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Assessing the Impacts of Climate Change on U.S. Agriculture by Combining Agroecosystem and Economic Models Robert Beach, RTI International; Allison Thomson,

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Presentation on theme: "Assessing the Impacts of Climate Change on U.S. Agriculture by Combining Agroecosystem and Economic Models Robert Beach, RTI International; Allison Thomson,"— Presentation transcript:

1 Assessing the Impacts of Climate Change on U.S. Agriculture by Combining Agroecosystem and Economic Models Robert Beach, RTI International; Allison Thomson, JGCRI; Bruce McCarl, Texas A&M University Presented at North American Carbon Project 4 th All-Investigators Meeting Albuquerque, NM, February 4-7, 2013 RTI International is a trade name of Research Triangle Institute 3040 Cornwallis Road ■ P.O. Box ■ Research Triangle Park, NC Phone Fax

2 Introduction Agricultural production is inherently risky, dependent upon weather and other factors Climate change may lead to more rapid changes in mean yields as well as yield and price variability than in the past, making adjustment more difficult Extreme events Historical experience less informative Faster depreciation of research/knowledge stock Complex set of interactions between impacts with heterogeneity across time and space Need for detailed characterization of potential effects on yields for future scenarios as inputs for decision support tools 2

3 3 IPCC Scenario and GCMs Used IPCC Scenario A1B was the scenario selected for all global circulation models Actual emissions have been above the A1B scenario so we felt it was reasonable to focus on that scenario vs. B1 and B2 scenarios with lower emissions given that there has also been interest in non- fossil fuel energy (vs. A1FI or A2) GCMs (daily simulation data available for and ) GFDL-CM2.0 and GFDL-CM2.1 models developed by the Geophysical Fluid Dynamics Laboratory (GFDL), USA  Both GFDL-CM2.0 and GFDL-CM2.1 are being included because they have substantially different seasonal precipitation patterns Coupled Global Climate Model (CGCM) 3.1 developed by the Canadian Centre for Climate Modelling and Analysis, Canada Meteorological Research Institute (MRI) Coupled atmosphere- ocean General Circulation Model (CGCM) 2.2 developed by the Meteorological Research Institute, Japan Meteorological Agency, Japan

4 4 Comparison of Average Changes in U.S. Temperature and Precipitation Across GCMs

5 5 EPIC Model Process-level agro-ecosystem model originally developed at USDA, currently used by multiple research groups Has been used previously to simulate regional productivity of corn, soybeans, wheat (winter and spring), cotton, hay, and switchgrass for the U.S. at the 8-digit hydrologic unit scale Multiple soil types are represented within each of the 1,450 hydrologic units, resulting in 7,540 total runs Modeling system modified to use baseline climatology simulations and future climate change projections for daily simulations from IPCC SRES scenario A1B Projections for this study incorporate GCM results from four different models for the time period Additional cropping systems were added: sorghum, rice, barley, and potatoes for the appropriate regions

6 Change in Average Spring and Summer Temperature and Precipitation: GFDL-CM2.0, Summer Spring

7 Change in Average Spring and Summer Temperature and Precipitation: GFDL-CM2.1, Summer Spring

8 Change in Average Spring and Summer Temperature and Precipitation: CGCM3.1, Summer Spring

9 Change in Average Spring and Summer Temperature and Precipitation: MRI-CGCM2.2, Summer Spring

10 10 Examining Potential Shifts in Production Regions Changes in yield distributions may alter production regions Areas where crops were modeled in EPIC were expanded outside recent historical range Focused on suitable cropland areas in proximity to historical range EPIC simulations of mean and variance of yield Equilibrium production is being simulated based on stochastic version of FASOM

11 11 Current and Expanded Crop Production Ranges Modeled in EPIC Barley Corn Cotton Potatoes Rice Wheat Soybeans Sorghum

12 Percentage Change in Dryland and Irrigated Corn Yields Simulated Using EPIC, Irrigated Yields Dryland Yields

13 Percentage Change in Dryland and Irrigated Soybean Yields Simulated Using EPIC, Irrigated Yields Dryland Yields

14 Percentage Change in Dryland and Irrigated Wheat Yields Simulated Using EPIC, Irrigated Yields Dryland Yields

15 Sample Comparison of Simulated Corn Yield Distributions Across Climate Scenarios 15

16 16 FASOM Model Structure Objective: Welfare Maximization Land is allocated between activities (and combined with other inputs) based on relative rents (including GHG payments) and suitability to maximize intertemporal welfare Sectoral and Land Coverage Forest – approximately 80 products from private timberland Agriculture – crops and pasture  Over 70 primary and about 60 processed commodities, 20 processed feeds Developed – Tracks conversion of forest, crop, and pastureland for development 3 GHGs — CO 2, N 2 O, CH 4 Stocks and flows of GHGs for more than 50 sources and sinks 63 US regions (11 market regions) and international trade with 37 major trading partners Detailed Bioenergy Market Forestry & agricultural dedicated and residue feedstocks Tracks production of starch- and sugar-based ethanol, cellulosic ethanol, biodiesel, and bioelectricity

17 17 FASOM Regions

18 Simulated Changes in Regional Acreage Relative to Baseline, Corn (Acres) 18 Note: CB=Corn Belt, GP=Great Plains, LS=Lake States, NE=New England, RM=Rocky Mountains, PSW=Pacific Southwest, PNWE=Pacific Northwest East Side, SC=Southcentral, SE=Southeast, SW=Southwest

19 Simulated Changes in Regional Acreage Relative to Baseline, Soybeans (Acres) 19 Note: CB=Corn Belt, GP=Great Plains, LS=Lake States, NE=New England, RM=Rocky Mountains, PSW=Pacific Southwest, PNWE=Pacific Northwest East Side, SC=Southcentral, SE=Southeast, SW=Southwest

20 Simulated Changes in Regional Acreage Relative to Baseline, Wheat (Acres) 20 Note: CB=Corn Belt, GP=Great Plains, LS=Lake States, NE=New England, RM=Rocky Mountains, PSW=Pacific Southwest, PNWE=Pacific Northwest East Side, SC=Southcentral, SE=Southeast, SW=Southwest

21 Equilibrium Changes in National Average Yield Relative to Baseline 21

22 Simulated Changes in Average Market Price Relative to the Baseline 22

23 Annual Welfare Change in US Agricultural Sector (2011$) 23

24 Summary Crop yields are affected by changes in temperature, precipitation, and other aspects of climate in a very heterogeneous way Important to consider changes in both mean and variance of yields Combined results from EPIC crop process model with FASOM model of forest and agricultural production and markets Potentially substantial shifts in crop production and commodity markets, though considerable uncertainty 24

25 25 Ongoing Research/Extensions Updated climate scenarios and new EPIC runs Incorporation of impacts on U.S. forest productivity International climate impacts on forests and agriculture Improved incorporation of variability and uncertainty Integration of insurance/risk management into market modeling Integration of climate impacts and adaptation with mitigation Energy prices Renewable fuels assumptions Technological progress assumptions Carbon price paths


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