ESTIMATING SOIL ORGANIC CARBON CHANGES: IS IT FEASIBLE?

Eleanor Milne, Mark Easter and Keith PaustianThe Natural Resource Ecology Laboratory,

Colorado State University (CSU)

GSOC17 - Global Symposium on Soil Organic Carbon21-23 March 2017 - FAO HQ - Rome, Italy

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

MODELS: CENTURY AND DAYCENT

Century(monthly)

Daycent(daily)

Parton et al. 1987

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

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

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

EquationFactors,USDA

Methods,IPCC

COMET-FarmTM

How it works

Historic Rotations

NRI, Cropping Practices Survey,CSRA

Climate & Soil

NARR&

SSURGO

Web Interface

CSU Server

Empirical

Models

Outputs

Results

SpecificLocation

SpecificActivitie

s

COMET-FarmTM & COMET-PlannerTM

What are they?Web-based Greenhouse Gas Inventory Tools for Land Use

Designed for Conservation Scenario Analysis

EquationFactors,USDA

Methods,IPCC

COMET-PlannerTM

How it works

Historic Rotations

NRI, Cropping Practices Survey,CSRA

Climate & Soil

NARR&

SSURGO

Web Interface

CSU Server

Empirical

Models

Outputs

Average Mitigation Effect

GeneralizedRegion

Conservation

Practices

COMET-Farm Team

www.comet-farm.com www.comet-planner.com

USDA-NRCSAdam Chambers (Portland)Greg Johnson (Portland)

USDA-ARSSteve DelGrosso

Colorado State University

USDA-OCSMarlen Eve (DC)

Keith Paustian (Team Leader)Shawn Archibeque

Allison BrownKevin BrownMark EasterRam GurungMelannie HartmanAdriane HuberBen JohnkeKen KillianStephen Ogle

Bill PartonGeoff PietzMatt StermerBen SuttonAmy SwanCrystal ToureeneSobha VelayudhanSteve WilliamsJustin Ziegler

USDA-OCECarolyn Olson (DC)Marci Baranski (DC)

Partner institutions:Colorado State University, USAJoint Research Center – European CommissionSpanish National Research Council (CSIC), SpainInstitut Français de Recherche pour le Développement (IRD), FranceUniversity Court of the University of Aberdeen, ScotlandQueensland University of Technology, Australia

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

AREAS WHERE DATA IS LIMITED –

• Assemble data sets, fill gaps• Parameterise and validate the models• Example – the GEFSOC project

AREAS WHERE DATA IS LIMITED – GEFSOC

Kenya

Brazilian Amazon

Indogangetic Plains, India

Jordan

Dynamic SOM models linked to spatial data bases

Spatial DatabasesSimulation

model Spatial Results

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

Soils DataA 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

Data Needs Model Runs

Climate

- Precipitation- Max temperature- Minimum temp

- Mean monthly precipitation- Mean monthly max temperature- Mean monthly min temp

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

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.

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.

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

•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)

Initial Land Use

Baseline Scenario

Project Scenario

Project activities:- Reduced grazing, protection of rangelands - Reforestation/Afforestation

Project scenario

Business as usual

Soil Carbon

Woody Carbon

Enteric CH4

Manure N2O, CH4Biomass Burning (CO2, N2O, CH4,CO, NOx

Synthetic Fertilizer N2O

CarbonBenefit

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

www.vivo.colostate.edu/lccrsp/reports/GrazingLandsLivestockClimateMitigation_Paper1_Final6Aug2015editedv4a.pdf

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

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/GrazingLandsLivestockClimateMitigation_Paper1_Final6Aug2015editedv4a.pdf

and Milne et al. 2016 Environmental Development Vol 19, 70-74