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
http://www.epa.gov/methanetomarkets/docs/methanemarkets-factsheet.pdf
USA greenhouse gas emissions by
economic sector, 2004
Kyoto 1990 target = 4,200 MMT CO2E
0
200
400
600
800
Potential Annual Carbon Sequestration (Tg) in USA Crop,
Forest, and range lands. Adapted from Metting FB, Smith JL,
Amthor JS. 1998.
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)
Carbon Sequestration Program
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
Dryland C-S
Site 1
Irrigated
continuous
maize
Site 2
Irrigated
maize –
soybean
Irrigated C-C
Irrigated C-S
Site 3
Rainfed
maize –
soybean
Carbon Sequestration Program Field Sites
Research Components
 Tower eddy covariance fluxes of CO2, 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 CO2, N2O and CH4: 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
Tower Flux Studies
Landscape-level
(Eddy Covariance)
Measurement of CO2
and Other Fluxes
Close-up of
Eddy Covariance
Flux Sensors
Measuring Components
of Solar Radiation
Verma, Suyker, & the team
Seasonal and Interannual Variability:
Net Ecosystem CO2 Exchange (NEE)
Mead, Nebraska
25
maize
Daily NEE (g C m-2 d-1)
20
maize
maize
maize
Site 1
maize
Irrigated
Continuous
Maize
15
10
P
H
P
H
P
H
P
H
P
H
5
0
-5
-10
5/1/01
8/29/01
12/27/01
4/26/02
8/24/02
12/22/02
4/21/03
8/19/03
12/17/03
4/15/04
8/13/04
12/11/04
4/10/05
8/8/05
12/6/05
4/5/06
Daily NEE (g C m-2 d-1)
25
soybean
maize
20
maize
soybean
maize
15
10
H
P
P
H
P
H
P
H
P
Site 2
Irrigated
Maize-Soybean
Rotation
H
5
0
-5
-10
5/1/01
8/29/01
12/27/01
4/26/02
8/24/02
12/22/02
4/21/03
8/19/03
12/17/03
4/15/04
8/13/04
12/11/04
4/10/05
8/8/05
12/6/05
4/5/06
Daily NEE (g C m-2 d-1)
25
maize
20
soybean
maize
soybean
15
H
P
P
H
P
P
H
H
Site 3
maize
P
10
Rainfed
Maize-Soybean
Rotation
5
H
0
-5
-10
5/1/01
8/29/01
12/27/01
4/26/02
8/24/02
12/22/02
4/21/03
8/19/03
12/17/03
4/15/04
8/13/04
12/11/04
4/10/05
8/8/05
12/6/05
4/5/06
Extrapolation to Regional Scales
Tower CO2 Flux vs Remotely Sensed Data
Maize-Soybean, Mead, Nebraska
3.5
2.0
Maize
Soybean
1.8
3.0
GPP (mg.m -2.s-1)
-2 -1
GPP (mg.m .s )
1.6
2.5
2.0
1.5
1.0
1.4
1.2
1.0
0.8
0.6
0.4
0.5
RMSE = 0.30 mg m-2s-1
RMSE = 0.20 mg m-2s-1
0.2
0.0
0.0
0
5000
10000
15000
20000
25000
0
5000
2
Predicted GPP (mg/m 2s)
10000
15000
20000
25000
2
[(rNIR/rGreen)-1] x PAR (mmol/m s)
[(rNIR/rGreen)-1] x PAR (mmol/m s)
3.0
2.5
2.0
1.5
1.0
RMSE=0.267 mg/m2s
0.5
0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Observed GPP (mg/m2s)
Chlorophyll Index, CIgreen = [(RNIR/Rgreen)-1], where Rgreen and RNIR
are reflectances in TM Landsat bands 2 (520-600 nm) and 4 (760900 nm), respectively. (Gitelson et al., 2005)
GPP distribution retrieved from
Landsat ETM+ imagery
1
2
3
6-30-2001
8-13-2001
8-29-2001
9-30-2001
1- Irrigated Continuous maize
2- Irrigated Maize-Soybean Rotation
3- Dryland Maize-Soybean Rotation
Scaling Process
Leaf/plot level
Landscape level
Remote Sensing Studies: Gitelson et al.; Walter-Shea et al.
Regional
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
0
Depth (cm)
-20
-40
Initial soil C profiles at CSP site
3, 2001
IMZ1
IMZ2
IMZ3
IMZ4
IMZ5
IMZ6
-60
-80
0
5
10
15
20
25
-1
Soil C (g kg )
Rainfed site: soil cores representative of the six soil
types within the 150 acre production field.
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!
NBP¶:
eddy co-variance towers
SOC¶:
direct measure
Irrigated continuous
maize
-53 to -33
-80
Irrigated maize-soybean
-106 to -89
-56
Rainfed maize–soybean
-22
-36
4-year average
(2001-2004)
¶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
Harvest
Removal C
AG
Live C
BG
Live C
Net Primary
Productivity
HybridHybrid-Maize
Model
http://www.nrel.colostate.edu/projects/century5
What are reasons for overpredition of soil C sequestration?

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
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
Points to critical role of detailed measurements
to validate ecosystem models, especially those
used to inform lawmakers and guide policy
Expansion of USA Maize-Ethanol Production
140
Maize requirement (MMt)
50
110
42%
34%
6
Ethanol Production (10 L/yr)
60
40
20%
30
Percentage
of projected
USA maize
production,
assuming 34
Mha area
harvested
and trendline yield
increase
70
20
10
0
1980
1985
1990
1995
Year
2000
2005
2010
Greenhouse Gas Mitigation and Net
Energy Yield of USA Maize-Ethanol

While there are many life-cycle analysis
(LCA) studies of maize-ethanol systems


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
Backward-looking vs forward-looking
LIFE-CYCLE ANALYSES



Previous studies use aggregate data
from the recent past
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?
Biofuel Energy Systems Simulator
(BESS)



Recently released life-cycle assessment
software available at: www.bess.unl.edu
Uses updated input values for maize yields
and production practices, energy
requirements for ethanol fermentationdistillation, and co-product processing and
utilization
Estimates much higher net energy
efficiency and greenhouse gas mitigation
potential than previous estimates
BESS LCA Analysis: GHG Emissions
Reduction (%, Mt CO2eq*)
-----Corn Production System----Type of
ethanol plant
USA
average
NE
average
Advanced
Iowa
High-Yield
average 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%,
600,623
Based on a 378 ML/yr maize-ethanol plant: from www.bess.unl.edu
Bottom line: Energy Efficiency and
GHG Mitigation
Current state-of-the-art USA
maize ethanol systems

Large net energy yield, 3075% net energy surplus, 2590% GHG reduction when
corn-ethanol replaces gasoline
Metric Tons
Thousands
NPPD
CO2
Projections
NPPDGeneration
Generation
CO
2 Projections
(excluding LES)
18,000
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
Potential C-credits from 1 billion gallons of NE ethanol
production (BESS software estimate: www.bess.unl.edu)
0
2005
2010
Projections
2015
2020
Est 1990
2025
2000
2005
2030
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