This presentation was presented during the Plenary 1, GSOC17 – Setting the scientific scene for GSOC17 of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Ms. Eleanor Milne from Colorado State University - USA, in FAO Hq, Rome

1. ESTIMATING SOIL ORGANIC CARBON
CHANGES: IS IT FEASIBLE?
Eleanor Milne, Mark Easter and Keith Paustian
The Natural Resource Ecology Laboratory,
Colorado State University (CSU)
GSOC17 - Global Symposium on Soil Organic Carbon
21-23 March 2017 - FAO HQ - Rome, Italy

2. ESTIMATING SOC CHANGES: IS IT FEASIBLE?
It depends on:
 Data availability for soils, climate, land use
and management (historical and current) for
the scale you are working at
 The availability of suitable models/methods
for those systems
 The level of uncertainty you are willing to
accept!
Where in the world you are working

3. MODELS: CENTURY AND DAYCENT
Century
(monthly)
Daycent
(daily)
Parton et al. 1987

4. EXAMPLE OF WHAT CAN BE DONE WHEN YOU
HAVE THE DATA AND THE MODELS

5. COMET-FarmTM
What is it?
Web-based Greenhouse Gas Inventory Tools for Land
Use
Designed for Conservation Scenario Analysis
•Works at the farm scale
•Estimate changes in SOC
using Daycent (plus other
models)
•Can consider effects of
different conservation practices
on SOC

6. DATA FOR COMET FARM
 Soils – SSURGO (web-served) (1:12,000 –
1:63,630)
 Climate – NARR (NCAR/NOAA)
 Regional specific land use/Management
Practices
 National Resources Inventory (NRI)
 USDA/ERS Cropping Practices Survey
 NRCS manure management
 CSRA – regional LU and management surveys
 User input of detailed management for recent
(> yr 2000) and projected practices

7. Equation
Factors,
USDA
Methods,
IPCC
COMET-FarmTM
How it works
Historic
Rotations
NRI, Cropping
Practices
Survey,
CSRA
Climate &
Soil
NARR
&
SSURGO
Web Interface
CSU
Server
Empirica
l Models
Outputs
Results
Specific
Location
Specific
Activities

8. COMET-FarmTM & COMET-
PlannerTM
What are they?
Web-based Greenhouse Gas Inventory Tools for Land
Use
Designed for Conservation Scenario Analysis

9. Equation
Factors,
USDA
Methods,
IPCC
COMET-PlannerTM
How it works
Historic
Rotations
NRI, Cropping
Practices
Survey,
CSRA
Climate &
Soil
NARR
&
SSURGO
Web Interface
CSU Server
Empirica
l Models
Outputs
Average Mitigation Effect
Generalized
Region
Conservatio
n
Practices

10. COMET-Farm Team
www.comet-farm.com www.comet-
planner.com
USDA-NRCS
Adam Chambers (Portland)
Greg Johnson (Portland)
USDA-ARS
Steve DelGrosso
Colorado State University
USDA-OCS
Marlen Eve (DC)
Keith Paustian (Team Leader)
Shawn Archibeque
Allison Brown
Kevin Brown
Mark Easter
Ram Gurung
Melannie Hartman
Adriane Huber
Ben Johnke
Ken Killian
Stephen Ogle
Bill Parton
Geoff Pietz
Matt Stermer
Ben Sutton
Amy Swan
Crystal Toureene
Sobha Velayudhan
Steve Williams
Justin Ziegler
USDA-OCE
Carolyn Olson (DC)
Marci Baranski (DC)

11. Partner institutions:
Colorado State University, USA
Joint Research Center – European Commission
Spanish National Research Council (CSIC), Spain
Institut Français de Recherche pour le Développement
(IRD), France
University Court of the University of Aberdeen,
Scotland
COMET-GLOBAL
•In progress
•Using the same approach for several places across
the globe
•Develop a globally applicable tool operational at the
farm entity
•Use Daycent,
RothC and
Ecosse

12. AREAS WHERE DATA IS LIMITED –
• Assemble data sets, fill gaps
• Parameterise and validate the models
• Example – the GEFSOC project

13. AREAS WHERE DATA IS LIMITED –
GEFSOC
Kenya
Brazilian
Amazon
Indogangetic Plains, India
Jordan

14. Dynamic SOM models linked to spatial data bases
Spatial Databases
Simulation
model Spatial Results

15. Data Needs Parameterisation
Model Parameterisation:
1. Any LTE or chronosequence data for model evaluation
- Ideally soil carbon + crop yields and/or plant production.
- Soil type (sand/silt/clay fraction + bulk density)
- Land use history going back 100 yrs if applicable or to the
time of land use change from native vegetation.
- Native vegetation type
- tillage, fertilization, organic matter additions, irrigation
amounts and timing.
- crops, dates of planting and harvest, extent of residue
removal.
- timing and extent of ditch and/or tile drainage, if applicable

16. Soils Data
A soils map with
- location and extent of soil type,
- drainage status,
- content of clay, sand and silt,
- SOC content,
- Bulk Density
GEFSOC DATA NEEDSData Needs Model Runs

17. Data Needs Model Runs
Climate
- Precipitation
- Max temperature
- Minimum temp
- Mean monthly precipitation
- Mean monthly max temperature
- Mean monthly min temp

18. Land Use
Land use and land use transitions
- Going back 100 or 50 yrs
- Or to the point land use was changed from native
vegetation
Land Management
Cropping practices (crop rotations, tillage, residue
management, fert inputs etc.), grassland
condition/management, forestland management (tree
types, wood removal) etc.
Data Needs Model Runs

19. Figure 14. Management sequence diagrams for MLRA 52. System abbreviations
are as follows: HG = heavy grazing, GH = grassland hay, IASG = irrigated alfalfa-
small grain (conventional tillage), IASGN = irrigated alfalfa-small grain (no tillage),
RG = rotational grazing, CSG = continuous small grains, DASG = dryland alfalfa-
small grain, FSG = fallow-small grain (conventional tillage), FSGO = fallow-small
grain-oilseed, FSGM = fallow-small grain (minimum tillage), FSGN = fallow-small
grain (no tillage), CRP = Conservation Reserve Program.

20. SOC stocks
(t C ha-1)
1990
2000
2030
Agricultural expansion
SOC stocks
(t C ha-1)
19901990
20002000
20302030
Agricultural expansion
Estimated SOC changes in a frontier
area of the Brazilian Amazon
Cerri et al. 2007. Ag Ec Env.

21. NON-DYNAMIC APPROACHES
 What if you don’t have data to parameterise
or populate models?
 Situation in many areas outside N. America
and Europe
 Take a computational approach – IPPC
method
 Several calculators available
 Example The Carbon Benefits Project tools

22. •Two tools utilising the IPCC approach
• Simple Assessment Tier 1
• Detailed Assessment Tier 2
• Aimed at landscape scale assessments
• NET GHG assessments includes estimates of
SOC stock change
• Takes no account of land use history so
doesn’t capture long term dynamic changes
• BUT simple to use, just needs land use and
management info (soils and climate defaults
provided)

23. Initial Land Use
Baseline Scenario
Project Scenario
Project activities:
- Reduced grazing, protection of rangelands
- Reforestation/Afforestation
Carbon
Benefit

26. ESTIMATING SOC CHANGES: IS IT FEASIBLE?
It depends on:
 Data availability for soils, climate, land use
and management (historical and current) for
the scale you are working at
 The availability of suitable models/methods
for those systems
 The level of uncertainty you are willing to
accept!
 Gaps in our understanding of the
determinants of C sequestration potential

27. www.vivo.colostate.edu/lccrsp/reports/GrazingLandsLivestockCl
imateMitigation_Paper1_Final6Aug2015editedv4a.pdf

28. GRAZING LANDS IN SUB-SAHARAN AFRICA
 P and the role it plays in C sequestration in
C4 grasslands
 Effect of ultraviolet radiation on
decomposition
 Termites- how they affect the amount and
distribution of OM and C in soils
 Shifts between shrublands and grasslands &
impact on above and below C stocks
 Rate of C sequestration and saturation levels
Milne et al. 2016 Environmental Development

29. THANK-YOU!
COMET Farm - http://cometfarm.nrel.colostate.edu/
GEFSOC – Vol 122, Issue 1 Agriculture Ecosystems and
Environment
Carbon Benefits Project - http://cbp-web1.nrel.colostate.edu/
Sub-Saharan Africa report -
www.vivo.colostate.edu/lccrsp/reports/GrazingLandsLivesto
ckClimateMitigation_Paper1_Final6Aug2015editedv4a.pdf
and Milne et al. 2016 Environmental Development Vol 19, 70-
74