1. Applying an agroecosystem model to inform integrated assessments of climate change mitigation opportunities
AM Thomson, RC Izaurralde, GP Kyle, X Zhang, V Bandaru, MA Wise, TO West, KV Calvin
Joint Global Change Research Institute, Pacific Northwest National Laboratory
February 6, North American Carbon Program All-Investigators Meeting
PNNL-SA-93391

2. Overview
Purpose: Explore tradeoffs in climate mitigation options in agricultural systems
Tradeoffs are modeled in the integrated assessment model GCAM
Focus on an important agricultural region – the US Midwest
GCAM is applied with additional data that allow higher spatial and agricultural practice resolution to resolve the US Midwest
Additional data sources are necessary for the higher resolutions
A process-based model is necessary to inform the difference in important C mitigation characteristics of difference crop production practices
We apply the EPIC agro-ecosystem model to generate these data
Scenarios of future climate mitigation policy simulated in GCAM
Results illustrate tradeoffs between different mitigation options

3. The Global Change Assessment Model
14 global geopolitical regions
151 agriculture and land-use subregions
Detailed energy system representation & full integration between energy and land use sectors
Data driven: Calibrated to base year conditions and projects forward based on socioeconomic drivers
Population
GDP
Carbon price

4. Climate Mitigation Options in Agriculture
Decision making needs: How would a global climate mitigation policy influence regional land use? What agricultural practices are most important for climate mitigation?
Agricultural mitigation options simulated here:
Conversion of land to no-tillage for soil C sequestration
Removal of crop residues for cellulosic biofuel feedstock
Cellulosic biofuel feedstock crops (e.g. switchgrass)
Method: Implement global carbon price that applies to all carbon from energy systems, land use change, and land management
GCAM model reallocates land use globally in the most economically efficient manner to meet a climate target
Data are needed for the higher spatial and technology resolution of this study

5. Higher spatial resolution
GCAM-MidwestUS Domain
Modeling regions (total of 37 regions) were created by combing state boundaries and crop management zones.
Projection: Albers Equal Area
GCAM is always run globally, but for this study also nests the US Midwest as a higher resolution region in the global system
EPIC agro-ecosystem model applied at 56m resolution across the 14 state region
Detailed cropping system management representation
Using the Spatially Explicit Integrated Modeling Framework by Zhang et al. (2010)
Additional data were compiled from USDA sources and from literature analysis

6. Data needed to calibrate GCAM for the Midwest - 2005
Crop Yield
Crop Area
Cost of production
Soil C potential
Crop residue yield
Bioenergy crop yield
Agricultural production
EPIC model
Literature
USDA
CDL

7. Cropping system resolution
Standard GCAM
Corn
Wheat
Rice
OtherGrain
OilCrop
Fruit&Veg
Roots&Tubers
FodderCrop
FiberCrop
SugarCrop
PalmFruit
Bioenergy crop
GCAM – USMidwest- AgLU
Continuous and in rotation with wheat and soy
With and without tillage
With and without residue removal
Soybean
Continuous and in rotation with corn
Switchgrass
Simulated in the EPIC model
Acquired from USDA
Acquired from FAO; Used for all GCAM regions outside the US Midwest

8. Regional data combined with standard GCAM global inputs
Crop Yield
Crop Area
Cost of production
Soil C potential
Crop residue yield
Bioenergy crop yield
Agricultural production
EPIC model
Literature
USDA
CDL
GCAM
Calibration Data
GCAM inputs for AgLU at 151 global subregions

9. GCAM Scenarios for analysis
No policy: No climate mitigation policy in place;
Mitigation – 4.5: A global carbon tax is imposed to limit total atmospheric radiative forcing to 4.5 W/m2 (~525 ppm CO2) in 2100.
Mitigation – 2.6: A global carbon tax is imposed to limit total atmospheric radiative forcing to 2.6 W/m2 (~380 ppm CO2) in 2100.

10. Mitigation scenarios change economic driving forces in agriculture
When the global carbon price includes terrestrial carbon, the value of land, and land products, increases.
This leads to changes in animal product consumption.
The GCAM model optimizes food production under these changed economic conditions, altering land use.

11. Climate policy drives land use change
Bioenergy crops are important even without a global carbon price.
A carbon price causes an expansion of agricultural land for food and feed in this region.
A carbon price intensifies land use - crop land expands at the expense of pasture and other arable land.

12. Carbon price increases no-till adoption
January 12, 2019

13. Midwest provides more crop residue for bioenergy than the rest of the USA
The residue generating cropping systems (e.g. corn) are concentrated in the Midwest
With a carbon price, there is a preference for cropping systems that produce residue for bioenergy

14. Crop technology adoption – focus on one sub-region in Iowa
Corn production diversifies into all available options
Overall production increases slightly over time
With mitigation, this region takes on a higher share of global corn production
Technology shifts occur faster due to the carbon price

15. Conclusions
How would a global climate mitigation policy influence regional land use?
The US Midwest is a highly productive region for crops, and as a consequence GCAM shifts additional future crop production into this region
Agricultural land is intensified when a carbon price is introduced, with conversion of pasture and other arable land to active crop production
What agricultural practices are most important for climate mitigation?
Under a carbon price, both residue removal and no-till practices increase,
This is indicative of the tradeoffs – these practices have costs (lower yield, higher economic cost) in addition to benefits (economic value of SOC conservation and residue) so these keep the expansion in check
There is no one “winner take all” mitigation technology. The model simulates adoption of all cost-effective options.
Next: Drive EPIC with climate change projections to understand how climate impacts on crop production might affect the mitigation options

16. Acknowledgements
Funding provided by Pacific Northwest National Laboratory Program for Regional Integrated Modeling and Assessment (PRIMA)
EPIC regional modeling system developed for the US DOE Great Lakes Bioenergy Research Center (GLBRC)
GCAM is a community model and available for download from
GCAM long term model development is funded by the US DOE Office of Science Integrated Assessment Research Program
Simulations in this study used the DOE SC supported Evergreen high performance computing cluster at PNNL/JGCRI.