Soil carbon simulation models for carbon accounting: Overview and research issues

FAO, CCAFS and CGIAR joint workshop: Towards a Framework for Smallholder Agricultural Mitigation: Soil Carbon Measurements and Simulation Models,

Rome July 13th 2010, presented by Timm Tennigkeit; UNIQUE forestry consultants

Steady state of soil carbon accounting methodologies

Methodology Status Targeted standard/ application and key features

Sustainable AgriculturalLand Management (SALM)Developer: World Bank BioCarbon Fund

1st validation completed, except clarification from VSC requested related to the conditions to use of soil carbon models

VCS, broad applicability with focus on smallholder agriculture.Production/activity monitoring and model based default values.

Adoption of Sustainable Grassland Mgmt through Adjustment of Fire & GrazingDeveloper: SyracuseUniversity, Soils for the Future LLC, Jadora Int. LLC

Public review process, 1st

validation initiatedVCS, broad applicability with focus on unfertilized grasslands, incooperatinglessons learned from SALM methodology.Activity monitoring & model based AND ex-post rectification of ex-ante estimates based on soil C measurements.

Others Still in the development/ review process or waiting until related business deals are signed before entering public review/domain

VCS, American Carbon Registry, Panda Standard, Alberta Standard

Means to quantify carbon credits

Source: Coalition of Agricultural GHG (C-AGG)

Activity

monitoring +

Remote

sensing based,

technology not

ready to use

Direct

emission

measurements

Direct soil

measurements

RothC, CENTURY,

DNDC

Most widely used carbon simulation models

Simulation model Application Input data requirements

RothC from the RothamstedAgricultural Research Centre in the UK

Originally developed for UK for agriculture

Most flexible and less demanding compared with CENTURY or DNDC with regards to input data. In particular the version used in the Australian carbon accounting system is very user friendly.

CENTURY from Natural Resource Ecology LaboratoryColorado State University in the US

Originally developed for grasslands in the US

Flexible and less demanding compared with DNDC with regards to input data

DNDC i.e. DeNitrification-DeComposition) is a computer simulation model of carbon and nitrogen biogeochemistry in agro-ecosystems from the University of New Hampshire

Mainly for projects that aim to improve the use of fertilizer

Very demanding with regards to input data and since the model is updated very frequently in-depth understanding of the model is required

Steps involved to develop a land based carbon accounting system for smallholder agriculture

The Western Kenya Smallholder Agriculture carbon project

Flowchart of carbon stock change estimation

ΔCActivity data

(ha)Emission factor (tCO2/ha/year)= X

Default value development• Literature & expert knowledge• C modeling• Existing data

Project inventory & survey system

-Soil Organic Carbon

BiomassProject

BiomassBaseline+

Project scenarioFactorMANAGEMEN

T

x xFactorLAND-USESOC ReferenceSOIL TYPE

Baseline scenario*FactorMANAGEMENTx xFactorLAND-USESOC ReferenceSOIL TYPE

-

RothC model calibration• Stratification project region based on crop production and soil clay

content

• Model inputs: Crop productivity Residue production, Clay content, climate parameters, additional residue inputs, additional manure inputs, soil cover in each month (bare or covered)

• Modelling equilibrium soil organic carbon stocks and with project stock changes

• Validation of model results with available research from similar agro-ecological zones using comparable management practices:

“Batjes N.H., Gicheru P. (2004). Soil data derived from SOTER for studies of carbon stocks and change in Kenya (ver. 1.0; GEFSOC Project). Report 2004/01, ISRIC - World Soil Information, Wageningen”

Key monitoring parameter of the SALM methodology

Crop production and activity monitoring:• Production

• Area, crop, amount of production• Residuals use

• Burning• Number and type of livestock• Manure use• Cover crops use• Nitrogen fixing species use• Fertilizer use• Estimate N2O emissions from N-fixing species and fertilizer

use• Measure woody perennial growth

• Trees and shrubs

Crop production and activity monitoring process

• Estimate number of farmers and the area where SALM activities will be adopted to generate carbon assets.

• Establish a transparent baseline and a monitoring system to reward farmer groups for generating carbon assets

• Receive a written commitment from farmers to adopt “climate smart“ land use practices

Design features:

• Pretesting survey design and sampling size

• Annual survey based on 200 farms (permanent samples), plus 20 temporary farm samples for annual retesting (to control biased treatment of permanent samples), plus 5 % additional plots (to consider late adopters)

• Structured interview + farm sketch map

RothC: Default value development

tC/ ha/ yr

for low/ high crop

production

Residues rem

oved from

the field

Residues left in the field

Residues rem

oved & 1 tC

/

ha/ year of manure

distributed

Residues rem

oved & 2 tC

/

ha/ year of manure

distributed

Residues rem

oved & 4tC

/

ha/ year of manure

distributed

Residues left &

1 tC/ ha/

year of manure distributed

Residues left &

2 tC/ ha/

year of manure distributed

Residues left &

4 tC/ ha/

year of manure distributed

Residues removed from the field 0.28/1.34 0.08/ 0.08 0.33/ 0.33 0.65/ 0.65 0.36/ 1.43 0.61/ 1.67 0.94/ 2.00

Residues left in the field - 0.20/ -1.26 0.04/ -1.02 0.37/ -0.69 0.08/ 0.08 0.33/ 0.33 0.65/ 0.65

Residues removed & 1 tC/ ha/

year of manure distributed

0.57/ 0.57 0.28/ 1.34 0.86/ 1.92

Residues removed & 2 tC/ ha/

year of manure distributed

Residues removed & 4tC/ ha/ year

of manure distributed

-0.29/ 0.77 0.28/ 1.34

Residues left & 1 tC/ ha/ year of

manure distributed

0.57/ 0.57

Residues left & 2 tC/ ha/ year of

manure distributed

Residues left & 4 tC/ ha/ year of

manure distributed

Kitale (ViA) t/ ha of productionLow production 1st season 2nd season

Maize 1,01 2,73

Beans 0,39 0,62

Potatoes 2,58 2,16

High production 1st season 2nd season

Maize 7,1 11,4

Beans 2,0 2,2

Potatoes 4,9 6,7

C Model sensitiveness:

• climate data

• soil clay content

• crop production/ residues

• manure application

Example: Mixed cropping with maize, beans, potatoes

Project monitoring costs (US$)

Direct soil measurement

Activity Cost estimate* Total cost % of carbon revenues

Project activity documentation 16 /day 80,000 3.2%

Sampling & reporting (incl. transport, contracted tonational research organization)

52 /sample 452,400 18.3%

Soil sample analysis (laboratory) 18 /sample 156,600 6.3%

Sample archiving 0.1 /sample/month 104,400 4.2%

Management and administration 10% 79,340 3.2%

Activity & productivity monitoring survey (APMS)

Survey pretesting and training 16/day 138,048 5.6%

APMS survey & reporting 8/survey 24,488 1%

Survey analysis & database management 8/survey 24,488 1%

SOC modeling 50,000 2%

Management and administration 10% 23,702 1%

* Data sources: Canada’s Greenhouse Gas Offset System 2006: Guide to developing a quantification

methodology and protocol; Winrock 2004: Measurement and monitoring costs: influence of parcel contiguity,

carbon variability, project size and timing of measurement events. Kenya project data.

Total cost comparison

Direct measurement Crop production & activity monitoring

Project cost item Total cost % of carbonrevenues

Total cost % of carbonrevenues

Carbon component 316,819 13% 316,819 13%

Carbon monitoring 872,740 35% 260,726 11%

Project implementation 1,293,600 52% 1,293,600 52%

Total costs 2,483,159 100% 1,871,145 76%

• Direct measurements would substantially increase carbon monitoring costs for

the Kenya BioCarbon Fund project without necessarily reducing uncertainty

• Major challenge in measuring SOC: measuring small changes against high

background levels, sampling costs

• Crop production and activity monitoring: Quality assurance mechanisms are

important

• Model: Application of models has to be standardized; i.e. model parameter

should be constant in the baseline and the project scenario run

Related research issues

• Long-term controlled soil carbon monitoring plots for all farming systems and agro-ecology zones (or any other more appropriate stratification system)

• Critical assessment of agricultural mitigation and adaptation technologies from a agronomy, ecological and socio-economic perspective (considering scale issues from plot to landscape)

• Suitable inventory and statistical monitoring design (combining micro-plots, with remote sensing based technologies and direct emission measurements)

• Cost/benefit benchmarking for soil carbon models and soil carbon monitoring systems

• Implications from soil health research results for agricultural extension (demand specific aggregation & disaggregation of soil research results)

• Farmer aggregation mechanisms (cooperatives, farmer groups, farmer field schools, outgrower shemes) and performance based agricultural monitoring and extension