Carbon fluxes and sequestration opportunities in grassland ecosystems

Transcription

1 GCP, Beijing, November Regional Carbon Budgets: from methodologies to quantification Carbon fluxes and sequestration opportunities in grassland ecosystems Jean-Francois Soussana INRA, Grassland Ecosystem Research Group, Clermont-Ferrand, France

2 Overview Introduction. C cycling and Net Biome Productivity in grasslands Soil C and land management CO 2 fluxes Farm gate budget and mitigation options Upscaling to regional scale Conclusions

3 Net Biome Productivity of a grazed grassland (tc ha -1 yr -1 ) CH Herbivore respiration 19 Gross primary productivity CO Animal excreta Herbivore Grazing Soil 3 Végétation 7 Root turnover Rhizodeposition Litter Vegetation 0 9 Shoot respiration 9.2 DOC, DIC? Net Biome Productivity: +0.5 Below-ground respiration Sown grassland with intensive grazing (SoussanaSoussana et et al., Soil Use and Management, 2004)

4 1. Grassland C stocks and land management

5 Land use changes from or to grasslands (Guo & Gifford, 2002) (+8%) (-59%) (+19%)

6 Land use change effects on soil carbon stocks Carbon stocks (tc/ha) Years after start of policy measure arable -> forest arable -> grassland forest -> arable grassland -> arable Land use change: carbon storage is slower than carbon relase (After INRA, 2002)

7 Soil C and grassland management Degraded Overgrazing Less productive Vegetation Degraded Sites Nominally-Managed Native Vegetation Pasture/Rangeland with no grazing problems or inputs IPCC Grassland Management Categories Improved (medium/high input) Fertilizers More Productive Grasses Irrigation Liming Seeding Legumes

8 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.

9 2. CO 2 fluxes

10 EC GreenGrass project FP5 - EESD ( ) Contributing to CarboEurope CNR-IATAA France (Co-ordination) Scotland Hungary Switzerland Ireland Italy The Netherlands Denmark

11 Activity 1.6. Grasslands & Wetlands 28 sites Main Grass. Main Wet. Anc. Grass. Anc. Wet.

12 Components of the Grassland Carbon Budget CO 2 exchange (NEE) harvest manure? C/ C/ t sequestration: C/ t = NEE + C imports- C exports

13 Global warming potential: N 2 O and CH 4 trade-offs with CO 2 CH4 N 2 O: automated static chambers and TDL SF6 SF6 pill CH 4, SF 6 CH 4 : in-situ SF 6 tracer method

14 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 N 2 O and CH 4 resulted in a 40 % offset of the NEE The attributed emission balance, including indirect emissions of CO 2 and CH 4 from the cut herbage, was on average neutral.

15 3. Upscaling C budgets at the farm scale

16 Structure of the farm model FARMSIM FARM SYSTEM Module 1: Farm structure Imports / Exports CO 2, N 2 O, CH 4 Module 2: Herd N 2 O, CH 4 PASIM Module 3: Grazing systems Module 4: Housing IPCC Module 5: Fertilis. & Cutting Module 8: Waste managem. Module 6: Crops management Module 9: Energy consump. Module 7: Feeding systems

17 PaSim model (FAL, LSCE, CEH, INRA) Nitrogen outputs Nitrogen Inputs Carbon Inputs CO 2 emissions NH3 N2O N2 N2O NO3- N deposition N fertiliser N fixation CO2 CH 4 emissions CH4 photosynthesis autotrophic respiration yield decomposition heterotrophic respiration volatilization nitri/ denitrification N uptake substrate allocation root lamina stem ear Carbon and Nitrogen BIOMASS mortality yield cutting grazing leaching Carbon and Nitrogen litter (structural / metabolic) animals decomposition urine dung active slow passive soil carbon and nitrogen

18 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 CO 2 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.

19 4. Upscaling grassland C budgets at the regional scale

20 Ecosystem Similarity Concept Gilmanov, Wylie, et al. 2004

21 Grasslands in Europe % land cover (CORINE PELCOM data)

22 Upscale Pasim at a regional scale Climate drivers Hourly values of irradiance temperature pressure humidity. wind speed Climatology from ECMWF Soil data Soil texture (Zoebler) Water content parameters (FAO) Land cover map Grassland fractional coverage Combined CORINE. PELCOM PaSim Equilibrium run at a spatial resolution of 1 degree GPP Respirations N2O emissions CH4 emissions Model outputs Management drivers dates of harvest. animal stocking rate and grazing periods. dates of application and amount of N-fertilizers Automatic optimal management module (Vuichard et al.)

23 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 CO 2, N 2 O and CH 4 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