LETTERSPUBLISHED ONLINE: 22 APRIL 2012 | DOI: 10.1038/NCLIMATE1491

Response of corn markets to climate volatilityunder alternative energy futuresNoah S. Diffenbaugh1,2,3*, ThomasW. Hertel3,4, Martin Scherer1 and Monika Verma1,4

Recent price spikes1,2 have raised concern that climate changecould increase food insecurity by reducing grain yields in thecoming decades3,4. However, commodity price volatility is alsoinfluenced by other factors5,6, which may either exacerbate orbuffer the effects of climate change. Here we show that UScorn price volatility exhibits higher sensitivity to near-termclimate change than to energy policy influences or agriculture–energy market integration, and that the presence of a biofuelsmandate enhances the sensitivity to climate change by morethan 50%. The climate change impact is driven primarilyby intensification of severe hot conditions in the primarycorn-growing region of the United States, which causes UScorn price volatility to increase sharply in response to globalwarming projected to occur over the next three decades. Closerintegration of agriculture and energy markets moderatesthe effects of climate change, unless the biofuels mandatebecomes binding, in which case corn price volatility is insteadexacerbated. However, in spite of the substantial impact onUS corn price volatility, we find relatively small impact onfood prices. Our findings highlight the critical importanceof interactions between energy policies, energy–agriculturelinkages and climate change.

Price volatility—particularly sharp upward price spikes—haslong characterized commodity markets1. However, the recentprice run-ups have been attributed to more pronounced ‘marketinelasticity’ associated with constraints on global land supply,biofuel policy mandates, depleted stocks and disruptive tradepolicies—all of which reduce the ease of adjustment in the face oftemporary scarcity2. In addition, grain supply shortfalls are oftencaused by adverse weather events in major producing regions of theworld. The likelihood of increasing occurrence of severe hot eventsin response to increasing global greenhouse-gas concentrations7poses a particular risk for field crops, which have historically shownhigh sensitivity to severe heat3,4.

A key unknown is whether increasing stress from climateextremes will influence yield volatility in addition to overall yieldlevels. Yield volatility can be measured in terms of deviationsfrom some long-term average, or as the variability in year-to-year changes in yields. In this work, we favour the latter metric,as it reflects those changes that are more likely to influencemarkets, including sensitivity to extreme events of the sort thatcause market disruptions. (By way of comparison, the 1980–2000standard deviation of year-on-year US national yield ratios (20%)is almost double the standard deviation of detrended US national

1Department of Environmental Earth System Science and Woods Institute for the Environment, Stanford University, 473 Via Ortega, Stanford, California94305-4216, USA, 2Department of Earth and Atmospheric Sciences, Purdue University, 550 Stadium Mall Drive, West Lafayette, Indiana 47907, USA,3Purdue Climate Change Research Center, Purdue University, 610 Purdue Mall, West Lafayette, Indiana 47907, USA, 4Department of AgriculturalEconomics and Global Trade Analysis Project, Purdue University, 403 West State Street, West Lafayette, Indiana 47907, USA.*e-mail: diffenbaugh@stanford.edu.

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Figure 1 | Standard deviation of year-on-year percentage change in UScorn prices under alternative climate, policy and economic scenarios.Each bar shows the standard deviation of US corn prices in the historic(blue) and future (red) climate, in the presence (hashed) or absence(unhashed) of the biofuels mandate and high (US$169 per bbl) or low(US$53 per bbl) oil price (see Methods and Supplementary Information fordetails). The number above each vertical bar reports the value ofyear-on-year price volatility in that model prescription. The white barshows price volatility for half of the future change in yield volatility. (SeeSupplementary Information for additional sensitivity analysis.).

yields (11%; ref. 8)). The potential for increasing yield volatilityis of particular concern within the context of the recent increasein market inelasticity2. Indeed, the combination of a bindingrenewable fuels standard for corn ethanol and capacity constraintson ethanol absorption (the so-called ‘blend wall’) in the US couldcause US corn price volatility to increase by more than 50% inresponse to historical supply shocks in the domesticmarket6.

We seek to quantify the sensitivity of US corn price volatilityto near-term changes in climate volatility, to the extent ofagriculture–energy market integration, to the presence of the USbiofuels mandate and to future oil price trajectories. To our

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LETTERS NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1491

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Figure 2 |US corn yield ratios in the historic and future climate. a, Observed and simulated distributions of yield ratios (yt/yt−1). The coloured numbersshow the standard deviation of year-on-year yield ratios, including the mean of the standard deviations from the five realizations. The confidence intervalsfor α and β reported by Schlenker and Roberts give volatility ranges of 20–24% and 43–53% for the simulated historic and future climates, respectively.b, Curves show the response of US corn yield ratio volatility to changes in the critical temperature threshold for different fractions (xβ0) of theSchlenker–Roberts β value denoting the associated yield penalty.

knowledge, this is one of the first attempts to draw out theprice effects of climate volatility—particularly in the context ofrelated economic policies. To enable this quantification, we employa one-way climate–agriculture–economic modelling framework(Supplementary Fig. S1). We first project twenty-first-centurychanges in temperature and precipitation using an ensemble ofhigh-resolution climate model experiments9 (see Methods). Wethen simulate the response of US corn yields to climatic conditionsusing a statistical model3 (see Methods). Finally, we simulate thecommodity market impacts of yield volatility using the GTAP-BIO-AEZ model10, which allows us to both project the futurebio-economy under both low- (US$53 per barrel (bbl)) andhigh- (US$169 per bbl) oil-price projections11 and examine theimpacts of biofuel mandates under each of these future economies(see Methods). Our approach simulates national-scale corn yieldvolatility that is very close to the observed value (standard deviationsof 22% and 20%, respectively, for simulated and observed year-on-year yield ratios over the 1980–2000 period; see SupplementaryInformation). Likewise, the standard deviation of year-on-yearUS percentage corn price changes induced by the supply-sideshocks to the economic model is very close to the observed value(25% and 28%, respectively, for simulated and observed year-on-year percentage price changes over the 1990–2009 period; seeSupplementary Information).

We find that climate change increases US corn price volatilityby a factor of 4.1 in the historic economy (from 43 to 177%;Fig. 1.) The amplification of corn price volatility results froma doubling of supply volatility in the future climate, with thestandard deviation of year-on-year corn yield ratios increasingfrom 22% in the historic period to 48% in the future period(Fig. 2). (An increase in yield volatility that is only half aslarge leads to a doubling of price volatility, indicating that theprice response to changing supply volatility is fairly linear inthe context of the historic economy; Fig. 1.) The response ofprice volatility to the climate-change-induced doubling of supplyvolatility is reduced to a factor of 3.1 (high oil price) and 3.4(low oil price) in the 2020 economy if the biofuels mandate isnot in place. This decline in sensitivity of price volatility arisesfrom the increased share of corn sales into the relatively price-responsive liquid-fuel market—in the absence of a mandate (seeSupplementary Information). In contrast, the effect of climatechange on US corn price volatility is increased to a factor of 5.3(high oil prices) and 4.8 (low oil prices) if the biofuels mandate iskept in place in 2020.

The sensitivity of US corn price volatility to developments in thebiofuels market is greater within the context of the future climatethan the historic climate (Fig. 1). For instance, in the context ofthe 2020 economy and low oil prices, elimination of the biofuelsmandate reduces corn price volatility from 41 to 32% in the historicclimate, whereas elimination of the mandate reduces price volatilityfrom 200 to 109% in the future-climate/low-oil-price scenario. It istherefore clear that the mandate, which has a substantial impact onUS corn price volatility under historic climate, has an even greaterimpact in the context of near-term climate change.

The impact of the mandate on US corn price volatilityis intimately related to the degree of integration between theagricultural and energy economies6. In principle, the liquid-fuelmarket offers a very price-elastic demand source, which can serveas a buffer against supply-side shocks in the corn market. Whenyields are above normal and prices are low, biofuel production canexpand to absorb the surplus; when yields are low and prices arehigh, biofuel plants can temporarily shut down and release cornsupplies for livestock and food uses. This is evidenced by an increasein the absolute value of the general equilibrium elasticity of demandfor US corn under the 2020 high-oil-price scenario, which is drivenby price-responsive biofuel sales (Supplementary Fig. S2, dark greenbar). Thus, the lowest price volatilities exist in the 2020 scenarios,with historic climate and nomandate (Fig. 1). The smallest absolutedemand elasticities and the highest price volatilities occur in the2020 scenario when the mandate remains in place. In this case,the larger share of corn sales to ethanol, coupled with the lackof price responsiveness in these sales, results in extremely volatilecorn prices in response to supply-side shocks. This volatility ismarkedly larger than in the 2020 high-oil-price scenario underfuture climate without the mandate in place. (We note that themandate is binding in 44% of the simulations under the combinedfuture-climate/high-oil-price scenario, and arises when the US cornyield shock is adverse.)

The national-level increase in yield volatility (Fig. 2) is drivenprimarily by increases in yield volatility in the US corn belt region(Supplementary Fig. S3), which exhibits increases of 100–160%in growing degree days (GDDs) above 29 ◦C, increases of up to(and in places exceeding) 50% in growing-season precipitationand increases of less than 6% in GDDs between 10 ◦C and 29 ◦C(Supplementary Fig. S3). Near-term climate change substantiallyincreases the yield-response-weighted volatility of all three climatevariables (including increases of greater than 100% in yield-response-weighted volatility of GDDs above 29 ◦C throughout

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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1491 LETTERS

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Figure 3 |Nearest distance to equivalent temperature envelope in the future climate. Colour contours show GDDs above 29 ◦C in the historic (leftpanels) and future (centre panels) climate. The blue area in the right panels shows the county weights in US corn production exceeding 0.18% in the1979–2000 period; the red area shows the grid points in the future climate that exhibit the minimum grid-point distance to a GDD value within 1 GDD ofeach of the blue grid points. Each grid point is allowed to be occupied only once in the future climate (see Supplementary Information for details).

the corn belt region; Supplementary Fig. S3), doubling thenational-level US corn yield volatility (Fig. 2).

The capacity to adapt to climate change through thedevelopment of new, heat-tolerant varieties and/or shifts in thegeographic concentration of corn production could help to alleviatethe impacts of severe heat on corn yields. Restoring the presentlevel of US corn yield ratio volatility within the context of futureclimate would require increasing the critical threshold from 29to 32.5 ◦C for the present regression coefficients, or to 31.0 ◦Cfor a severe-heat penalty that is 0.7 of the present value (Fig. 2).In addition, in the future climate, the nearest areas of equivalentmean and standard deviation of GDDs between 10 and 29 ◦Cexhibit a high degree of overlap with the present US corn belt(Supplementary Fig. S4), but the nearest areas of equivalentmean and standard deviation of GDDs above 29 ◦C are locatedconsiderably northward (Fig. 3). The need for changes in heattolerance or growing locationwill be reduced if the actual near-termclimate change is smaller than projected here, although the CMIP3global climatemodel ensemble shows twenty-first-centurywarmingof at least 2 ◦Cper century over the central US (ref. 12). Likewise, thephysiological response to increasing ambient CO2 concentrationscould alter the crop response to climate change13,14, although theeffect on the year-on-year corn yield ratio volatility is uncertain14.

The price response to increased supply volatility could alsobe altered by several factors from which we have abstracted(Supplementary Fig. S5). Any increase in year-on-year pricevolatility will increase the incentive for stockholding as a strategy forbenefiting from greater price spikes. However, as withmany studies,we subsume the stockholding response into consumers’ overallprice responsiveness of demand, thereby overstating the latter1.This abstraction makes it impossible to examine the interplaybetween increased year-on-year volatility and the private-sectorincentives for accumulating stocks and releasing them in low-yield

years—which will have a moderating influence on prices. Likewise,we have not factored in the response of corn producers to theincreased price risk, which is likely to further moderate theirresponse to price shocks15. In addition, we abstract from theimpacts of changes in climate volatility on other crops in theUS as well as impacts in the rest of the world. As a result, theimpact of the climate-induced increase in corn price volatility onoverall food prices in the US and other countries is quite small(see Supplementary Information). However, these effects could bemuch larger if similar increases in the frequency of severe eventsoccur simultaneously in other growing regions16, as is projected bynumerous global climate model simulations7.

Our treatment of the ethanolmandate and energy prices presentsa final set of limitations.We treat thismandate as being commodity-specific, on the assumption that other biofuels are unlikely todisplace corn ethanol over the near-term period17. However, inreality the US Renewable Fuels Standard is far more complex18.Furthermore, incremental waivers19 relaxing the stringency of theethanol mandate are more likely than the complete eliminationthat we have prescribed here. Indeed, such waivers have alreadybeen granted for cellulosic biofuels, and the waiver option is agreat source of potential uncertainty in these markets20. Finally, thestrengthened linkages between the corn and fuel markets indicatedby our results would increase exposure to interannual variability inoil prices, presenting an additional source of uncertainty.

Notwithstanding these important caveats, we conclude thateconomic influences can interact with increased climate volatilityto generate significant commodity price variability in the near-termdecades (Supplementary Fig. S5).Our results indicate that increasedgreenhouse-gas concentrations are likely to lead to increasedfrequency and intensity of severely hot temperatures in the UScorn belt, leading to increases in year-on-year variability in UScorn yields and increases in US corn price volatility. This increased

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LETTERS NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1491

price volatility in response to supply-side shocks will be dampenedby closer integration of the corn and energy markets. However,such integration increases the exposure to oil price uncertainty, andincreases the influence of biofuel mandates, which amplify the priceresponse to yield volatility by promoting market inelasticity. Ourresults therefore indicate that energy markets and associated policydecisions could substantially exacerbate the impacts of climatechange, even for the relatively modest levels of global warming thatare likely to occur over the near-term decades.

MethodsWe employ a high-resolution climate model ensemble9 in which the ICTP RegCM3limited-area climate model21 is nested within the NCAR CCSM3 global climatemodel22 at 25-km horizontal resolution over the continental United States23,generating five high-resolution simulations of the 1950–2040 period in the SpecialReport on Emissions Scenarios A1B emissions scenario24. We remove the bias in thefive climate model realizations using a quantile-based bias correction method25,26,which substantially improves the simulation of temperature extremes over thecentral United States25. Further details of the climate model simulations and thebias correction are included in the Supplementary Information.

We calculate corn yield ratios using the Schlenker–Roberts yield function3,which predicts corn yields in county i and year t from GDDs, precipitation,county-fixed effects and a time trend for technology. By taking the differencebetween two successive years, this statistical model can be used to calculate theyear-on-year yield change as the yield ratio:

YRi,t =yi,tyi,t−1

= exp(α · (Φ−i,t −Φ−i,t−1)+β · (Φ+

i,t −Φ+i,t−1)

+ δ1 · (Pi,t −Pi,t−1)−δ2 · (P2i,t −P

2i,t−1))

where Φ−i,t are the growing-season (1 March–31 August) GDDs betweenthe base of 10 ◦C and 29 ◦C, Φ+i,t are the growing-season GDDs above 29 ◦Cand Pi,t are the total growing-season precipitation (cmm−2). The estimatedcoefficients3 are α= 3.15512×10−4, β=−6.43807×10−3, δ1= 1.02821×10−2 andδ2=8.15140×10−5. For purposes of analysing climate-induced commodity marketvolatility, we aggregate to the national level using county production sharesωi:

YRUS,t =∑i

ωi ·YRi,t

We calculate the yield ratios in both the historic (1979–2000) and future(2019–2040) climate. The standard deviation of historical yield ratios calculatedusing the bias-corrected high-resolution climate simulations (22%) agrees with theobserved value (20%; see also Fig. 2). Further details of the yield calculation areincluded in the Supplementary Information.

We employ the global economic model GTAP-BIO-AEZ10, which integratesagricultural and energy markets and allows for explicit modelling of biofuelpolicies. The model has been validated in stochastic mode in the context ofrandom shocks to supply and demand in agriculture27 and energy markets28.We validate the model by undertaking a historic simulation over the 2001–2008period as well as by undertaking a stochastic simulation analysis wherein wesample from the observed 1990–2009 year-on-year yield distribution and predictcorn price volatility in the context of the 2008 economy. The resulting standarddeviation of model-predicted year-on-year price changes (25%) is in agreementwith the standard deviation of observed, detrended, year-on-year percentage pricechanges (28%). We also examine the sensitivity of our findings to uncertaintiesin the economic model parameters, and find the results to be robust (seeSupplementary Information).

We generate a baseline scenario for the period 2008–2020 in which we shocktotal factor productivity, population, labour force, investment and oil prices(see Supplementary Information for list of variables). We focus special attentionon the state of the biofuel mandate in 2020. We allow the mandated level ofethanol production to rise in accordance with present estimates, factoring indevelopments in other renewable fuels from FAPRI (ref. 29). We find that ourmodel conforms with FAPRI’s prediction that the mandate will be binding in 2020.Having established this baseline projection for the global economy to 2020, wethen re-run these economic projections from 2008 to 2020 imposing alternativelythe low- and high-oil-price trajectories. We find that, without climate change,the biofuels mandate is severely binding in the low-oil-price scenario (recall thatit was already binding in the reference scenario), but that the mandate becomesnon-binding in the high-oil-price scenario, causing ethanol production to exceedthe mandate by 17%. Further details of the economic modelling are included in theSupplementary Information.

Given that the calculated US corn yield volatility is twice as high in the futureclimate as in the historic climate (Fig. 2), we investigate the effect of climate changeby doubling the US corn yield volatility in the GTAP model. This doubling isapplied in three sets of ‘multi-factor’ economic model simulations that examine

the interaction of climate change with energy markets and policies (Fig. 1): the2001 economy, with corn yield volatility calculated from either historic or futureclimate (2 simulations); the 2020 economy with high oil prices, with yield volatilitycalculated from either the historic or future climate, with or without the biofuelsmandate (4 simulations); and the 2020 economy with low oil prices, with yieldvolatility calculated from either the historic or future climate, with or without themandate (4 simulations).

Received 19 December 2011; accepted 20March 2012;published online 22 April 2012

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AcknowledgementsWe thank W. Schlenker for sharing his data and parameter estimates with us. We aregrateful for insightful and constructive comments from participants in the InternationalAgricultural Trade Consortium theme day and the Stanford Environmental EconomicsSeminar series. We thank NCEP for providing access to the NARR data set, and thePRISM Climate Group for providing access to the PRISM observational data set.We thank the Rosen Center for Advanced Computing (RCAC) at Purdue Universityand the Center for Computational Earth and Environmental Science (CEES) atStanford University for access to computing resources. The research reported herewas primarily supported by the US DOE, Office of Science, Office of Biological

and Environmental Research, Integrated Assessment Research Program, Grant No.DE-SC005171, along with supplementary support from NSF award 0955283 and NIHaward 1R01AI090159-01.

Author contributionsN.S.D. designed and performed the climate modelling, designed theclimate–yield–economic modelling approach, analysed the results and wrotethe paper. T.W.H. designed the climate–yield–economic modelling approach,designed the economic modelling, analysed the results and wrote the paper. M.S.designed the climate–yield–economic modelling approach, performed the yieldcalculations and analysed the results. M.V. designed the climate–yield–economicmodelling approach, performed the economic modelling, analysed the resultsand wrote the paper.

Additional informationThe authors declare no competing financial interests. Supplementary informationaccompanies this paper on www.nature.com/natureclimatechange. Reprints andpermissions information is available online at www.nature.com/reprints. Correspondenceand requests for materials should be addressed to N.S.D.

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