Environ Res Lett 10 (2015) 095004 doi1010881748-9326109095004

LETTER

Climate change impacts on US agriculture and forestry benefits ofglobal climate stabilization

RobertHBeach18 Yongxia Cai1 AllisonThomson2 XuesongZhang2 Russell Jones3 BruceAMcCarl4AllisonCrimmins5 JeremyMartinich5 JeffersonCole5 SaraOhrel5 BenjaminDeAngelo5JamesMcFarland5 Kenneth Strzepek6 andBrent Boehlert7

1 RTI International 3040Cornwallis Road POBox 12194 Research Triangle Park NC27709USA2 Joint Global Change Research Institute PacificNorthwest National Laboratory 5825University ResearchCourt Suite 3500 College

ParkMD20740USA3 Stratus Consulting Inc 1881Ninth Street Suite 201 Boulder CO80302USA4 Texas AampMUniversity College Station TX 77843-2124 USA5 Climate ChangeDivisionUS Environmental ProtectionAgency 1200 Pennsylvania AvenueNW (6207-J)WashingtonDC 20460USA6 Massachusetts Institute of Technology Joint Programon the ScienceampPolicy ofGlobal Change 77Massachusetts Avenue Cambridge

MA02139USA7 Industrial Economics Inc USA8 Author towhomany correspondence should be addressed

E-mail rbeachrtiorg ycairtiorg athomsonfieldtomarketorg XuesongZhangpnnlgov RJonesstratusconsultingcommccarltamuedu CrimminsAllisonepagovMartinichJeremyepagov ColeJeffersonepagovOhrelSaraepagovDeAngeloBenepagovMcFarlandJamesepagov strzepekmitedu and bboehlertindeconcom

Keywords climate change impacts agriculturalmodeling forestrymodelingmitigation scenarios EPICMC1 FASOM-GHG

Supplementarymaterial for this article is available online

AbstractIncreasing atmospheric carbondioxide levels higher temperatures alteredprecipitationpatterns andother climate change impacts have alreadybegun to affectUS agriculture and forestrywith impactsexpected to becomemore substantial in the future There havebeennumerous studies of climate changeimpacts on agriculture or forestry but relatively little research examining the long-termnet impacts of astabilization scenario relative to a casewith unabated climate changeWeprovide an analysis of thepotential benefits of global climate changemitigation forUSagriculture and forestry through2100accounting for landowner decisions regarding landuse cropmix andmanagement practices Theanalytic approach involves a combinationof climatemodels a crop processmodel (EPIC) a dynamicvegetationmodel used for forests (MC1) and an economicmodel of theUS forestry and agriculturalsector (FASOM-GHG)Wefind substantial impacts onproductivity commoditymarkets andconsumer andproducerwelfare for the stabilization scenario relative tounabated climate changethough themagnitude anddirectionof impacts vary across regions and commodities Although there isvariability inwelfare impacts across climate simulationswefindpositive net benefits fromstabilizationin all cases with cumulative impacts ranging from$327 billion to $545 billionover theperiod2015ndash2100Our estimates contribute to the literature onpotential benefits ofGHGmitigation and canhelp informpolicy decisionsweighing alternativemitigation andadaptation actions

1 Introduction

Agricultural and forestry production are highly sensitiveto climate conditions Climate change is projected toalter the spatial and temporal distributionof temperatureand precipitation as well as the frequency and severity ofextreme events such as storms flooding drought andwildfires as well as pest and disease outbreaks(IPCC 2013 2014) These changes are all likely to affect

future agriculture and forestry productivity influencingthe global supply of agricultural and forestry commod-ities In addition to changes in mean yields climatechange has already been linked to increased yieldvariability that has led to greater price volatility (Diffen-baugh et al2012)9

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RECEIVED

18December 2014

REVISED

30 July 2015

ACCEPTED FOR PUBLICATION

31 July 2015

PUBLISHED

1 September 2015

Content from this workmay be used under theterms of theCreativeCommonsAttribution 30licence

Any further distribution ofthis workmustmaintainattribution to theauthor(s) and the title ofthework journal citationandDOI

9When referring to yields we are always defining them in terms of

output per unit area whether for crops or forestry

copy 2015 IOPPublishing Ltd

Given the likelihood that future climate conditionswill be outside the range of recent historical experi-ence landowners will likely find it more difficult toaccurately assess the future risks they are facing and tosuccessfully adapt by such means as land use changecrop switching or modifying production practices Asa result it may become more difficult for landownerstomanage risk and domestic and global market volati-lity for agricultural and forest product commoditieswill likely increase (Wheeler and vonBraun 2013)

A number of papers have explored the potentialimpacts of climate change on US agriculture (egAdams et al 1990 Mendelsohn et al 1994 Booteet al 1996 Boote et al 1997 Reilly et al 2003 Schlenkeret al 2005 2007 Long et al 2006 Schlenker andRoberts 2006 Greenstone and Deschenes 2007 US Cli-mate Change Science Program (CCSP) 2008 Beachet al 2010a) In addition there have been studies examin-ing the potential impacts of climate change on naturalvegetation including forests in the United States (egDaly et al 2000 Irland et al 2001 Bachelet et al 2004Lenihan et al 2008 Shaw et al 2009) There have alsobeen a few studies that explore climate change impactson both agriculture and forests simultaneously includ-ing the interactions between alternative land uses andimplications formarket outcomes (eg Irland et al 2001Alig et al 2002) However there is a total lack of detailedanalyses of the effects of stabilization scenarios relative tounabated emissions scenarios Such analyses are impor-tant for developing estimates of the benefits of those sta-bilization scenarios which can play a vital role inassessing tradeoffs associated with allocating resourcesacross alternativemitigation and adaptation activities

The effects of climate change on agriculture andforests are extremely complex because of multiplevegetation types regional and temporal differencesand interaction effects among numerous categories ofimpacts For instance amoderate increase in tempera-tures may positively affect growth rates of some cropsparticularly if water availability is increasing but willalso tend to increase damages fromweeds insects andelevated ozone levels (IPCC 2014) Past a certainthreshold increases in temperature are likely to havedamaging effects on crop yields (Schlenker andRoberts 2006) However below such thresholdschanges in climate in conjunction with carbon dioxide(CO2) fertilizationmay result in higher yields and ben-efits to US agriculture (Mendelsohn et al 1994 Men-delsohn and Dinar 2003 Massetti and Mendelsohn2011 Attavanich and McCarl 2014) Climate impactsare expected to become more significant in the futureas temperatures more frequently exceed thresholdsthat result in significant reductions in crop growth andwater availability becomes more severely constrainedinmany regions

However net impacts on regional and nationalyields could be positive or negative depending onchanges in precipitation temperature CO2 fertiliza-tion and other factors relative to the baseline climate

for a given region (Adams et al 1990 Reilly et al 2003)In addition location-specific differences in resourceavailability will affect adaptation possibilities Thusthe ability of landowners to adapt to climate changeimpacts is expected to vary considerably across USregions It is vital to reflect this regional variabilitywhen quantifying changes in productivity in order toobtain amore accurate picture of the national impacts

In this study we quantify the potential impacts ofclimate change on agriculture and forests based on aspecific set of stabilization scenarios developed underthe US Environmental Protection Agencyrsquos ClimateChange Impacts and Risk Analysis (CIRA) project(Waldhoff et al 2015) Using a consistent set of inputsand assumptions across a large number of impact sec-tors the CIRA project seeks to quantify and monetizethe risks of inaction and the potential benefits (ie avoi-ded impacts) to theUnited States of globalGHGmitiga-tion in the formof stabilization scenarios Theuse of theCIRA scenarios for this study on agriculture and for-estry ensures that these impacts are more directly com-parable with those in other sectors of the US economyas estimated within the CIRA framework and providesthe first instance of totally consistent agricultural andforestry responses under a stabilization scenario

We used projected impacts of climate change onUS crop yields for the period 2010ndash2115 from theEnvironmental Policy Integrated Climate (EPIC ver-sion 1120) crop process model and forest yields fromthe MC1 dynamic global vegetation model (OregonState University 2011 Bachelet et al 2001)10 Thesebiophysical impacts were then incorporated into theForest and Agricultural Sector Optimization Modelwith Greenhouse Gases (FASOM-GHG Adamset al 2005 Beach et al 2010b) which is an economicoptimization model of the US forest and agriculturalsectors that includes land transfers between these sec-tors Yield impacts were incorporated following theprocedures used in Reilly et al (2003) and Irland et al(2001) which applied the percentage changes in yieldsunder climate change estimated in biophysical modelsto shift the baseline yields in FASOM-GHG for climatechange scenarios Similarly we do not calibrateFASOM-GHG yields to match those in EPIC or MC1but assume that the relative yield changes simulated inthose models can be used to represent the impacts ofclimate change within FASOM-GHG The use ofFASOM-GHG captures interactions between alter-native land uses at a spatially disaggregated level for theUS In applying the CIRA GHG emission and climatescenarios we are able to compare the estimated eco-nomic impacts of unmitigated climate change andGHG mitigation on US agriculture and forests in the

10Although EPIC generated impacts through 2115 the current

version of the FASOM-GHG model only reports outputs throughthe end of the century Thus we do not present results for economicimpacts past 2100

2

Environ Res Lett 10 (2015) 095004 RHBeach et al

form of a stabilization scenario as part of a coordi-natedmulti-sector set of analyses

2 Emission scenarios and climateprojections

Emissions scenarios for the CIRA project were devel-oped using the Massachusetts Institute of Technol-ogyrsquos Integrated Global SystemModel (IGSM) version23 (Dutkiewicz et al 2005 Sokolov et al 2005)Descriptions of the specific scenarios developed areprovided in Paltsev et al (2015) and Waldhoffet al (2015)

We use two of the CIRA emissions scenarios Thefirst is the Reference scenario where global emissionsare unconstrained and a total radiative forcing of10Wmminus2 is reached by 2100 The second is a stabiliza-tion scenariowith a total radiative forcing of 37Wmminus2

by 2100 (labeled Policy from now on)11 For the pur-poses of this paper we do not incorporate any incen-tives for GHG mitigation from the US forestry oragriculture sectors (including bioenergy expansionbiofuels volumes required under the US RenewableFuel Standard (RFS) are included in all scenarios butthere are no policy incentives included for increasingvolumes above RFS levels) RFS biofuels volumes areincluded in all scenarios) implicitly assuming thatmiti-gation under the Policy scenario takes place in otherregions and sectors This enables us to focus specificallyon the benefits of global mitigation for the sectors duesolely to changes in climate impacts

The climate projections used in this analysis weredeveloped using the IGSM Community AtmosphericModel (CAM) framework (Monier et al 2015) TheIGSM-CAM which projects a relatively wetter futurefor Eastern and Central regions of the contiguous Uni-ted States than historical conditions was simulatedfive times for each scenario using slightly differentinitial conditions to generate five ensemble mem-bers12 Because there are major uncertainties asso-ciated with future climate projections and substantialvariability across GCMs (Beach et al 2010a) we alsoused another climate projection from a GCM thattends to be comparatively drier to provide anothercomparison point and a more balanced assessmentthe Model for Interdisciplinary Research on Climate(MIROC32-medres) The IGSM pattern scaling

methodology was employed to develop a balanced setof regional patterns of climatic change (see Monieret al 2015 formethodological details and comparisonsamongst the different projections) This approach pre-serves all of the CIRA socioeconomic and emissiondrivers but for an alternative set of climate conditionsAdditional detail regarding the climate projections isprovided in the online supplementarymaterial

3Methods

The climate projections for each emission scenariowereused in the biophysical simulation models Griddedresults for temperature precipitation vapor pressureand other climate characteristics were mapped to beconsistent with the spatial disaggregation necessary foruse in the EPIC crop simulationmodel (Williams 1995)and the MC1 dynamic vegetation model (Bacheletet al 2001 2003) andwere bias-corrected using the deltamethod (see Mills et al 2015 for a discussion ofadjustments to the climate data)13 EPIC andMC1weresimulated under three sets of climate assumptions (1)no climate change (lsquofixed climatersquo) (2) Reference and(3) Policy Percentage changes in crop and forest yieldsfor the Policy and Reference scenarios were calculatedrelative to yields with no climate change Thesepercentage changes were incorporated into FASOM-GHG to generate results for the Reference and Policycases associated with differences in yields relative to thebaseline FASOM-GHG scenario which does notinclude climate change impacts

Differences in the FASOM-GHG results betweenthe Reference and Policy cases were then calculated inorder to assess differences in market outcomes givenclimate-induced shifts in regional yields similar toprevious analyses of climate change impacts onmarketoutcomes in the US forestry and agriculture sectorsthat implemented yield shifts in a similar manner (seeIrland et al 2001 and Reilly et al 2003) although thisstudy focuses specifically on the benefits of mitigationrather than only the impacts of climate change Each ofthese models and their application in this study aredescribed below

31 EPIC crop yield simulationClimate data were incorporated into the EPIC modelto estimate annual impacts of alternative climatescenarios on crop yields for barley corn cotton haypotatoes rice sorghum soybeans and wheat over theperiod 2010ndash2115 in addition to a baseline period of1980-2009 The EPICmodel is a field level biophysical

11There are multiple methods used to calculate radiative forcing

Using the simplified equations for calculating radiative forcing suchas those defined in IPCCrsquos Third Assessment Report and used indevelopment of the representative concentration pathways (RCPs)the CIRA Reference scenario has a total radiative forcing of88 W mminus2 and the Policy has a value of 36 W mminus2 The CIRAscenarios are independent of the RCPs but a comparison of the totalradiative forcing indicates that our Reference has forcing a bit higherthan RCP85 while our Policy scenario provides mitigation betweenthat of RCP45 andRCP2612

In this paper when we refer to ensemble members we mean thefive different initializations of the IGSM-CAM model that we areusing to represent natural variability within the IGSM-CAMmodel

13EPIC was simulated at the 8-digit hydrologic unit code (HUC)

level (there are about 2100 8-digit HUCs in the US about 1400 ofwhich have crop production in the baseline period) MC1 wassimulated at a 05degtimes05deg grid level (latitudelongitudesim1600 km2 per cell) The EPIC andMC1 data were thenmapped tothe FASOM-GHG 6-region level covering the contiguous UnitedStates (see Adams et al 2005 or Beach et al 2010b for moreinformation on FASOM-GHG regions)

3

Environ Res Lett 10 (2015) 095004 RHBeach et al

process model that simulates crop yieldbiomassproduction soil evolution and their mutual interac-tion given detailed farm management practices andclimate data (Williams 1995) Crop growth is simu-lated by calculating the potential daily photosyntheticproduction of biomass

Daily potential growth is based on the concept ofradiation-use efficiency by which a fraction of dailyphotosynthetically-active solar radiation is interceptedby the plant canopy and converted into plant biomassDaily gains in plant biomass are affected by vapor pres-sure deficits atmospheric CO2 concentrations envir-onmental controls and stresses such as limitations inwater and nutrients Thus EPIC can account for theeffects of climate-induced changes in temperatureprecipitation and other variables including extremetemperature and precipitation events affecting agri-culture on potential yields EPIC also accounts for theeffects of change in CO2 concentration and vaporpressure deficit on the radiation-use efficiency leafresistance and transpiration of crops (Stockleet al 1992a) This feature combined with EPICrsquos com-prehensive representation of agroecosystems pro-cesses makes it able to simulate the probable effects ofCO2 and climate change on complex cropping systemsthat vary in soil crops crop rotations and manage-ment practices (Stockle et al 1992b)14

Consistent with the expectation that crop produc-tion regions may change over time under climatechange scenarios the EPIC model was used to simu-late the potential migration of crops into new produc-tion areas (following Adams et al 2004 and Beachet al 2010a) In particular crop production was simu-lated on potentially suitable areas within 100 km ofhistorical production regions in all directions15 16

The entire range of potential area for each crop wassimulated under both dryland and irrigated condi-tions for each of the IGSM-CAM ensemble membersand for the MIROC climate projections as were chan-ges in crop timing and maturity dates The main focuswas on generating results for potential yields and irri-gated crop water under existing cropping practicesand possible adaptations for incorporation intoFASOM-GHG These scenarios are based on climateand environmental conditions but unconstrained bypotential limits on water availability for irrigated cropuse for incorporation into FASOM-GHG (seesection 33 below) which establishes crop mix andproduction practices based on economic considera-tions and irrigation constraints

Finally EPIC was simulated with the CO2 con-centration pathways from each scenario (reachingabout 830 ppm by 2100 under the Reference and460 ppm under the Policy scenario) as well as withholding CO2 concentrations constant at baseline levels(400 ppm) in both scenarios throughout the simula-tion period This sensitivity analysis enables the exam-ination of the influence of other climate impactsseparately from the influence of CO2 fertilization ofcrops

32MC1 forest yield simulationThe IGSM-CAM (all five ensemble members) andMIROC climate projections were incorporated intothe MC1 dynamic vegetation model to assess differ-ences in forest growth rates MC1 is a spatially explicitgridded model that contains linked modules simulat-ing biogeography biogeochemistry and fire distur-bance (Bachelet et al 2001 2003) The MC1simulations (Mills et al 2015) accounted for theestimated effects of CO2 fertilization on plant growthThe model was simulated annually from 2010ndash2115for each of the scenarios along with a baseline periodof 1980ndash2009

The variable used to capture differences in growthwas net primary productivity of aboveground forestmapped to FASOM-GHG forest types and regions Tosmooth out the large level of annual variability fromdynamic vegetationmodels likeMC1 (Mills et al 2015)and to provide FASOM-GHG with long-term yieldtrends a 30 year moving average was used17 The dif-ference in forest yield was assumed to be equal to thepercentage difference in net primary productivitybetween future years and the average for the baselineas in Irland et al (2001) Importantly the effects of

14It is now well known that CO2 fertilization response of crops can

be reduced by environmental factors such as ozone damage to cropspests diseases and weeds that are not currently simulated by EPICTherefore it is unlikely that increased CO2 will be as ideallybeneficial as simulated in EPIC based on chamber studies (Stockleet al 1992a 1992b) Updating the CO2 fertilization responseequations within EPIC to reflect lower realized yield gains fromhigher CO2 concentrations would tend to increase the estimatedbenefits ofmitigation15

Although we considered focusing expanded production inregions to the north of current production areas we decided againstthat for two main reasons The first is the importance of precipita-tion in determining yield potential It is possible that an area to thesouth of an existing production region could receive sufficientadditional precipitation that its yield would increase relative to thebaseline even with higher temperatures The second is that shifts incrop production depend on relative productivity of alternativecrops There could be cases where crops would move into newproduction regions because they are relatively less impacted by thechange in climate than crops currently grown in the region Suchrelative productivity effects could potentially lead to shifts incropping patterns for individual crops that differ from the overallnorthward trend typically projected to occur in the US under highertemperatures16

To define parameters for regions that do not contain historicalproduction data in the model but could potentially start producingunder climate change we use the parameters from the closest regionavailable that is producing the relevant cropproduction processcombination in the base period

17As noted by an anonymous reviewer using a 30-year moving

average for yields tends to smooth out the impacts of yield shocksdue to extreme events and associated price shocks Howeverbecause we have a long-term forward-looking model that does notmodel short-term price dynamics we chose to rely on changes inoverall trends over time to drive behavior in ourmodel

4

Environ Res Lett 10 (2015) 095004 RHBeach et al

wildfire pest and disease on yield are not reflected inthe net primary productivity estimates18

33 FASOM-GHG forest and agricultural sectormarket simulationThe effects of climate change on production cropmixandmarkets were simulated using themodel FASOM-GHG for a six-region aggregation of the UnitedStates19 This regional differentiation is important forreflecting the different conditions across the UnitedStates and therefore generating an accurate picture ofthe potential national-level impacts FASOM-GHG(Adams et al 2005 Beach et al 2010b) is a forward-lookingmodel of the forest and agriculture sectors thatsimulates the allocation of land across both sectorsover time and the associated impacts on commoditymarkets The model solves a constrained dynamicoptimization problem that maximizes the net presentvalue of the sum of producersrsquo and consumersrsquo surplusover time The model is constrained such that totalproduction is equal to total consumption technicalinputoutput relationships hold and total US land useremains constant (with some land migrating out todeveloped uses and becoming unavailable for forestryand agriculture) In addition the model includesdetailed accounting for changes in net GHGemissions

Land categories included in the model are speci-fied as follows

bull Cropland is land suitable for crop production that isbeing used to produce either traditional crops (egcorn soybeans) or dedicated energy crops (egswitchgrass)

bull Cropland pasture is managed land suitable for cropproduction (eg relatively high productivity) that isbeing used as pasture but can be used for cropswithout additional improvement

bull Pasture is grassland pasture that is less productivethan cropland pasture andwould need to incur coststo be improved before it could be used as cropland

bull Grazed forest is woodland area used for livestockgrazing Grazed forest areas are assumed to produce

no forest products and cannot be converted to anyother alternative use

bull Rangeland is unimproved land where a significantportion of the natural vegetation is native grassesand shrubs This land is used for livestock grazingbut is considerably less productive than our othergrassland categories and is not permitted to transferto other land uses

bull Forestland refers to private timberland which hasseveral subcategories based on productivity some ofwhich can convert to cropland or pasture Publicforestland is not explicitly tracked and is assumed toremain constant over time but supplies an exogen-ous amount of timber into FASOM-GHG forestcommoditymarkets consistent with recent policy

bull Developed land is assumed to increase over time atan exogenous rate for each region based on pro-jected changes in population and economic growth

bull Conservation Reserve Program (CRP) land is speci-fied as land that is voluntarily taken out of cropproduction and enrolled in the USDArsquos CRP Thisland is generallymarginal cropland

Landowners choose how to allocate land based onthe net present value of the relative returns to alter-native forest types and management regimes com-pared with the returns to alternative crop productionactivities all within constraints on land suitability foralternative activities For instance when a forest land-owner harvests their timber they would comparewhether they could receive higher returns from refor-esting (under any one of multiple potential manage-ment regimes) relative to other potential uses of theirland and could decide to plant crops or convert to pas-ture instead of reforesting

FASOM-GHG was run under the modelrsquos typicalbaseline assumptions which include fixed climate(future climate is assumed not to change from histor-ical averages so there are no climate change effects onyields) The yield data in FASOM-GHG was thenmodified relative to the fixed climate baseline usingEPIC crop yields and MC1 forest yield projectionsunder the Reference and Policy climate scenarios (allassuming full CO2 fertilization) The percentage dif-ferences in potential agricultural and forestry yieldsfrom EPIC and MC1 were calculated for regions con-sistent with those in FASOM-GHG and applied to theFASOM-GHG fixed climate baseline regional cropand forest yield data20 Agricultural yield data arebased on USDA data for historical values and pro-jected out to 2115 to represent technological

18As described in Mills et al (2015) wildfire-burned area as

projected using MC1 is estimated to increase across the US in bothof the CIRA scenarios Rogers et al (2011) similarly found very largepotential increases in forest area burned by wildfires in the PacificNorthwest by the end of the century using MC1 and multiplealternative GCMs They found that future climate change couldincrease area burned for the Pacific Northwest by 76 to 310accounting for up to 12 petagrams of cumulative carbon emissionsby the end of the century Further climate change will likely changepest and disease incidence Given this the forestry yields used in theFASOM-GHGmodeling are likely overestimates of potential futureyields19

The model can be run at a couple different levels of spatialaggregation but was run using the most aggregated version for thisstudy given resource constraints

20Although the biophysical models simulate yields we do not use

the yields from those models directly Instead we apply thepercentage changes in simulated yields between scenarios to thehistorical and baseline projection data in FASOM-GHG See thesupplementary material for more information on the use of EPICandMC1outputs to informFASOM-GHGmodeling

5

Environ Res Lett 10 (2015) 095004 RHBeach et al

improvements using annual percentage or absolutedifferences in yields calculated from historical data oncrop yield improvements (Beach et al 2010b) Annualyield growth rates for crops range between 0 (rye) to19 (silage) while those for livestock range between04 (eggs) to 23 (milk) Therefore baseline fixedclimate yields in 2100may be several times higher thanin 2000 for some commodities21 Yields have grownrapidly over past decades and most models projectingfuture production include technological progressthough there is of course uncertainty regarding itsmagnitude Forestry yields are assumed constant for agiven management intensity classification thoughlandowners can shift between management intensityclassifications following harvest FASOM-GHG wassolved out to 2115 in 5 year intervals yielding adynamic simulation of prices production manage-ment consumption and GHG emissions along withother environmental and economic indicators22

As noted in section 31 EPIC was used to simulatepotential yields in areas adjacent to historical produc-tion areas and for both dryland and irrigated produc-tion for each crop To account for climate effects onwater availability for irrigation water supply shiftswere supplied to FASOM-GHG based on dataobtained from a water supply-and-demand modelused in the CIRA project (Boehlert et al in review)23However the actual production areas yields outputsand other market outcomes simulated for this studyare all determined within FASOM-GHG In additionto accommodate potential crop expansion andoradoption of irrigation in new areas it is also possiblethat production andor irrigation for certain cropswill cease in some existing production areas depend-ing on how differences in yields and production costsaffect relative profitability of different land uses

4 Results and discussion

Our primary emphasis in this paper is assessment ofthe benefits of a stabilization scenario relative tounabated climate change accounting for agriculturendashforestry interactions within a market setting A sum-mary of results is presented below with additionalinformation provided in the online supplementarymaterial

41 Crop yieldThe biophysical simulation results reveal sizableeffects on productivity from the Policy scenariorelative to the Reference scenario mostly positive butsome negative Yield impacts vary considerably acrossscenarios and between dryland versus irrigated areasGenerally yields are most positively affected by theemissions stabilization scenario in the Southern USregion while the Northeast benefits the least Greateryield benefits of stabilization in the Southern US areconsistent with expectations and with the results ofour Reference scenario which indicate that the South-ern US would experience some of the most negativeeffects on yields with unabated climate change How-ever the patterns of simulated yield differences for agiven climate scenario are complex anddepend heavilyon the individual crop irrigation status interactionswith changes in precipitation that affect water avail-ability regional soils andmany other factors

The IGSM-CAM (wetter of our two GCMs) simu-lations project increases in national average yields forall three of these crops under the Policy scenario rela-tive to the Reference case for both irrigated and dry-land conditions (see figure 1) The relative yieldbenefits of the Policy scenario increase over timeacross all regions for irrigated crops and across mostregions for dryland crops as well Exceptions are dry-land corn and soybeans in the Plains and WesternUnited States where the yield gains are very smallandor flat over time and are even negative for soy-beans in the Western United States In the MIROC(drier) model the results for both irrigated and dry-land crops are consistently positive for all crops acrossall regions

Wheat is typically considered a cooler weathercrop than corn or soybeans However as shown infigure 1 percentage increases in wheat yields are lessthan or similar to corn and soybeans for IGSM-CAMFor MIROC scenarios percentage increases in wheatyields are similar to or higher than corn and soybeansIn general the level of temperature increase simulatedin MIROC scenarios results in less temperature stressand more favorable conditions then the IGSM-CAMscenarios for wheat Overall our simulations of futuredifferences in wheat yields are of similar magnitude toRosenzweig et al (2013)

Although both Reference and Policy scenarioshave similar directional effects on yields in many but

21The biophysical models hold crop characteristics constant to

generate estimates of climate change impacts independent oftechnical progress Thus we are assuming that the percentagechanges relative to the baseline yields are consistent with thepercentage changes that would be experienced in the future eventhough yields would have changed For instance if EPIC estimatedthat the yield of a particular crop was reduced by 10 in 2050 buttechnical progress would have increased the yield of that crop by50 by 2050 within the model then the net yield under climatechange would be a 10 reduction relative to the no climate changecase or 15times09=135 times higher than the base year22

Although the model was run out to 2115 results are presentedthrough 2100 FASOM-GHG (and many other simulation models)is typically run beyond thefinal year reported to reduce the influenceof terminal period effects23

The EPIC simulations assume that crops can be irrigated to a levelthat eliminates water stress which does not capture the risk ofreduced water supply in some areas To fully capture this riskrequires integration of crop modeling with hydrologic modeling toproject estimates of future water supply A full linkage withhydrologic models is outside the scope of the current studyFASOM-GHG characterizes supply and demand for water and thereis an upward-sloping supply curve that increases costs of irrigationas the quantity of water used for irrigation increases

6

Environ Res Lett 10 (2015) 095004 RHBeach et al

not all (eg dryland hay and cotton) cases our focushere is on the difference between the scenarios Ourstudy differs from Nelson et al (2013) for instance inthat we are focusing on the benefits of stabilizationThus while both Policy and Reference cases may leadto negative impacts on crop yields consistent withtheir findings on potential yield effects under climatechange our focus on the benefits of reducing theimpacts of climate change results in an improvementin yields relative to unabated climate change Table 1summarizes the relative difference in yields for majorcrops under the Policy case relative to the Referencecase after the biophysical yield effects in EPIC (dis-cussed above) are incorporated into FASOM-GHGwhich determines production practices (eg irriga-tion) and the regional distribution of productionbased on economic optimization Therefore theseyield effects reflect both the biophysical differences inyields and the net adjustments in production practices(eg irrigation) and regional distribution of produc-tion simulated in FASOM-GHG Overall the differ-ences in yields tend to be fairly consistent in thedirection of impacts across the IGSM-CAM andMIROC climate models but yield impacts for drylandcrops tend to be less positive or more negative underIGSM-CAM This is consistent with the wetter modelfinding less negative climate change impacts and con-versely smaller benefits of avoiding climate changeAlthough the Policy case generally exhibits increasedyields under both GCMs IGSM-CAM and MIROCare consistent in simulating negative impacts for dry-land wheat and sorghum and irrigated cotton by theend of the century The crop that experiences the lar-gest difference in yield impacts between the GCMs is

dryland hay which has rising yields over time with thePolicy under MIROC but falling yields over time withthe Policy under IGSM-CAM

Although our primary focus in this study is on theresults with CO2 fertilization because the literaturefinds there will be benefits of higher CO2 concentra-tions for plant growth (Ainsworth and Long 2005Sharkey et al 2007) we also conducted a sensitivityanalysis to explore the impacts of climate change inisolation from CO2 benefits However we found rela-tively small differences in the benefits of the stabiliza-tion scenario for yields with both increases anddecreases in the potential yield benefits across indivi-dual crops Refer to the supplemental online materialfor selected results from the EPIC scenarios simulatedwithout CO2 fertilization

42 Forest yieldForest productivity generally increases with climatechange in our simulations rising under both Refer-ence and Policy cases compared with fixed climateThe yield gains using the IGSM-CAM are larger underthe Reference than the Policy especially for hard-woods indicating that abatement would reduce yieldscompared with the Reference (see figure 2) Onepotential driver of this higher forest productivityunder the IGSM-CAM Reference case in the future islikely the enhanced positive effects of CO2 fertilizationalong with the response to increases in precipitationthat take place across much of the forested area in theUS The MIROC climate projections on the otherhand have higher hardwood yields under the Refer-ence case through 2050 with abatement resulting in

Figure 1Percentage difference in dryland and irrigated corn soybean andwheat yields simulated using IGSM-CAMandMIROCpolicy case relative to Reference case

7

Environ Res Lett 10 (2015) 095004 RHBeach et al

not all (eg dryland hay and cotton) cases our focushere is on the difference between the scenarios Ourstudy differs from Nelson et al (2013) for instance inthat we are focusing on the benefits of stabilizationThus while both Policy and Reference cases may leadto negative impacts on crop yields consistent withtheir findings on potential yield effects under climatechange our focus on the benefits of reducing theimpacts of climate change results in an improvementin yields relative to unabated climate change Table 1summarizes the relative difference in yields for majorcrops under the Policy case relative to the Referencecase after the biophysical yield effects in EPIC (dis-cussed above) are incorporated into FASOM-GHGwhich determines production practices (eg irriga-tion) and the regional distribution of productionbased on economic optimization Therefore theseyield effects reflect both the biophysical differences inyields and the net adjustments in production practices(eg irrigation) and regional distribution of produc-tion simulated in FASOM-GHG Overall the differ-ences in yields tend to be fairly consistent in thedirection of impacts across the IGSM-CAM andMIROC climate models but yield impacts for drylandcrops tend to be less positive or more negative underIGSM-CAM This is consistent with the wetter modelfinding less negative climate change impacts and con-versely smaller benefits of avoiding climate changeAlthough the Policy case generally exhibits increasedyields under both GCMs IGSM-CAM and MIROCare consistent in simulating negative impacts for dry-land wheat and sorghum and irrigated cotton by theend of the century The crop that experiences the lar-gest difference in yield impacts between the GCMs is

dryland hay which has rising yields over time with thePolicy under MIROC but falling yields over time withthe Policy under IGSM-CAM

Although our primary focus in this study is on theresults with CO2 fertilization because the literaturefinds there will be benefits of higher CO2 concentra-tions for plant growth (Ainsworth and Long 2005Sharkey et al 2007) we also conducted a sensitivityanalysis to explore the impacts of climate change inisolation from CO2 benefits However we found rela-tively small differences in the benefits of the stabiliza-tion scenario for yields with both increases anddecreases in the potential yield benefits across indivi-dual crops Refer to the supplemental online materialfor selected results from the EPIC scenarios simulatedwithout CO2 fertilization

42 Forest yieldForest productivity generally increases with climatechange in our simulations rising under both Refer-ence and Policy cases compared with fixed climateThe yield gains using the IGSM-CAM are larger underthe Reference than the Policy especially for hard-woods indicating that abatement would reduce yieldscompared with the Reference (see figure 2) Onepotential driver of this higher forest productivityunder the IGSM-CAM Reference case in the future islikely the enhanced positive effects of CO2 fertilizationalong with the response to increases in precipitationthat take place across much of the forested area in theUS The MIROC climate projections on the otherhand have higher hardwood yields under the Refer-ence case through 2050 with abatement resulting in

Figure 1Percentage difference in dryland and irrigated corn soybean andwheat yields simulated using IGSM-CAMandMIROCpolicy case relative to Reference case

7

Environ Res Lett 10 (2015) 095004 RHBeach et al

higher yields in 2060 and beyond For softwoodsMIROC results show little differentiation until 2045when abatement begins to result in higher yield growthand maintains that advantage through 2100 Thesegains from the Policy case in later yearsmay stem fromthe increasingly negative effects of the hotter and drierfuture under theMIROCGCM It is important to notethat these yield estimates do not include the effects ofwildfire which would likely decrease simulated pro-ductivity especially under unmitigated climate change(Rogers et al 2011 Mills et al 2015) Our results onpotential changes in forest productivity in the US aremuch more positive than some European studies Forinstance Hanewinkel et al (2013) finds that between21ndash60 of European forestlands will be suitableonly for a low-valued oak forest by the end of thecentury One reason for such differences is that weplaced more restrictions on the ability of forests toswitch types based on standing inventory and eco-nomic considerations rather than permitting changes

in forest types based primarily on ecologicalconsiderations

In contrast our study of the US has much smallernegative or even net positive impacts on forests due toCO2 fertilization that largely outweighs negative cli-mate impacts and reallocation of forests amongstother marketable species This result is consistent withKirilenko and Sedjo (2007) who examined the poten-tial impacts of climate change on global forestry Theyfound that there may be sizable impacts on global for-estry production due to climate change as forests shif-ted towards the poles but that the United States mayface relatively small net impacts of climate change onthe forestry sector due to the large stock of existing for-ests across multiple climate zones rapid technologicalchange in timber production and the ability to adaptquickly Susaeta et al (2014) examined the impact ofclimate change and new disturbance regimes for tim-ber production in the US South and found that the netimpacts depended on tradeoffs between increased

Table 1Differences in national average yields ofmajor crops followingmarket adjustments for bothclimatemodels Policy case relative to Reference case (change)

IGSM-CAM MIROC

2010 2050 2100 2010 2050 2100

Barley dryland 18 116 487 05 115 537

Barley irrigated 08 108 563 02 64 356

Corn dryland 13 39 166 01 41 218

Corn irrigated 07 54 147 01 47 231

Cotton dryland 12 minus83 95 01 79 199

Cotton irrigated minus02 52 minus44 minus02 96 minus109

Hay dryland minus01 minus48 minus118 minus02 61 103

Hay irrigated 01 minus04 17 01 02 09

Rice irrigated 03 12 86 01 26 27

Sorghum dryland 19 25 minus60 00 54 minus40

Sorghum irrigated 06 78 122 01 42 199

Soybeans dryland 13 35 141 minus01 45 142

Soybeans irrigated 00 70 324 01 59 438

Wheat dryland 09 08 minus16 03 42 minus06

Wheat irrigated 02 90 103 01 19 752

Figure 2Percentage difference in hardwood and softwood yields using IGSM-CAMandMIROCClimate Projections Policy andReferences cases relative to fixed climate (no climate change)

8

Environ Res Lett 10 (2015) 095004 RHBeach et al

forest productivity and higher probabilities of loss dueto disturbances When landowners were able to adaptthrough switching tree species and other adjustmentsSusaeta et al (2014) found that it was possible for themto substantially reduce potential losses or even experi-ence net benefits The fact that our forest yield impactsare relatively small in magnitude and can be higherunder the Reference case than Policy case for someregionclimate scenario combinations is consistentwith our model reflecting the higher productivityassociated with climate change but not capturing thechange in disturbances other than through the impactoffire on average yields

43 Crop pricesConsistent with the observed differences in yields overtime and across crops global GHG mitigation underthe Policy scenario generally results in lower cropprices compared to the Reference case although thereis almost no difference through 2020 and prices do notreally start diverging substantially until 2050 Thedifferences in prices becoming more sizable over timeis reflective of the increasing climate change impactson yields and hence the greater impact of avoidingthem through stabilization

A decline in prices does not necessarily have to bethe outcome of higher yields but often results fromthe greater production efficiency experienced leadingto higher production volumes That is what we are see-ing in our scenarios an increase in production ofmostcommodities due to higher yields even though they areusing less land in many cases As a consequence ofhigher production and lower prices US exportsbecome more competitive and exports rise for themajority of traded agricultural commodities Theincreased diversion of domestic production to exportmarkets will tend to increase domestic prices relative

to having no increase in exports but we are still seeingdomestic prices fall over time

Under the IGSM-CAMprojections the Policy sce-nario results in lower crop prices for most of the cen-tury for seven out of the nine crops directly simulatedusing EPIC with hay and barley being the only cropswith consistently higher prices under the Policy caseFor the MIROC projections the Policy scenarioresults in lower crop prices compared to the Referencecase for eight out of the nine crops with hay being theonly crop with higher prices under the Policy caseFigure 3 presents the crop price index generated usingFASOM-GHG which is a weighted average of all cropprice changes in the model As would be expectedgenerally rising yields under the Policy case relative tothe Reference case result in less pressure on landresources and declining commodity prices BecauseMIROC has more positive impacts on yields overall itis not too surprising that the crop price index declinesmore underMIROC than under IGSM-CAM

44 Land coverThe differences in expected yields and prices as well asin yield and price variability for both crops and forestsbetween the Policy and Reference scenarios result indifferences in acreage allocation Figure 4 presents theaverage simulated differences in land cover at theregional level under both IGSM-CAM and MIROCclimate conditions over the latter half of thiscentury Overall the higher crop yields experiencedunder the Policy case compared with the Referenceresult in less cropland being brought into productionrelative to the Reference case under MIROC climateconditions as less land is required to meet demandCropland tends to convert to cropland pasture andforests with most of the cropland reductions andforest increases concentrated in the Midwest region

Figure 3Percentage difference in crop price index using the IGSM-CAMandMIROCClimate Projections Policy case relative toReference case

9

Environ Res Lett 10 (2015) 095004 RHBeach et al

Under IGSM-CAM climate conditions there is asmall net increase in cropland occurring primarily inthe Plains region while the Midwest experiences a

decline in cropland Forest productivity is relativelyhigher under the Policy scenario under MIROCclimate conditions but is just the opposite for IGSM-

Figure 4Difference in regional land cover using IGSM-CAMandMIROCclimate projections average for 2050ndash2100 Policy caserelative toReference case (thousands of acres) Cropland pasturemdashland of sufficient quality to be classified as cropland but that isbeing used as pasture forestmdashprivate forest CRPmdashConservation Reserve Program pasturemdashpastureland that is not of sufficientquality to be used as croplandwithout incurring costs of land improvement croplandmdasharea of harvested cropland There is noagricultural production in the PNWWest region in FASOM-GHG only forestry production Thus there is no difference in land coverin that region by definition

10

Environ Res Lett 10 (2015) 095004 RHBeach et al

CAM which influences decisions about land conver-sion based on the relative profitability of alternativeland uses

The changes experienced within individualregions are up to a few million acres but are generallyrelatively small in percentage terms given the large USland areaMost changes in land cover are less than 1though there are some exceptions such as croplandpasture in the Plains region which declines by almost13 in the Policy relative to the Reference as that landshifts into cropland

Reallocation of land between cropland and otheruses has implications for the provision of ecosystemservices including promoting habitat and biodiversity(Lawler et al 2014) Given the large quantities of car-bon sequestered in forests even relatively smallland movements can have sizable impacts on netGHG emissions In addition given that we are findingincreases in crop yields under the Policy caserelative to the Reference case that result in reduceddemand for cropland global stabilization may resultin an increased provision of ecosystem servicesand wildlife habitat relative to unabated climatechange This is particularly true in the Midwestwhich experiences the largest reduction in croplandFor instance the prairie pothole region which isimportant habitat for migratory birds would poten-tially face less pressure to convert natural lands tocropland in the Policy case compared with the Refer-ence case

45 Crop allocationWe find substantial reallocation of where crops aregrown as an adaptation in response to climate changeimpacts on relative agricultural productivity consis-tent with adjustments found by Adams et al (19902004 2005) and Reilly et al (2003) (see figure 5) Thiseffect is overshadowed by a general reduction in croparea however due to increasing yields under thePolicy case relative to the Reference that results in alower overall demand for land Switching betweencrops varies across regions as production of majorcrops other than wheat tends to decrease substantiallyin the major production center of the Midwest andshift to the Northeast Plains and Southern UnitedStates Under IGSM-CAM there is movement ofcotton from the Midwest to the Plains region andlsquoother cropsrsquo moving from the Midwest and Plainsregions to the Northeast and Southern regions Hayand wheat tend to be expanding in multiple regions asthey have relatively low or even negative yield impactsunder the Policy case resulting in a demand formore land to meet demand for those commoditiesUnder MIROC we see sorghum moving from theWestern US Midwest and Northeast into the Plainsregion while lsquoother croprsquo shift from the Midwest andNortheast to the other three regions As with IGSM-CAM wheat is again increasing in most regions given

its relative lack of yield benefit from the Policy caseHay on the other hand experiences a net decrease inarea under MIROC with a large decrease in theMidwest and only small increases in the Plains andNortheast as the net increase in hay yields results indecreased demand for hay land area relative to theReference

46 GHGemissionsAs is readily apparent from figure 6 the IGSM-CAMresults show large increases in net GHG emissionsfrom agriculture and forestry under the IGSM-CAMPolicy case almost all of which are due to differencesin forest management (ie net carbon losses orreduced gains in carbon storage on existing forest-land)24 Under the IGSM-CAM projections the gen-erally lower forest productivity under the Policyscenario results in substantially less forest carbonsequestration over time because of the direct effects ofslower forest growth (less sequestration) UnderMIROC climate conditions however forests becomea larger sink under the Policy than occurs in theReference case as forest productivity is enhancedEmissions from livestock rise under the Policy case forboth GCMs as higher crop yields result in lower feedprices and expanded livestock production but GHGemissions related to crop production are generallydeclining as less area is devoted to crops given thehigher yields As is often the case in assessingagricultural and forestry sector emissions changes inforest carbon sequestration greatly outweigh all othersources In this case differences in forest productivityeffects between the GCMs analyzed yield oppositesigns for the change in net GHG emissions with GHGstabilization Policy

One clear implication is that the climate modelused has a profound influence on the estimatedchange in net GHG emissions Our findings are con-sistent with a major difference in precipitation pat-terns observed across thesemodelsWhile theMIROCmodel tends to havemore precipitation under the Pol-icy case than the Reference case for many key forest-growing regions the IGSM-CAM model is just theopposite The effect on forest growth rates and asso-ciated carbon storage is reflected very clearly infigure 6 While climate conditions such as those underIGSM-CAMwould reduce the US LULUCF sink con-ditions such as those under MIROC would increase itother things being equal

Figure 6 is showing only the change in cumulativeemissions however not the absolute emissions TotalUSGHG emissions from agriculture and forestry landuse and land use change are estimated at about minus343MT CO2eq in 2013 (USEPA 2015) (forestry sink morethan outweighs emissions so net emissions from thosesectors are currently negative) FASOM-GHG does

24We do not incorporate feedback of these changes in emissions

into the calculation of climate impacts in this study

11

Environ Res Lett 10 (2015) 095004 RHBeach et al

not fully capture all emissions components that are inthe US inventory but based on the average cumulativechanges from 2015ndash2100 (cumulative net source of5394 MT CO2eq for Policy relative to Reference over90 years or average of 599 MT CO2eq increase in netGHG emissions per year over that timeframe) theaverage change under the Policy with IGSM-CAM is

equal to a reduction in the 2013 net sink by about15ndash20UnderMIROC on the other hand the Policyscenario provides a cumulative net sink of about 7685MT CO2eq or an average sink of about 854 MTCO2eq which is equal to about 40 of the US sinkfrom these sectors in 2013 Other things being equalthe effect of the Policy on net US GHG emissions will

Figure 5Difference in regional cropland allocation using IGSM-CAMandMIROCClimate Projections average for 2050ndash2100Policy case relative toReference case (million acres)

12

Environ Res Lett 10 (2015) 095004 RHBeach et al

become larger over time as seen in figure 6 though thepercentage impactwill dependon the evolutionof theUSinventoryover time

47WelfareThe differences in prices and the level of productionand consumptionwill lead to impacts on the economicwelfare of consumers and commodity producers Onecommon method of measuring these effects on well-being is in terms of differences in economic surpluswhich is a monetary measure of welfare Consumersrsquosurplus is the difference between the maximumamount consumers would have been willing to pay fora commodity and the actual price paid whereasproducersrsquo surplus is the difference between theminimum amount producers would have accepted tosupply a commodity and the actual price received

Overall the results of this study show that stabili-zation results in transfers from producers to con-sumers as commodity prices decline for goods withrelatively inelastic demands Although the magnitudeand temporal distribution of benefits under the Policycase vary across the IGSM-CAM model initializationsand MIROC projection each of our scenarios results

in cumulative net gains to consumers that more thanoutweigh any losses to producers as has been found inprior studies (eg Irland et al 2001) Based on theMC1and EPIC simulated impacts on forest and agriculturalproductivity the Policy case results in sizable increasesin overall yields and reduced commodity prices com-pared to unabated climate change in the Referencecase As shown in tables 2 and 3 reducing global GHGemissions under the Policy case is found to increasetotal surplus in the forest and agriculture sector by acumulative $327 billion to $545 billion for the2015ndash2100 period across the two GCMs and the dif-ferent model initializations of IGSM-CAM The mag-nitude of welfare impacts in the agricultural sector aremuch larger than in the forestry sector

Figure 6Difference in cumulative net GHG emissions by type using IGSM-CAMandMIROCClimate Projections Policy caserelative toReference case

Table 2Present value of cumulative differences in consumer and producer welfare using IGSM-CAMclimate projections Policy caserelative to Reference case 2015ndash2100 (million $2005)

Consumer surplus Producer surplus TOTAL

Minimum Average Maximum Minimum Average Maximum Minimum Average Maximum

Agriculture $6318 $24 921 $37 077 minus$4330 $11 224 $27 168

Forestry minus$162 $57 $440 $29 $298 $782

TOTAL $6211 $24 978 $36 915 minus$4151 $11 522 $27 495 $32 764 $36 500 $43 100

Note Agriculture + Forestry does not necessarily sum to totals because the table is independently calculating minimum average and

maximumvalues for agriculture forestry and the combined totals across thefive IGSM-CAM initializations

Table 3Present value of cumulative differences in consumer andproducer welfare usingMIROCclimate projections Policy versusReference case 2015ndash2100 (million $2005)

Consumer surplus Producer surplus TOTAL

Agriculture $52 665 minus$2809

Forestry minus$138 $777

TOTAL $52 527 minus$2032 $50 495

13

Environ Res Lett 10 (2015) 095004 RHBeach et al

There have been several previous studies examin-ing the potential economic impacts of climate changeon US agriculture but many focus only on majorcrops they do not necessarily report national welfarevalues and they do not explicitly examine the benefitsof mitigation For instance Reilly et al (2003) usingpredictions from GCMs from the Canadian CenterClimate Model and Hadley Centre Model found thatclimate change from 2030ndash2090 would have positiveimpacts on welfare ranging between $08 billion (2000$) in 2030 and $122 billion in 2090 The main driverof these productivity gains were projected increases inprecipitation levels under climate change Greenstoneand Deschenes (2007) also found positive climatechange impacts using the Hadleymodel estimating anincrease of $13 billion Schlenker et al (2005) findmuch more negative impacts estimating that eco-nomic effects of climate change on agriculture need tobe assessed differently in dryland and irrigated areasand that pooling the dryland and irrigated countiescould potentially yield biased estimates Based onavailable data they estimate the model for drylandcounties and the estimated annual losses are about $5to $53 billion for dryland non-urban counties aloneTemperature increases affect crop responses in a non-linear fashion Using a 55 year panel data on cropyields Schlenker and Roberts (2006) found increasesin crop yields (for corn soybeans and cotton) withhigher temperatures until reaching threshold valuesTheir results show very large decreases in crop yieldstoward the end of the century as temperatures exceedthese threshold levels The study estimates that yieldsof these three crops are expected to decline by 25ndash44under a slow warming scenario and 60ndash79 respec-tively under a quick warming scenario at the end ofthe century Thus the negative effects on agriculturecould become very large in the long-term future iftemperatures begin to reach threshold levels

In general the findings from previous studies havedepended heavily on the net changes in precipitationexperienced under climate change but the welfareresults from this study fall within the general range ofestimates that have been generated

5 Conclusions

Our analytical approach of a combination of climatemodels a crop process model a dynamic vegetationmodel and an economic model of the forest andagricultural sectors was applied to a Reference andPolicy case enabling us to conduct an integratedassessment of the potential benefits of climate stabili-zation onUS forests and agriculture The results of thispaper are generally consistent with published studiesfocused on impacts in these sectors Projections ofadverse yields resulting from unmitigated climatechange large regional differences in crop responseincreasing vulnerability of water supplies for

irrigation and the ability of adaptation to reduceadverse effects are consistent with the findings of themajor climate science assessments (eg Nelsonet al 2013 Hatfield et al 2014) However our resultsaugment previous studies through the use of anintegrated forest and agricultural model to study thepotential benefits of GHG mitigation which can helpinform policy decisions weighing the relative costs andbenefits ofmitigation and adaptation strategies

First global GHG mitigation generally results inhigher agricultural yields across most of our dis-aggregated crop types compared to the unabated cli-mate change scenario even across different climatemodels largely though avoiding decreases in yieldsdue to climate change The majority of the yield bene-fits occur after 2050 though as the Reference climateimpacts being avoided by Policy are relatively smallerover the next few decades and increase over time Sec-ond under both contrasting futures for precipitationin the United States many irrigated crops benefitmore from mitigation than dryland crops In generalirrigated crops tend to gain from the reduced tempera-tures while being unaffected by changes in precipita-tion since they already irrigate to meet their waterneeds This demonstrates the importance of assump-tions concerning water availability and incidence forirrigation Third the effect of global GHG stabilizationon projected forest yields is substantially (almost anorder of magnitude) less than estimated differences incrop yields and the direction of the effect dependsstrongly upon climate model and forest type (hard-wood versus softwood) Consistent with the findingson crop prices the majority of the impacts are con-centrated in 2050 and beyond One important caveatto that finding is that this study is using smoothedtrends of forest productivity and does not reflectincreases in disturbances such as pest damages whichmay influence forest investment decisions when thepossibility of large capital losses are reflected Fourththe impacts of climate change and the effects of stabili-zation are not equally distributed with variationsdepending upon crop type irrigation status forestvegetation type and other factors This finding is con-sistent with previous studies that found that there maybe modest agricultural impacts of climate change inthe United States at the national level but substantialdistributional effects (Beach et al 2010a) Fifth becauseof the positive impacts on crop yields global GHGmitigation is projected to lower crop prices in generalcompared to the Reference scenario This creates sub-stantial benefits of mitigation for consumers that out-weigh the losses incurred by producers Sixth we finda significant effect on net GHG emissions that is highlydependent on the climate scenario used for forestryyields While the MIROC Policy case raises forestyields and increases the size of the US sink from agri-culture and forests IGSM-CAM has exactly the oppo-site effect Additional work to refine estimates of thechanges in forest carbon associated with mitigation

14

Environ Res Lett 10 (2015) 095004 RHBeach et al

policy would be valuable and could be one piece of alarger future research effort to assess interactionsbetween mitigation and adaptation activities Finallyanother interesting finding is that there are gains toconsumers that outweigh losses to producers underour mitigation scenarios Although producers mayreceive higher yields under the Policy scenario it doesnot necessarily mean that they will becomemore prof-itable In fact the opposite is often the case Whenyields rise for a large group of products making inelas-tic goods such as most food commodities the pricewill tend to fall by more than the quantity sold risesleading to a drop in revenue and profits Depending onthe structure and incidence of the costs of a potentialmitigation policy producers could be concernedabout bearing costs of mitigation while also losingproducer surplus due to the change in climate impactswhichmay be a consideration for policy design

One limitation of this study is that it does notreflect climate change impacts on international forestsand agriculture which would also affect relativereturns to different uses of land and trade patterns andtherefore affect land use decisions (Leclegravere et al 2014)For instance including negative impacts on yields inthe rest of the world would tend to drive up global pri-ces and make US exports more competitive Alsonumerous uncertainties remain regarding issues suchas future changes in crop technology energy policyand other interactions that will substantially affectmarket outcomes which is one rationale for conduct-ing model comparisons (Nelson et al 2013) The use ofjust one crop process model one biophysical forestsimulation model and one market model each ofwhich contain their own structural uncertaintiesshould be noted given the importance of these uncer-tainties raised in recent model intercomparisons (Nel-son et al 2013 Rosenzweig et al 2013) Finally thisanalysis omits important aspects of climate changeimpacts to agriculture and forestry including damagesfrom extreme weather events wildfire and changes inweeds pests disease and ozone damage Additionalresearch is necessary to better characterize and under-stand a variety of model uncertainties explore theinteractive effects between land-using sectors in moredetail and incorporate additional interactions withinternationalmarkets

Acknowledgments

The authors wish to acknowledge thefinancial supportof the US Environmental Protection Agencyrsquos ClimateChange Division (Contract EP-BPA-12-H-0023 CallOrder EP-B13H-00143) Kaiguang Zhao and ErwanMonier provided technical contributions We are alsograteful for comments from two anonymousreviewers which greatly improved the manuscriptThe views expressed in this paper are solely those of

the authors and do not necessarily reflect those of theUS Environmental Protection Agency

References

AdamsD Alig RMcCarl BA andMurray BC 2005 FASOMGHGconceptual structure and specification documentation(httpagecon2tamuedupeoplefacultymccarl-brucepapers1212FASOMGHG_docpdf)

AdamsRMMcCarl BA SegersonK Rosenzweig C Bryant K JDixonBL Conner J R EvensonRE andOjimaD S 2004Theeconomic effects of climate change onUS agricultureTheImpact of Climate Change on theUnited States Economy edRMendelsohn and JNewmann (London CambridgeUniversity Press) pp 18ndash54

AdamsRMRosenzweig C Peart RM Ritchie J TMcCarl BAGlyer J D Curry RB Jones JW Boote K J andAllen LH Jr1990Global climate change andUS agricultureNature 345219ndash24

Ainsworth EA andLong S P 2005What havewe learned from15years of free-airCO2 enrichment (FACE) Ameta-analyticreviewof the responses of photosynthesis canopy propertiesandplant production to risingCO2NewPhytology 165 351ndash71

Alig R J AdamsDMandMcCarl BA 2002 Projecting impacts ofglobal climate change on theUS forest and agriculture sectorsand carbon budgets Forest EcologyManage 169 3ndash14

AttavanichWandMcCarl BA 2014How isCO2 affecting yields andtechnological progress a statistical analysisClim Change 124747ndash62

BacheletD Lenihan JMDaly CNeilsonRPOjimaDS andPartonW J 2001MC1 A dynamic vegetationmodel forestimating the distribution of vegetation and associatedecosystem fluxes of carbon nutrients andwater technicaldocumentationVersion 10 General Technical ReportPNWGTR-508USDepartment of Agriculture ForestService PacificNorthwest Research Station PortlandOR(wwwfslorstedudgvmmcgtr508pdf)

BacheletDNeilsonRPHickler TDrapek R J Lenihan JMSykesMT Smith B Sitch S andThonickeD2003 Simulatingpast and future dynamics of natural ecosystems in theUnitedStatesGlob Biogeochemical Cycles 17 1045

BacheletDNeilsonRP Lenihan JMandDrapek R J 2004Regionaldifferences in the carbon source-sink potential of naturalvegetation in theUSA EnvironManage 33 S23ndash43

BeachRH ZhenC ThomsonAMRejesus RM Sinha PLentz AWVedenovDV andMcCarl B 2010aClimateChange Impacts on Crop Insurance Prepared forUSDepartment of Agriculture RiskManagement Agency

BeachRHAdamsDMAlig R J Baker J S Latta G SMcCarl BMurray BC Rose SK andWhite EM2010bModelDocumentation for the Forest andAgricultural SectorOptimizationModel withGreenhouse Gases (FASOMGHG)Prepared forUS Environmental ProtectionAgency (wwwcoforsteducoffrresearchtamm

FASOMGHG_Model_Documentation)Boehlert B StrzepekKMChapra SC FantC Gebretsadik Y

LickleyM SwansonRMcCluskey ANeumann J E andMartinich J 2015Climate change impacts and greenhouse gasmitigation effects onUSwater quality J AdvModel EarthSystems accepted ( doi1010022014MS000400)

Boote K J Jones JWandPickeringNB 1996 Potential uses andlimitations of cropmodelsAgronomy J 88 704ndash16

Boote K J PickeringNB andAllen LH Jr 1997 Plantmodelingadvances and gaps in our capability to project future cropgrowth and yield in response to global climate changeAdvances in CarbonDioxide Effects Research ed LHAllen JrMBKirkmanDMOlszyk andCEWhitman ASA SpecialPublicationNo 61 (MadisonWI ASA-CSSA-SSSA)

DalyC Bachelet D Lenihan JM PartonWNeilson RP andOjimaD2000Dynamic simulations of tree-grass interactionsfor global change studies Ecological Appl 10 449ndash69

15

Environ Res Lett 10 (2015) 095004 RHBeach et al

Under IGSM-CAM climate conditions there is asmall net increase in cropland occurring primarily inthe Plains region while the Midwest experiences a

decline in cropland Forest productivity is relativelyhigher under the Policy scenario under MIROCclimate conditions but is just the opposite for IGSM-

Figure 4Difference in regional land cover using IGSM-CAMandMIROCclimate projections average for 2050ndash2100 Policy caserelative toReference case (thousands of acres) Cropland pasturemdashland of sufficient quality to be classified as cropland but that isbeing used as pasture forestmdashprivate forest CRPmdashConservation Reserve Program pasturemdashpastureland that is not of sufficientquality to be used as croplandwithout incurring costs of land improvement croplandmdasharea of harvested cropland There is noagricultural production in the PNWWest region in FASOM-GHG only forestry production Thus there is no difference in land coverin that region by definition

10

Environ Res Lett 10 (2015) 095004 RHBeach et al

CAM which influences decisions about land conver-sion based on the relative profitability of alternativeland uses

The changes experienced within individualregions are up to a few million acres but are generallyrelatively small in percentage terms given the large USland areaMost changes in land cover are less than 1though there are some exceptions such as croplandpasture in the Plains region which declines by almost13 in the Policy relative to the Reference as that landshifts into cropland

Reallocation of land between cropland and otheruses has implications for the provision of ecosystemservices including promoting habitat and biodiversity(Lawler et al 2014) Given the large quantities of car-bon sequestered in forests even relatively smallland movements can have sizable impacts on netGHG emissions In addition given that we are findingincreases in crop yields under the Policy caserelative to the Reference case that result in reduceddemand for cropland global stabilization may resultin an increased provision of ecosystem servicesand wildlife habitat relative to unabated climatechange This is particularly true in the Midwestwhich experiences the largest reduction in croplandFor instance the prairie pothole region which isimportant habitat for migratory birds would poten-tially face less pressure to convert natural lands tocropland in the Policy case compared with the Refer-ence case

45 Crop allocationWe find substantial reallocation of where crops aregrown as an adaptation in response to climate changeimpacts on relative agricultural productivity consis-tent with adjustments found by Adams et al (19902004 2005) and Reilly et al (2003) (see figure 5) Thiseffect is overshadowed by a general reduction in croparea however due to increasing yields under thePolicy case relative to the Reference that results in alower overall demand for land Switching betweencrops varies across regions as production of majorcrops other than wheat tends to decrease substantiallyin the major production center of the Midwest andshift to the Northeast Plains and Southern UnitedStates Under IGSM-CAM there is movement ofcotton from the Midwest to the Plains region andlsquoother cropsrsquo moving from the Midwest and Plainsregions to the Northeast and Southern regions Hayand wheat tend to be expanding in multiple regions asthey have relatively low or even negative yield impactsunder the Policy case resulting in a demand formore land to meet demand for those commoditiesUnder MIROC we see sorghum moving from theWestern US Midwest and Northeast into the Plainsregion while lsquoother croprsquo shift from the Midwest andNortheast to the other three regions As with IGSM-CAM wheat is again increasing in most regions given

its relative lack of yield benefit from the Policy caseHay on the other hand experiences a net decrease inarea under MIROC with a large decrease in theMidwest and only small increases in the Plains andNortheast as the net increase in hay yields results indecreased demand for hay land area relative to theReference

46 GHGemissionsAs is readily apparent from figure 6 the IGSM-CAMresults show large increases in net GHG emissionsfrom agriculture and forestry under the IGSM-CAMPolicy case almost all of which are due to differencesin forest management (ie net carbon losses orreduced gains in carbon storage on existing forest-land)24 Under the IGSM-CAM projections the gen-erally lower forest productivity under the Policyscenario results in substantially less forest carbonsequestration over time because of the direct effects ofslower forest growth (less sequestration) UnderMIROC climate conditions however forests becomea larger sink under the Policy than occurs in theReference case as forest productivity is enhancedEmissions from livestock rise under the Policy case forboth GCMs as higher crop yields result in lower feedprices and expanded livestock production but GHGemissions related to crop production are generallydeclining as less area is devoted to crops given thehigher yields As is often the case in assessingagricultural and forestry sector emissions changes inforest carbon sequestration greatly outweigh all othersources In this case differences in forest productivityeffects between the GCMs analyzed yield oppositesigns for the change in net GHG emissions with GHGstabilization Policy

One clear implication is that the climate modelused has a profound influence on the estimatedchange in net GHG emissions Our findings are con-sistent with a major difference in precipitation pat-terns observed across thesemodelsWhile theMIROCmodel tends to havemore precipitation under the Pol-icy case than the Reference case for many key forest-growing regions the IGSM-CAM model is just theopposite The effect on forest growth rates and asso-ciated carbon storage is reflected very clearly infigure 6 While climate conditions such as those underIGSM-CAMwould reduce the US LULUCF sink con-ditions such as those under MIROC would increase itother things being equal

Figure 6 is showing only the change in cumulativeemissions however not the absolute emissions TotalUSGHG emissions from agriculture and forestry landuse and land use change are estimated at about minus343MT CO2eq in 2013 (USEPA 2015) (forestry sink morethan outweighs emissions so net emissions from thosesectors are currently negative) FASOM-GHG does

24We do not incorporate feedback of these changes in emissions

into the calculation of climate impacts in this study

11

Environ Res Lett 10 (2015) 095004 RHBeach et al

not fully capture all emissions components that are inthe US inventory but based on the average cumulativechanges from 2015ndash2100 (cumulative net source of5394 MT CO2eq for Policy relative to Reference over90 years or average of 599 MT CO2eq increase in netGHG emissions per year over that timeframe) theaverage change under the Policy with IGSM-CAM is

equal to a reduction in the 2013 net sink by about15ndash20UnderMIROC on the other hand the Policyscenario provides a cumulative net sink of about 7685MT CO2eq or an average sink of about 854 MTCO2eq which is equal to about 40 of the US sinkfrom these sectors in 2013 Other things being equalthe effect of the Policy on net US GHG emissions will

Figure 5Difference in regional cropland allocation using IGSM-CAMandMIROCClimate Projections average for 2050ndash2100Policy case relative toReference case (million acres)

12

Environ Res Lett 10 (2015) 095004 RHBeach et al

become larger over time as seen in figure 6 though thepercentage impactwill dependon the evolutionof theUSinventoryover time

47WelfareThe differences in prices and the level of productionand consumptionwill lead to impacts on the economicwelfare of consumers and commodity producers Onecommon method of measuring these effects on well-being is in terms of differences in economic surpluswhich is a monetary measure of welfare Consumersrsquosurplus is the difference between the maximumamount consumers would have been willing to pay fora commodity and the actual price paid whereasproducersrsquo surplus is the difference between theminimum amount producers would have accepted tosupply a commodity and the actual price received

Overall the results of this study show that stabili-zation results in transfers from producers to con-sumers as commodity prices decline for goods withrelatively inelastic demands Although the magnitudeand temporal distribution of benefits under the Policycase vary across the IGSM-CAM model initializationsand MIROC projection each of our scenarios results

in cumulative net gains to consumers that more thanoutweigh any losses to producers as has been found inprior studies (eg Irland et al 2001) Based on theMC1and EPIC simulated impacts on forest and agriculturalproductivity the Policy case results in sizable increasesin overall yields and reduced commodity prices com-pared to unabated climate change in the Referencecase As shown in tables 2 and 3 reducing global GHGemissions under the Policy case is found to increasetotal surplus in the forest and agriculture sector by acumulative $327 billion to $545 billion for the2015ndash2100 period across the two GCMs and the dif-ferent model initializations of IGSM-CAM The mag-nitude of welfare impacts in the agricultural sector aremuch larger than in the forestry sector

Figure 6Difference in cumulative net GHG emissions by type using IGSM-CAMandMIROCClimate Projections Policy caserelative toReference case

Table 2Present value of cumulative differences in consumer and producer welfare using IGSM-CAMclimate projections Policy caserelative to Reference case 2015ndash2100 (million $2005)

Consumer surplus Producer surplus TOTAL

Minimum Average Maximum Minimum Average Maximum Minimum Average Maximum

Agriculture $6318 $24 921 $37 077 minus$4330 $11 224 $27 168

Forestry minus$162 $57 $440 $29 $298 $782

TOTAL $6211 $24 978 $36 915 minus$4151 $11 522 $27 495 $32 764 $36 500 $43 100

Note Agriculture + Forestry does not necessarily sum to totals because the table is independently calculating minimum average and

maximumvalues for agriculture forestry and the combined totals across thefive IGSM-CAM initializations

Table 3Present value of cumulative differences in consumer andproducer welfare usingMIROCclimate projections Policy versusReference case 2015ndash2100 (million $2005)

Consumer surplus Producer surplus TOTAL

Agriculture $52 665 minus$2809

Forestry minus$138 $777

TOTAL $52 527 minus$2032 $50 495

13

Environ Res Lett 10 (2015) 095004 RHBeach et al

There have been several previous studies examin-ing the potential economic impacts of climate changeon US agriculture but many focus only on majorcrops they do not necessarily report national welfarevalues and they do not explicitly examine the benefitsof mitigation For instance Reilly et al (2003) usingpredictions from GCMs from the Canadian CenterClimate Model and Hadley Centre Model found thatclimate change from 2030ndash2090 would have positiveimpacts on welfare ranging between $08 billion (2000$) in 2030 and $122 billion in 2090 The main driverof these productivity gains were projected increases inprecipitation levels under climate change Greenstoneand Deschenes (2007) also found positive climatechange impacts using the Hadleymodel estimating anincrease of $13 billion Schlenker et al (2005) findmuch more negative impacts estimating that eco-nomic effects of climate change on agriculture need tobe assessed differently in dryland and irrigated areasand that pooling the dryland and irrigated countiescould potentially yield biased estimates Based onavailable data they estimate the model for drylandcounties and the estimated annual losses are about $5to $53 billion for dryland non-urban counties aloneTemperature increases affect crop responses in a non-linear fashion Using a 55 year panel data on cropyields Schlenker and Roberts (2006) found increasesin crop yields (for corn soybeans and cotton) withhigher temperatures until reaching threshold valuesTheir results show very large decreases in crop yieldstoward the end of the century as temperatures exceedthese threshold levels The study estimates that yieldsof these three crops are expected to decline by 25ndash44under a slow warming scenario and 60ndash79 respec-tively under a quick warming scenario at the end ofthe century Thus the negative effects on agriculturecould become very large in the long-term future iftemperatures begin to reach threshold levels

In general the findings from previous studies havedepended heavily on the net changes in precipitationexperienced under climate change but the welfareresults from this study fall within the general range ofestimates that have been generated

5 Conclusions

Our analytical approach of a combination of climatemodels a crop process model a dynamic vegetationmodel and an economic model of the forest andagricultural sectors was applied to a Reference andPolicy case enabling us to conduct an integratedassessment of the potential benefits of climate stabili-zation onUS forests and agriculture The results of thispaper are generally consistent with published studiesfocused on impacts in these sectors Projections ofadverse yields resulting from unmitigated climatechange large regional differences in crop responseincreasing vulnerability of water supplies for

irrigation and the ability of adaptation to reduceadverse effects are consistent with the findings of themajor climate science assessments (eg Nelsonet al 2013 Hatfield et al 2014) However our resultsaugment previous studies through the use of anintegrated forest and agricultural model to study thepotential benefits of GHG mitigation which can helpinform policy decisions weighing the relative costs andbenefits ofmitigation and adaptation strategies

First global GHG mitigation generally results inhigher agricultural yields across most of our dis-aggregated crop types compared to the unabated cli-mate change scenario even across different climatemodels largely though avoiding decreases in yieldsdue to climate change The majority of the yield bene-fits occur after 2050 though as the Reference climateimpacts being avoided by Policy are relatively smallerover the next few decades and increase over time Sec-ond under both contrasting futures for precipitationin the United States many irrigated crops benefitmore from mitigation than dryland crops In generalirrigated crops tend to gain from the reduced tempera-tures while being unaffected by changes in precipita-tion since they already irrigate to meet their waterneeds This demonstrates the importance of assump-tions concerning water availability and incidence forirrigation Third the effect of global GHG stabilizationon projected forest yields is substantially (almost anorder of magnitude) less than estimated differences incrop yields and the direction of the effect dependsstrongly upon climate model and forest type (hard-wood versus softwood) Consistent with the findingson crop prices the majority of the impacts are con-centrated in 2050 and beyond One important caveatto that finding is that this study is using smoothedtrends of forest productivity and does not reflectincreases in disturbances such as pest damages whichmay influence forest investment decisions when thepossibility of large capital losses are reflected Fourththe impacts of climate change and the effects of stabili-zation are not equally distributed with variationsdepending upon crop type irrigation status forestvegetation type and other factors This finding is con-sistent with previous studies that found that there maybe modest agricultural impacts of climate change inthe United States at the national level but substantialdistributional effects (Beach et al 2010a) Fifth becauseof the positive impacts on crop yields global GHGmitigation is projected to lower crop prices in generalcompared to the Reference scenario This creates sub-stantial benefits of mitigation for consumers that out-weigh the losses incurred by producers Sixth we finda significant effect on net GHG emissions that is highlydependent on the climate scenario used for forestryyields While the MIROC Policy case raises forestyields and increases the size of the US sink from agri-culture and forests IGSM-CAM has exactly the oppo-site effect Additional work to refine estimates of thechanges in forest carbon associated with mitigation

14

Environ Res Lett 10 (2015) 095004 RHBeach et al

policy would be valuable and could be one piece of alarger future research effort to assess interactionsbetween mitigation and adaptation activities Finallyanother interesting finding is that there are gains toconsumers that outweigh losses to producers underour mitigation scenarios Although producers mayreceive higher yields under the Policy scenario it doesnot necessarily mean that they will becomemore prof-itable In fact the opposite is often the case Whenyields rise for a large group of products making inelas-tic goods such as most food commodities the pricewill tend to fall by more than the quantity sold risesleading to a drop in revenue and profits Depending onthe structure and incidence of the costs of a potentialmitigation policy producers could be concernedabout bearing costs of mitigation while also losingproducer surplus due to the change in climate impactswhichmay be a consideration for policy design

One limitation of this study is that it does notreflect climate change impacts on international forestsand agriculture which would also affect relativereturns to different uses of land and trade patterns andtherefore affect land use decisions (Leclegravere et al 2014)For instance including negative impacts on yields inthe rest of the world would tend to drive up global pri-ces and make US exports more competitive Alsonumerous uncertainties remain regarding issues suchas future changes in crop technology energy policyand other interactions that will substantially affectmarket outcomes which is one rationale for conduct-ing model comparisons (Nelson et al 2013) The use ofjust one crop process model one biophysical forestsimulation model and one market model each ofwhich contain their own structural uncertaintiesshould be noted given the importance of these uncer-tainties raised in recent model intercomparisons (Nel-son et al 2013 Rosenzweig et al 2013) Finally thisanalysis omits important aspects of climate changeimpacts to agriculture and forestry including damagesfrom extreme weather events wildfire and changes inweeds pests disease and ozone damage Additionalresearch is necessary to better characterize and under-stand a variety of model uncertainties explore theinteractive effects between land-using sectors in moredetail and incorporate additional interactions withinternationalmarkets

Acknowledgments

The authors wish to acknowledge thefinancial supportof the US Environmental Protection Agencyrsquos ClimateChange Division (Contract EP-BPA-12-H-0023 CallOrder EP-B13H-00143) Kaiguang Zhao and ErwanMonier provided technical contributions We are alsograteful for comments from two anonymousreviewers which greatly improved the manuscriptThe views expressed in this paper are solely those of

the authors and do not necessarily reflect those of theUS Environmental Protection Agency

References

AdamsD Alig RMcCarl BA andMurray BC 2005 FASOMGHGconceptual structure and specification documentation(httpagecon2tamuedupeoplefacultymccarl-brucepapers1212FASOMGHG_docpdf)

AdamsRMMcCarl BA SegersonK Rosenzweig C Bryant K JDixonBL Conner J R EvensonRE andOjimaD S 2004Theeconomic effects of climate change onUS agricultureTheImpact of Climate Change on theUnited States Economy edRMendelsohn and JNewmann (London CambridgeUniversity Press) pp 18ndash54

AdamsRMRosenzweig C Peart RM Ritchie J TMcCarl BAGlyer J D Curry RB Jones JW Boote K J andAllen LH Jr1990Global climate change andUS agricultureNature 345219ndash24

Ainsworth EA andLong S P 2005What havewe learned from15years of free-airCO2 enrichment (FACE) Ameta-analyticreviewof the responses of photosynthesis canopy propertiesandplant production to risingCO2NewPhytology 165 351ndash71

Alig R J AdamsDMandMcCarl BA 2002 Projecting impacts ofglobal climate change on theUS forest and agriculture sectorsand carbon budgets Forest EcologyManage 169 3ndash14

AttavanichWandMcCarl BA 2014How isCO2 affecting yields andtechnological progress a statistical analysisClim Change 124747ndash62

BacheletD Lenihan JMDaly CNeilsonRPOjimaDS andPartonW J 2001MC1 A dynamic vegetationmodel forestimating the distribution of vegetation and associatedecosystem fluxes of carbon nutrients andwater technicaldocumentationVersion 10 General Technical ReportPNWGTR-508USDepartment of Agriculture ForestService PacificNorthwest Research Station PortlandOR(wwwfslorstedudgvmmcgtr508pdf)

BacheletDNeilsonRPHickler TDrapek R J Lenihan JMSykesMT Smith B Sitch S andThonickeD2003 Simulatingpast and future dynamics of natural ecosystems in theUnitedStatesGlob Biogeochemical Cycles 17 1045

BacheletDNeilsonRP Lenihan JMandDrapek R J 2004Regionaldifferences in the carbon source-sink potential of naturalvegetation in theUSA EnvironManage 33 S23ndash43

BeachRH ZhenC ThomsonAMRejesus RM Sinha PLentz AWVedenovDV andMcCarl B 2010aClimateChange Impacts on Crop Insurance Prepared forUSDepartment of Agriculture RiskManagement Agency

BeachRHAdamsDMAlig R J Baker J S Latta G SMcCarl BMurray BC Rose SK andWhite EM2010bModelDocumentation for the Forest andAgricultural SectorOptimizationModel withGreenhouse Gases (FASOMGHG)Prepared forUS Environmental ProtectionAgency (wwwcoforsteducoffrresearchtamm

FASOMGHG_Model_Documentation)Boehlert B StrzepekKMChapra SC FantC Gebretsadik Y

LickleyM SwansonRMcCluskey ANeumann J E andMartinich J 2015Climate change impacts and greenhouse gasmitigation effects onUSwater quality J AdvModel EarthSystems accepted ( doi1010022014MS000400)

Boote K J Jones JWandPickeringNB 1996 Potential uses andlimitations of cropmodelsAgronomy J 88 704ndash16

Boote K J PickeringNB andAllen LH Jr 1997 Plantmodelingadvances and gaps in our capability to project future cropgrowth and yield in response to global climate changeAdvances in CarbonDioxide Effects Research ed LHAllen JrMBKirkmanDMOlszyk andCEWhitman ASA SpecialPublicationNo 61 (MadisonWI ASA-CSSA-SSSA)

DalyC Bachelet D Lenihan JM PartonWNeilson RP andOjimaD2000Dynamic simulations of tree-grass interactionsfor global change studies Ecological Appl 10 449ndash69

15

Environ Res Lett 10 (2015) 095004 RHBeach et al

CAM which influences decisions about land conver-sion based on the relative profitability of alternativeland uses

The changes experienced within individualregions are up to a few million acres but are generallyrelatively small in percentage terms given the large USland areaMost changes in land cover are less than 1though there are some exceptions such as croplandpasture in the Plains region which declines by almost13 in the Policy relative to the Reference as that landshifts into cropland

Reallocation of land between cropland and otheruses has implications for the provision of ecosystemservices including promoting habitat and biodiversity(Lawler et al 2014) Given the large quantities of car-bon sequestered in forests even relatively smallland movements can have sizable impacts on netGHG emissions In addition given that we are findingincreases in crop yields under the Policy caserelative to the Reference case that result in reduceddemand for cropland global stabilization may resultin an increased provision of ecosystem servicesand wildlife habitat relative to unabated climatechange This is particularly true in the Midwestwhich experiences the largest reduction in croplandFor instance the prairie pothole region which isimportant habitat for migratory birds would poten-tially face less pressure to convert natural lands tocropland in the Policy case compared with the Refer-ence case

45 Crop allocationWe find substantial reallocation of where crops aregrown as an adaptation in response to climate changeimpacts on relative agricultural productivity consis-tent with adjustments found by Adams et al (19902004 2005) and Reilly et al (2003) (see figure 5) Thiseffect is overshadowed by a general reduction in croparea however due to increasing yields under thePolicy case relative to the Reference that results in alower overall demand for land Switching betweencrops varies across regions as production of majorcrops other than wheat tends to decrease substantiallyin the major production center of the Midwest andshift to the Northeast Plains and Southern UnitedStates Under IGSM-CAM there is movement ofcotton from the Midwest to the Plains region andlsquoother cropsrsquo moving from the Midwest and Plainsregions to the Northeast and Southern regions Hayand wheat tend to be expanding in multiple regions asthey have relatively low or even negative yield impactsunder the Policy case resulting in a demand formore land to meet demand for those commoditiesUnder MIROC we see sorghum moving from theWestern US Midwest and Northeast into the Plainsregion while lsquoother croprsquo shift from the Midwest andNortheast to the other three regions As with IGSM-CAM wheat is again increasing in most regions given

its relative lack of yield benefit from the Policy caseHay on the other hand experiences a net decrease inarea under MIROC with a large decrease in theMidwest and only small increases in the Plains andNortheast as the net increase in hay yields results indecreased demand for hay land area relative to theReference

46 GHGemissionsAs is readily apparent from figure 6 the IGSM-CAMresults show large increases in net GHG emissionsfrom agriculture and forestry under the IGSM-CAMPolicy case almost all of which are due to differencesin forest management (ie net carbon losses orreduced gains in carbon storage on existing forest-land)24 Under the IGSM-CAM projections the gen-erally lower forest productivity under the Policyscenario results in substantially less forest carbonsequestration over time because of the direct effects ofslower forest growth (less sequestration) UnderMIROC climate conditions however forests becomea larger sink under the Policy than occurs in theReference case as forest productivity is enhancedEmissions from livestock rise under the Policy case forboth GCMs as higher crop yields result in lower feedprices and expanded livestock production but GHGemissions related to crop production are generallydeclining as less area is devoted to crops given thehigher yields As is often the case in assessingagricultural and forestry sector emissions changes inforest carbon sequestration greatly outweigh all othersources In this case differences in forest productivityeffects between the GCMs analyzed yield oppositesigns for the change in net GHG emissions with GHGstabilization Policy

One clear implication is that the climate modelused has a profound influence on the estimatedchange in net GHG emissions Our findings are con-sistent with a major difference in precipitation pat-terns observed across thesemodelsWhile theMIROCmodel tends to havemore precipitation under the Pol-icy case than the Reference case for many key forest-growing regions the IGSM-CAM model is just theopposite The effect on forest growth rates and asso-ciated carbon storage is reflected very clearly infigure 6 While climate conditions such as those underIGSM-CAMwould reduce the US LULUCF sink con-ditions such as those under MIROC would increase itother things being equal

Figure 6 is showing only the change in cumulativeemissions however not the absolute emissions TotalUSGHG emissions from agriculture and forestry landuse and land use change are estimated at about minus343MT CO2eq in 2013 (USEPA 2015) (forestry sink morethan outweighs emissions so net emissions from thosesectors are currently negative) FASOM-GHG does

24We do not incorporate feedback of these changes in emissions

into the calculation of climate impacts in this study

11

Environ Res Lett 10 (2015) 095004 RHBeach et al

not fully capture all emissions components that are inthe US inventory but based on the average cumulativechanges from 2015ndash2100 (cumulative net source of5394 MT CO2eq for Policy relative to Reference over90 years or average of 599 MT CO2eq increase in netGHG emissions per year over that timeframe) theaverage change under the Policy with IGSM-CAM is

equal to a reduction in the 2013 net sink by about15ndash20UnderMIROC on the other hand the Policyscenario provides a cumulative net sink of about 7685MT CO2eq or an average sink of about 854 MTCO2eq which is equal to about 40 of the US sinkfrom these sectors in 2013 Other things being equalthe effect of the Policy on net US GHG emissions will

Figure 5Difference in regional cropland allocation using IGSM-CAMandMIROCClimate Projections average for 2050ndash2100Policy case relative toReference case (million acres)

12

Environ Res Lett 10 (2015) 095004 RHBeach et al

become larger over time as seen in figure 6 though thepercentage impactwill dependon the evolutionof theUSinventoryover time

47WelfareThe differences in prices and the level of productionand consumptionwill lead to impacts on the economicwelfare of consumers and commodity producers Onecommon method of measuring these effects on well-being is in terms of differences in economic surpluswhich is a monetary measure of welfare Consumersrsquosurplus is the difference between the maximumamount consumers would have been willing to pay fora commodity and the actual price paid whereasproducersrsquo surplus is the difference between theminimum amount producers would have accepted tosupply a commodity and the actual price received

Overall the results of this study show that stabili-zation results in transfers from producers to con-sumers as commodity prices decline for goods withrelatively inelastic demands Although the magnitudeand temporal distribution of benefits under the Policycase vary across the IGSM-CAM model initializationsand MIROC projection each of our scenarios results

in cumulative net gains to consumers that more thanoutweigh any losses to producers as has been found inprior studies (eg Irland et al 2001) Based on theMC1and EPIC simulated impacts on forest and agriculturalproductivity the Policy case results in sizable increasesin overall yields and reduced commodity prices com-pared to unabated climate change in the Referencecase As shown in tables 2 and 3 reducing global GHGemissions under the Policy case is found to increasetotal surplus in the forest and agriculture sector by acumulative $327 billion to $545 billion for the2015ndash2100 period across the two GCMs and the dif-ferent model initializations of IGSM-CAM The mag-nitude of welfare impacts in the agricultural sector aremuch larger than in the forestry sector

Figure 6Difference in cumulative net GHG emissions by type using IGSM-CAMandMIROCClimate Projections Policy caserelative toReference case

Table 2Present value of cumulative differences in consumer and producer welfare using IGSM-CAMclimate projections Policy caserelative to Reference case 2015ndash2100 (million $2005)

Consumer surplus Producer surplus TOTAL

Minimum Average Maximum Minimum Average Maximum Minimum Average Maximum

Agriculture $6318 $24 921 $37 077 minus$4330 $11 224 $27 168

Forestry minus$162 $57 $440 $29 $298 $782

TOTAL $6211 $24 978 $36 915 minus$4151 $11 522 $27 495 $32 764 $36 500 $43 100

Note Agriculture + Forestry does not necessarily sum to totals because the table is independently calculating minimum average and

maximumvalues for agriculture forestry and the combined totals across thefive IGSM-CAM initializations

Table 3Present value of cumulative differences in consumer andproducer welfare usingMIROCclimate projections Policy versusReference case 2015ndash2100 (million $2005)

Consumer surplus Producer surplus TOTAL

Agriculture $52 665 minus$2809

Forestry minus$138 $777

TOTAL $52 527 minus$2032 $50 495

13

Environ Res Lett 10 (2015) 095004 RHBeach et al

There have been several previous studies examin-ing the potential economic impacts of climate changeon US agriculture but many focus only on majorcrops they do not necessarily report national welfarevalues and they do not explicitly examine the benefitsof mitigation For instance Reilly et al (2003) usingpredictions from GCMs from the Canadian CenterClimate Model and Hadley Centre Model found thatclimate change from 2030ndash2090 would have positiveimpacts on welfare ranging between $08 billion (2000$) in 2030 and $122 billion in 2090 The main driverof these productivity gains were projected increases inprecipitation levels under climate change Greenstoneand Deschenes (2007) also found positive climatechange impacts using the Hadleymodel estimating anincrease of $13 billion Schlenker et al (2005) findmuch more negative impacts estimating that eco-nomic effects of climate change on agriculture need tobe assessed differently in dryland and irrigated areasand that pooling the dryland and irrigated countiescould potentially yield biased estimates Based onavailable data they estimate the model for drylandcounties and the estimated annual losses are about $5to $53 billion for dryland non-urban counties aloneTemperature increases affect crop responses in a non-linear fashion Using a 55 year panel data on cropyields Schlenker and Roberts (2006) found increasesin crop yields (for corn soybeans and cotton) withhigher temperatures until reaching threshold valuesTheir results show very large decreases in crop yieldstoward the end of the century as temperatures exceedthese threshold levels The study estimates that yieldsof these three crops are expected to decline by 25ndash44under a slow warming scenario and 60ndash79 respec-tively under a quick warming scenario at the end ofthe century Thus the negative effects on agriculturecould become very large in the long-term future iftemperatures begin to reach threshold levels

In general the findings from previous studies havedepended heavily on the net changes in precipitationexperienced under climate change but the welfareresults from this study fall within the general range ofestimates that have been generated

5 Conclusions

Our analytical approach of a combination of climatemodels a crop process model a dynamic vegetationmodel and an economic model of the forest andagricultural sectors was applied to a Reference andPolicy case enabling us to conduct an integratedassessment of the potential benefits of climate stabili-zation onUS forests and agriculture The results of thispaper are generally consistent with published studiesfocused on impacts in these sectors Projections ofadverse yields resulting from unmitigated climatechange large regional differences in crop responseincreasing vulnerability of water supplies for

irrigation and the ability of adaptation to reduceadverse effects are consistent with the findings of themajor climate science assessments (eg Nelsonet al 2013 Hatfield et al 2014) However our resultsaugment previous studies through the use of anintegrated forest and agricultural model to study thepotential benefits of GHG mitigation which can helpinform policy decisions weighing the relative costs andbenefits ofmitigation and adaptation strategies

First global GHG mitigation generally results inhigher agricultural yields across most of our dis-aggregated crop types compared to the unabated cli-mate change scenario even across different climatemodels largely though avoiding decreases in yieldsdue to climate change The majority of the yield bene-fits occur after 2050 though as the Reference climateimpacts being avoided by Policy are relatively smallerover the next few decades and increase over time Sec-ond under both contrasting futures for precipitationin the United States many irrigated crops benefitmore from mitigation than dryland crops In generalirrigated crops tend to gain from the reduced tempera-tures while being unaffected by changes in precipita-tion since they already irrigate to meet their waterneeds This demonstrates the importance of assump-tions concerning water availability and incidence forirrigation Third the effect of global GHG stabilizationon projected forest yields is substantially (almost anorder of magnitude) less than estimated differences incrop yields and the direction of the effect dependsstrongly upon climate model and forest type (hard-wood versus softwood) Consistent with the findingson crop prices the majority of the impacts are con-centrated in 2050 and beyond One important caveatto that finding is that this study is using smoothedtrends of forest productivity and does not reflectincreases in disturbances such as pest damages whichmay influence forest investment decisions when thepossibility of large capital losses are reflected Fourththe impacts of climate change and the effects of stabili-zation are not equally distributed with variationsdepending upon crop type irrigation status forestvegetation type and other factors This finding is con-sistent with previous studies that found that there maybe modest agricultural impacts of climate change inthe United States at the national level but substantialdistributional effects (Beach et al 2010a) Fifth becauseof the positive impacts on crop yields global GHGmitigation is projected to lower crop prices in generalcompared to the Reference scenario This creates sub-stantial benefits of mitigation for consumers that out-weigh the losses incurred by producers Sixth we finda significant effect on net GHG emissions that is highlydependent on the climate scenario used for forestryyields While the MIROC Policy case raises forestyields and increases the size of the US sink from agri-culture and forests IGSM-CAM has exactly the oppo-site effect Additional work to refine estimates of thechanges in forest carbon associated with mitigation

14

Environ Res Lett 10 (2015) 095004 RHBeach et al

policy would be valuable and could be one piece of alarger future research effort to assess interactionsbetween mitigation and adaptation activities Finallyanother interesting finding is that there are gains toconsumers that outweigh losses to producers underour mitigation scenarios Although producers mayreceive higher yields under the Policy scenario it doesnot necessarily mean that they will becomemore prof-itable In fact the opposite is often the case Whenyields rise for a large group of products making inelas-tic goods such as most food commodities the pricewill tend to fall by more than the quantity sold risesleading to a drop in revenue and profits Depending onthe structure and incidence of the costs of a potentialmitigation policy producers could be concernedabout bearing costs of mitigation while also losingproducer surplus due to the change in climate impactswhichmay be a consideration for policy design

One limitation of this study is that it does notreflect climate change impacts on international forestsand agriculture which would also affect relativereturns to different uses of land and trade patterns andtherefore affect land use decisions (Leclegravere et al 2014)For instance including negative impacts on yields inthe rest of the world would tend to drive up global pri-ces and make US exports more competitive Alsonumerous uncertainties remain regarding issues suchas future changes in crop technology energy policyand other interactions that will substantially affectmarket outcomes which is one rationale for conduct-ing model comparisons (Nelson et al 2013) The use ofjust one crop process model one biophysical forestsimulation model and one market model each ofwhich contain their own structural uncertaintiesshould be noted given the importance of these uncer-tainties raised in recent model intercomparisons (Nel-son et al 2013 Rosenzweig et al 2013) Finally thisanalysis omits important aspects of climate changeimpacts to agriculture and forestry including damagesfrom extreme weather events wildfire and changes inweeds pests disease and ozone damage Additionalresearch is necessary to better characterize and under-stand a variety of model uncertainties explore theinteractive effects between land-using sectors in moredetail and incorporate additional interactions withinternationalmarkets

Acknowledgments

The authors wish to acknowledge thefinancial supportof the US Environmental Protection Agencyrsquos ClimateChange Division (Contract EP-BPA-12-H-0023 CallOrder EP-B13H-00143) Kaiguang Zhao and ErwanMonier provided technical contributions We are alsograteful for comments from two anonymousreviewers which greatly improved the manuscriptThe views expressed in this paper are solely those of

the authors and do not necessarily reflect those of theUS Environmental Protection Agency

References

AdamsD Alig RMcCarl BA andMurray BC 2005 FASOMGHGconceptual structure and specification documentation(httpagecon2tamuedupeoplefacultymccarl-brucepapers1212FASOMGHG_docpdf)

AdamsRMMcCarl BA SegersonK Rosenzweig C Bryant K JDixonBL Conner J R EvensonRE andOjimaD S 2004Theeconomic effects of climate change onUS agricultureTheImpact of Climate Change on theUnited States Economy edRMendelsohn and JNewmann (London CambridgeUniversity Press) pp 18ndash54

AdamsRMRosenzweig C Peart RM Ritchie J TMcCarl BAGlyer J D Curry RB Jones JW Boote K J andAllen LH Jr1990Global climate change andUS agricultureNature 345219ndash24

Ainsworth EA andLong S P 2005What havewe learned from15years of free-airCO2 enrichment (FACE) Ameta-analyticreviewof the responses of photosynthesis canopy propertiesandplant production to risingCO2NewPhytology 165 351ndash71

Alig R J AdamsDMandMcCarl BA 2002 Projecting impacts ofglobal climate change on theUS forest and agriculture sectorsand carbon budgets Forest EcologyManage 169 3ndash14

AttavanichWandMcCarl BA 2014How isCO2 affecting yields andtechnological progress a statistical analysisClim Change 124747ndash62

BacheletD Lenihan JMDaly CNeilsonRPOjimaDS andPartonW J 2001MC1 A dynamic vegetationmodel forestimating the distribution of vegetation and associatedecosystem fluxes of carbon nutrients andwater technicaldocumentationVersion 10 General Technical ReportPNWGTR-508USDepartment of Agriculture ForestService PacificNorthwest Research Station PortlandOR(wwwfslorstedudgvmmcgtr508pdf)

BacheletDNeilsonRPHickler TDrapek R J Lenihan JMSykesMT Smith B Sitch S andThonickeD2003 Simulatingpast and future dynamics of natural ecosystems in theUnitedStatesGlob Biogeochemical Cycles 17 1045

BacheletDNeilsonRP Lenihan JMandDrapek R J 2004Regionaldifferences in the carbon source-sink potential of naturalvegetation in theUSA EnvironManage 33 S23ndash43

BeachRH ZhenC ThomsonAMRejesus RM Sinha PLentz AWVedenovDV andMcCarl B 2010aClimateChange Impacts on Crop Insurance Prepared forUSDepartment of Agriculture RiskManagement Agency

BeachRHAdamsDMAlig R J Baker J S Latta G SMcCarl BMurray BC Rose SK andWhite EM2010bModelDocumentation for the Forest andAgricultural SectorOptimizationModel withGreenhouse Gases (FASOMGHG)Prepared forUS Environmental ProtectionAgency (wwwcoforsteducoffrresearchtamm

FASOMGHG_Model_Documentation)Boehlert B StrzepekKMChapra SC FantC Gebretsadik Y

LickleyM SwansonRMcCluskey ANeumann J E andMartinich J 2015Climate change impacts and greenhouse gasmitigation effects onUSwater quality J AdvModel EarthSystems accepted ( doi1010022014MS000400)

Boote K J Jones JWandPickeringNB 1996 Potential uses andlimitations of cropmodelsAgronomy J 88 704ndash16

Boote K J PickeringNB andAllen LH Jr 1997 Plantmodelingadvances and gaps in our capability to project future cropgrowth and yield in response to global climate changeAdvances in CarbonDioxide Effects Research ed LHAllen JrMBKirkmanDMOlszyk andCEWhitman ASA SpecialPublicationNo 61 (MadisonWI ASA-CSSA-SSSA)

DalyC Bachelet D Lenihan JM PartonWNeilson RP andOjimaD2000Dynamic simulations of tree-grass interactionsfor global change studies Ecological Appl 10 449ndash69

15

Environ Res Lett 10 (2015) 095004 RHBeach et al

not fully capture all emissions components that are inthe US inventory but based on the average cumulativechanges from 2015ndash2100 (cumulative net source of5394 MT CO2eq for Policy relative to Reference over90 years or average of 599 MT CO2eq increase in netGHG emissions per year over that timeframe) theaverage change under the Policy with IGSM-CAM is

equal to a reduction in the 2013 net sink by about15ndash20UnderMIROC on the other hand the Policyscenario provides a cumulative net sink of about 7685MT CO2eq or an average sink of about 854 MTCO2eq which is equal to about 40 of the US sinkfrom these sectors in 2013 Other things being equalthe effect of the Policy on net US GHG emissions will

Figure 5Difference in regional cropland allocation using IGSM-CAMandMIROCClimate Projections average for 2050ndash2100Policy case relative toReference case (million acres)

12

Environ Res Lett 10 (2015) 095004 RHBeach et al

become larger over time as seen in figure 6 though thepercentage impactwill dependon the evolutionof theUSinventoryover time

47WelfareThe differences in prices and the level of productionand consumptionwill lead to impacts on the economicwelfare of consumers and commodity producers Onecommon method of measuring these effects on well-being is in terms of differences in economic surpluswhich is a monetary measure of welfare Consumersrsquosurplus is the difference between the maximumamount consumers would have been willing to pay fora commodity and the actual price paid whereasproducersrsquo surplus is the difference between theminimum amount producers would have accepted tosupply a commodity and the actual price received

Overall the results of this study show that stabili-zation results in transfers from producers to con-sumers as commodity prices decline for goods withrelatively inelastic demands Although the magnitudeand temporal distribution of benefits under the Policycase vary across the IGSM-CAM model initializationsand MIROC projection each of our scenarios results

in cumulative net gains to consumers that more thanoutweigh any losses to producers as has been found inprior studies (eg Irland et al 2001) Based on theMC1and EPIC simulated impacts on forest and agriculturalproductivity the Policy case results in sizable increasesin overall yields and reduced commodity prices com-pared to unabated climate change in the Referencecase As shown in tables 2 and 3 reducing global GHGemissions under the Policy case is found to increasetotal surplus in the forest and agriculture sector by acumulative $327 billion to $545 billion for the2015ndash2100 period across the two GCMs and the dif-ferent model initializations of IGSM-CAM The mag-nitude of welfare impacts in the agricultural sector aremuch larger than in the forestry sector

Figure 6Difference in cumulative net GHG emissions by type using IGSM-CAMandMIROCClimate Projections Policy caserelative toReference case

Table 2Present value of cumulative differences in consumer and producer welfare using IGSM-CAMclimate projections Policy caserelative to Reference case 2015ndash2100 (million $2005)

Consumer surplus Producer surplus TOTAL

Minimum Average Maximum Minimum Average Maximum Minimum Average Maximum

Agriculture $6318 $24 921 $37 077 minus$4330 $11 224 $27 168

Forestry minus$162 $57 $440 $29 $298 $782

TOTAL $6211 $24 978 $36 915 minus$4151 $11 522 $27 495 $32 764 $36 500 $43 100

Note Agriculture + Forestry does not necessarily sum to totals because the table is independently calculating minimum average and

maximumvalues for agriculture forestry and the combined totals across thefive IGSM-CAM initializations

Table 3Present value of cumulative differences in consumer andproducer welfare usingMIROCclimate projections Policy versusReference case 2015ndash2100 (million $2005)

Consumer surplus Producer surplus TOTAL

Agriculture $52 665 minus$2809

Forestry minus$138 $777

TOTAL $52 527 minus$2032 $50 495

13

Environ Res Lett 10 (2015) 095004 RHBeach et al

There have been several previous studies examin-ing the potential economic impacts of climate changeon US agriculture but many focus only on majorcrops they do not necessarily report national welfarevalues and they do not explicitly examine the benefitsof mitigation For instance Reilly et al (2003) usingpredictions from GCMs from the Canadian CenterClimate Model and Hadley Centre Model found thatclimate change from 2030ndash2090 would have positiveimpacts on welfare ranging between $08 billion (2000$) in 2030 and $122 billion in 2090 The main driverof these productivity gains were projected increases inprecipitation levels under climate change Greenstoneand Deschenes (2007) also found positive climatechange impacts using the Hadleymodel estimating anincrease of $13 billion Schlenker et al (2005) findmuch more negative impacts estimating that eco-nomic effects of climate change on agriculture need tobe assessed differently in dryland and irrigated areasand that pooling the dryland and irrigated countiescould potentially yield biased estimates Based onavailable data they estimate the model for drylandcounties and the estimated annual losses are about $5to $53 billion for dryland non-urban counties aloneTemperature increases affect crop responses in a non-linear fashion Using a 55 year panel data on cropyields Schlenker and Roberts (2006) found increasesin crop yields (for corn soybeans and cotton) withhigher temperatures until reaching threshold valuesTheir results show very large decreases in crop yieldstoward the end of the century as temperatures exceedthese threshold levels The study estimates that yieldsof these three crops are expected to decline by 25ndash44under a slow warming scenario and 60ndash79 respec-tively under a quick warming scenario at the end ofthe century Thus the negative effects on agriculturecould become very large in the long-term future iftemperatures begin to reach threshold levels

In general the findings from previous studies havedepended heavily on the net changes in precipitationexperienced under climate change but the welfareresults from this study fall within the general range ofestimates that have been generated

5 Conclusions

Our analytical approach of a combination of climatemodels a crop process model a dynamic vegetationmodel and an economic model of the forest andagricultural sectors was applied to a Reference andPolicy case enabling us to conduct an integratedassessment of the potential benefits of climate stabili-zation onUS forests and agriculture The results of thispaper are generally consistent with published studiesfocused on impacts in these sectors Projections ofadverse yields resulting from unmitigated climatechange large regional differences in crop responseincreasing vulnerability of water supplies for

irrigation and the ability of adaptation to reduceadverse effects are consistent with the findings of themajor climate science assessments (eg Nelsonet al 2013 Hatfield et al 2014) However our resultsaugment previous studies through the use of anintegrated forest and agricultural model to study thepotential benefits of GHG mitigation which can helpinform policy decisions weighing the relative costs andbenefits ofmitigation and adaptation strategies

First global GHG mitigation generally results inhigher agricultural yields across most of our dis-aggregated crop types compared to the unabated cli-mate change scenario even across different climatemodels largely though avoiding decreases in yieldsdue to climate change The majority of the yield bene-fits occur after 2050 though as the Reference climateimpacts being avoided by Policy are relatively smallerover the next few decades and increase over time Sec-ond under both contrasting futures for precipitationin the United States many irrigated crops benefitmore from mitigation than dryland crops In generalirrigated crops tend to gain from the reduced tempera-tures while being unaffected by changes in precipita-tion since they already irrigate to meet their waterneeds This demonstrates the importance of assump-tions concerning water availability and incidence forirrigation Third the effect of global GHG stabilizationon projected forest yields is substantially (almost anorder of magnitude) less than estimated differences incrop yields and the direction of the effect dependsstrongly upon climate model and forest type (hard-wood versus softwood) Consistent with the findingson crop prices the majority of the impacts are con-centrated in 2050 and beyond One important caveatto that finding is that this study is using smoothedtrends of forest productivity and does not reflectincreases in disturbances such as pest damages whichmay influence forest investment decisions when thepossibility of large capital losses are reflected Fourththe impacts of climate change and the effects of stabili-zation are not equally distributed with variationsdepending upon crop type irrigation status forestvegetation type and other factors This finding is con-sistent with previous studies that found that there maybe modest agricultural impacts of climate change inthe United States at the national level but substantialdistributional effects (Beach et al 2010a) Fifth becauseof the positive impacts on crop yields global GHGmitigation is projected to lower crop prices in generalcompared to the Reference scenario This creates sub-stantial benefits of mitigation for consumers that out-weigh the losses incurred by producers Sixth we finda significant effect on net GHG emissions that is highlydependent on the climate scenario used for forestryyields While the MIROC Policy case raises forestyields and increases the size of the US sink from agri-culture and forests IGSM-CAM has exactly the oppo-site effect Additional work to refine estimates of thechanges in forest carbon associated with mitigation

14

Environ Res Lett 10 (2015) 095004 RHBeach et al

policy would be valuable and could be one piece of alarger future research effort to assess interactionsbetween mitigation and adaptation activities Finallyanother interesting finding is that there are gains toconsumers that outweigh losses to producers underour mitigation scenarios Although producers mayreceive higher yields under the Policy scenario it doesnot necessarily mean that they will becomemore prof-itable In fact the opposite is often the case Whenyields rise for a large group of products making inelas-tic goods such as most food commodities the pricewill tend to fall by more than the quantity sold risesleading to a drop in revenue and profits Depending onthe structure and incidence of the costs of a potentialmitigation policy producers could be concernedabout bearing costs of mitigation while also losingproducer surplus due to the change in climate impactswhichmay be a consideration for policy design

One limitation of this study is that it does notreflect climate change impacts on international forestsand agriculture which would also affect relativereturns to different uses of land and trade patterns andtherefore affect land use decisions (Leclegravere et al 2014)For instance including negative impacts on yields inthe rest of the world would tend to drive up global pri-ces and make US exports more competitive Alsonumerous uncertainties remain regarding issues suchas future changes in crop technology energy policyand other interactions that will substantially affectmarket outcomes which is one rationale for conduct-ing model comparisons (Nelson et al 2013) The use ofjust one crop process model one biophysical forestsimulation model and one market model each ofwhich contain their own structural uncertaintiesshould be noted given the importance of these uncer-tainties raised in recent model intercomparisons (Nel-son et al 2013 Rosenzweig et al 2013) Finally thisanalysis omits important aspects of climate changeimpacts to agriculture and forestry including damagesfrom extreme weather events wildfire and changes inweeds pests disease and ozone damage Additionalresearch is necessary to better characterize and under-stand a variety of model uncertainties explore theinteractive effects between land-using sectors in moredetail and incorporate additional interactions withinternationalmarkets

Acknowledgments

The authors wish to acknowledge thefinancial supportof the US Environmental Protection Agencyrsquos ClimateChange Division (Contract EP-BPA-12-H-0023 CallOrder EP-B13H-00143) Kaiguang Zhao and ErwanMonier provided technical contributions We are alsograteful for comments from two anonymousreviewers which greatly improved the manuscriptThe views expressed in this paper are solely those of

the authors and do not necessarily reflect those of theUS Environmental Protection Agency

References

AdamsD Alig RMcCarl BA andMurray BC 2005 FASOMGHGconceptual structure and specification documentation(httpagecon2tamuedupeoplefacultymccarl-brucepapers1212FASOMGHG_docpdf)

AdamsRMMcCarl BA SegersonK Rosenzweig C Bryant K JDixonBL Conner J R EvensonRE andOjimaD S 2004Theeconomic effects of climate change onUS agricultureTheImpact of Climate Change on theUnited States Economy edRMendelsohn and JNewmann (London CambridgeUniversity Press) pp 18ndash54

AdamsRMRosenzweig C Peart RM Ritchie J TMcCarl BAGlyer J D Curry RB Jones JW Boote K J andAllen LH Jr1990Global climate change andUS agricultureNature 345219ndash24

Ainsworth EA andLong S P 2005What havewe learned from15years of free-airCO2 enrichment (FACE) Ameta-analyticreviewof the responses of photosynthesis canopy propertiesandplant production to risingCO2NewPhytology 165 351ndash71

Alig R J AdamsDMandMcCarl BA 2002 Projecting impacts ofglobal climate change on theUS forest and agriculture sectorsand carbon budgets Forest EcologyManage 169 3ndash14

AttavanichWandMcCarl BA 2014How isCO2 affecting yields andtechnological progress a statistical analysisClim Change 124747ndash62

BacheletD Lenihan JMDaly CNeilsonRPOjimaDS andPartonW J 2001MC1 A dynamic vegetationmodel forestimating the distribution of vegetation and associatedecosystem fluxes of carbon nutrients andwater technicaldocumentationVersion 10 General Technical ReportPNWGTR-508USDepartment of Agriculture ForestService PacificNorthwest Research Station PortlandOR(wwwfslorstedudgvmmcgtr508pdf)

BacheletDNeilsonRPHickler TDrapek R J Lenihan JMSykesMT Smith B Sitch S andThonickeD2003 Simulatingpast and future dynamics of natural ecosystems in theUnitedStatesGlob Biogeochemical Cycles 17 1045

BacheletDNeilsonRP Lenihan JMandDrapek R J 2004Regionaldifferences in the carbon source-sink potential of naturalvegetation in theUSA EnvironManage 33 S23ndash43

BeachRH ZhenC ThomsonAMRejesus RM Sinha PLentz AWVedenovDV andMcCarl B 2010aClimateChange Impacts on Crop Insurance Prepared forUSDepartment of Agriculture RiskManagement Agency

BeachRHAdamsDMAlig R J Baker J S Latta G SMcCarl BMurray BC Rose SK andWhite EM2010bModelDocumentation for the Forest andAgricultural SectorOptimizationModel withGreenhouse Gases (FASOMGHG)Prepared forUS Environmental ProtectionAgency (wwwcoforsteducoffrresearchtamm

FASOMGHG_Model_Documentation)Boehlert B StrzepekKMChapra SC FantC Gebretsadik Y

LickleyM SwansonRMcCluskey ANeumann J E andMartinich J 2015Climate change impacts and greenhouse gasmitigation effects onUSwater quality J AdvModel EarthSystems accepted ( doi1010022014MS000400)

Boote K J Jones JWandPickeringNB 1996 Potential uses andlimitations of cropmodelsAgronomy J 88 704ndash16

Boote K J PickeringNB andAllen LH Jr 1997 Plantmodelingadvances and gaps in our capability to project future cropgrowth and yield in response to global climate changeAdvances in CarbonDioxide Effects Research ed LHAllen JrMBKirkmanDMOlszyk andCEWhitman ASA SpecialPublicationNo 61 (MadisonWI ASA-CSSA-SSSA)

DalyC Bachelet D Lenihan JM PartonWNeilson RP andOjimaD2000Dynamic simulations of tree-grass interactionsfor global change studies Ecological Appl 10 449ndash69

15

Environ Res Lett 10 (2015) 095004 RHBeach et al

become larger over time as seen in figure 6 though thepercentage impactwill dependon the evolutionof theUSinventoryover time

47WelfareThe differences in prices and the level of productionand consumptionwill lead to impacts on the economicwelfare of consumers and commodity producers Onecommon method of measuring these effects on well-being is in terms of differences in economic surpluswhich is a monetary measure of welfare Consumersrsquosurplus is the difference between the maximumamount consumers would have been willing to pay fora commodity and the actual price paid whereasproducersrsquo surplus is the difference between theminimum amount producers would have accepted tosupply a commodity and the actual price received

Overall the results of this study show that stabili-zation results in transfers from producers to con-sumers as commodity prices decline for goods withrelatively inelastic demands Although the magnitudeand temporal distribution of benefits under the Policycase vary across the IGSM-CAM model initializationsand MIROC projection each of our scenarios results

in cumulative net gains to consumers that more thanoutweigh any losses to producers as has been found inprior studies (eg Irland et al 2001) Based on theMC1and EPIC simulated impacts on forest and agriculturalproductivity the Policy case results in sizable increasesin overall yields and reduced commodity prices com-pared to unabated climate change in the Referencecase As shown in tables 2 and 3 reducing global GHGemissions under the Policy case is found to increasetotal surplus in the forest and agriculture sector by acumulative $327 billion to $545 billion for the2015ndash2100 period across the two GCMs and the dif-ferent model initializations of IGSM-CAM The mag-nitude of welfare impacts in the agricultural sector aremuch larger than in the forestry sector

Figure 6Difference in cumulative net GHG emissions by type using IGSM-CAMandMIROCClimate Projections Policy caserelative toReference case

Table 2Present value of cumulative differences in consumer and producer welfare using IGSM-CAMclimate projections Policy caserelative to Reference case 2015ndash2100 (million $2005)

Consumer surplus Producer surplus TOTAL

Minimum Average Maximum Minimum Average Maximum Minimum Average Maximum

Agriculture $6318 $24 921 $37 077 minus$4330 $11 224 $27 168

Forestry minus$162 $57 $440 $29 $298 $782

TOTAL $6211 $24 978 $36 915 minus$4151 $11 522 $27 495 $32 764 $36 500 $43 100

Note Agriculture + Forestry does not necessarily sum to totals because the table is independently calculating minimum average and

maximumvalues for agriculture forestry and the combined totals across thefive IGSM-CAM initializations

Table 3Present value of cumulative differences in consumer andproducer welfare usingMIROCclimate projections Policy versusReference case 2015ndash2100 (million $2005)

Consumer surplus Producer surplus TOTAL

Agriculture $52 665 minus$2809

Forestry minus$138 $777

TOTAL $52 527 minus$2032 $50 495

13

Environ Res Lett 10 (2015) 095004 RHBeach et al

There have been several previous studies examin-ing the potential economic impacts of climate changeon US agriculture but many focus only on majorcrops they do not necessarily report national welfarevalues and they do not explicitly examine the benefitsof mitigation For instance Reilly et al (2003) usingpredictions from GCMs from the Canadian CenterClimate Model and Hadley Centre Model found thatclimate change from 2030ndash2090 would have positiveimpacts on welfare ranging between $08 billion (2000$) in 2030 and $122 billion in 2090 The main driverof these productivity gains were projected increases inprecipitation levels under climate change Greenstoneand Deschenes (2007) also found positive climatechange impacts using the Hadleymodel estimating anincrease of $13 billion Schlenker et al (2005) findmuch more negative impacts estimating that eco-nomic effects of climate change on agriculture need tobe assessed differently in dryland and irrigated areasand that pooling the dryland and irrigated countiescould potentially yield biased estimates Based onavailable data they estimate the model for drylandcounties and the estimated annual losses are about $5to $53 billion for dryland non-urban counties aloneTemperature increases affect crop responses in a non-linear fashion Using a 55 year panel data on cropyields Schlenker and Roberts (2006) found increasesin crop yields (for corn soybeans and cotton) withhigher temperatures until reaching threshold valuesTheir results show very large decreases in crop yieldstoward the end of the century as temperatures exceedthese threshold levels The study estimates that yieldsof these three crops are expected to decline by 25ndash44under a slow warming scenario and 60ndash79 respec-tively under a quick warming scenario at the end ofthe century Thus the negative effects on agriculturecould become very large in the long-term future iftemperatures begin to reach threshold levels

In general the findings from previous studies havedepended heavily on the net changes in precipitationexperienced under climate change but the welfareresults from this study fall within the general range ofestimates that have been generated

5 Conclusions

Our analytical approach of a combination of climatemodels a crop process model a dynamic vegetationmodel and an economic model of the forest andagricultural sectors was applied to a Reference andPolicy case enabling us to conduct an integratedassessment of the potential benefits of climate stabili-zation onUS forests and agriculture The results of thispaper are generally consistent with published studiesfocused on impacts in these sectors Projections ofadverse yields resulting from unmitigated climatechange large regional differences in crop responseincreasing vulnerability of water supplies for

irrigation and the ability of adaptation to reduceadverse effects are consistent with the findings of themajor climate science assessments (eg Nelsonet al 2013 Hatfield et al 2014) However our resultsaugment previous studies through the use of anintegrated forest and agricultural model to study thepotential benefits of GHG mitigation which can helpinform policy decisions weighing the relative costs andbenefits ofmitigation and adaptation strategies

First global GHG mitigation generally results inhigher agricultural yields across most of our dis-aggregated crop types compared to the unabated cli-mate change scenario even across different climatemodels largely though avoiding decreases in yieldsdue to climate change The majority of the yield bene-fits occur after 2050 though as the Reference climateimpacts being avoided by Policy are relatively smallerover the next few decades and increase over time Sec-ond under both contrasting futures for precipitationin the United States many irrigated crops benefitmore from mitigation than dryland crops In generalirrigated crops tend to gain from the reduced tempera-tures while being unaffected by changes in precipita-tion since they already irrigate to meet their waterneeds This demonstrates the importance of assump-tions concerning water availability and incidence forirrigation Third the effect of global GHG stabilizationon projected forest yields is substantially (almost anorder of magnitude) less than estimated differences incrop yields and the direction of the effect dependsstrongly upon climate model and forest type (hard-wood versus softwood) Consistent with the findingson crop prices the majority of the impacts are con-centrated in 2050 and beyond One important caveatto that finding is that this study is using smoothedtrends of forest productivity and does not reflectincreases in disturbances such as pest damages whichmay influence forest investment decisions when thepossibility of large capital losses are reflected Fourththe impacts of climate change and the effects of stabili-zation are not equally distributed with variationsdepending upon crop type irrigation status forestvegetation type and other factors This finding is con-sistent with previous studies that found that there maybe modest agricultural impacts of climate change inthe United States at the national level but substantialdistributional effects (Beach et al 2010a) Fifth becauseof the positive impacts on crop yields global GHGmitigation is projected to lower crop prices in generalcompared to the Reference scenario This creates sub-stantial benefits of mitigation for consumers that out-weigh the losses incurred by producers Sixth we finda significant effect on net GHG emissions that is highlydependent on the climate scenario used for forestryyields While the MIROC Policy case raises forestyields and increases the size of the US sink from agri-culture and forests IGSM-CAM has exactly the oppo-site effect Additional work to refine estimates of thechanges in forest carbon associated with mitigation

14

Environ Res Lett 10 (2015) 095004 RHBeach et al

policy would be valuable and could be one piece of alarger future research effort to assess interactionsbetween mitigation and adaptation activities Finallyanother interesting finding is that there are gains toconsumers that outweigh losses to producers underour mitigation scenarios Although producers mayreceive higher yields under the Policy scenario it doesnot necessarily mean that they will becomemore prof-itable In fact the opposite is often the case Whenyields rise for a large group of products making inelas-tic goods such as most food commodities the pricewill tend to fall by more than the quantity sold risesleading to a drop in revenue and profits Depending onthe structure and incidence of the costs of a potentialmitigation policy producers could be concernedabout bearing costs of mitigation while also losingproducer surplus due to the change in climate impactswhichmay be a consideration for policy design

One limitation of this study is that it does notreflect climate change impacts on international forestsand agriculture which would also affect relativereturns to different uses of land and trade patterns andtherefore affect land use decisions (Leclegravere et al 2014)For instance including negative impacts on yields inthe rest of the world would tend to drive up global pri-ces and make US exports more competitive Alsonumerous uncertainties remain regarding issues suchas future changes in crop technology energy policyand other interactions that will substantially affectmarket outcomes which is one rationale for conduct-ing model comparisons (Nelson et al 2013) The use ofjust one crop process model one biophysical forestsimulation model and one market model each ofwhich contain their own structural uncertaintiesshould be noted given the importance of these uncer-tainties raised in recent model intercomparisons (Nel-son et al 2013 Rosenzweig et al 2013) Finally thisanalysis omits important aspects of climate changeimpacts to agriculture and forestry including damagesfrom extreme weather events wildfire and changes inweeds pests disease and ozone damage Additionalresearch is necessary to better characterize and under-stand a variety of model uncertainties explore theinteractive effects between land-using sectors in moredetail and incorporate additional interactions withinternationalmarkets

Acknowledgments

The authors wish to acknowledge thefinancial supportof the US Environmental Protection Agencyrsquos ClimateChange Division (Contract EP-BPA-12-H-0023 CallOrder EP-B13H-00143) Kaiguang Zhao and ErwanMonier provided technical contributions We are alsograteful for comments from two anonymousreviewers which greatly improved the manuscriptThe views expressed in this paper are solely those of

the authors and do not necessarily reflect those of theUS Environmental Protection Agency

References

AdamsD Alig RMcCarl BA andMurray BC 2005 FASOMGHGconceptual structure and specification documentation(httpagecon2tamuedupeoplefacultymccarl-brucepapers1212FASOMGHG_docpdf)

AdamsRMMcCarl BA SegersonK Rosenzweig C Bryant K JDixonBL Conner J R EvensonRE andOjimaD S 2004Theeconomic effects of climate change onUS agricultureTheImpact of Climate Change on theUnited States Economy edRMendelsohn and JNewmann (London CambridgeUniversity Press) pp 18ndash54

AdamsRMRosenzweig C Peart RM Ritchie J TMcCarl BAGlyer J D Curry RB Jones JW Boote K J andAllen LH Jr1990Global climate change andUS agricultureNature 345219ndash24

Ainsworth EA andLong S P 2005What havewe learned from15years of free-airCO2 enrichment (FACE) Ameta-analyticreviewof the responses of photosynthesis canopy propertiesandplant production to risingCO2NewPhytology 165 351ndash71

Alig R J AdamsDMandMcCarl BA 2002 Projecting impacts ofglobal climate change on theUS forest and agriculture sectorsand carbon budgets Forest EcologyManage 169 3ndash14

AttavanichWandMcCarl BA 2014How isCO2 affecting yields andtechnological progress a statistical analysisClim Change 124747ndash62

BacheletD Lenihan JMDaly CNeilsonRPOjimaDS andPartonW J 2001MC1 A dynamic vegetationmodel forestimating the distribution of vegetation and associatedecosystem fluxes of carbon nutrients andwater technicaldocumentationVersion 10 General Technical ReportPNWGTR-508USDepartment of Agriculture ForestService PacificNorthwest Research Station PortlandOR(wwwfslorstedudgvmmcgtr508pdf)

BacheletDNeilsonRPHickler TDrapek R J Lenihan JMSykesMT Smith B Sitch S andThonickeD2003 Simulatingpast and future dynamics of natural ecosystems in theUnitedStatesGlob Biogeochemical Cycles 17 1045

BacheletDNeilsonRP Lenihan JMandDrapek R J 2004Regionaldifferences in the carbon source-sink potential of naturalvegetation in theUSA EnvironManage 33 S23ndash43

BeachRH ZhenC ThomsonAMRejesus RM Sinha PLentz AWVedenovDV andMcCarl B 2010aClimateChange Impacts on Crop Insurance Prepared forUSDepartment of Agriculture RiskManagement Agency

BeachRHAdamsDMAlig R J Baker J S Latta G SMcCarl BMurray BC Rose SK andWhite EM2010bModelDocumentation for the Forest andAgricultural SectorOptimizationModel withGreenhouse Gases (FASOMGHG)Prepared forUS Environmental ProtectionAgency (wwwcoforsteducoffrresearchtamm

FASOMGHG_Model_Documentation)Boehlert B StrzepekKMChapra SC FantC Gebretsadik Y

LickleyM SwansonRMcCluskey ANeumann J E andMartinich J 2015Climate change impacts and greenhouse gasmitigation effects onUSwater quality J AdvModel EarthSystems accepted ( doi1010022014MS000400)

Boote K J Jones JWandPickeringNB 1996 Potential uses andlimitations of cropmodelsAgronomy J 88 704ndash16

Boote K J PickeringNB andAllen LH Jr 1997 Plantmodelingadvances and gaps in our capability to project future cropgrowth and yield in response to global climate changeAdvances in CarbonDioxide Effects Research ed LHAllen JrMBKirkmanDMOlszyk andCEWhitman ASA SpecialPublicationNo 61 (MadisonWI ASA-CSSA-SSSA)

DalyC Bachelet D Lenihan JM PartonWNeilson RP andOjimaD2000Dynamic simulations of tree-grass interactionsfor global change studies Ecological Appl 10 449ndash69

15

Environ Res Lett 10 (2015) 095004 RHBeach et al

There have been several previous studies examin-ing the potential economic impacts of climate changeon US agriculture but many focus only on majorcrops they do not necessarily report national welfarevalues and they do not explicitly examine the benefitsof mitigation For instance Reilly et al (2003) usingpredictions from GCMs from the Canadian CenterClimate Model and Hadley Centre Model found thatclimate change from 2030ndash2090 would have positiveimpacts on welfare ranging between $08 billion (2000$) in 2030 and $122 billion in 2090 The main driverof these productivity gains were projected increases inprecipitation levels under climate change Greenstoneand Deschenes (2007) also found positive climatechange impacts using the Hadleymodel estimating anincrease of $13 billion Schlenker et al (2005) findmuch more negative impacts estimating that eco-nomic effects of climate change on agriculture need tobe assessed differently in dryland and irrigated areasand that pooling the dryland and irrigated countiescould potentially yield biased estimates Based onavailable data they estimate the model for drylandcounties and the estimated annual losses are about $5to $53 billion for dryland non-urban counties aloneTemperature increases affect crop responses in a non-linear fashion Using a 55 year panel data on cropyields Schlenker and Roberts (2006) found increasesin crop yields (for corn soybeans and cotton) withhigher temperatures until reaching threshold valuesTheir results show very large decreases in crop yieldstoward the end of the century as temperatures exceedthese threshold levels The study estimates that yieldsof these three crops are expected to decline by 25ndash44under a slow warming scenario and 60ndash79 respec-tively under a quick warming scenario at the end ofthe century Thus the negative effects on agriculturecould become very large in the long-term future iftemperatures begin to reach threshold levels

In general the findings from previous studies havedepended heavily on the net changes in precipitationexperienced under climate change but the welfareresults from this study fall within the general range ofestimates that have been generated

5 Conclusions

Our analytical approach of a combination of climatemodels a crop process model a dynamic vegetationmodel and an economic model of the forest andagricultural sectors was applied to a Reference andPolicy case enabling us to conduct an integratedassessment of the potential benefits of climate stabili-zation onUS forests and agriculture The results of thispaper are generally consistent with published studiesfocused on impacts in these sectors Projections ofadverse yields resulting from unmitigated climatechange large regional differences in crop responseincreasing vulnerability of water supplies for

irrigation and the ability of adaptation to reduceadverse effects are consistent with the findings of themajor climate science assessments (eg Nelsonet al 2013 Hatfield et al 2014) However our resultsaugment previous studies through the use of anintegrated forest and agricultural model to study thepotential benefits of GHG mitigation which can helpinform policy decisions weighing the relative costs andbenefits ofmitigation and adaptation strategies

First global GHG mitigation generally results inhigher agricultural yields across most of our dis-aggregated crop types compared to the unabated cli-mate change scenario even across different climatemodels largely though avoiding decreases in yieldsdue to climate change The majority of the yield bene-fits occur after 2050 though as the Reference climateimpacts being avoided by Policy are relatively smallerover the next few decades and increase over time Sec-ond under both contrasting futures for precipitationin the United States many irrigated crops benefitmore from mitigation than dryland crops In generalirrigated crops tend to gain from the reduced tempera-tures while being unaffected by changes in precipita-tion since they already irrigate to meet their waterneeds This demonstrates the importance of assump-tions concerning water availability and incidence forirrigation Third the effect of global GHG stabilizationon projected forest yields is substantially (almost anorder of magnitude) less than estimated differences incrop yields and the direction of the effect dependsstrongly upon climate model and forest type (hard-wood versus softwood) Consistent with the findingson crop prices the majority of the impacts are con-centrated in 2050 and beyond One important caveatto that finding is that this study is using smoothedtrends of forest productivity and does not reflectincreases in disturbances such as pest damages whichmay influence forest investment decisions when thepossibility of large capital losses are reflected Fourththe impacts of climate change and the effects of stabili-zation are not equally distributed with variationsdepending upon crop type irrigation status forestvegetation type and other factors This finding is con-sistent with previous studies that found that there maybe modest agricultural impacts of climate change inthe United States at the national level but substantialdistributional effects (Beach et al 2010a) Fifth becauseof the positive impacts on crop yields global GHGmitigation is projected to lower crop prices in generalcompared to the Reference scenario This creates sub-stantial benefits of mitigation for consumers that out-weigh the losses incurred by producers Sixth we finda significant effect on net GHG emissions that is highlydependent on the climate scenario used for forestryyields While the MIROC Policy case raises forestyields and increases the size of the US sink from agri-culture and forests IGSM-CAM has exactly the oppo-site effect Additional work to refine estimates of thechanges in forest carbon associated with mitigation

14

Environ Res Lett 10 (2015) 095004 RHBeach et al

policy would be valuable and could be one piece of alarger future research effort to assess interactionsbetween mitigation and adaptation activities Finallyanother interesting finding is that there are gains toconsumers that outweigh losses to producers underour mitigation scenarios Although producers mayreceive higher yields under the Policy scenario it doesnot necessarily mean that they will becomemore prof-itable In fact the opposite is often the case Whenyields rise for a large group of products making inelas-tic goods such as most food commodities the pricewill tend to fall by more than the quantity sold risesleading to a drop in revenue and profits Depending onthe structure and incidence of the costs of a potentialmitigation policy producers could be concernedabout bearing costs of mitigation while also losingproducer surplus due to the change in climate impactswhichmay be a consideration for policy design

One limitation of this study is that it does notreflect climate change impacts on international forestsand agriculture which would also affect relativereturns to different uses of land and trade patterns andtherefore affect land use decisions (Leclegravere et al 2014)For instance including negative impacts on yields inthe rest of the world would tend to drive up global pri-ces and make US exports more competitive Alsonumerous uncertainties remain regarding issues suchas future changes in crop technology energy policyand other interactions that will substantially affectmarket outcomes which is one rationale for conduct-ing model comparisons (Nelson et al 2013) The use ofjust one crop process model one biophysical forestsimulation model and one market model each ofwhich contain their own structural uncertaintiesshould be noted given the importance of these uncer-tainties raised in recent model intercomparisons (Nel-son et al 2013 Rosenzweig et al 2013) Finally thisanalysis omits important aspects of climate changeimpacts to agriculture and forestry including damagesfrom extreme weather events wildfire and changes inweeds pests disease and ozone damage Additionalresearch is necessary to better characterize and under-stand a variety of model uncertainties explore theinteractive effects between land-using sectors in moredetail and incorporate additional interactions withinternationalmarkets

Acknowledgments

The authors wish to acknowledge thefinancial supportof the US Environmental Protection Agencyrsquos ClimateChange Division (Contract EP-BPA-12-H-0023 CallOrder EP-B13H-00143) Kaiguang Zhao and ErwanMonier provided technical contributions We are alsograteful for comments from two anonymousreviewers which greatly improved the manuscriptThe views expressed in this paper are solely those of

the authors and do not necessarily reflect those of theUS Environmental Protection Agency

References

AdamsD Alig RMcCarl BA andMurray BC 2005 FASOMGHGconceptual structure and specification documentation(httpagecon2tamuedupeoplefacultymccarl-brucepapers1212FASOMGHG_docpdf)

AdamsRMMcCarl BA SegersonK Rosenzweig C Bryant K JDixonBL Conner J R EvensonRE andOjimaD S 2004Theeconomic effects of climate change onUS agricultureTheImpact of Climate Change on theUnited States Economy edRMendelsohn and JNewmann (London CambridgeUniversity Press) pp 18ndash54

AdamsRMRosenzweig C Peart RM Ritchie J TMcCarl BAGlyer J D Curry RB Jones JW Boote K J andAllen LH Jr1990Global climate change andUS agricultureNature 345219ndash24

Ainsworth EA andLong S P 2005What havewe learned from15years of free-airCO2 enrichment (FACE) Ameta-analyticreviewof the responses of photosynthesis canopy propertiesandplant production to risingCO2NewPhytology 165 351ndash71

Alig R J AdamsDMandMcCarl BA 2002 Projecting impacts ofglobal climate change on theUS forest and agriculture sectorsand carbon budgets Forest EcologyManage 169 3ndash14

AttavanichWandMcCarl BA 2014How isCO2 affecting yields andtechnological progress a statistical analysisClim Change 124747ndash62

BacheletD Lenihan JMDaly CNeilsonRPOjimaDS andPartonW J 2001MC1 A dynamic vegetationmodel forestimating the distribution of vegetation and associatedecosystem fluxes of carbon nutrients andwater technicaldocumentationVersion 10 General Technical ReportPNWGTR-508USDepartment of Agriculture ForestService PacificNorthwest Research Station PortlandOR(wwwfslorstedudgvmmcgtr508pdf)

BacheletDNeilsonRPHickler TDrapek R J Lenihan JMSykesMT Smith B Sitch S andThonickeD2003 Simulatingpast and future dynamics of natural ecosystems in theUnitedStatesGlob Biogeochemical Cycles 17 1045

BacheletDNeilsonRP Lenihan JMandDrapek R J 2004Regionaldifferences in the carbon source-sink potential of naturalvegetation in theUSA EnvironManage 33 S23ndash43

BeachRH ZhenC ThomsonAMRejesus RM Sinha PLentz AWVedenovDV andMcCarl B 2010aClimateChange Impacts on Crop Insurance Prepared forUSDepartment of Agriculture RiskManagement Agency

BeachRHAdamsDMAlig R J Baker J S Latta G SMcCarl BMurray BC Rose SK andWhite EM2010bModelDocumentation for the Forest andAgricultural SectorOptimizationModel withGreenhouse Gases (FASOMGHG)Prepared forUS Environmental ProtectionAgency (wwwcoforsteducoffrresearchtamm

FASOMGHG_Model_Documentation)Boehlert B StrzepekKMChapra SC FantC Gebretsadik Y

LickleyM SwansonRMcCluskey ANeumann J E andMartinich J 2015Climate change impacts and greenhouse gasmitigation effects onUSwater quality J AdvModel EarthSystems accepted ( doi1010022014MS000400)

Boote K J Jones JWandPickeringNB 1996 Potential uses andlimitations of cropmodelsAgronomy J 88 704ndash16

Boote K J PickeringNB andAllen LH Jr 1997 Plantmodelingadvances and gaps in our capability to project future cropgrowth and yield in response to global climate changeAdvances in CarbonDioxide Effects Research ed LHAllen JrMBKirkmanDMOlszyk andCEWhitman ASA SpecialPublicationNo 61 (MadisonWI ASA-CSSA-SSSA)

DalyC Bachelet D Lenihan JM PartonWNeilson RP andOjimaD2000Dynamic simulations of tree-grass interactionsfor global change studies Ecological Appl 10 449ndash69

15

Environ Res Lett 10 (2015) 095004 RHBeach et al

policy would be valuable and could be one piece of alarger future research effort to assess interactionsbetween mitigation and adaptation activities Finallyanother interesting finding is that there are gains toconsumers that outweigh losses to producers underour mitigation scenarios Although producers mayreceive higher yields under the Policy scenario it doesnot necessarily mean that they will becomemore prof-itable In fact the opposite is often the case Whenyields rise for a large group of products making inelas-tic goods such as most food commodities the pricewill tend to fall by more than the quantity sold risesleading to a drop in revenue and profits Depending onthe structure and incidence of the costs of a potentialmitigation policy producers could be concernedabout bearing costs of mitigation while also losingproducer surplus due to the change in climate impactswhichmay be a consideration for policy design

One limitation of this study is that it does notreflect climate change impacts on international forestsand agriculture which would also affect relativereturns to different uses of land and trade patterns andtherefore affect land use decisions (Leclegravere et al 2014)For instance including negative impacts on yields inthe rest of the world would tend to drive up global pri-ces and make US exports more competitive Alsonumerous uncertainties remain regarding issues suchas future changes in crop technology energy policyand other interactions that will substantially affectmarket outcomes which is one rationale for conduct-ing model comparisons (Nelson et al 2013) The use ofjust one crop process model one biophysical forestsimulation model and one market model each ofwhich contain their own structural uncertaintiesshould be noted given the importance of these uncer-tainties raised in recent model intercomparisons (Nel-son et al 2013 Rosenzweig et al 2013) Finally thisanalysis omits important aspects of climate changeimpacts to agriculture and forestry including damagesfrom extreme weather events wildfire and changes inweeds pests disease and ozone damage Additionalresearch is necessary to better characterize and under-stand a variety of model uncertainties explore theinteractive effects between land-using sectors in moredetail and incorporate additional interactions withinternationalmarkets

Acknowledgments

The authors wish to acknowledge thefinancial supportof the US Environmental Protection Agencyrsquos ClimateChange Division (Contract EP-BPA-12-H-0023 CallOrder EP-B13H-00143) Kaiguang Zhao and ErwanMonier provided technical contributions We are alsograteful for comments from two anonymousreviewers which greatly improved the manuscriptThe views expressed in this paper are solely those of

the authors and do not necessarily reflect those of theUS Environmental Protection Agency

References

AdamsD Alig RMcCarl BA andMurray BC 2005 FASOMGHGconceptual structure and specification documentation(httpagecon2tamuedupeoplefacultymccarl-brucepapers1212FASOMGHG_docpdf)

AdamsRMMcCarl BA SegersonK Rosenzweig C Bryant K JDixonBL Conner J R EvensonRE andOjimaD S 2004Theeconomic effects of climate change onUS agricultureTheImpact of Climate Change on theUnited States Economy edRMendelsohn and JNewmann (London CambridgeUniversity Press) pp 18ndash54

AdamsRMRosenzweig C Peart RM Ritchie J TMcCarl BAGlyer J D Curry RB Jones JW Boote K J andAllen LH Jr1990Global climate change andUS agricultureNature 345219ndash24

Ainsworth EA andLong S P 2005What havewe learned from15years of free-airCO2 enrichment (FACE) Ameta-analyticreviewof the responses of photosynthesis canopy propertiesandplant production to risingCO2NewPhytology 165 351ndash71

Alig R J AdamsDMandMcCarl BA 2002 Projecting impacts ofglobal climate change on theUS forest and agriculture sectorsand carbon budgets Forest EcologyManage 169 3ndash14

AttavanichWandMcCarl BA 2014How isCO2 affecting yields andtechnological progress a statistical analysisClim Change 124747ndash62

BacheletD Lenihan JMDaly CNeilsonRPOjimaDS andPartonW J 2001MC1 A dynamic vegetationmodel forestimating the distribution of vegetation and associatedecosystem fluxes of carbon nutrients andwater technicaldocumentationVersion 10 General Technical ReportPNWGTR-508USDepartment of Agriculture ForestService PacificNorthwest Research Station PortlandOR(wwwfslorstedudgvmmcgtr508pdf)

BacheletDNeilsonRPHickler TDrapek R J Lenihan JMSykesMT Smith B Sitch S andThonickeD2003 Simulatingpast and future dynamics of natural ecosystems in theUnitedStatesGlob Biogeochemical Cycles 17 1045

BacheletDNeilsonRP Lenihan JMandDrapek R J 2004Regionaldifferences in the carbon source-sink potential of naturalvegetation in theUSA EnvironManage 33 S23ndash43

BeachRH ZhenC ThomsonAMRejesus RM Sinha PLentz AWVedenovDV andMcCarl B 2010aClimateChange Impacts on Crop Insurance Prepared forUSDepartment of Agriculture RiskManagement Agency

BeachRHAdamsDMAlig R J Baker J S Latta G SMcCarl BMurray BC Rose SK andWhite EM2010bModelDocumentation for the Forest andAgricultural SectorOptimizationModel withGreenhouse Gases (FASOMGHG)Prepared forUS Environmental ProtectionAgency (wwwcoforsteducoffrresearchtamm

FASOMGHG_Model_Documentation)Boehlert B StrzepekKMChapra SC FantC Gebretsadik Y

LickleyM SwansonRMcCluskey ANeumann J E andMartinich J 2015Climate change impacts and greenhouse gasmitigation effects onUSwater quality J AdvModel EarthSystems accepted ( doi1010022014MS000400)

Boote K J Jones JWandPickeringNB 1996 Potential uses andlimitations of cropmodelsAgronomy J 88 704ndash16

Boote K J PickeringNB andAllen LH Jr 1997 Plantmodelingadvances and gaps in our capability to project future cropgrowth and yield in response to global climate changeAdvances in CarbonDioxide Effects Research ed LHAllen JrMBKirkmanDMOlszyk andCEWhitman ASA SpecialPublicationNo 61 (MadisonWI ASA-CSSA-SSSA)

DalyC Bachelet D Lenihan JM PartonWNeilson RP andOjimaD2000Dynamic simulations of tree-grass interactionsfor global change studies Ecological Appl 10 449ndash69

15

Environ Res Lett 10 (2015) 095004 RHBeach et al

DiffenbaughNSHertel TW SchererM andVermaM2012Response of cornmarkets to climate volatility underalternative energy futuresNat Clim Change 2 514ndash8

Dutkiewicz S Sokolov AP Scott J and Stone P 2005A three-dimensional ocean-sea ice-carbon cyclemodel and itscoupling to a two-dimensional atmosphericmodel uses inclimate change studiesReport 122MIT Joint Programon theScience and Policy ofGlobal Change (httpglobalchangemitedufilesdocumentMITJPSPGC_Rpt122pdf)

GreenstoneMandDeschenesO2007The economic impacts ofclimate change evidence from agricultural output andrandomfluctuations inweatherAm Econ Rev 97 354ndash85

HanewinkelM CullmannDA SchelhaasM-J Nabuurs G-J andZimmermannNE2013Climate changemay cause severe lossin the economic value of European forest landNat ClimChange 3 203ndash7

Hatfield J TakleG GrotjahnRHolden P Izaurralde RCMader TMarshall E and LivermanD2014Agriculture Climate ChangeImpacts in theUnited States The ThirdNational ClimateAssessment ed JMMelillo TCRichmond andGWYohe USGlobal Change Research Program ch 6 pp 150ndash74

IPCC2013Climate Change 2013 The Physical Science BasisContribution ofWorkingGroup I to the Fifth Assessment Reportof the Intergovernmental Panel on Climate Change(Cambridge CambridgeUniversity Press)

IPCC2014Climate Change 2014 Impacts Adaptation andVulnerability Part A Global and Sectoral AspectsContribution ofWorkingGroup II to the Fifth AssessmentReport of the Intergovernmental Panel onClimate Change edCB Field et al (Cambridge CambridgeUniversityPress) p 1132

Irland LC AdamsDMAlig R J Betz C J ChenCCHutchinsMMcCarl BA SkogK and SohngenB L 2001Assessingsocioeconomic impacts of climate change onUS forestswood-productmarkets and forest recreationBioscience 51753ndash64

KirilenkoAP and Sedjo RA 2007Climate change impacts onforestry Proc Natl Acad Sci USA 104 19697ndash702

Lawler J J LewisD JNelson E PlantingaA J Polasky SWithey J CHelmersDPMartinuzzi S PenningtonD andRadeloff VC2014 Projected land-use change impacts on ecosystemservices in theUnited States Proc Natl Acad Sci USA 1117492ndash7

LeclegravereDHavlik P Fuss S Schmid EMosnier AWalsh B ValinHHerreroMKhabarovN andObersteinerM2014Climate change induced transformations of agriculturalsystems insights from a globalmodel Environ Res Lett 9124018

Lenihan JM Bachelet DNeilsonRP andDrapek R 2008 Simulatedresponse of coterminousUnited States ecosystems to climatechange at different levels of fire suppression CO2 emissionrate and growth response toCO2Glob Planet Change 6416ndash25

Long S P Ainsworth EA Leakey ADBNoumlsberger J andOrtDR2006 Food for thought lower-than-expected crop yieldstimulationwith rising CO2 concentrations Science 3121918ndash21

Massetti E andMendelsohnR 2011 Estimating Ricardian functionswith panel dataClim Change Econ 2 301ndash19

MendelsohnR andDinar A 2003Climate water and agricultureLand Econ 79 328ndash41

MendelsohnRNordhausWand ShawD1994The impact of globalwarming on agriculture a Ricardian analysisAm Econ Rev84 753ndash71

MillsD Jones R Carney K St JulianaA ReadyR Crimmins AMartinich J Shouse KDeAngelo B andMonier E 2015Quantifying andmonetizing potential climate change policyimpacts on terrestrial ecosystem carbon storage andwildfiresin theUnited StatesClim Change 131 163ndash78

Monier E GaoX Scott J Sokolov A and Schlosser A 2015Aframework formodeling uncertainty in regional climatechangeClim Change 131 51ndash66

NelsonGC et al 2013Climate change effects on agricultureEconomic responses to biophysical shocks Proc Natl AcadSci USA 111 3274ndash9

Oregon StateUniversity 2011 MC1DynamicVegetationModel(httpfslorstedudgvm)Accessed 9May 2012

Paltsev SMonier E Scott J Sokolov A andReilly J 2015 Integratedeconomic and climate projections for impact assessmentClim Change 131 21ndash33

Reilly J et al 2003US agriculture and climate change new resultsClim Change 57 43ndash69

Rogers BNeilson RDrapek R Lenihan JWells J Bachelet D andLawB2011 Impacts of climate change onfire regimes andcarbon stocks of theUSPacificNorthwest J Geophys ResBiogeosciences 116G03037

Rosenzweig C et al 2013Assessing agricultural risks of climatechange in the 21st century in a global gridded cropmodelintercomparison Proc Natl Acad Sci 111 3268ndash73

SchlenkerWHannemanMandFisherAC2005WillUSagriculturereally benefit fromglobalwarmingAccounting for irrigationin the hedonic approachAm EconRev95395ndash406

SchlenkerWHannemanMand Fisher AC 2007Water availabilitydegree days and the potential impact of climate change onirrigated agriculture inCaliforniaClim Change 81 19ndash38

SchlenkerWandRobertsM2006 Estimating the impact of climatechange on crop yields non-linear effects of weather on cornyieldsRev Agric Econ 28 391ndash8

Sharkey TD Bernacchi C J FarquharGD and Singsaas E L 2007Fitting photosynthetic carbon dioxide response curves for C(3) leaves Plant Cell Environ 30 1035ndash40

ShawMR et al 2009The Impact of Climate Change onCaliforniarsquosEcosystem Services CEC-500-2009-025-FCalifornia EnergyCommission California Climate ChangeCenter (wwwenergycagov2009publicationsCEC-500-2009-025CEC-500-2009-025-DPDF)

SokolovA et al 2005 MIT integrated global systemmodel (IGSM)version 2Model description and baseline evaluationReport124MIT Joint Programon the Science and Policy of GlobalChange (httpglobalchangemitedufilesdocumentMITJPSPGCRpt124pdf)

Stockle COWilliams J R RosenbergN J and Jones CA 1992aAmethod for estimating the direct and climatic effects of risingatmospheric carbon dioxide on growth and yield of crops IModification of the EPICmodel for climate change analysisAgric Syst 38 225ndash38

Stockle CODyke PTWilliams J R Jones CA andRosenbergN J1992bAmethod for estimating the direct and climatic effectsof rising atmospheric carbon dioxide on growth and yield ofcrops IIModification of the EPICmodel for climate changeanalysisAgric Syst 38 239ndash56

Susaeta A Carter DR andAdamsDC2014 Impacts of climatechange on economics of forestry and adaptation strategies intheUnited States South J Agric Appl Econo 46 257ndash72

USClimate Change Science Program (CCSP) 2008The Effects ofClimate Change onAgriculture LandResourcesWaterResources and Biodiversity in theUnited States AReport bytheUSClimate Change Science Program and theSubcommittee onGlobal Change ResearchUSDepartmentof AgricultureWashington DC (wwwusdagovoceclimate_changeSAP4_3CCSPFinalReportpdf)

USEnvironmental ProtectionAgency (USEPA) 2015 Inventory ofUS greenhouse gas emissions and sinks1990ndash2013TechnicalReportEPA430-R-15-004

Waldhoff SMartinich J SarofimMDeAngelo BMcFarland JJantarasami L Shouse K Crimmins A and Li J 2015Overviewof the special issue amulti-model framework to achieveconsistent evaluation of climate change impacts in theUnitedStatesClim Change 131 1ndash20

Wheeler T and von Braun J 2013Climate change impacts on globalfood security Science 341 508ndash13

Williams J R 1995The EPICmodelComputerModels inWatershedHydrology edVP Singh (Highlands Ranch COWaterResources Publication) pp 909ndash1000

16

Environ Res Lett 10 (2015) 095004 RHBeach et al