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, 2013 - North American Carbon Program All-Investigators Meeting PNNL-SA-93391

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

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

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

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

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

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

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

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.

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.

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.

Carbon price increases no-till adoption January 12, 2019

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

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

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

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 http://www.globalchange.umd.edu/models/gcam 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.