannualmaintenancecostsforeachfarmirrigationsystem scenario.

2.6. Criteria,attributesandpriorities

Inordertocharacterize theirrigationsystemscenarios, per-formance indicators weredeﬁned including the economic land productivity,irrigationcosts,totalproductioncosts,BWUF,TWU, WPandEWPR.TheadoptedcriteriatoperformMCAwere repre-sentedbyattributesand scaledaccordingtomeasuresofutility

usingutilityfunctionsthatenablevariableshavingdifferentunits tobecompared.TheutilitiesUjrelatingtoanycriterionjwere

nor-malizedintothe[0–1]interval,withzeroforthemoreadverseand 1forthemostadvantageousresult.Linearutilityfunctionswere applied:

Uj(xj)=˛·xj+ˇ (10)

wherexjistheattribute,˛isthegraphslopeandˇistheutility

valueUj(xj)foranullvalueoftheattribute.Theslope,˛,is

neg-ativeforcriterialikecostsand wateruse,whose highestvalues aretheworst,andpositiveforothercriterialikeWPandEWPR, wherehighervaluesarethebest.Criteriaattributesandutility func-tionsarepresentedinTable7.Thisapproachissimilartotheone describedbyGonc¸alvesetal.(2011)andDarouichetal.(2012).

TheMCA methodappliedis thelinear weightedsummation (Pomerol andRomero,2000),a fullcompensatoryand aggrega-tivemethod,whichhasthemajoradvantageofitshighsimplicity, allowingan easierunderstandingof the procedureand results. However,thismethodhasthedisadvantageoffullcompensatory assumption,whichmeansthatanycriterionwithlowerresultcan becompensatedbyanotheronewithabetterresult,whichisa trade-offthatmaynotbewellacceptedbythedecisionmakers. Foreachalternative,adoptinguserdeﬁnedweights(j)forevery

criterionj,aglobalutilityU,thatrepresentsitsintegrativescore performance,wascomputedas:

U=

7

j=1

j×Uj (11)

Thedifferentirrigationsystemsscenarioswereranked accord-ingtotheglobalutilityvalues.Inthisstudy,differentsetsofweights wereadoptedtocharacterizeassigningprioritiestowatersaving, economicresultsandabalancebetweentheformer(Table7). 3. Results

3.1. Irrigationtreatmentsandyield

TheSIMDualKcmodelwasvalidatedforthevarioustreatments referredinSection2.1(4treatmentsandatotalof16replications). ResultsareshowninFig.1comparingﬁeldmeasuredandsimulated

Fig.1. Comparisonbetweenﬁeldestimatedandsimulatedadjustedcrop evapo-transpiration (ETc adj)cumulated between successiveirrigation events for all treatmentsandreplications.

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Table7

Criteriaattributes,utilityfunctionsandcriteriaweights.

Attributes(x) Units Utilityfunction Weights(%)fortheattributesinconditionof Balanceamong economicsand watersaving Prioritytowater saving Priorityto economicresults Economic

Economiclandproductivity Dha−1 _U(x)₌_0.22_×₁₀−3_x ₁₄ ₅ ₂₂

Irrigationcosts Dm−3 _U(x)₌₁₋_1.47x ₁₄ ₆ ₂₂

Totalproductioncosts Dm−3 ₁₄ ₆ ₂₂

Economicwaterproductivityratio – U(x)=0.60x 14 5 22

Watersaving

Beneﬁcialwaterusefraction – U(x)=1.02x 14 26 4

Totalwateruse m3_ha−1 _U(x)₌_5.41₋_0.82_×₁₀−3_x ₁₅ ₂₆ ₄

Waterproductivity kgm−3 _U(x)₌_0.35x ₁₅ ₂₆ ₄

ETvaluescumulatedfortheperiodsbetweensuccessiveirrigation

events.Theregressioncoefﬁcientis0.98,indicatingagoodmodel

ﬁt,andR2_is_0.86_showing_that_most_of_the_variance_is_explained_by

themodel.TheestimatedRMSEis4.8mm,i.e.,7.7%ofmaximum

cumulatedETobserved.ResultsofusingETcomputedfrom

obser-vationsofthesoilwaterbalanceformodelcalibration/validation

inmaizearereportedbyCameiraetal.(2005),Popovaetal.(2006)

andHongetal.(2013).Therespectiveindicatorsofmodelﬁtare similartothosepresentedabove.

Thereferred fourirrigationtreatments (A,B, C andD)were adoptedinthisstudy,appliedtotheVigiaIrrigationSystem.The irrigationmanagementscenarios simulatedwerebuiltadopting differentASWDthresholdsatvariouscropstagesasgiveninTable5. Theexceptionwastheﬂoweringstagebecausemaizeisparticularly sensitivetowaterstressatmidseason(Alvesetal.,1991;C¸akir, 2004;FarréandFaci,2009).

Table8presentsthenetirrigationdepths,adjustedcrop evapo-transpiration(ETcadj),adjustedtranspiration(Tadj),transpiration

deﬁcit (Td), and simulated actual yield (Ya) for all treatments

obtainedwiththeSIMDualKcmodelfortheVigiaﬁeldsin2011 consideringsprinkleranddripirrigationmethods.Resultsreferto theS2model(Eq.(2)).Themaximumtranspirationfortheentire seasonwas480mmforanon-stresseddripTreatmentA.

ResultsinTable8showthatgreaterDI(TreatmentD)leadsto considerableyieldlossesduetoareductionofETcadj,mainly

tran-spiration,Tadj,andthusanincreaseofthetranspirationdeﬁcit,with

Td=47 and 41mmrespectively for sprinkleranddripirrigation

methods.ThedripirrigationTreatmentCpresentsaloweryield thanTreatmentBdespitehavingthesameTdduetostressimposed

duringthelateseason,whichproducesanincreasedyieldimpact. Yieldsfordripirrigationarehigherthanforsprinklerbecause whenadoptingsmallerandmorefrequentirrigationeventsstress ismoreeasilyavoided.Thisisapparentintranspirationdeﬁcits reportedinTable8,whicharehigherforthesprinklerirrigation systems.However,thenetirrigationdepthsaregreaterthanfor sprinklersystemsduetohighersoilevaporationthatresultsfrom thehigherfrequencyofsoilwettings.Asforsprinkler,yieldtends todecreaseforthedripsystemwhenadoptingaDIschedule.

Con-trarilytosprinkling,TreatmentCunderdripirrigationhasalower TadjandhigherTdwhencomparedwithTreatmentB;thisisdueto

differencesinirrigationtiming.

3.2. Waterproductivityasinﬂuencedbycommoditypricesand irrigationsystems

Resultscomparingthebeneﬁcialwaterusefraction(BWUF)and physicalandeconomicwaterproductivity(WPandEWP)forall treatmentsandirrigationsystemsaswellasforbothﬁeldsizesof 5and32haandcommoditypricesof154and264Dt−1 are pre-sentedinTable9.Resultsshowthatdripsystemsleadtohigher BWUFthansetsprinklerandcenter-pivotsystems.Thisisdueto lowersoilevaporationsincethewettedfractionofthesoilisfw=0.6,

lessthanforsprinklerirrigation,whereallareaiswetted;therefore, soilevaporationislessfordripthanforsprinkling.Adopting Treat-mentsAandCleadtohigherBWUFthanTreatmentsBandDfor allirrigationsystemsandallcasesanalyzed.SinceBWUFisherein deﬁnedastheratioofETadjtoTWU,thatsituationisduetothefact

thatETadjissmallerforBandD,thusdecreasingthatratio.

Treat-mentBpresentsthelowestBWUFamongallcasesanalyzed,which resultsfromthedecreaseofETadjcausedbythestressimposed

dur-ingthevegetativestage.Comparingthesmallandthelargerfield, BWUFaresimilarfordripandsetsprinklersystemsbutaresmaller forthecenter-pivotsystemsincaseofthe5hafieldcomparatively tothe32hafield.

Whenadoptingfullirrigation(TreatmentA)ahigherWPthan for other treatments is generally obtained. Similar results are obtainedfortheC treatment,wherestressisinducedonly dur-ingthelateseason.BecauseBWUFisalsohighforbothtreatments, yieldlossesarenullorminimized.ForBandDtreatmentsTWU alsodecreasesbutproportionallylessthanforC,thusresultingin lowerWP.ThehighestvaluesforWPcorrespondtothenonstressed TreatmentA,varyingbetween2.60and2.80kgm−3forallsystems andmanagementconditions.ThelowerWPvaluesareobtained forTreatmentDunderdripirrigationandTreatmentCfor center-pivot.Thisoccursbecausethewatersavingsthatareattainedwith thestressimposedduringthedifferentcropstagesarenotenough

Table8

Adjustedcropevapotranspiration(ETc adj),adjustedtranspiration(Tadj),transpirationdeﬁcit(Td),netirrigationandsimulatedactualgrainyield(Ya)relativetoeachtreatment. Irrigationmethod Treatments ETcadj(mm) Tadj(mm) Td(mm) Netirrigation(mm) Ya(kgha−1)

Sprinkler A 568 471 4 372 16,554 B 521 438 19 360 16,074 C 569 441 28 336 14,784 D 492 413 47 300 14,279 Drip A 579 480 0 432 16,858 B 579 434 17 440 16,161 C 536 429 17 320 15,614 D 546 409 41 384 14,468

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Table9

ComparisonofBWUF,WPandEWPforalltreatments,irrigationsystemsandmanagementprecision,andﬁeldsizes.

Field Irrigationsystem Irrigationtreatment BWUF WP(kgm−3) EWP(Dm−3)

Lowprice Highprice

5ha Drip A 0.96 2.80 0.43 0.74 B 0.88 2.47 0.38 0.65 C 0.96 2.79 0.43 0.74 D 0.90 2.37 0.37 0.63 Setsprinkler A 0.90 2.62 0.40 0.69 B 0.85 2.62 0.40 0.69 C 0.91 2.36 0.36 0.62 D 0.86 2.49 0.38 0.66 Center-pivot A 0.89 2.60 0.40 0.69 B 0.85 2.61 0.40 0.69 C 0.90 2.35 0.36 0.62 D 0.86 2.48 0.38 0.66 32ha Drip A 0.96 2.80 0.43 0.74 B 0.88 2.47 0.38 0.65 C 0.96 2.79 0.43 0.74 D 0.90 2.37 0.37 0.63 Setsprinkler A 0.90 2.62 0.40 0.69 B 0.85 2.62 0.40 0.69 C 0.91 2.36 0.36 0.62 D 0.86 2.49 0.38 0.66 Center-pivot A 0.92 2.69 0.41 0.71 B 0.87 2.69 0.41 0.71 C 0.93 2.42 0.37 0.64 D 0.88 2.55 0.39 0.67

BWUF:beneﬁcialwaterusefunction;WP:waterproductivity;EWP:economicwaterproductivity.

toovercomethecorrespondentyieldlosses.WPdoesnotchange

fromthe5hatothe32haﬁeldincaseofdripandsetsprinkler

sys-temsbutWParelargerforthe32haﬁeldundercenter-pivotdue

tohigherBWUF.Thisisduetohigherdistributionuniformityfor

center-pivotinalargerﬁeld(Table4),thatleadstoalowerTWU.

EWPvariesinaccordancewithWP.Bothindicatorshavea sim-ilarbehaviour,withEWP dependingonly upon thecommodity pricesthoughthisindicatorvarieslinearlywiththem.Thehighest valueisachievedwhenadoptingTreatmentAunderadripsystem. AsforWP,TreatmentChasthelowestEWPvalueamongall sprin-klertreatments,butthelowestvaluefordripreferstoTreatment D.Thisdifferentbehaviour,alsoobservedforWP,resultsfromthe factthatthesmallerandfrequentnetirrigationdepthsappliedwith dripirrigationleadtoovercomestressproducedwithTreatment Cbetterthansprinklerirrigation.Variousstudiescompareddrip andsprinklerirrigationandfoundhigheryieldsandWPfordrip irrigation,e.g.,Tognettietal.(2003)forsugarbeet,Colaizzietal. (2004)forsorghum,andAlmarshadiandIsmail(2011)foralfalfa. Thegreater advantage ofdrip systemswas foundwhen deﬁcit irrigationwasapplied.However,Albajietal.(2010)found contra-dictoryresultsbecausetherelativeadvantagesofdriporsprinkler systemsdependeduponvariousfactorsincludingsoil characteris-tics,salinityandwaterquality.

EWPRwasusedtocomparetheyieldvaluesperunitof farm-ingcosts consideringboth scenarios of commodityprices. This approach allows assessing the feasibility of different irrigation treatmentsinordertodefinetheeconomicalreturnthresholdfor whichfarmingbecomesprofitable.Forthispurpose,Fig.2shows thevariationofEWPRforalltheirrigationtreatmentsand both commodityprices.EWPRforalltreatments,allirrigationsystems andbothfieldsizesrangedfrom0.64to0.97,thusindicatingthat notreatmentwouldbefeasiblewiththatlowcommodityprice, irrespectiveof theadoptedirrigationsystem.TreatmentA, cor-respondingtofullirrigation,wastheoneapproachingfeasibility forcenter-pivotincaseofthelargefield,andsetsprinklerforthe smallone.Dripsystemswerefarfromeconomicviabilityforlow commodityprices,withEWPRvalueslowerthan0.80inallcases. TreatmentsCandDhadEWPRsmallerthanTreatmentsAandB,

thusindicatingthatstressimposedduringthelateseasonledto non-negligibleimpacts.

Forhighcommodityprices(Fig.2),mostscenariosleadto posi-tiveincomes,i.e.,EWPR>1.0.Anegativeincomewasonlyobserved whenadoptingTreatmentDunderacenter-pivotsystemina5ha field,thusconfirmingthenon-appropriatenessofthis combina-tiontreatment/systeminsmallfields.Whenfarmingmaize ina 32hafield,theadoptionofdeficitirrigationwouldleadtoa pos-itive income in all cases,with EWPRvalues rangingfrom 1.18 to1.67.Forthis fieldsize,irrigating witha center-pivotsystem would produce a farm income 1.40 to 1.67times greater than theannualproduction costs.For5hafieldsthebestEWPR val-uescorrespondtoasprinklersetsystem,rangingfrom1.42to1.61 (Fig.2).Positivevalueswereobtainedfordripsystems(1.18–1.35) but lowerthan for setor center-pivotsprinklersystems. How-ever,EWPRvalues changewiththepricesof waterasanalyzed forBrazilianconditions(Rodriguesetal.,2013).Onecanconclude thattheadoptionofdeficitirrigationiswellsupportedforthishigh pricescenarioandthatadoptingdripirrigationformaizewould notbeselectedbyafarmerunlesshewouldassignhighpriority forwatersaving,asanalyzedinthefollowingchapter.Resultsby Heumesseretal.(2012)alsofoundthatsprinklerirrigationwas moreprofitablethandripincaseofmaize.Theyalsofoundthat dripirrigationadoption would requiresubsidies forequipment investing.

3.3. Rankingofdifferentalternatives

Theglobalutilitiesofallthealternativescombiningirrigation treatmentsand irrigation systemsare shown inFig. 3. Compu-tations refer to the highcommodity price only because when consideringthelowpricescenarioa negativefarmincome was obtained for all the alternatives as shown in Fig.2. The three prioritizationschemesdeﬁnedinTable7arehereinconsidered. Resultsshowthattheglobalutilitiesareverydifferentforthe var-iousprioritizationschemesconsidered,showingadisagreement betweenwatersavingandeconomiccriteria.Changingtheweights assignedtoeachcriterionwouldchangetheutilitiesvalues.Using

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Fig.2. Economicwaterproductivityratio(EWPR)foralldeﬁcitirrigationtreatmentsandirrigationsystemsappliedtosmall(a)andlarge(b)farmsizeswhenadoptinglow ( )andhigh( ),commodityprices.

theweightsreferred inTable7itis noticeablethathigher util-ityvaluescorrespondtotheCtreatment(waterstressduringthe late season) underdrip irrigationifwater saving is prioritized. Differently,whenthepriorityreferstotheeconomicreturns,the highestvaluesoftheutilitiescorrespondtotheAtreatment(full irrigation)forsetsprinklersystemsincaseofthesmallﬁeldand

forcenter-pivotincaseofthelargefield(Fig.3).Theseresultsare largelyexplainedbythecostsassociatedwiththeirrigation sys-tems,higherfordripthanforsprinklerand,whenconsideringthe sizeofthefield,becauseofthehigherinvestmentcostof center-pivotsystemsvs.setsystemsforsmallfields.Differencesbetween smallandlargerfieldswerealreadyreferredbyO’Brienetal.(1998)

Fig.3.Globalutilitiesrelativetotheprioritizationschemesadopted:watersaving(),farmeconomicreturns(䊉),orabalancebetweenboth( ),whenconsideringvarious deﬁcitirrigationtreatmentsAthroughD,dripandsprinklersystemsaswellassmallandlargeﬁelds.

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Table10

Theﬁvebestalternativesrelativestotheconsideredprioritizationschemeforbothirrigationmanagements,ﬁeldsizesandcommodityprice.

Priorities Rank Treatment Fieldsize

5ha 32ha

Irrigationsystem Utility Treatment Irrigationsystem Utility

Watersaving 1 C Drip 0.85 C Drip 0.85

2 D Setsprinkler 0.79 D Center-pivot 0.83

3 A Drip 0.77 D Setsprinkler 0.79

4 D Center-pivot 0.74 B Center-pivot 0.77

5 B Setsprinkler 0.72 A Drip 0.77

Economicresults 1 A Setsprinkler 0.78 A Center-pivot 0.80

2 B Setsprinkler 0.77 B Center-pivot 0.79

3 C Setsprinkler 0.74 A Setsprinkler 0.78

4 D Setsprinkler 0.73 B Setsprinkler 0.77

5 A Drip 0.69 C Center-pivot 0.76

Balancebetweenwater savingandeconomic results

1 D Setsprinkler 0.76 D Center-pivot 0.79

2 C Drip 0.76 B Center-pivot 0.79

3 B Setsprinkler 0.75 A Center-pivot 0.78

4 A Setsprinkler 0.75 D Setsprinkler 0.76

5 A Drip 0.74 C Drip 0.76

andLammetal.(2002),reportingthatcenter-pivotirrigationwas moreadvantageousforlargeﬁelds.

High utilities when prioritizing for water savings are also assignedtoDtreatments(DIduringallstagesexceptmidseason) forcenter-pivotsystemsinthecaseofthelargefield,andset sprin-klersystemsforthesmallone.Differently,otherhighutilityvalues whenprioritizingfor economicreturnsrefertotheBtreatment (stressedduringthevegetativestageonly)forsetsprinklersand thesmallfieldorcenter-pivotsinlargefields.Theadvantagein usingMCAforrankingisevidencedbythesedifferencesinresults. The top five alternatives relative to the three prioritization schemesdefinedinTable7areshowninTable10forbothfield sizes(5and32ha).Rankingsaredefinitelydifferentwhen consider-ingthevariousprioritizationschemes.Theyalsochangewithfield sizes.Forthe32hafield,therearedifferencesinrankingsforall priorityschemesbutdifferencesinutilityvaluesaresmall.

Forallcasesandwatersavingprioritiesthefirstrankisassigned fordripirrigationandtheCtreatment(stressedduringthelate sea-sononly),giventhewatersavingeffectslinkedtotheirrigation methodandtheadoptionofDI.Thesecondplacegoesto Treat-mentD(DIduringallstagesbutmidseason)withsetorcenter-pivot sprinklersforthesmallandlargefields,respectively.Fullirrigation (TreatmentA)withdripisthirdforthe5hafieldbutisfifthforthe 32hafield.Differently,whenpriorityisgiventoeconomicreturns, setsprinklersystemswithschedulingTreatmentsA,B,C,Darein thefirstfourranks;incaseoflargefields,thecenter-pivotsystems A,BandCrankfirst,secondandfifth.Theserankingsclearlyidentify theimpactofsystemscostscombinedwithyieldvalues.Inthecase ofbalancedprioritization,dripCcomesinsecondplacewhiledrip Acomesinfifthplaceforthesmallfield.Theotherranking pos-itionsaregiventothesetsprinklingsystems.Forthe32hafield, center-pivotsystemscomesinthefirst3rankingpositions,while dripCcomesinthefifthposition.

Theseresultsclearlyshowtheimportanceofinvestmentcosts inrelationtothewatersavingpotential.Comparisonsweremade forwelldesignedandmanagedsystemswhich are,allofthem, abletoproducehighBWUFandsupporthighWP.Therefore,the preferencesevidencedbytherankingsidentifythepossibleuseof variousalternatives,bothintermsofwatersavingandeconomic returnsdependinguponthedecision-makerpreferences.

Resultsshowthatthevariationoftheproductioncosts,mainly duetotheinvestmentannuityandthemaintenanceannualcosts, largelyinterfereintheeconomicrankingofthebestalternatives when comparing different farm sizes. For a larger area, a center-pivotsystemprovestobethemosteconomicallyfeasible;

however,forasmallerfield,thebestoptionistheadoptionofset sprinklersystems.Onecanalsoconcludethattheinvestmentand maintenancecostsplayanimportantrolewhencomparing differ-entfieldsizes,sinceitwidelyinterferesinthechoosingofthebest alternativestobeadopted.Marquesetal.(2005)andO’Brienetal. (2010)alsoreferredthatvariousfactorsinfluencingproductionand irrigationcostsandyieldlevelandvalueplayamajorrolein deter-miningwhichirrigationsystemsshouldbeselected.Thus,rankings shownabovemaydeeplychangewhenthesefactorsaremodified. Overall results show that the selection of the best design alternativeshighlydependsuponthedecisionmaker,mainlyon theprioritizationschemeandweightsadopted.Theweightsand prioritygiven tocriteriamustthereforeinvolvetheenduserin ordertochoosethescenariothatsuitshim/herthebest. Adopt-ingadecision supportsystemwithMCArequires thedefinition ofthemainpurpose,choosingthemostappropriateprioritization schemesandrelatedcriteriaweights.Forsupportingthedefinition oftheadoptedprioritiesandweightsandtheanalysisofresultsby users,oneneedstotakeintoaccountsomeadditionalfactorssuch asthewateravailability,whichismoreimportantincaseofmore waterdemandingalternatives,thecommodityprices,whichcould haveagreaterimpactonthealternativeshavinglowerland pro-ductivity;ortheproductioncosts,thataffectthealternativesthat requirehigherinvestment.Resultsalsoshowthatitisnecessary tosearchforsolutionsthatassurecompatibilityamongwater sav-ing,irrigationperformanceandeconomicviabilityforfarmers,i.e., assuringconditionsforsustainableirrigation,whichis in agree-mentwithfindingsbyWichelnsandOster(2006).Furthermore, adoptingwatersavingapproachesrequiresadequatemeasuresto supportfarmersontheselectionofthemostappropriateirrigation systemsand management optionssincejust usinga MCA deci-sionsupporttoolrequiresgoodknowledgeoffactorsinfluencing rankings.Resultsalsoindicatethatappropriatesupportmustbe giventofarmerswhenadoptinghighperformanceirrigation sys-tems,whichrepresentahighinvestment,aswellastoadoptmild deficitirrigationmanagementstrategiesthatallowforsustainable cropprofitability.

4. Conclusions

Thisstudyshowsthateconomicwaterproductivityindicators maybeanappropriateapproachforassessingtheimpactsofdeﬁcit irrigation,mainlyconsideringcommodityprices.Comparing differ-entscenariosofeconomicwaterproductivitiesmayhelptoassess whendeﬁcitirrigationisorisnotfeasible.Theeconomicwater

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productivityratioEWPR,relatingtheyieldvaluesperunitof farm-ingcosts,revealstobeadequatetoassessthefeasibilityofdeficit irrigationasinfluenced bycommoditypricesandirrigation sys-tems.Resultsshowthatviabilityofdeficitirrigationstrategiesis extremelydependentonthecommodityprices.Iflow commod-itypricesareconsideredallthetreatmentsforalltheirrigation systemsandfieldsizesleadtoanegativeincome.Contrarily,for highercommodityprices,mostscenariosleadtopositiveincomes. Dripirrigationsystemswerefoundtoleadtohigherwateruse performanceintermsofbeneficialwateruseandwater produc-tivitywhencomparedwithsprinklersystems.However,theEWPR werelowerfordripthanforbothsetandcenter-pivotsprinkler systemsduetorespectiveinvestmentcosts.Resultswerealso dif-ferentwhencomparinga5hawitha32hafield:bestresultsforall treatmentswereforsetsprinklerincaseofthesmallerfieldandfor center-pivotincaseofthelargeone,thusevidencingtheinfluence ofhighercostsofcenter-pivotsystemswhenasmallfieldis consid-ered.Thisstudydemonstratesthattheadoptionofwelldesigned andmanagedirrigationsystemsmayleadtocontradictoryresults whentheachievedwatersavingdoesnotallowthedesired recov-eryoftheinvestmentcosts,alsodependingonthefarmsize.This mayhelppolicymakerstounderstandthecontradictionsbetween watersavingandfarmeconomicresults.

Rankingirrigationsystemalternativesforwatersavingleadsto theselectionofdripanddeficitirrigationforbothtypesoffields. Contrarily,relativetoeconomicresults,sprinklerandfullirrigation treatmentsarefirstranked.Center-pivotrankabovesetsprinklers whenalargefieldisconsidered.Firstrankingpositionsforwater savingare notcommon tothose obtainedwhen thepriorityis assignedtofarmeconomicresults.Nevertheless,whenadopting aprioritizationschemethatbalanceswatersavingandeconomic results,it ispossibletohave arankingthat representsa trade-offbetweenwatersavingandeconomicreturns.Thisstudyshows theneedtoappropriatelyselectingtheweightstobeassignedto eachcriterion,whichrequiresappropriatesupporttofarmerswhen theywanttoselectanewirrigationsystemallowingsustainable cropprofitability.Resultsofthisresearchmaybeusefulfor farm-ers,managersandpolicymakerswhenaimingatimprovingwater managementatfieldscale,particularlyforunderstandingthe eco-nomiclimitsofdeficitirrigation,aswellaseconomicandwater savingissueswhencomparingdripandsprinklersystems.

Acknowledgements

Scholarships provided by FCT to G.C. Rodrigues and P. Paredesareacknowledged.Thesupportoftheproject PTDCAGR-AAM/105432/2008 and of the CEER-Biosystems Engineering (ProjectPEst-OE/AGR/UI0245/2011)arealsoacknowledged.

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