Quantifying Soil Carbon for Greenhouse Gas Mitigation

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Quantifying Soil Carbon for Greenhouse Gas
Mitigation
Richard T. Conant Keith Paustian Natural
Resource Ecology Laboratory Colorado State
University
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Average Cultivated soil C 35 Mg C ha-1
Accumulation rate 0.5 Mg C ha-1 yr-1
100 cm
Soil C pool
2 years change 2.8
25 years change 35.7
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Average Cultivated soil C (top 20cm) 14.5 Mg C
ha-1
Accumulation rate (top 20cm) 0.27 Mg C ha-1 yr-1
20cm
2 years change 3.7
25 years change 46.6
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Average Cultivated soil C () 14.5 Mg C ha-1
Accumulation rate (top 20cm) 0.27 Mg C ha-1 yr-1
20cm
2 years change 3.7
25 years change 46.6
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Average Cultivated soil C (top 20cm) 22.5 Mg C
ha-1
Accumulation rate (top 20cm) 0.47 Mg C ha-1 yr-1
Soil C pool
40cm
2 years change 4.2
25 years change 52.2
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Soil C trajectories
agriculture
Soil carbon
?Clt0
?C? 0
?Cgt0
Initial cultivation
Management change
Effect on atmospheric CO2

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Soil C trajectories
agriculture
Shape? Slope?
Amount? Characteristics?
Amount? Stability?
Soil carbon
Amount? Influences?
Duration?
Rate?
Initial cultivation
Management change
Location? Types? Reasons?
Effect on
Agricultural production? Energy
consumption? Agronomic decisions?
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Pedon database variability Implications for
measuring changes in soil C
Spatial extent
Landscape uniformity
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North American Agroecosystem research network
Useful for Multiple site comparisons Experiments
across climatic zones Evaluating multiple
controlling variables Refining regional
extrapolation/interpolation
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Total C (Mg C ha-1)
NT - CT (Mg C ha-1)
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(No Transcript)
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CENTURY MODEL VALIDATION
Cropping Systems
Pendleton, OR varying inputs
r2 0.83
r2 0.96
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Agroecosystems Research at NREL
Projections (Economic)
Regional analysis Integrated assessment
Economic Models
Mgmt choices
Auditing
Responses to management
Monitoring sites
Projections
Auditing
Site Networks
Agroecological Models
Process Studies
Interpretation
Integration
Model Validation
Field Experiments
Driving variables
Agroecozone delineation
Remote sensing regional statistics
Regional databases
Management changes
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National-level CO2 Inventory for U.S. Cropland
Soils
Approach
Regional analysis Integrated assessment
Auditing

• Contributors
• Marlen D. Eve
• Keith Paustian
• Ronald F. Follett
• E. Ted Elliott

Monitoring sites
Projections
Site Networks
Agroecological Models
Driving variables
Agroecozone delineation
Regional databases
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OBJECTIVES

• Estimate changes in soil Carbon storage (i.e.
  net CO2 flux) in U.S. croplands using
• (1) The IPCC inventory approach, and
• (2) A simulation model (CENTURY) approach
• Evaluate the differences in results between the
  two methods.
• Evaluate uncertainty components associated with
  varying degrees of spatial aggregation.

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DATA
Quantitative
Spatial

• USDA-NRCS National Resources Inventory.
• USDA-ERS Cropping Practices Survey.
• Conservation Technology Information Center -
  Crop Residue Management Survey.
• USDA-NASS Agricultural Statistics.

• USDA-NRCS Major Land Resource Areas.
• USDA-ERS USMP Cropping Regions.
• USDA-NRCS / OSU PRISM Climatic Data.
• USDA-NRCS STATSGO Soils Data.

Validation

• Long-term field experiment data.

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MLRA 102B lies at the western edge of the corn
belt. Average annual precipitation is 500 - 650
mm. Average annual temperature ranges from 9 -
11 degrees C.
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Carbon Emissions by Climatic Region Preliminary
Estimates
Graph depicts emissions, negative values indicate
soil C sequestration.
Using the IPCC inventory approach. Based on
analysis of the 1992 NRI data. Tillage data
acquired from CTIC. Climatic data based on
PRISM. Estimates are preliminary only, and are
currently being refined to decrease uncertainty
and incorporate the 1997 NRI data.
Cool Temperate Dry
Cool Temperate Moist
Warm Temperate Moist
Warm Temperate Dry
Sub-Tropical Dry
Sub-Tropical Moist
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State-level Carbon Sequestration A New
Opportunity in Agricultural Soils
Approach
Regional analysis Integrated assessment

• Contributors
• Keith Paustian
• John Brenner
• Kendrick Killian
• Jan Cipra
• Brian Dudek
• George Bluhm
• Steve Williams
• Ted Elliott
• Mark Easter
• Roel Vining
• Tim Kautza

Projections
Agroecological Models
Driving variables
Agroecozone delineation
Remote sensing regional statistics
Regional databases
Management changes
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Phase II
Phase I

• County-level information
• drainage (year)
• irrigation
• land capability class
• crop history
• conservation practices

• National databases for state-level information
• soils
• texture
• hydric soils
• depth
• soil series
• broad land use
• temperature
• precipitation
• native vegetation

Rural Appraisals submitted to conservation
districts
Land Use
Drainage
Soil Texture (STATSGO)
Land Cover (MRLC)
Irrigation
Conservation
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Rates of soil C change (MMT C) for 1996
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Phase I National databases
Remote sensing regional statistics
Phase II CSRAs
CENTURY Model
Driving variables
Driving variables
County-scale Integrated assessment
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Carbon sequestration through improved pasture
management
Approach
Regional analysis Integrated assessment
Auditing

• Contributors
• Richard Conant
• Keith Paustian
• E. Ted Elliott

Monitoring sites
Projections
Auditing
Agroecological Models
Driving variables
Agroecozone delineation
Remote sensing regional statistics
Regional databases
Management changes
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Comparative soil sampling

• Intensive grazing management
• conversion from forest
• (chronosequence)
• conversion from cultivation

• Intensive grazing management
• long term hay production

• Intensive grazing management

• Intensive grazing management

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Design of microsites

Sample location
5m
Magnetic marker
2m
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Effects of land use change on soil C in VA
Minimum detectable differences were 3.6 tC ha-1
and 401 kg N ha-1
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Comparative soil sampling

• Ecosystems
• Crops
• Conv.-till
• No-till
• Pastures
• well managed
• poorly managed
• Forests
• coniferous
• deciduous
• native
• plantation
• Rangeland

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• Assessments Based on
• Land use
• Land management
• Soil physical characteristics
• Economics

• Assessment Scale
• County
• State
• National

• Ecosystems
• Cropland
• Pasture
• Forest
• Rangeland

• Future plans
• Improve models
• Improve data/integration
• Integrate other GHGs
• Expand area
• Improve assessment resolution

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Quantifying Soil Carbon Credits ForGreenhouse
Gas MitigationConclusions

• Soil C is highly variable, dependant on many
  factors
• Changes in soil C are small relative to amount of
  C in soil
• Measuring changes requires precise measurements
• best if stratified by factors affecting soil C
• Modeling can be used to extrapolate
  measured/historical data
• Measured/historical data can be used to validate
  model results