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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