Download Introduction to the Global Trade Analysis Project

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-agriculturemarkets-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
Exploring the climate-agriculturemarkets-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
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
GDD above 29C
rise sharply
throughout Corn
Belt; leads to drop
in yields
Precipitation changes
less pronounced
Exploring the climate-agriculturemarkets-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
Temperature Sensitivity of Yields is Key
US Corn Yield Response to Temp
Schlenker and Roberts, PNAS, 2009
Climate variability translates
into increased year-on-year
yield volatility:
Std deviation of
yield ratio rises
in future climate
Historic climate Future climate Ratio: Future/Historic Std Dev
What is driving the increased
variability?
% change in standard deviation of weighted individual drivers of YR
9
Combine county yield ratios with national
production weights to get national ratio
Production weights
YRUS,t = åw i × YRi,t
i
10
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
Source: Berry, Schlenker and Roberts, NBER, 2012
The excess heat was made much worse by the
drought: Cumulative Precipitation
Source: Berry, Schlenker and Roberts, NBER, 2012
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
Exploring the climate-agriculturemarkets-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
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
160
7
140
6
Oil
5
Corn
100
4
80
3
60
2
40
20
January 01 - August 07
Correlation = .32
Sep 07 Oct 08
r = .92
Nov 08 May 09
1
r = .56
0
0
20
Central Illinois No. 2, Yellow ($/bushel)
Cushing, OK Spot Price FOB ($/barrel)
120
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
S'
DL
S
DH
S
P'0
P'0
P0
Q'0 Q0
High Oil Prices (assuming blend wall is
relaxed by 2020) → more elastic corn
demand due to price-responsive sales to
liquid fuel market
P0
Q'0 Q0
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 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
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)
27
Simulations with GTAP-BIO-AEZ Model
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
28
Simulations with GTAP-BIO-AEZ Model
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
29
Simulations with GTAP-BIO-AEZ Model
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
0.53
-0.07
-0.27
-0.08
Normalized Standard Deviation of US Corn price
relative to Baseline Case (=1)
Adaptation wedges under future
climate: metric = SD of year on
year corn price changes
in 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
Analysis ignores the role of
soils and infrastructure in
determining the location
of production
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.
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
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 geospatial data and analysis tools.
GEOSHARE features a scalable structure
which can be readily expanded
Global Livestock
Mario 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
Over-prediction
42
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
Overprediction
once again
43
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 (E85) vehicles as well