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

1. 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

2. 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

4. 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 the US • Combine with estimated yield function for maize in US • Integrate within economic model to assess interplay with energy policies/energy futures

5. 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)

6. Climate is changing in Corn Belt where crops are sensitive to heat GDD below 29C rise in Northern regions; improves growing conditions GDD above 29C rise sharply throughout Corn Belt; leads to drop in yields Precipitation changes less pronounced

7. 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 effects 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

8. Temperature Sensitivity of Yields is Key US Corn Yield Response to Temp Schlenkerand Roberts, PNAS, 2009 Climate variability translates into increased year-on-year yield volatility: Std deviation of yield ratio rises in future climate Historic climateFuture climate Ratio: Future/Historic Std Dev

9. What is driving the increased variability? % change in standard deviation of weighted individual drivers of YR

10. Combine county yield ratios with national production weights to get national ratio Production weights

11. 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!

12. The variability (SD) of the national yield ratio doubles under future climate with historic yield function(will evaluate changes in yield function later on)

13. 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

14. 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

15. After which it becomes a bad thing if it leads to excessive heat during critical stages of crop development: Days over 29C = Excess Heat Source: Berry, Schlenker and Roberts, NBER, 2012

16. The excess heat was made much worse by the drought: Cumulative Precipitation Source: Berry, Schlenker and Roberts, NBER, 2012

17. 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% Source: Berry, Schlenker and Roberts, 2012

18. 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 the US • Combine with estimated yield function for maize in US • Integrate within economic model to assess interplay with energy policies/energy futures

19. 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

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

21. So oil prices matter, but they are uncertain

22. 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

23. RFS binding at the end of 2008

24. DL S S DH P'0 P'0 P0 P0 Q'0 Q'0 Q0 Q0 The inter-annual price response to commodity supply volatility depends on interplay between oil prices and RFS S' High Oil Prices (assuming blend wall is relaxed by 2020) → more elastic corn demand due to price-responsive sales to liquid fuel market Low Oil Prices → Inelastic corn demand as ethanol production is dictated by policies instead of markets

25. 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)

26. Economic Model Scenarios We combine 5 alternative economic scenarios with historic and future climates 1) Economy in 2001 2) Economy in 2020 with High Oil Prices and a. RFS mandate (corn ethanol only) in place (15bgy not initially binding) b. RFS mandate waived 3) 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

27. 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) Simulations with GTAP-BIO-AEZ Model

28. 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) • 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 Simulations with GTAP-BIO-AEZ Model

29. 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) Under low oil future, price volatility is even higher, particularly in context of mandate, which is binding in 2020 benchmark Simulations with GTAP-BIO-AEZ Model

30. Summary of economic integration and adaptation • Intersectoralintegration: • Market driven (e.g. higher energy prices) • Policy driven (RFS) • International integration: • Partial: fix tariffs at currently applied rates • Eliminate tariffs: full trade liberalization 0.53 Normalized Standard Deviation of US Corn price relative to Baseline Case (=1) -0.07 -0.08 -0.27 Adaptation wedges under future climate: metric = SD of year on year corn price changes in 2020

31. 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

32. 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

33. 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

34. 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%. Thered 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 of soils and infrastructure in determining the location of production

35. 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....

36. 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

37. 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.

38. GEOSHARE features a scalable structure which can be readily expanded Global Livestock Mario Herrero, ILRI Land Tenure Klaus Deininger, World Bank

39. 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

40. Supplementary slides

41. Further validation and scrutiny of the historical data • If shorten period to 1980-90, observed yield volatility is even higher: 0.22

42. 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 Over-prediction

43. 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! Volatility diminished Over- prediction once again

44. 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