ContentslistsavailableatScienceDirect
NJAS
-
Wageningen
Journal
of
Life
Sciences
j ou rn a l h o m e p a g e :w w w . e l s e v i e r . c om / l o c a t e / n j a s
Projections
of
long-term
food
security
with
R&D
driven
technical
change—A
CGE
analysis
Z.
Smeets
Kristkova
a,b,∗,
M.
Van
Dijk
a,1,
H.
Van
Meijl
aaLEIWageningenURAlexanderveld5,2585DBDenHaag,Netherlands
bCzechUniversityofLifeSciencesinPrague,FacultyofEconomicsandManagementKamycka129,16521Prague6,CzechRepublic
a
r
t
i
c
l
e
i
n
f
o
Articlehistory:Received1September2015
Receivedinrevisedform11March2016 Accepted11March2016
Availableonline25April2016 Keywords:
PublicagriculturalR& Dinvestments
Land-augmentingtechnicalchange Agriculturalproductivity CGEmodel
Magnet Foodsecurity
a
b
s
t
r
a
c
t
Inthispaper,theimpactofpublicR&Dinvestmentonagriculturalproductivityandlong-termfood secu-rityviaR&Ddrivenendogenoustechnicalchangeisanalysed.ThefindingsshowthatR&Dgrowthrates atthelevelreachedin2000s,particularlythoseforChina,wouldnotbeexpectedanylonger. Concern-ingtheimpactofprojectedR&Dinvestmentsonagriculturalproductivity,itisfoundthatendogenous growthratesofland-augmentingtechnicalchangearecomparablylowerthanthestandardexogenous ratesusedinlongtermprojectionsofagri-foodmarkets.ThissuggeststhatpublicR&Dinvestmentsare notabletostimulateagriculturalproductiontothelevelsthatwouldbeexpectedfromthestandard baselineoutcomes.
©2016TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
Therearevariouschallengesforreachinglong-termsustainable agriculturalproductionandfoodsecurity.Ontheonehand,there
areincreaseddemandpressuresresultingfromongoing
popula-tiongrowth,improvinglivingstandardsindeveloping countries
andincreaseddemandfromnon-foodsourcessuchasbiofuelsand
othersourcesofrenewableenergy.Ontheotherhand,thereare
constraintsattheproductionside,duetolimitedspacefor expan-sionofagriculturalland, climatechangeand migration ofrural labourtourbanareas.RecentlytheFAOestimatedthatfood pro-ductionneedstobeincreasedwith60percenttofeedtheglobal populationof9billionpeoplein2050.Around80%oftheprojected
growthwillhavetocomefromintensification,predominantlyan
increaseinyieldsthroughbetteruseofinputs(Alexandratosand Bruinsma,[1]).Increasingagriculturalproductivityandcropyield
isbecomingevenmoreimportantconsideringthefactthatland
∗ Correspondingauthorat:LEIWageningenURAlexanderveld5,2585DBDen Haag,Netherlands;CzechUniversityofLifeSciencesinPrague,FacultyofEconomics andManagementKamycka129,16521Prague6,CzechRepublic.
E-mailaddresses:[email protected],[email protected]
(Z.SmeetsKristkova),[email protected](M.VanDijk),[email protected] (H.VanMeijl).
1 Co-authoraddress:LEIWageningenURDepartment ofInternationalPolicy
Alexanderveld5,2585DBDenHaag.
andwaterresourcesarebecomingscarce,whichmakesextensive
agriculturemoreandmoreproblematic.
AgriculturalR&DinvestmentsinbiotechnologiessuchasGMO representapossiblesolution,inadditiontothediffusionofexisting technologies,forthefoodsecuritychallenge,especiallyin devel-opingcountrieswherecerealyieldsarestillwellbelowtheglobal averagelevel.ContinuousinvestmentsinR&Dareimportantfrom theperspectiveofallfoodsecuritydimensions(FAO,[2]).The avail-abilitydimensionoffoodsecurityisassociatedwiththephysical supplyoffood.Accordingtovariousscholars(suchasAvilaand Evenson,[3],Fuglie,[4],Pardeyetal.[5],Alston,[6]),investments inR&Dareimportantdriversofagriculturalproductivityandfood availability.AsPardeyandAlston[7]pointout,U.S.agricultural R&Dhasfuelledproductivitygrowthandfoodsuppliesnotonlyin U.S.agriculturebutalsogloballyviaR&Dandtechnologyspillovers. Theaccessibilitydimensionoffoodsecuritylooksatthe
eco-nomic determinants of the access to food suchas households’
incomeandtheevolutionandvariabilityoffoodprices. Particu-larlyforthepoor,whospendaround50%oftheirincomeonfood consumption,changesinthepricesofmayorstaplecropssuchas
rice,wheatandmaize,canhaveadramaticimpact.Thepositive
occurrenceoftheperiodoflowagriculturalpricesin1980s-1990s waspredominantlyachievedbyR&Dinvestmentsinbetterseeds andvarietiesduringtheGreenRevolution.
The utilization dimension of food security refers mostly to thepopulation’sabilitytoobtainsufficientnutritionalintake.As
http://dx.doi.org/10.1016/j.njas.2016.03.001
1573-5214/©2016TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4. 0/).
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 19711973197519771979198119831985198719891991199319951997199920012003 USA Australia New Zealand UK China India Indonesia Brazil Colombia Argenna Ghana Kenya Nigeria South Africa Linear (India) Fig.1.Long-termevolutionoftheshareofagriculturalR&DexpendituresinGrossAgriculturalOutput.
Note:R&Ddatacompiledfromvarioussources,dataforGrossAgriculturalOutputtakenfromFugliedataset[4]. highlighted by Mogues et al. [8], the potential for agricultural
investmentstohavesignificantandobservableeffectsonhealth
andnutritionisgreat.Byincreasingagriculturalproductivity,the correspondingfarmerincomegainscantranslateintobetter
nutri-tion through greater calorie consumption and gains in dietary
diversity,aswellasimprovedhealththrougha betterabilityto purchasemedicineandaccesshealthservices.
In viewof this, therole of R&D investmentsas a key tech-nologydriverinachievingvariousdimensionsoffoodsecurityis undisputable.However,onlylimitedattentionispaidtoR&Das
akeytechnologydriverinmostoftheleadingassessment
mod-elsthatintendtoprojectfoodsecurityandcorrespondingchanges
infoodproductionandprices. Recentworkaspartofthe
Agri-culturalModelIntercomparisonandImprovementProject(AgMIP)
hasexamineddifferencesinlongrunfoodpricedevelopmentsinto thefuturethroughsystematicmodelintercomparison(Nelsonetal. [9],[10]andvonLampeetal.[11]).VonLampeetal.intheoverview
paperconcludedthata vast areaof uncertaintyis the
account-ingoftechnicalprogressinagriculturalproduction.Robinsonetal.
[12]showthatassumptionsdifferwidelyamongmodelsandare
anotherimportantdriverbehindthedifferentresults.They con-cludethatmoreempiricalresearchisneededtoopentheblackbox ofmacroandsectoraltechnicalchange.Asaresult,theabilityto guidepolicymakersindefininglong-termfoodsecuritystrategies isweakened.
Theobjectiveofthispaperistoprovideprojectionsof agricul-turalproduction, foodprices andother foodsecurityindicators
towards2050usinga global CGEmodel withendogenous R&D
driventechnicalchangeinagriculture.TheR&Ddrivenproductivity developmentsobtainedintheseprojectionswillbecomparedwith establishedyieldprojectionsusedinkeyglobalimpactassessment
modelsandanalyses.
Thecontributionofthisresearchistwofold:i)methodological, byincorporatingadynamicaccumulationofR&Dstocksincluding regionspecifictimelagsandtheirlinkstoagriculturalproductivity inastate-of-the-artCGEmodel,ii)policy-oriented,byexploring thepossibledirectionsofR&Dinvestmentsworldwideandtheir
impacts onagricultural productivity and consequently onfood
security.TheexplicitinclusionoftheR&Dsectorandcorresponding R&DstockaccumulationinthisCGEmodelisadistinctivefeature fromallotherglobalimpactassessmentmodelsusedinfood secu-rityprojections.
Thepaperisstructuredasfollows:chapter2containsthe liter-aturereviewwhichservedasabasisforincorporatingpublicR&D investmentsintheCGEmodel,asdescribedinchapter3.Inchapter 4,outcomesofthemodelareanalysedandchapter5concludes.
2. Literaturereview
2.1. PublicagriculturalR&Dinvestments—highreturnsbutlong lags
ThereisrichempiricalevidenceontheeffectsofR&D invest-mentsonproductivitywithgenerallysignificantlypositiveresults. Accordingtothemeta-analysisof289studiesconductedbyAlston etal.[13],theaveragereturnsonR&Dinagriculturereached82% (mean)and44%(median).Recently,Hurleyetal.[14]re-examined theratesofreturnin372separatestudiesfrom1958to2011and confirmedthepositiveevidenceofR&Dinvestments,althoughwith lowerreturnsthanpreviouslyadvocated.Similarly,Moguesetal. [8]presentedupdatedevidencefromcountrycasestudiesfocused ondevelopingcountries.Theyconcludethatliteratureonpublic investmentsstronglysuggeststhatreturnstoresearchand exten-sionaresignificant.Nexttothattheypointoutthreeobservations
−i)higherR&DreturnsarefoundinR&Dforshorterproduction cycles,suchasfield cropsii)higherreturnshavebeenfoundin R&DinAsiaanddevelopedcountriesandiii)R&Disassociatedwith higherreturnsthanagriculturalextension.
Although public R&D investments undisputedly bring large
returns,theirbenefitsaccruewithconsiderablelags,contraryto
industrial research,which hasa more short-term experimental
character.2 Thus,specificapproachesmustbeadoptedthatallow for alternativeaccumulation of R&Dinvestmentsto reflect this delayintheconstructionofknowledgestocksinagriculture. Trape-zoidallagmodels,polynomial-distributedlaggedforms(PDL)and
gammalagdistributionsarethemostcommonandrecommended
formsformodellingR&Dstocksinagriculture.Thirtleetal.[15] comment,thatthegammadistributionisofinterestsinceitoffers thesmoothformofatrapezoid,whichcanbeestimatedratherthan
imposed.Byfittingknowledgestockscalculatedfromalternative
distributionspecificationsinaTFPregression,Alston[6]foundthat
inadoublelogfunction,agammadistributionwithamaximum
50-yearlagandpeakafter24yearsyieldsthebestresult.Forthe
2AsAlstonetal.[4]explainsresearchanddevelopmentmighttake5–10years
beforethevarietyisadopted,duetotimespentonexperimentaltrialsandregulatory approvals.Afterthevarietyisadopted,farmershavetolearnhowtoproduceit,and consumershavetoacceptthenewproductinnovationonthemarket.Therefore,the peakofbenefitsonlycomes15–25yearsaftertheinitialinvestment.Eventually,the varietymaybecomeobsolete,asitmaybelesseffectiveagainstevolvingpestsor diseases.
-2% 0% 2% 4% 6% 8% 10% 12% 1960-19701970-1980 1980-1990 1990-2000 2000-2010 2010-20202020-2030 2030-20402040-2050 d o i r e p n o i t a l u m i S d o i r e p l a c i r o t s i H USA Brazil NoAfrica WeAfrica SoAfrica India EaAfrica EU16 China
Fig.2. HistoricalandprojectedannualgrowthratesofrealR&Dinvestments. Source:authors’calculationsbasedonhistoricaldataandMAGNEToutput.
calculationofknowledgestockwiththisdistribution,Alstonused thefollowingformulas:
RDstocki,t= 50
k=0 bk×Ri,t−k where 50 k=0 bk=1 andb(k)=(k+1) ı 1−ı×k (1–3)whereRDstocki,trepresentstheaccumulatedknowledgestockper state,Ri,t-krepresentstheR&Dexpendituresinlaggedperiodt-k,bk
aregammaweightsthatsumtoone,kisthemaximumlagofthe
distributionandand␦aregammadistributionparameters.
Variousstudieshave adoptedtheabove-mentioned
distribu-tions in modelling R&D stocks. Recently, Andersen and Song
[16]quantifiedtheeffectsofcumulativeR&DinvestmentsonUS agriculturalmulti-factorproductivity,adoptingAlton’sgamma distributionwith50yearslagandfoundpositiveevidence,withthe elasticityofTFPwithrespecttoR&Drangingaround0.3%.Sheng, etal.[17]tested10differentalternativesofgamma,trapezoidal
andgeometricdistributionforconstructingknowledgestocksin
Australianagriculturefrom1953to2007.Theauthorsconcluded thatthegammadistributionwithapeakafter7yearsandalagof 35yearsperformedthebest.Underthisdistribution,theestimated elasticityofTFPwithrespecttopublicR&Dknowledgestockswas 0.23%,withaninternalrateofreturnonpublicR&Dreaching28%. Similarly,HallandScobie[18]founda17%rateofreturnon pub-licR&DinNewZealandagriculture,usingtheperpetualinventory method,aKoycktransformationandapolynomiallagstructureon
annualdatafrom1927to2000.AsfortheEuropeanagriculture,
similarstudiesthatwouldquantifytheeffectofpublicR&D invest-mentsonproductivityarescarce.Anotableexceptionisfoundby Thirtleetal.[19]fortheUK.Theauthorsappliedalternative distri-butionstothegammadistributionwithlagsof25yearsandtheir calculatedelasticityrangedbetween0.1–0.3%.
Concerningdevelopingcountries,areviewofstudiesand cal-culatedelasticitiesispresentedinNinnPrattandFan[20]whouse alagof10yearsandelasticitiesaround0.1%tosimulatethe opti-malallocationofR&DinvestmentsacrossregionsofAsia,Africaand LatinAmerica.Theirchoiceofparametersislargelybasedonthe studyofThirtleetal.[19]thatanalysedtheimpactofresearch-led agriculturalproductivitygrowthonpovertyreductionand calcu-latedelasticitiesofR&Ddrivenlandproductivityintherangeof0.3% forAsia,AfricaandtheAmericas.AsinglecountrystudyforIndia wasperformedbyFan[21]whomodelledR&Dinvestmentsusinga PDLfunctionalformwithamaximumlagof13yearsandderivedan elasticityof0.255%.Fanfoundthatamongalltheruralinvestments
consideredinhisstudy,agriculturalresearchhasthelargestimpact onurbanpovertyreductioninIndiaperadditionalunitof
invest-ment.OtherevidencefromAsiawasprovidedbySuphannachart
andWar[22]forThailandwhoconsideredonlysevenyearlagsof R&Dinvestmentswithcorrespondingelasticitiesrangingaround 0.07%.AshorterlagofR&Dinvestmentsisjustifiableindeveloping countries,whereresearchisoftenclosertoextension.Asarguedby Alene[23]and[24],muchoftheR&DinAfricanagricultureisof adaptivenaturewithashortergestationlagthanwouldbethecase forbasicresearch.ApplyingaSecondDegreePDLfunctionwitha16 yearslag,AlenequantifiedelasticityofSub-SaharanAfrican agri-culturalproductivitywithrespecttoR&Drangingaround0.2%(for TFP)and0.38%(forvalueaddedperhectare).Aleneconcludesthat agriculturalR&DhassignificanteffectsonproductivityinAfrican agriculturewitharateofreturnof33%peryearandbeingthusa sociallyprofitableinvestmentinAfricanagriculture.AsforLatin America,asimilarstudywasconductedbyBervejilloetal.[25] whofoundagammadistributionwitha25yearslagandapeakin the24thyeartoperformthebestwithcorrespondingelasticities ofTFPwithrespecttopublicR&Dstockintherangeof0.5%.
Finally,empiricalevidence forcountriesof Centraland
East-ernEuropeandtheFormerSovietBlockisalmostnon-existent.
FortheCzechRepublic,RatingerandKristkova[26]foundpositive evidenceofR&Dstocksmodelledbyagammadistributionwith lagsrangingfrom7to15years.Theyarguedthatshortertimelags
comparedtoevidencefromtheUKorUSAcanbeexplainedbythe
transitionperiodwhichhasseenarapidupgradingoftechnologies, likelyinducedbytheurgentneedtoenhancethecompetitiveness ofagriculturalproduction.
MostoftheempiricalstudiesmentionedabovefocusontheR&D effectsofneutraltechnicalchange,assumingthatallfactorsbenefit equallyfromtheinnovationeffort.However,thereisevidencethat
someproductionfactorsbenefitfromtechnologicalchangemore
thanothers:asshownbyAcemoglu[27].Factor-biasedtechnical
changemightresultfrominducedinnovation(Hayamiand
Rut-tan,[28])thatdirectstechnicalchangetowardsthescarcerand
hence moreexpensiveproduction factor (for instancein Japan,
specificcropvarietiesweredevelopedthatincreasethe productiv-ityofland).Theempiricalteststhatweredevelopedtoverifythe
presenceofinducedtechnicalchangehavebeenwidelyapplied,
mostimportantlybyBinswanger[29],Antle[30],andHuffmanand Evenson[31].MorerecentlyThirtleetal.[32],studyinginduced innovationinUSAagriculture,showthatpublicresearch
expendi-Fig.3. EvolutionofknowledgestocksinBaselineVintage. Source:MAGNEToutput.
turesareimportantdeterminantsofbiasedtechnologicalchange. Moreover,theauthorsconfirmthatpublicR&Dexpenditures gen-erateland-savingtechnicalchange,whichisconsistentwithearlier workofdeJanvryetal.(cit.inThirtleetal.).Similarly,Piesseand
Schimmelpfennig[33]inastudyforUKagricultureconfirmthat
publicR&Dinplantbreedinghasbeenlandsaving,while mechan-icaltechnologyhasbeenlaboursaving.Theyarguethatitisthe
privatesectorthatdominatestheinducedinnovationprocessin
machinery.Theauthorsalsoconcludethatthepublicsector’sR&D impacttakeslongerthanprivateR&Dsectorimpact(18yearslagvs. 14years),butistwiceaslarge(long-runelasticityof0.30vs.0.15), attainingthistotheresponsibilityofthepublicsectorfor biologi-caltechnicalchange.Wangetal.[34]explainthatprivateresearch
maybemoreappliedthanpublicsectorresearch,andtherefore
mayhaveashorterlagstructurecomparedwithpublicR&D.
2.2. ApproachesformodellingR&DinvestmentsinCGEmodels
VariousapproachesexistthatincorporatetheR&Dsectorinto aCGEframework,suchaslinkingR&DeffectstoTotalFactor Pro-ductivity(TFP),asdoneearlierbyLejourandNahuis[35]inthe WorldscanCGEmodelorVerbic[36]forSlovenia,orvia incorpo-ratingacumulatedR&Dstockintheformofknowledgeasanew productionfactor(asappliedforinstancebyKristkova,[37]).Fully
dynamicRomerbasedendogenousgrowthCGEmodelsincorporate
effectsviaR&Dproductionofcapitalvarietieswithapublicgoods featureandwereappliedbyGhosh[38]forCanada.Themodelsof directedtechnicalchangeareafurtherextensionoftheRomerstyle CGEmodelswithtwo-varietycapitalsectorscapturingthe trade-offbetweenimprovingproductivityofoneinputversusothers,as usedbyPopp[39]intheENTICEmodelorOtto,Löschel,etal.[40]. ThesefullyendogenousgrowthCGEmodelshavetypically
forward-lookingdynamicbehaviourandareverystrongintheory.Onthe
otherhand,certainfeaturesmakethesemodelslessattractivefor agriculturalpolicyorientedanalysis.First,ahighlydisaggregated productionstructurethatcapturesallindividualagricultural
com-moditiesmaycomplicatethecomputabilityofthemodel,because
oftheinter-temporalsolution.Second,themodelsarebasedon
stylizedassumptions that are not yet adequately supportedby
empirics.Forinstance,limitedempiricalestimatesexistregarding theknowledgeproductionfunctionthatlinkspatentsasanR&D outputtoR&Dlabourasaninput.Thesameappliesforthelack ofempiricalevidenceforthevalueoftheelasticityofsubstitution betweencapitalvarietiesintheDixit-Stiglitzproductionfunction,
orbetweenknowledgestockandcapitalandlabourbundlesinthe CESproductionfunction.Third,amorefundamentalissueisthat intheabovementionedmodelsthemodellingofinnovativeeffort
issolelybasedonpatentedknowledgestock,whilenon-patented
knowledgesuchaspublicagriculturalR&Disnotconsidered.
Aninteresting and empirically-based approach tomodelling
endogenous factor-biased technical change in a CGE model is
presented byParado and deCian [41]. Theauthorslink
factor-augmentingtechnologyparameterstospilloversembodiedinthe
tradeof capitalgoods.Theparameters thatlinkspilloversfrom tradetoproductivityareempiricallyestimated(seeCarraroand deCian,[42]).
Fuelledbyanincreasinginteresttoassesstheimpactof agricul-turalR&Dinvestmentonglobalagriculturalproductionandfood security,variousglobalmodelshaveattemptedtoincorporate agri-culturalR&Dinvestmentsinanumberofapproaches.Hoddinott
etal.[43]andPerezandRosegrant[44]applytheIMPACTmodel
toassesstheimpactofinvestmentinR&Dontheprevalenceof
hungerand childmalnutrition. Tomodeltechnical changethey
taketheelasticityofyieldswithrespecttoresearchexpenditures onagriculturefromtheliterature.Dietrichetal.[45]endogenise technologicalchangeintheMAgPIEmodelbyrelatingtheratioof investmentin(publicandprivate)R&D(andinfrastructure)and yield(usinga15yearlag),toameasureoflanduseintensity.The resultingelasticityofagriculturalinvestmentonyieldsof0.30is
usedto simulatetheimpact ofinvestment in technicalchange
onlandusechange.Finally,Baldosetal.[46],exploredifferent publicR&Dinvestmentscenariosonglobalfoodandnutrition
secu-rityusingtheSIMPLEmodel.Tomodeltherelationshipbetween
agriculturalR&Dandtechnicalchange(measuredastotalfactor productivity)inthemodelanelasticityof0.25isusedfor devel-opedcountriesand0.16–0.28fordevelopingcountries.Allthree
models(IMPACT,MAgPIEandSIMPLE)arepartialequilibrium(PE)
modelsthat onlysimulatetheagriculturalsectorand therefore
arenotabletoaddresstheimpactofagriculturalR&Dinvestment onthewidereconomy(forinstancethroughlowerpricesof agri-culturalcommodities).Furthermore,althoughtechnicalchangeis madeendogenoustoR&DinvestmentinthePEmodelstudies,R&D investmentisstillexogenousandmodelledasa‘free’inputthat doesnotrequireresources(i.e.governmentbudgetincaseof pub-licR&D),whichisnotthecaseinreality.Weaimtoaddressthese issuesinthispaperbyusingaCGEmodelthatprovidesapictureof thetotalglobaleconomy.
Fig.4. Annualgrowthoflandaugmentingtechnicalchangeacrossbaselines(2010–2050). Source:MAGNEToutput.
3. Methodologicalapproach
3.1. TheMagnetCGEmodel
Inthisstudy,asophisticatedvariantoftheGlobalTradeAnalysis Project(GTAP)model(Hertel,[47])isemployed,knownasthe
Mod-ularAppliedGeNeralEquilibriumTool(MAGNET−Woltjer,Kuiper
etal.[48]).TheCGEmodelMAGNETisanextendedversionofthe GTAP(GlobalTradeAnalysisProject)model,awidelyusedtoolfor globaltradeanalysis.MAGNETisaneo-classicalrecursivedynamic
multi-sector,multi-regioncomputablegeneralequilibrium(CGE)
modelthathasbeenwidelyusedtosimulatetheimpactsof agri-cultural,trade,landuseandbiofuelpoliciesonglobaleconomic
development.Themodelhasbeenappliedtoanalysethemedium
andlongruneffectsofglobalandEUagricultural,trade,landuse, andbiofuelspolicies(Francoisetal.[49],VanMeijletal.[50],Banse et al.[51],Nowicki et al. [52],and Nelson etal. [9], [10]).The
modelis calibratedupon aninput-outputstructurethat
explic-itlylinksindustriesinavalueaddedchainfromprimarygoods, overcontinuouslyhigherstagesofintermediateprocessing,tothe finalassemblingofgoodsandservicesforconsumption.Incommon
withthestandardGTAPmodel,economicbehaviouris‘demand’
driven,withbehaviouralequationscharacterisedbymulti-stage
neo-classical optimisationtosegregatefactor, intermediate and finaldemandsinto‘nests’.Producersareperfectlycompetitiveand exhibitconstantreturnstoscaletechnology.Theequilibrium
solu-tionsarefoundbysolvingthedemand,supplyandpricesystem
ofalargenumberofinteractingfactorandproductmarketsthat
togethercover theglobal economy. Medium to longrun
base-linesareobtainedbycalibratingthemodeltoexogenousmacro
assumptionsofexpectedGDPandpopulationgrowth.Themain
outputofMAGNETisasetofeconomicindicatorsthatdescribe
thedevelopmentoftheglobaleconomy,includingsectoralgrowth,
employment,(food)consumption,pricesandtrade.Animportant
featureofMAGNET,incomparisontothestandard GTAPmodel,
isthatlanduseismadeendogenousbyincludingalandsupply
curve.Alandsupplycurveisestimatedusinghistorical informa-tiononlandpricesandlandsupplyaswellasbio-physicaldataon actuallandavailablethatcanbeusedforcommercialpurposes(e.g. croplandandpasturelandversusparks)VanMeijletal.[50].
Fortheanalysisinthispaper,MAGNETusestheGTAPdatabase version8,finalrelease(Narayananetal.[53]),whichcontainsdata ontheeconomicstructureof140countriesfor2007.Thesectoral divisiondistinguishes12agricultural(landusing)sectorsavailable inGTAPatthehighestlevelofdetail,includingpaddyrice,wheat andothergrains,variousothercropsandlivestockandanimal pro-ducesectors aswellasa (commercial)forestrysector,a fishing sector,manufacturingandservices.
3.2. IncorporationofR&D-driventechnicalchangeinMagnet
Inthispaper,wemakeanimportantdistinctionbetween
pri-vate and publicR&D activities.We focus onpublicagricultural R&Dtargetedtomajorimprovementsofseedsandvarietiesinthe styleoftheGreenrevolution,developedinspecificpubliclyfunded researchinstitutes.Inotherwords,we assumethatpublic agri-culturalR&Disresponsibleforbiologicaltechnicalchange,inline
withthereasoningofPiesseandSchimmelpfennig[33].Although
onemightarguethatpublicR&Dcomprisesmorethanjust land-orientedresearch,investmentsinimprovingcropvarietiesarestill thekeyfocusofapubliclyfundedresearch.3
AsopposedtoprivateagriculturalR&Dwheretechnologymight bedevelopedmore“in-house”,4publicR&Drequiresa representa-tionofaspecificproductionsectorandtechnology(forinstance,
3ThisisalsoconfirmedbyCowanetal.([54],TableA1)whoisolatedR&D
expendi-turespercategory,andshowthatlandresearchrepresentsbyfarthelargestcategory ofR&DorientedresearchintheUSA.
4SuchasdevelopingoffarmmachinerybyJohnDeeroragriculturalchemicalsby
Fig.5.Indexofproductionvolumeofagrifoodsector2050vs2010. Source:MAGNEToutput.
independentCGIARinstitutes developing newvarieties).A
sec-onddistinctive featurecompared toprivateagriculturalR&D is thattheeffectsaccrueonlyafterlonglags(rangingto50years). ThisexplainswhypublicR&Dstillrepresentsthemajorfinancing sourceofagriculturalresearch.AsforprivateR&D,ashortertime lagisexpectedbecauseofthemoreappliednatureofresearchand also,intuitively,itisexpectedthatprivateinvestorswanttosee theirreturnsassoonaspossible.Forinstance,inindustrial busi-nessR&D,R&Dstocksaretypicallybuiltwithageometricalrate
of15%(Kumbhakar[55]),whichmeansthatinlessthan7years,
thevalueofinvestmentistotallydepreciated.Asforprivate agri-culturalR&D,thelagismostlycausedbytheregulatoryapprovals, whichmaytakeupto7yearsincaseofGMOcrops(Qaim,[56]).
Giventheoftennationalfocusandhighlevelofstylizationin
mostof theabovementioned approaches, we proposea global
empiricallybasedapproachtolinkR&Dwithproductivity coeffi-cientsinthefunctionofConstantElasticityofSubstitution(CES)
productionstructuresin aglobalmodelling framework.Besides
beingempiricallybased,theadvantageoflinkingpublicR&Dto pro-ductivitycoefficientsisthattheagriculturalsectorbenefits“freely” frompublicR&Dinvestmentbutitisthegovernmentwhopaysfor
theexpenditures(andtheincreasedgovernmentalconsumptionis
reflectedinreducedsavingsintherestoftheeconomy).Thus,the publicgoodscomponentofagriculturalR&Diswellcaptured.5
InsteadoflinkingR&Dtototalfactor productivityconsistent
with a Cobb-Douglas production function, we consider
factor-augmentingtechnicalchangethatconsistsofanexogenouspart
andanendogenouspartfollowingtheapproachofParadoandde
Cian[41],inlinewithaCESproductionframework.The
endoge-5 ThealternativeandmorecommonapproachintheCGEliteratureistoinclude
knowledgeasanewproductionfactorwhichresultsfromcumulativeR&Defforts. Inthisway,however,knowledgeispartoftheproducers’costminimization prob-lem,meaningagriculturalproducerspayforR&Dinvestment.Thisapproachismore appropriateformodellingprivateR&Deffects.
nouspartdependsondomesticcumulativepublicagriculturalR&D investmentsinallcountries,theexogenouspartissettozero.
FollowingtheassumptionthatthenatureofpublicR&Dresearch ismostlytargetedtoimprovementsincropvarieties,welink pub-licR&Dinvestmentstoland-augmentingtechnicalchange.6This
assumptionisalsosupportedbytheevidenceofinduced
techni-calchangebyThirtleetal.[32],andPiesseandSchimmelpfennig [33]citedabove.Contrarytotheland-augmentingeffectofpublic R&Dinvestments,privateR&Dinvestments,whichlargelyresult
in improvementsinmechanical technology, mayhave typically
labour-savingeffectontechnicalchange.7 3.2.1. R&DdatausedforSAMdisaggregation
SocialAccountingMatrix(SAM)isabasicdatastructurethat reflectsallmarkettransactionsinaneconomy,whichisusedasa startingpointforaCGEmodelinthebaseyear.Inlinewithour assumptions,aseparateR&Dsectorwasdisaggregatedfromthe sectorofpublicservicesintheSAM.Asimpleprocedureofapplying theshareofpublicR&Dexpendituresinthevalueofoutputofpublic serviceswasappliedtoallcostcomponents.Thismeansthatthe publicR&Dsectoremploysthesameshareofskilledandunskilled labourasotherpublicservices.Inmostoftheregions,theshareof skilledlabourreachesmorethan50%,whichisrealistic.
InordertoimplementtheR&DsectorinMAGNET,variousdata sourceswerecompiledtoderivethevalueofpublicR&D expendi-turesforall140regions,namelyi)AstiPublicdatabaseformostof thedevelopingcountries[57],ii)OECD[58]andEUROSTAT[59]for Europeancountriesandiii)UNESCODatabase[60]forthe remain-ingcountries.Nexttothat,datapublishedinPardeyetal.[5]were usedforagriculturalR&Dseriesforsomedevelopingcountries.The
6Paralleltothisresearch,empiricalestimateshavebeencarriedoutto
quan-tifythedirectionofR&Dinfactor-augmentingtechnicalchangeontheaggregate agriculturallevel.
7InclusionofprivateR&D investmentswill beconsideredinthefollowup
Fig.6. Growthofrealagriculturalpricesbetween2010and2050(%). Source:MAGNEToutput.
InsTeppDatabaseSummariesprovidedinthepaperofPardeyetal. [61]wasusedtoobtainagriculturalR&Dexpendituresfor impor-tantEUcountrieswhichdonotsharethedatawithEUROSTAT,such asGermany,France,SpainorItaly.Finally,allvalueswereconverted from2005PPPdollarsto2007currentDollarstohomogenizewith valuesofothervariablesintheSAM.
3.2.2. ModellingdomesticR&DstocksinMagnet
Followingtheempiricalevidenceonthespecificshapeofthe
knowledge stocksdistributionover time, a gammadistribution
functionwasincorporatedinMAGNETforbuildingR&Dstocksfrom publicR&Dexpenditures.Inlinewiththeevidenceintheliterature, regionsweregroupedintosixvintagegroups.R&Dinvestmentsin highincomeregionssuchastheUSAexhibitthelongestlags corre-spondingtothenatureoftheresearch(basicresearchprevails).On theotherhand,developingregionsareallocatedtovintagegroups
withshorter lags due tothe more adaptive natureof research
(Tables 1and Fig.A1).Similarly, theelasticityvaluesvary with vintagegroupsandgenerallyfollowthepatternthatthelongeris theR&Ddistributionlag,thehigheristhereturnandthe elastic-ityoftechnicalchangewithrespecttoR&D(thelagsandobtained elasticitiesfromneutralandfactor-biasedstudiesarecomparable). Giventhechoiceofthevintagegroups,R&Dstocksineachregion
werereconstructedbackwardsfrom1960to2010usingformulas
1–3.Intheprocessofthiscalculation,amatrixofR&Dvintagesis
constructedwhereeachrowindicatesthedistributionofannual
investmentovertheproductionperiod(dependingonthe
maxi-mumlag)andeachcolumnindicatesthecontributionoft-kR&D investmenttothecurrentR&Dstock.
GammaweightsandR&Dvintagematrixfortheperiodofthe
simulationhorizonwereaggregatedtothelengthofthe
simula-tionperiods.ThegrowthofthecumulatedR&Dstocksfromthe gammadistributionislinkedtoland-augmentingtechnicalchange asshowninthefollowingequation:
alandj,r=elasRDr×rdstockr (4)
where aland represents the land-augmenting technical change
parameter,whichenterstheCESproductionfunction,elasRDisthe
elasticityofalandwithrespecttoR&Dgrowth(valuesarereported inTable1)andrdstockisthegrowthrateofdomesticR&Dstocks.
TheCESfunctionalformwithland-augmentingtechnicalchange
alandisprovidedinEquation5:
VAj,r=
˛×aland×Dj,r KL,D−1 KL,D +(1−˛)×KLj,r KL,D−1 KL,D KL,D KL,D−1 (5) whereVAtstandsforvalueadded,Dtislandinput,KLt iscapital-labour bundle (in case of a nested production structre), KL,D
representstheelasticityofsubstitutionbetweenlandand
capital-labourinputand ␣representstheshare ofeach inputinvalue
added.Landaugmentingtechnicalchangeisdefinedas:
∂
VA(aland,D,KL)∂
aland >0 (6)wherevalueaddedgrowswithaconstantleveloflandinput.
3.3. Modelaggregation,definitionofscenariosandbaseline assumptions
Theproductionandregionaggregationchoicesappliedin
MAG-NET areprovided inTable A1.Thereare 21aggregated regions
and25productionsectors,fromwhich11areprimaryagricultural
sectors. Industrysectors areaggregated inlow and high
indus-try;servicescontainsectorsofbusinessservices(othser),public services(pubser)andthepublicagriculturalR&Dsector(rd).
TwoscenariosaremodelledwithMAGNET.Eachofthem
repre-sentsanalternativebaselinescenario:
•Baseline ALEX: this is the usual baseline in which
land-augmentingtechnicalchangeisdeterminedexogenouslybased
onthehistoricalgrowthratesofyieldsorexogenous scenario relatedassumptions,whichmeansthereisnoR&D-driven tech-nicalchangeinthemodel.
•BaselineVINTAGE:In thisbaseline scenario,land-augmenting
technicalchangegrowsaccordingtothegrowthofthedomestic R&DstockthatrespectsthelaggeddistributionofR&D invest-ments(vintageapproach).TheR&Dinvestmentsaredetermined
Table1
ParametersofthegammadistributionfunctionofR&Dstockaccumulationpervintagegroup.
Group TypicalRegions MaxLagyears Lambda Delta ElasticityalandtoRD Peak
A USA 50 0.7 0.9 0.5 24
B AustraliaandNewZealand 35 0.7 0.8 0.4 10
C EU-15andotherHighIncome 25 0.6 0.85 0.4 10
D EU-12andRussianFederation 15 0.4 0.8 0.4 3
E LatinAmerica 25 0.7 0.9 0.3 24
F AsiaPacificandAfrica 15 0.5 0.8 0.3 5
Source:Authorselaboration.
Fig.7.EvolutionoftheindexofrealagriculturalpricesintheBaselineVintagescenario. Source:MAGNEToutput.
asafixedshareofagriculturalvalueaddedinthebaseyear.This impliesthatR&D expenditures growaccording toagricultural valueaddedgrowth.
Fig.1plotslong-termsharesofR&Dinvestmentsinagricultural productionforcountrieswheresufficientlylongR&Ddataseries areavailable.Itcanbenotedthat,exceptforIndia,theR&D expen-dituresseemtofollowaconstantshareinagriculturalproduction,
whichoscillatesbetween1%and4%dependingontheregion,and
whichsupportstheassumptionofaconstantshareofR&D expen-dituresinourmodel.
TodevelopthebaselinescenarioswebuildontheShared
Socio-economicPathways(SSPs),whichhavebeenrecentlydeveloped
toassesstheimpactofglobalclimatechange(Kriegleretal.[62], O’Neilletal.[63],and [64]).TheSSPsareasetofplausibleand alternativeassumptionsthatdescribethepotentialfuture socioe-conomicdevelopmentintheabsenceofclimatepoliciesorclimate change.Theyconsistoftwoelements:anarrativestorylineanda
quantificationofkeydrivers,mainlypopulationgrowthand
eco-nomicdevelopment.Fortheassessmentinthepaperweonlyuse
oneofthefiveSSPs,theso-calledMiddleoftheRoad(SSP2) sce-nario,whichreflectsabusiness-as-usualfuture.Inthisscenario, trendsthataretypicalofrecent decadescontinuein thefuture (O’Neilletal.[63]).Therewillbesomeprogresstowards
achiev-ingdevelopmentgoalsbutdevelopmentoflow-incomecountries
proceedsunevenly. Most economies are politically stable with
partiallyfunctioningand globallyconnectedmarkets.Per-capita
incomelevelsgrowatamediumpaceontheglobalaverage,with
slowlyconvergingincomelevelsbetweendevelopingand
indus-trialised countries. Intra-regional income distributions improve slightlywithincreasingnational income,but disparitiesremain
highin some regions. In the Baseline ALEXScenario, the SSP2
consistentratesofexogenouslandaugmentingtechnicalchange
(aland)arebasedonexpertprojectionsofyieldsintothefuture.In
theBaselineVINTAGEScenariotheyaredeterminedendogenously
fromR&Dstocks,followingequation4.
4. ImpactofpublicR&Dinvestmentsonproductivityand foodsecurity
4.1. ProjectionsofagriculturalR&Dinvestments
Inthissection,theevolutionofR&Dinvestmentstowards2050 isanalysed.Twointerestinginsightscanbederived here−first
a comparisonof historical and projectedgrowth ratesand
sec-ond,anintervalinwhichfutureR&Dinvestmentsmightoscillate in eachregion. Theevolution ofreal R&Dinvestmentstowards 2050thatfollowvalueaddedgrowthinagricultureisdisplayedin Fig.2.Comparedtothehistoricalperiod(1960–2010),R&Dgrowth ratesofChinawillbenegative,whichisinlinewiththe
assump-tionofgradualslowdownofChineseGDPgrowth.Inthecourse
ofeconomicdevelopmentofChinaandcorrespondingstructural
change,thedemandforagriculturalcommoditiesrelativetomore processedgoodswilldecline,whichwillresultinadeclineof
agri-culturalvalueaddedtowards2050.Regionsthatmightcontinue
withhighR&D investmentrates areSub-Saharan Africanstates
whereratescouldexceed5%growth.
TheevolutionofdomesticR&Dstockscalculatedasaweighted averageof all past R&D investmentsusing gammadistribution weightsisprovidedinFig.3.InthisFigure,R&Dstocksarebuilt fromR&Dinvestmentsfollowingagrowthrateofagriculturalvalue added.Clearly,thebiggestvolumeofpublicR&Dstockswouldbe
accumulatedintheEU-16, alsoastheeffectof theaggregation
of16highincomeeconomies.After2020,R&Dstocksinthe EU-16willstarttodecline.Itisalsovisible,thatChineseR&Dstocks
wouldgrowdynamicallyinthefirsttwodecadesbenefitingfrom
theexcessiveinvestmentsin2000s, butafter2020,R&D stocks
wouldgraduallydeclineduetolowinvestmentlevelsprojected
inthefuture.AninterestingevolutionoccursinthecaseoftheUSA whereR&Dstocksgrowprogressivelyuntil2020butin2030,their levelfallsby50%.Suchadramaticdeclineisattributedtothelong lagofR&Dinvestments.Clearly,inthefirsttwodecadesafter2000, theUSagriculturalsectorbenefitsimportantlyfromR&D invest-mentscarriedoutbeforethe1990s.In2030,theslowdownofR&D investmentsintheUSAafter2000s,asadvocatedinworksofPardey
Fig.8. Landpressure(demandoflandtoavailableagriculturalland). Source:MAGNEToutput.
[62],and[67],isreflectedinaseriousdropofR&Dstock.Thisis con-sequentlyreflectedinthegrowthofagriculturalpricesdrivenby adeclineofproductivity,whichinturntriggersanincreasedR&D spendingandleadstoaneventualrecoveryofR&Dstocks.Thisis animportantobservationthatshowsthatevenifR&Dinvestments arestimulatedlargelytoday,theireffectsinbuildingR&Dstocks willbeseenonlyinthenext20–30years.
ConcerningBrazilandIndia,R&Dstocksareprojectedtohave
asustainedgrowthalongthewholesimulationperiod,whichis
fuelledbyagriculturalGDPgrowthandshorterR&Dlags.
4.2. EvolutionofagriculturalproductivitywithR&D-driven technicalchange
Asexplainedin themethodologicalsection, wemodel
R&D-driven land augmentingtechnical change (aland)as a function
ofgrowthofcumulateddomesticR&Dstocks.Fig.4displaysthe averagegrowthratesofalandacrossbothbaselinescenarios.This
exerciseallowstocomparetheendogenousgrowthratesofland
augmentingtechnicalchangewithexogenousgrowthratesthatare
modelledexogenouslyinstandardbaselines.Thiscanalsoserve
asavalidationoftheproductivitygrowthratesthatareusually assumedintheex-anteprojectionexercisessuchasthose
elabo-ratedintheAgriculturalModelIntercomparisonandImprovement
Project (AgMIP, www.agmip.org), which compared agricultural
outputprojectionsforalargenumberofglobalPEandCGEmodels
(alsoincludingMAGNET)[vonLampe,[11]].Tomeasure
techni-calchange,AgMIPusestheso-calledIntrinsicProductivityGrowth
Rates (IPRs), which were originally developed by IFPRI for the
IMPACT model. The IPRs are commodity- and country-specific
assumptionsonexogenousproductivitygrowthupto2050,based
onexpertopinionsconcerningthefuturereturnsofagricultureR&D (seeannexinWiebeetal.[66]formoreinformation).
The first conclusion when inspecting Fig. 4 shows that the
endogenous growth rates of land productivity are for most developingcountriescomparablylowerthantheAgMIP exoge-nousrates(particularlyforEU-12,China,Brazil,CentralAmerica,
SouthEastAsiaandHighIncome Asia).Inthesecases,standard
assumptions aretoooptimisticwithregard toyield changesas
lowervalueaddeddevelopmentsinagricultureleadtolowerR&D
investmentsandloweryieldgrowth.
4.3. Projectionsofagriculturalproduction,pricesandcaloric consumption
Animportantquestionthatariseswheninspectingthe
evolu-tionoflandproductivityishowthesedevelopmentsaretranslated inagriculturalproductionandfoodsecurity.Fig.5showstheindex
ofthevolumeofagri-foodproductionin2050comparedto2010
underthealternativebaselinescenarios.Itisapparentthatexcept
forCanada,thequantityofproductiongrowslowerinthe
Base-lineVintageScenario,whichisattributedtolowergrowthofland productivitycomparedtotheBaselineAlexScenario(asshownin Fig.4).ItisalsopartiallyattributedtoindirectR&Deffectsthrough foreigntrademarketsandgrowthofagriculturalprices.Thelargest deviationsintheprojectedproductionvolumeoccurintheregions
ofSub-SaharanAfricaandIndiawhichsuggeststhatour
assump-tionsaboutfuturegrowthratesofagriculturalproductionin Sub-SaharanAfricaandIndiabasedonAgMipexogenousyield growthratesareoverestimated.
Theavailabilityoffoodisonlyoneoftheindicatorsoffood secu-rity.Nexttothat,itisalsoimportanttoassesstheeconomicaccess tofoodinthefuture projections.Fig.6 showsthat theaverage growthratesofrealagriculturalpricesin2050comparedto2010 areconsiderablyhigherintheBaselineVintagescenario,compared
to theBaselineAlex scenario. Particularly for theSub-Saharan
regions, the projections are highly alarming, as agricultural
pricescouldgrowfromabout30%incaseoftheexogenousaland
scenario (Baseline Alex)upto 80%if land-augmentingtechnical changeisdrivenbypublicR&Dinvestments(BaselineVintage).An extremedivergenceintheprojectionofagriculturalpricesis foundinthecaseofIndia,whereinsteadofa30%decline(Baseline Alex),priceswouldgrowby40%.Inmostotherregions,agricultural pricesareprojectedtodecline,buttoalowerextentthanpredicted byBaselineAlex.
Theevolution of prices in time isfurtherdepictedin Fig.7. Whereasfoodpricesremainstableoverthewholeperiodforhigh incomecountriesincludingBrazilandChina,pricesinIndiaand Sub-SaharanAfricarapidlydivergeandescalatetowards2050.
Fig.9.Ratioofunskilledlabourwagestofoodpriceindex. Source:MAGNEToutput.
Anexplanationfortheescalationofagriculturalpricesliesin thepressureonland(see,Meijletal.[50],Schmitzetal.[67]).With
increasingpopulationgrowthanddemandforfood,thepressure
onagriculturallandisincreasing.Insomeregions,theavailability ofagriculturallandisalreadylargelylimitednow,andthiswillbe furtheraccentuatedinthefuture(Fig.8).Underbasicallyno avail-ableagriculturalland,landpriceswillincreasedramaticallyand thiswillbetransmittedtopricesoffood.Interestingly,inHigh
IncomeAsiawherelandpressurealreadyreachesamaximum,land
availabilitywillincreasetowards2050duetodecliningdemandfor foodasaresultofhigheconomicgrowthandnegativepopulation growth.
Theaccesstofoodasoneofthekeydimensionsoffoodsecurity canalsobemeasuredintermsofincomethatdetermines
purchas-ingpowerof households.Asan appropriateindicator, theratio
ofoverallwagesofunskilledlabourtothefoodpriceindexwas chosen.Fig.9comparesthisratiobetween2010and2050 includ-ing also projections of standard baselines withoutR&D driven
technical change. Results show that the purchasing power of
householdsdependentonlow-skilled labourin Sub-Saharan Africaisexpectedtoremainlowordeteriorate,especiallyin
East-ernandWesternAfrica,wherethegrowthofwageswouldbarely
covertheexpectedgrowthoffoodprices.Anotabledifferencein projectionsisfoundinthecaseofIndia.UndertheR&Ddriven tech-nicalchange(BaselineVintagescenario),livingstandardsofIndian
householdswouldgrowmuchlessthanintheBaselineAlex
Sce-nario,whichisdrivenmainlybygrowthofagriculturalpricesas
nominalwages wouldin bothscenarios growinthesame
pro-portion.AninterestingdevelopmentoccursinthecaseofChina,
wheremassivegrowthofbothskilledandunskilledlabourwages
isexpectedasaresultofashrinkingpopulationandhigheconomic growth.Fromthefoodsecurityperspective,thiswillbeapositive factorasfoodaccessibilitywillimproveovertimeinChina.
Finally,Fig.10showshowtheexcessivegrowthofagricultural pricesisreflectedinimportsofcalories,whichshowstheresilience ofregionstoanymajorfoodpriceshock.Wheninspectingthe fig-uresacrossregions,nexttoNorthAfrica,RestofSouthAsiaemerges asaregionwiththehighestshareofimportedcalories.Theshare ofimportedcaloriesisalsoexpectedtogrowsignificantlyin
Sub-SaharanAfricaregions,particularlyinSouthAfrica(from9%to16% in2050)andinWesternAfrica(from10%to15%)andIndia(from 5%to11%)
5. Discussionandconclusion
Inthispaper,theprojectionsoffoodsecuritytowards2050with anR&Ddrivenendogenoustechnicalchangewereanalysed.The methodologicalapproachwasbasedontheapplicationofthe
state-of-theartCGEmodelMAGNETwithnewlybuiltR&Dmodule.By
endogenizingR&DinglobalCGEmodels,itis possibletoassess theimpactofdifferentpublicR&Dpoliciesonfoodsecurity.Such analysisisparticularlyimportantfordevelopingcountrieswhere foodsecurityissuesarethemostpertinentandpublicR&Dplaysa muchbiggerroleinfinancingresearchthanprivateR&D.
ThefindingsshowedthatR&Dgrowthratesatthelevelreached inthe2000s,particularlythoseforChina,wouldnotbeexpected anylonger.RegionsthatmightcontinuewithhighR&Dinvestment ratesareSub-SaharanAfricanstateswhereratescouldexceed5% growth.Asforhighincomecountries,simulationsshowedthatthe
slowdowninR&Dspending whichoccurredafter2000wastoo
restrictiveandthereisroomforboostingfutureR&Dinvestments inagriculture,ifwewanttopreventacontinuousdeclineinR&D stocksandproductivity,asprojectedforthecaseoftheUSA.Thisis inlinewiththeargumentsofPardey[65]whoalertedthatpublic supportforagriculturalsciencehasbroadlywanedandan increas-ingshareisbeingdirectedtowardoff-farmissues.Pardeyetal.[68] warnthattheincreaseinnewfundingdirectedtoresearchinthe NewUSFarmBillisinsufficienttoreversethedramaticdeclinein theUSshareofglobalpublicspending.ThesameappliesfortheEU, whereinspiteofthepositiveeffortofincreasedfinancingof agri-culturalresearchinHorizon2020andthenewEuropeanInnovation Partnershipinitiativeinagriculture,aconflictbetweenobjectivesof sustainableintensificationexists(paralleladvancementin produc-tivityandsustainability)asraisedbyMatthews,[69].Nexttothat, itmustbehighlightedthatintheprocessofconvertingtheEUand
otherhighincomeregions onbio-basedeconomies, agricultural
innovationmustgohandinhandwithbio-industryinnovationsto
Fig.10.Caloricdependency(shareofimportsoftotalcaloriesconsumed). Source:MAGNEToutput.
ConcerningtheimpactofprojectedR&Dinvestmentson agri-culturalproductivity,itwasfoundthatendogenousgrowthrates oflandproductivityarecomparablylowerthanthestandard
exoge-nousrates based onhistorical projections and expert opinions.
Thisshowsthat publicR&D investmentsarenotableto stimu-lateagriculturalproductiontothelevelsthatwouldbeexpected
fromthestandard baselineoutcomesusedin projectionstudies
ofe.g.IPCCSSPscenarios.Regardingfoodprices,projectionsfor Sub-Saharanregionsarealarming.ThisalsoappliesforIndiawhich clearlyshowsthatR&Dinvestmentsarenotsufficienttoprevent foodprices fromrising. Asa resultofthat,anincreased depen-denceoncaloricimportsisexpectedwhichweakenstheresilience ofdevelopingregionstoanyfoodpriceshocks.Highprice volatil-ityofagriculturalcropsand theirrelationtopoliticalinstability inAfricaisadvocatedbymanyscholars(seeforinstanceAyinde etal.,[70]forNigeria).Growthofunskilledlabourwageswould
insomecasesnotadequatelycompensatefortheexpectedgrowth
offoodpriceswhichwillresultinthedeteriorationofliving
stan-dardsofhouseholdsdependentontheincomeoftheirunskilled
labour.
Thepolicyimplicationsfollowingfromthispaperarelargely directedtowardshighersupportofnationalR&Dinvestmentsin thedevelopingregions.Clearly,asthemostlimitedfactorof pro-ductionwillbecomeagriculturalland,itwillbecrucialtofocus
more R&D investments onland-augmenting technologies, such
asnewseeds. Asadvocatedby Qaim[56],GMtechnologiesare
potentiallymoresuccessfulindevelopingcountriesbecausethese
regions suffer more from pests and disease problems. Already
now there aremany interesting GM technologies tested inthe
field that are targeted toAfrican agriculture suchas pest- and
disease-resistantrice,cassavaormaizewithhighernitrogenuse efficiency.
Variousfutureextensionsofthisresearchcanbeconsidered, suchastheinclusionofprivateagriculturalandnon-agricultural R&Dasanimportantdeterminantofproductivityinhighincome countriesand theincorporation of internationalR&D spillovers anddiffusionofknowledge.Fromthepolicyperspective,research canbedirectedtoestimatingadesirablelevelofR&Dinvestments neededtoavoidadversefoodsecurityimpactsofexcessivebiofuels policyinthefuture.
Acknowledgements
This work was supported by a Marie Curie Intra European
Fellowshipwithinthe7thEuropeanCommunityFramework
Pro-grammeMETCAFOSwhichaimsatinvestigatingthelinksbetween
driversoftechnicalchangeandsectoralgrowththatwillbe
inte-grated into a global CGE model MAGNET with thepurpose of
improvingprojectionsoffoodsecurity. AppendixA.
TableA1
Descriptionofregions,productionsectorsandperiodsappliedinMAGNET.
REGIONS PROD.SECTORS PERIODS
1Canada 1pdr* 1p[1] 2007–2010 2USA 2wht* 2p[2] 2010–2020 3CentrAmer 3grain* 3p[3] 2020–2030 4Brazil 4oils* 4p[4] 2030–2040 5RestSoAmer 5sug* 5p[5] 2040–2050 6NoAfrica 6hort* 7WeAfrica 7crops* 8REaEurope 8cattle* 9RWeEurope 9pigpoul* 10SoAfrica 10milk* 11MiddleEast 11cmt 12India 12omt 13ReSoAsia 13dairy 14HighIncAsia 14sugar 15SoEaAsia 15vol 16EaAfrica 16ofd 17EU16 17fish 18EU12 18lowind
19China 19othser
20Oceania 20oagr*
21RussiaStan 21pubser 22highind 23rd 24fossilfuel 25CGDS Total
Note:primaryagriculturalsectorsarenotedwith*.SectordescriptionfollowsGTAP terminology(seesectorlistingat:https://www.gtap.agecon.purdue.edu/databases/ v9/v9sectors.asp).
Fig.A1.WeightsofgammadistributionpervintagegroupusedintheCGEmodel. Source:author’selaboration.
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