Presented by Thomas Hertel, Purdue University Based on joint work with

Implications of Climate Extremes for US Corn Prices Under Alternative Economic

and Energy Scenarios

Presented by Thomas Hertel, Purdue University

Based on joint work with Noah Diffenbaugh, Martin Scherer, and Monika Verma

Stanford University

Presentation to the Department of Agricultural Economics, Purdue University, October 16, 2012 Based in part on a paper published in Nature Climate Change, April 23, 2012

Exploring the climate-agriculture-markets-energy policy nexus

• Agricultural production depends on climate • Frequency and Intensity of extreme events is

anticipated to increase in the future• Crops are sensitive to climate extremes, but these can

be quite localized• Capitalize on recent high resolution climate results for

continental US• Combine with estimated yield function for maize in US• Integrate within economic model to assess market

impacts



anticipated to increase in the future • Crops are sensitive to climate extremes, but

these can be quite localized• Capitalize on recent high resolution climate

results for the US• Combine with estimated yield function for

maize in US• Integrate within economic model to assess

interplay with energy policies/energy futures

Climate Model Supports Hypothesis of Increased Extreme Events

• Regional Climate Model for USA (RegCM3) nested in Global Climate Model (CCSM3)

• High resolution (25km)• A1B Scenario for GHG forcing• Five (physically uniform) realizations: difference between the

realizations arises due to internal climate system variability• Average results across five realizations in keeping with best

practice in climate science• Compare:

– 1980-2000 (historic climate) to – 2020-2040 (future climate)

Climate is changing in Corn Belt where crops are sensitive to heat

GDD below 29C rise in Northern regions; improves growing conditions

Precipitation changesless pronounced

GDD above 29C rise sharplythroughout Corn Belt; leads to drop in yields




these effects can be quite localized• Capitalize on recent high resolution climate




Temperature Sensitivity of Yields is Key

Climate variability translates into increased year-on-year yield volatility:

US Corn Yield Response to Temp Schlenker and Roberts, PNAS, 2009

Std deviation of yield ratio rises in future climate

Historic climate Future climate Ratio: Future/Historic Std Dev

What is driving the increased variability?

9

% change in standard deviation of weighted individual drivers of YR

Combine county yield ratios with national production weights to get national ratio

10

Production weights

Validation 1: The combination of high resolution climate results with the Schlenker-Roberts yield

regression performs well vs. history

As with economics – micro-theory works better at the macro-level!

The variability (SD) of the national yield ratio doubles under future climate with historic yield function

(will evaluate changes in yield function later on)

Validation 2: What does this framework predict for the current crop year (2012)?

• Results produced by Schlenker and Roberts and presented at NBER meetings in August, revised in September using weather data up to August 31

• Based on cumulative heat and precipitation indexes; applied at county level

• Following slides come from their paper

2012 was warm early: this is a good thing for getting into the fields earlier: Days under 29C = Good Heat

Source: Berry, Schlenker and Roberts, NBER, 2012

After which it becomes a bad thing if it leads to excessive heat during critical stages of crop development: Days over

29C = Excess Heat


The excess heat was made much worse by the drought: Cumulative Precipitation


Decline in National Yields depends on model specification (BSR, 2012)

Predicted production impacts, by county

15% with simpler yield model used by us (see above)

20% BRS when adjust growing season

24% BRS when effect of heat is allowed to vary over the growing season

Latest estimates suggest a decline of 24.7%

17

Source: Berry, Schlenker and Roberts, 2012




these can be quite localized• Capitalize on recent high resolution climate




The biofuel boom and high oil prices altered the landscape

• Prior to 2006 growth in ethanol demand from use as an oxygenator; not linked to energy: Corn-crude price correlation 2001/07 = 0.32

• After 2006 this was satiated, leaving ethanol with just the energy substitution margin

• High oil prices from Sept. 2007 – Oct. 2008 encouraged significant substitution at this margin; further expansion of ethanol production with corn prices rising to choke off excess profits: Corn-crude price correlation: 2007/08 = 0.92

The correlation between corn and oil prices was strong in the high price regime of 2007/08

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Correlation = .32 r = .92 r = .56

January 01 - August 07Sep 07 - Oct 08

Nov 08 - May 09

So oil prices matter, but they are uncertain

Policies and institutional constraints matter for market price transmission

• Blend wall is currently serious issue – expect this to be relaxed over next decade

• Refinery flexibility is another key issue (see Abbott, NBER, 2012)

• Renewable Fuel Standard (RFS) represents a lower bound constraint on production

• Binding at end of 2008 when oil price fell, permitting separation of oil and corn prices, with RINs attaining positive values

• Corn-crude price correlation: 2008/09 = 0.56

RFS binding at the end of 2008

The inter-annual price response to commodity supply volatility depends on

interplay between oil prices and RFS

P'0P0

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High Oil Prices (assuming blend wall is relaxed by 2020) → more elastic corn demand due to price-responsive sales to liquid fuel market

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SDL

Low Oil Prices → Inelastic corn demand as ethanol production is dictated by policies instead of markets

Validation 3: Economic model• GTAP-BIO-AEZ extensively used to examine

biofuels policy & energy linkages• Focus is on price volatility, yet benefits many• Validate this in three ways:

- Historical simulation from 2001-2008: compare to obs changes- Stochastic simulation off 2008 base:

Use historic yield volatility: 1990-2009 (base period matters -- Higher volatility in earlier period.)

Seek to reproduce historic price volatility Actual SD year-on-year price changes was 28% Simulated value is 25%; very close to actual when add

energy price volatility- Reproduce 2012 drought: 20% yield reduction (as of early August) gives 50% price rise; comparable to observation at the time impact (assumes pre-drought expected price of $5.26/bu)

Economic Model ScenariosWe combine 5 alternative economic scenarios with historic and future climates1) Economy in 20012) Economy in 2020 with High Oil Prices and a. RFS mandate (corn ethanol only) in place (15bgy not initially binding) b. RFS mandate waived3) Economy in 2020 with Low Oil Prices a. RFS mandate in place ((corn ethanol only:

15bgy binding in 2020) b. RFS mandate waived, but only in 2020 26

Impact of corn supply shocks on US corn price volatility across climate regime, under two energy futures: No Adaptation

(standard deviation in inter-annual % price change)

• Future climate doubles yield volatility, quadruples price volatility

• Price volatility dampened under economic growth, high oil prices due to integration of agr, energy markets (higher sales share to ethanol)

27Simulations with GTAP-BIO-AEZ Model

Impact of corn supply shocks on US corn price volatility across climate regime, under two energy futures: : No Adaptation


• When add mandate, sensitivity to future climate is exacerbated (factor of 5.3 under binding mandate – shaded bars), even though not initially binding in 2020, high oil price scenario


Impact of corn supply shocks on US corn price volatility across climate regime, under two energy futures: : No Adaptation


Under low oil future,price volatility is evenhigher, particularly incontext of mandate, which is binding in 2020 benchmark


Summary of economic integration and adaptation

• Intersectoral integration:– Market driven (e.g.

higher energy prices)– Policy driven (RFS)

• International integration:– Partial: fix tariffs at

currently applied rates– Eliminate tariffs: full

trade liberalization

Normalized Standard Deviation of US Corn price relative to Baseline Case (=1)-0.27

0.53

-0.07 -0.08

Adaptation wedges under future climate: metric = SD of year on year corn price changesin 2020

What about biophysical adaptation?

• Farmers and agribusinesses have a proven capacity to adapt to a changing environment

• Adaptation through adoption of heat resistant crops• Shifting areas where crops are grown

Plant breeding to adapt yield function for high temperatures could limit volatility

• X-axis varies critical threshold at which damages arise; if increase from 29 to 32.5˚C, no change in yield volatility

• If moderate rate of yield loss due to excess heat by 0.7, then increase in critical threshold to 31˚C is sufficient

What about biophysical adaptation?

• Farmers and agribusinesses have a proven capacity to adapt to a changing environment

• Adaptation through adoption of heat resistant crops• Shifting areas where crops are grown

Adapting location of production may also limit future climate impacts

Mean GDD above 29˚C doubles over much of current corn belt, with current values found northward in the future climate.

Std Deviation of GDD above 29˚C increases more sharply under future climate

Blue area shows shows the county weights in US production that exceed 0.18%. The red area shows the grid points with the minimum distance to a GDD value within 1 GDD of the original value under future climate.

Analysis ignores the role ofsoils and infrastructure indetermining the locationof production

Conclusions• Evidence suggests increased frequency and intensity of extreme

climate events will exacerbate corn production volatility in future• Impact on commodity price volatility depends on energy regime:

– Low energy prices, binding biofuel mandates, leads to high price volatility in response to supply-side shocks

– High energy prices, non-binding mandates, high energy demands could serve as a buffer for climate-driven production volatility; provided oil refiners become more flexible

– Increased exposure to volatile energy markets could destabilize corn markets – but we don’t find this to be the case

• Analysis has abstracted from a number of key considerations:– Increased rate of stockholding will moderate price changes– Movement of RINs between years (built-in RFS flexibility)– Interaction with other mandates: Meyer & Thompson, AEPP,

forthcoming, for a complete analysis of this issue– Potential changes in yield volatility in the rest of the world....

Conclusions (2)• This work illustrates the great potential for research and policy

analysis at the interface between economic and biophysical systems. However, in order to extend this work to global scale, need greatly enhanced data base infrastructure.

• As part of his Foresight project on the long run sustainability of the global food system, UK Science Advisor, Sir John Beddington, commissioned a report from us on the current state of global geospatial data infrastructure for agriculture and the environment

• Report concludes (http://www.agecon.purdue.edu/foresight/) that most geospatial datasets for agriculture are severely limited: – Regional or national in scope, not global– If global, then one-time efforts, lacking inter-operability– Limited attention to time series data needed for science– If they are publicly available, specialized knowledge and costly

software licenses significantly limit access• Led us to propose GEOSHARE: www.geoshareproject.org

http://www.agecon.purdue.edu/foresight/

http://www.agecon.purdue.edu/foresight/

http://www.geoshareproject.org/

GEOSHARE Objectives• Provide a globally consistent, temporally opportune, and

locally relevant database for better decision making.• Assist decision makers, policy analysts and researchers

seeking to use geospatial data and analysis tools to inform activities relating to agriculture, poverty, land use and the environment.

• Build capacity throughout the world in individuals who can effectively bridge disciplines to make decisions and to identify solutions to complex resource use and development problems using geo-spatial data and analysis tools.

GEOSHARE features a scalable structure which can be readily expanded

Global LivestockMario Herrero, ILRI

Land Tenure Klaus Deininger,

World Bank

Funding and Timetable• Funding from three sources:

– UK Department For International Development– UK Department for Environment, Food and Rural

Affairs– USDA’s Economic Research Service

• Proof of concept: – Two regional case studies supporting decision makers in

Asia and Africa – Integration regional and global data bases for subset of

countries in these two regions– Delivery of data and decision tools through NSF-funded

HubZero infrastructure

Supplementary slides

40

Further validation and scrutiny of the historical data

• If shorten period to 1980-90, observed yield volatility is even higher: 0.22

41

Examining earlier period• If shorten period to 1980-90,

observed yield volatility is even higher: 0.22

• For this earlier period, our model over-predicts variability – possibly due to:– Increased temperature

sensitivity of crops– Omitted sources of

variability

42

Over-prediction

What about the recent decrease in yield volatility?

• Red dots confirm diminished volatility

• Blue dots show model’s ability to pick up this effect

• In fact, over-predict reduction, possibly due to:– Increased temperature

sensitivity of crops– Changes in omitted

sources of variation• So our model is fully

consistent with recent lessening of volatility;

• This is not inconsistent with increasing volatility in the future: It’s all about climate!

43

Volatility diminished

Over-prediction once again

The Blend Wall is also important• Blend Wall (BW): constraint on max usage• In theory it is now 15% of 135 billion gallons of

gasoline consumption; however, the effective blend limit is much lower due to infrastructure limitations, including old auto stock; therefore binding

• At BW: capacity > market absorption and ethanol price falls to breakeven: ‘warm shutdown’ of production facilities

• In future economy (2020), assume that the blend wall is no longer a constraint due to turnover in auto stock, predominance of E-15 gasoline with more flex-fuel (E-85) vehicles as well

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Presented by Thomas Hertel, Purdue University Based on joint work with