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GCP, Beijing, 15-18 November 2004. Regional Carbon Budgets: from methodologies to quantification
Carbon fluxes and sequestrationopportunities
in grassland ecosystems
Jean-Francois Soussana
INRA, Grassland Ecosystem Research Group, Clermont-Ferrand, France
Overview
Introduction.C cycling and Net Biome Productivity in grasslands
• Soil C and land management• CO2 fluxes• Farm gate budget and mitigation options• Upscaling to regional scale
Conclusions
Net Biome Productivity of a grazed grassland(tC ha-1 yr-1)
Végétation
Soil
Root turnoverRhizodeposition
Litter
CO2
9.2
Gross primaryproductivity
Below-groundrespiration
Vegetation0 Shoot
respiration
9
3 7
0.7 Animalexcreta
Net Biome Productivity: +0.5
Herbivorerespiration
CH4
0.2
Herbivore+0.05
Grazing
192.1
DOC, DIC?
SownSown grasslandgrassland withwith intensive intensive grazinggrazing ((SoussanaSoussana et et alal., ., SoilSoil Use Use andand Management, 2004Management, 2004))
1. Grassland C stocks and land management
Land use changes from or to grasslands(Guo & Gifford, 2002)
(-59%)
(+8%)
(+19%)
Land use change effects on soil carbon stocks
-40
-30
-20
-10
0
10
20
30
40
0 20 40 60 80 100 120Years after start of policy measure
Carb
onsto
cks (
tC/ha
)
arable -> forestarable -> grasslandforest -> arable
grassland -> arable
Land use change: carbon storage is slower than carbon relase(After INRA, 2002)
Soil C and grassland management
Improved (medium/high input)FertilizersMore Productive
GrassesIrrigationLimingSeeding Legumes
Nominally-ManagedNative VegetationPasture/Rangeland
with no grazingproblems or inputs
DegradedOvergrazingLess productive
VegetationDegraded Sites
IPCC Grassland Management Categories
Changes in grassland C stocks• Rates of land use and grassland management changes
– Need to develop improved systems for the collection of statistics on types and timing of land use change and of agricultural management events (e.g. manuring, cutting, extent of grazing etc.)
- Need to know past history of land use and grassland management
• Soil organic carbon change per unit area:– a) non-linear, more rapid during the early years after adopting a
practice. – b) asymmetric: accumulation is slower than release – Any estimate of soil C storage must refer to a given time period
and both to the previous and current management.– Interrupting stock-enhancing practices usually results in a rapid
release of carbon to the atmosphere.
2. CO2 fluxes
EC ‘GreenGrass’ project FP5 - EESD (2002-2004)
Contributing to CarboEurope
France (Co-ordination) Scotland Hungary Switzerland ItalyIreland DenmarkThe Netherlands
CNR-IATAA
Activity 1.6. Grasslands & Wetlands
28sites
Main Grass.
Main Wet.Anc. Grass.
Anc. Wet.
Components of the Grassland Carbon BudgetComponents of the Grassland Carbon Budget
∆∆C/C/∆∆tt
manure
?
CO2 exchange (NEE) harvest
sequestration: ∆C/∆t = NEE + C imports- C exports
Global warming potential: N2O and CH4 trade-offs with CO2
N2O: automated staticchambers and TDL SF6 pill
SF6
CH4
CH4 , SF6
CH4: in-situ SF6 tracer method
Field scale flux measurements
• In terms of NEE, managed grasslands are large apparent sinks of C• C imports from manures and C exports from cuts should be taken into
account to estimate NBP. • Uncertainty associated to NBP is high• The NBP was found to be a carbon sink, with approximately half of the
sink activity resulting from imports of C from manures• Emissions of N2O and CH4 resulted in a 40 % offset of the NEE • The attributed emission balance, including indirect emissions of CO2
and CH4 from the cut herbage, was on average neutral.
3. Upscaling C budgets at the farm scale
PASIM
CO2, N2O,CH4
IPCC
N2O,CH4
Imports /Exports
Module 7:Feeding systems
Module 7:Feeding systems
Module 1:Farm structure
Module 1:Farm structure
Module 2:Herd
Module 2:Herd
Module 3:Grazing systems
Module 3:Grazing systems
Module 5:Fertilis. & Cutting
Module 5:Fertilis. & Cutting
Module 6:Crops management
Module 6:Crops management
Module 4:Housing
Module 4:Housing
Module 9:Energy consump.
Module 9:Energy consump.
Module 8:Waste managem.
Module 8:Waste managem.
FARMSYSTEM
Structure of the farm model FARMSIM
PaSim model (FAL, LSCE, CEH, INRA)
photosynthesis
substrate
allocation
lamina stem earCarbon and Nitrogen BIOMASS
mortality
Carbon and Nitrogen litter(structural / metabolic)
decomposition
soil carbon and nitrogen
cutting
yield
grazing
animals
CH4
autotrophicrespiration
active slow passive
heterotrophicrespiration
CO2
root
N uptake
N fertiliser
N deposition
nitri/denitrification
volatilization
leaching
N2O NO3-NH3
N2O N2
N fixation
urinedung
Nitrogen outputs Nitrogen Inputs Carbon Inputs CO2 emissions CH4emissions
yielddecomposition
Farm scale C and GHG balance
• All simulated cattle farms were sources of GHG• Indoors emissions are a major component of the farm budget• Non CO2 trace gases play a major role in the GHG budget• Rate of emission per unit land area increased from extensive to
intensive farming systems. • Possibility to upscale budgets per region according to farm type. This
allows to account for changes in farming practices.• Pre-chain emissions may lead to much higher emission than farm gate
budgets.
4. Upscaling grassland C budgets at the regional scale
Ecosystem Similarity Concept
Gilmanov, Wylie, et al. 2004
Grasslands in Europe
% land cover (CORINE – PELCOM data)
Upscale Pasim at a regional scale
PaSim
Hourly values of • irradiance• temperature• pressure• humidity. • wind speedClimatology from ECMWF
Grassland fractional coverage
Combined CORINE. PELCOM
• Soil texture (Zoebler)• Water content parameters(FAO)
Soil data
dates of harvest. animal stocking rate and grazing periods. dates of
application and amount of N-fertilizers
Management drivers
Equilibrium run at a spatial resolution of 1 degree
• GPP• Respirations• N2O emissions• CH4 emissions• …
Model outputs
Automatic optimal management module
Climate drivers Land cover map
(Vuichard et al.)
Upscaling C budgets at regional scale
• Phenomenological models (with NDVI and GIS)– Light-response functions method works with grasslands– Provides estimates compatible with ecological theory and data– There are significant relationships between ecosystem scale
characteristics and remotely sensed and on-site factors
• Process-based simulation models– CO2, N2O and CH4 fluxes can be predicted by a process driven model
at the European scale. It takes into account management– Optimal management scenario predicts
• realistic values of yield • realistic dependencies with temperature and precipitation
– Refine runs by using “real” management data– Develop “mitigation” runs