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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

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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

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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

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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

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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

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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

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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

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Websiteswww.soilcarboncenter.k-state.edu/

K-State Research and ExtensionK-State Research and Extension

Chuck RicePhone: 785-532-7217Cell: 785-587-7215 cwrice@ksu.edu