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1
Methods and Tools for Measurement, Monitoring and Verification for Soil Carbon Sequestration
Markets
Charles W. Rice
Department of Agronomy
K-State Research and ExtensionK-State Research and Extension
2
Measurement, Monitoring and Verification
• Verifiable and transparent for reporting changes in soil carbon stocks
– (i.e., withstand reasonable scrutiny by an independent third party as to completeness, consistency, and correctness)
• Cost efficient if soil C will be competitive with other C offsets
• Based on best science possible
• Meet requirements that are specified by international conventions
• Designed to work with data currently available but compatible with different types of data or new methods of data collection
3
Measurement, Monitoring and Verification
• Provide accounts and associated uncertainties for soil C measurements
• Flexible to accommodate new scientific developments (e.g., instrumentation, process or empirical models)
• Reporting structures that are flexible to meet the needs of different users
4
Measurement, Monitoring and Verification Detecting soil C changes
• Difficult on short time scales• Amount of change small compared to total C
Methods for detecting and projecting soil C changes (Post et al. 2001)
• Direct methods– Field measurements
• Indirect methods– Accounting
- Stratified accounting- Remote sensing- Models
Root C
LitterC
Eroded C
Cropland C
Wetland C
Eddy flux
Sampleprobe
Soil profile
Remotesensor
Respired C
Captured C
HeavyfractionC
Woodlot C
Harvested C
Buried C
Lightfraction
C
Respired C
Soil organic C
Soil inorganic C
Simulation modelsDatabases / GIS
SOCt = SOC0 + Cc + Cb - Ch - Cr - Ce
Post et al. (2001)
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Monitoring and Verification
Level Resolution Cost Producer Acceptance
Practiced Based
IndividualFields
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Estimating Changes in Soil Organic Carbon
IssuesChoice of baseline
Comparison to current practiceStart and end time points• Measure C sequestration or avoided C loss
Uncertainty and cost in estimating soil C• Measure and report mean and variation
SeasonalitySoil sampling
• Depths• Roots• Carbonates• Rocks
7
7
15
20
25
30
35
40
0 30 60 90 120 150
Years of Cultivation
So
il O
rg. C
(M
g h
a-1)
ConventionalManagement
Steady State
Improved Practice
Carbon SequesteringPractice
Practice Change
Soil Measurement
D
C
B
A
O
8
Sampling strategies: account for variable landscapes
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Geo-reference microsites
Microsites reduces spatial variability
Simple and inexpensive
Used to improve models
Used to adopt new technology
Soil C changes detected in 3 yr• 0.71 Mg C ha-1 – semiarid• 1.25 Mg C ha-1 – subhumid
Ellert et al. (2001)
Sampling location: initial subsequent electromagnetic marker
4 m
7 m
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Methods to Extrapolate Measurements and Model Predictions from Sites to Regional Scales
Models• CENTURY
• EPIC • RothC• Other models are also being developed
Spatial aggregation of soil carbon distribution• Remote sensing and climatic data• Indices:
– Productivity– Practice monitoring
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Resources Available for National-level Assessments
• NRCS/STATSGO soil data
• Daily Climate data from NOAA
• 1997 NRI area weights
• NRCS/ERS Cropping Practices Survey
• NRCS/National Soils Laboratory Pedon Database
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Crop identification for spatial modeling. Courtesy: P Doraiswamy, USDA-ARS, Beltsville, MD
Remote Sensing and Carbon Sequestration
Remote sensing useful for assessing• Vegetation
– Type– Cover– Productivity
• Water, soil temperature• Tillage intensity?
Remote sensing cannot be used to measure soil C directly unless soil is bare
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Several satellite and airborne sensors can estimate LAI, NPP, crop yields, and litter cover
Traditional sources of land cover data:• AVHRR and Landsat
Increased resolution being obtained with MODIS Good temporal resolution
• MODIS and AVHRR Excellent spatial detail provided by
• Landsat and SPOT IKONOS and Quickbird offer excellent spatial and temporal resolution Two airborne sensors
• AVIRIS• LIDAR
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CSiTE and CASMGS terrestrial ecosystem models Century
• Century• DayCent• C-STORE
EPIC• EPIC• APEX
Soil Processes
Water movement Erosion
Temp & Moisture
Density Changes
Above Gr. LiveAbove Gr. DeadBelow Gr. LiveBelow Gr. Dead
Harvest
Plant Growth
Leaching
Soil Properties, Management, Weather, CO2
PesticidesSurface residuesSubsoil residues
Humus
OrganicTransformations
CO2
NitrificationNH3 Volatilization
DenitrificationPi reactions
InorganicTransformations
NH3, N2O, N2
Processes and drivers
Residue C
Metabolic Litter Biomass C Passive C
Slow C Leached C
Carbon and nitrogen flows
Structural Litter
In Situ Measurements of Soil Carbon with Advanced Technologies
R.C. Izaurralde, M.H. Ebinger, J.B. Reeves, C.W. Rice, L. Wielopolski, B.A. Francis, R.D. Harris, S. Mitra, A.M. Thomson, J.D. Etchevers, K.D. Sayre, A. Rappaport, and B. Govaerts
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Laser Induced Breakdown Spectroscopy: LIBS Based on atomic
emission spectroscopy
Portable A laser pulse is focused on a soil
sample, creating high temperatures and electric fields that break all chemical bonds and generating a white-hot plasma
The spectrum generated contains atomic emission peaks at wavelengths characteristic of the sample’s constituent elements
Cremers et al. (2001) J. Environ. Qual. 30:2202-2206
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Emerging technologies for measuring soil C: MIR / NIR
Mid Infrared / Near Infrared Spectroscopy (MIR / NIR)• Non-destructive method
measurement of C in soils based on the reflectance spectra of illuminated soil
• Spectral regions– NIR: 400–2500 nm– MIR: 2500–25000 nm
• Excellent potential for assessment of spatial distribution of belowground C
MIR and NIR spectra of a calcareous soil before and after treatment with acid for removal of carbonates. Source: McCarty et al. (2002)
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Field Test: CIMMYT, Mexico; April 2007 Conducted at CIMMYT on a 17-year
old crop rotation, tillage, residue study Treatments sampled:
• Maize (m) and wheat (w) grown in monoculture (M) or in rotation (R)
• Grown with conventional (CT) or no tillage (ZT), and with (+) or without (-) removal of crop residues
• Each treatment is replicated twice A composite soil sample made of 12
subsamples per soil depth (0-5, 5-10, and 10-20 cm) was taken from each of the 22 x 7.5 m plots.
General view of plotsGeneral view of plots
No Till w/o residuesNo Till w/o residues
No Till with residuesNo Till with residues
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Mean soil C density (kg C m-2) by treatment and summary statistics in the CIMMYT experiment
DC LIBS MIRS
1.306 1.440 1.413
0.301 0.393 0.134
Max 2.315 2.300 1.791
Min 0.814 0.600 1.166
Range 1.500 1.700 0.625
n 112 112 112
Although LIBS and MIRS followed the C density trends detected by DC method
Correlation between methods was low LIBS vs. DC: R2 = 0.174 MIRS vs. DC: R2 = 0.329
20
Further calibration of CIMMYT data Partial Least Squares method was used to improve calibration curves A calibration curve was developed using 31 samples run 3 times each (1
missing value) Re-estimation of data points improved significantly (see graph on the
right)
y = 1.003x
R2 = 0.919
0.5
1.0
1.5
2.0
2.5
0.5 1.0 1.5 2.0 2.5
LIBS
DC
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Further calibration of MIRS of CIMMYT data Original estimation of data using MIRS was developed with the calibration
curve based on Maryland samples and 8 samples from Mexico Eleven samples from the set of 112 were added to the calibration curve Prediction of the remainder 101 points improved significantly with the
revised calibration curve that used the Maryland data points plus the 19 Mexican data points
With the MIRS method, the greatest difficulty in predicting the correct values seems to be associated with high C samples
y = 0.7x + 0.4
R2 = 0.8
0.5
1.0
1.5
2.0
2.5
0.5 1.0 1.5 2.0 2.5
MIRS
DC
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Conclusions By using principles of soil science
• Minimize spatial variability• Reduce number of samples
–Decrease costs–Increase efficiency
• Increase sensitivity for detecting change• Allow adoption of new technology
Extrapolation• Modeling• Remote sensing
23
Websiteswww.soilcarboncenter.k-state.edu/
K-State Research and ExtensionK-State Research and Extension
Chuck RicePhone: 785-532-7217Cell: 785-587-7215 [email protected]