Carbon Sequestration: Magnitude, Measurement, and Potential to Mitigate Climate Change Ken Cassman, Director, Nebraska Center for Energy Sciences Research Shashi Verma, School of Natural Resources

Kyoto 1990 target = 4,200 MMT CO 2 E USA greenhouse gas emissions by economic sector, 2004

Potential Annual Carbon Sequestration (Tg) in USA Crop, Forest, and range lands. Adapted from Metting FB, Smith JL, Amthor JS

UNL Carbon Sequestration Program: Goals  Quantify the annual amounts of carbon (C) sequestered in major rainfed and irrigated agroecosystems in the north-central USA.  Improve our basic understanding of the biophysical processes that govern C exchange in these ecosystems.

University of Nebraska Ecological Intensification Project What is the potential for maximizing yield, carbon sequestration, greenhouse gas mitigation, and nutrient use efficiency concomitantly with progressive management that achieves high yields and high input efficiencies? N fertigation and precise N and water management, Bt hybrids, Round-up Ready soybeans, higher plant densities, no tillage

Annually Integrated NEE (g C m -2 y -1 ) Maize, NE 300 to 500 (Verma et al., 2005) Harvard Forest, MA 200 (Barford et al., 2003) Howland Forest, ME 174 (Hollinger et al., 2004) Univ. of Michigan Biological St 80 to 170 (Schmid et al., 2003) Wind River, WA -50 to 200 (Pers. Comm.) Douglas Fir, B.C. 270 to 420 (Morgenstern et al., 2004) Tallgrass Prairie, OK 50 to 275 (Suyker et al., 2003) Northern Temperate Grassland, Alberta -18 to 20 (Flanagan et al., 2002) Mediterranean, Annual Grassland, CA -30 to 130 (Xu and Baldocchi, 2003) Soybean, NE -10 to -75 (Verma et al., 2005)

Co-Principal Investigators Shashi B. Verma School of Natural Resources Kenneth G. Cassman Agronomy and Horticulture Co-Investigators Timothy J. Arkebauer Agronomy and Horticulture Achim Dobermann Agronomy and Horticulture Anatoly A. Gitelson School of Natural Resources Kenneth G. Hubbard School of Natural Resources Johannes M. Knops School or Biological Sciences Gary D. Lynne Agricultural Economics Madhavan Soundararajan Biochemistry Andrew E. Suyker School of Natural Resources Elizabeth A. Walter-Shea School of Natural Resources Daniel T. Walters Agronomy and Horticulture Haishun Yang Agronomy and Horticulture Carbon Sequestration Program

Carbon Sequestration Program Field Sites Site 1 Irrigated continuous maize Site 2 Irrigated maize – soybean Site 3 Rainfed maize – soybean Irrigated C-C Irrigated C-S Irrigated C-S Dryland C-S

Research Components  Tower eddy covariance fluxes of CO 2, water vapor and energy: Verma, Suyker  Monitoring and mapping soil C stocks: Dobermann, Walters  Litter decomposition: Knops  Above biomass and leaf area index: Arkebauer  Leaf gas exchange: Arkebauer  Soil surface fluxes of CO 2, N 2 O and CH 4 : Arkebauer  Belowground processes: Walters  Monitoring soil water: Hubbard, Schimelfenig  Ecosystem modeling: Yang, Cassman  Remote sensing: Gitelson, Walter-Shea  Life-cycle GHG emissions analysis for both the cropping system and when crops are used for biofuel production: Walters, Cassman, Liska

Landscape-level (Eddy Covariance) Measurement of CO 2 and Other Fluxes Measuring Components of Solar Radiation Close-up of Eddy Covariance Flux Sensors Tower Flux Studies Verma, Suyker, & the team

Seasonal and Interannual Variability: Net Ecosystem CO 2 Exchange (NEE) Mead, Nebraska

Extrapolation to Regional Scales Tower CO 2 Flux vs Remotely Sensed Data Maize-Soybean, Mead, Nebraska Chlorophyll Index, CI green = [(R NIR /R green )-1], where R green and R NIR are reflectances in TM Landsat bands 2 ( nm) and 4 ( nm), respectively. (Gitelson et al., 2005)

GPP distribution retrieved from Landsat ETM+ imagery 1- Irrigated Continuous maize 2- Irrigated Maize-Soybean Rotation 3- Dryland Maize-Soybean Rotation 1 2 3

Leaf/plot levelLandscape levelRegional Scaling Process Remote Sensing Studies: Gitelson et al.; Walter-Shea et al.

Biomass and Leaf Area Index Arkebauer et al.

Leaf Gas Exchange Arkebauer et al.

Soil Surface Fluxes Arkebauer et al.

Below Ground Processes Walters et al.

Monitoring Soil Water Hubbard, Schimelfenig & the team

Litter Decomposition Knops et al.

Mapping Soil Carbon Stocks Dobermann et al.

Site 1: Irrigated Continuous Maize Fuzzy soil classes, intensive measurement zones for scaling to the whole field

Initial soil C profiles at CSP site 3, 2001 Rainfed site: soil cores representative of the six soil types within the 150 acre production field.

4-year average ( ) NBP ¶ : eddy co-variance towers  SOC ¶ : direct measure Irrigated continuous maize -53 to Irrigated maize-soybean-106 to Rainfed maize–soybean Average annual change in soil carbon stocks in a four-year period that included two complete rotation cycles for the corn-soybean rotation treatments: based on eddy tower CO2 flux measurements or direct measurement of changes in soil carbon content. BOTTOM LINE: no detectable C sequestration! ¶ Negative value indicates net loss of soil C.

Modified Century Soil Carbon Model: overpredicts C sequestration potential of our CSP sites; we find no net sequestration, i.e. C neutral

What are reasons for over- predition of soil C sequestration? Ecosystem C models calibrated to long-term field experiments that: Ecosystem C models calibrated to long-term field experiments that: Only evaluated soil C changes in upper foot of soil; ignored full active root zone profile Only evaluated soil C changes in upper foot of soil; ignored full active root zone profile Did not account for the decrease in soil bulk density that occurs when soil organic matter content increases Did not account for the decrease in soil bulk density that occurs when soil organic matter content increases While soil C turnover model components were mechanistic, crop productivity components were empirical and not robust While soil C turnover model components were mechanistic, crop productivity components were empirical and not robust Points to critical role of detailed measurements to validate ecosystem models, especially those used to inform lawmakers and guide policy Points to critical role of detailed measurements to validate ecosystem models, especially those used to inform lawmakers and guide policy

42% 34% Percentage of projected USA maize production, assuming 34 Mha area harvested and trend- line yield increase Expansion of USA Maize-Ethanol Production 20%

Greenhouse Gas Mitigation and Net Energy Yield of USA Maize-Ethanol While there are many life-cycle analysis (LCA) studies of maize-ethanol systems While there are many life-cycle analysis (LCA) studies of maize-ethanol systems Includes crop production, ethanol conversion, co-product processing and utilization Includes crop production, ethanol conversion, co-product processing and utilization Results vary depending on selection of system boundaries, energy content of crop inputs, crop yields and input levels, energy use in ethanol plant Results vary depending on selection of system boundaries, energy content of crop inputs, crop yields and input levels, energy use in ethanol plant

Backward-looking vs forward-looking LIFE-CYCLE ANALYSES Previous studies use aggregate data from the recent past Previous studies use aggregate data from the recent past But efficiencies of maize production and ethanol conversion are continually improving But efficiencies of maize production and ethanol conversion are continually improving More relevant question: what is the energy efficiency and greenhouse gas mitigation potential of current and future maize-ethanol systems? More relevant question: what is the energy efficiency and greenhouse gas mitigation potential of current and future maize-ethanol systems?

Biofuel Energy Systems Simulator (BESS) Recently released life-cycle assessment software available at: Recently released life-cycle assessment software available at: Uses updated input values for maize yields and production practices, energy requirements for ethanol fermentation- distillation, and co-product processing and utilization Uses updated input values for maize yields and production practices, energy requirements for ethanol fermentation- distillation, and co-product processing and utilization Estimates much higher net energy efficiency and greenhouse gas mitigation potential than previous estimates Estimates much higher net energy efficiency and greenhouse gas mitigation potential than previous estimates

BESS LCA Analysis: GHG Emissions Reduction (%, Mt CO 2 eq*) Type of ethanol plant USA average NE average Iowa average Advanced High-Yield Irrigated Coal, dry DG 26%, 197,817 36%, 270,668 46%, 342,359 39%, 294,171 natural gas, dry DG 51%, 381,213 61%, 454,064 70%, 525,756 63%, 477,567 natural gas, wet DG 60%, 447,462 69%, 520,313 79%, 592,004 73%, 543,816 closed-loop facility 67%, 504,269 77%, 577,120 87%, 648,812 80%, 600, Corn Production System----- Based on a 378 ML/yr maize-ethanol plant: from

Bottom line: Energy Efficiency and GHG Mitigation Current state-of-the-art USA maize ethanol systems Large net energy yield, % net energy surplus, % GHG reduction when corn-ethanol replaces gasoline Large net energy yield, % net energy surplus, % GHG reduction when corn-ethanol replaces gasoline

NPPD Generation CO 2 Projections Potential C-credits from 1 billion gallons of NE ethanol production (BESS software estimate:

Annually Integrated NEE (g C m -2 y -1 ) Maize, NE 300 to 500 (Verma et al., 2005) Harvard Forest, MA 200 (Barford et al., 2003) Howland Forest, ME 174 (Hollinger et al., 2004) Univ. of Michigan Biological St 80 to 170 (Schmid et al., 2003) Wind River, WA -50 to 200 (Pers. Comm.) Douglas Fir, B.C. 270 to 420 (Morgenstern et al., 2004) Tallgrass Prairie, OK 50 to 275 (Suyker et al., 2003) Northern Temperate Grassland, Alberta -18 to 20 (Flanagan et al., 2002) Mediterranean, Annual Grassland, CA -30 to 130 (Xu and Baldocchi, 2003) Soybean, NE -10 to -75 (Verma et al., 2005)

Contribution of other biomes to GHG emissions or mitigation, and impact on water quality? CRP land and parks Prairie grass biofuel systems Nutrient storage and fluxes Biological diversity