Soil Carbon & Nitrogen Cycling Cross-Cutting Research Group Outputs of Orléans 1st meeting ACTION PLAN / Short-term

Step 1. Benchmark an ensemble of models at GHG/ soil C measurement sites Which sites? Flux sites or long term experiments Including management/mitigation options => Two pilot flux sites, each comparing two managements: Grignon(F), arable Oensingen (CH), grassland How to apply models? Bayesian calibration? Already done Initialisation? Spin-up runs (current management) Pilot CN-MIP Model Intercomparison, benchmarking & imProvement

Two test sites Oensingen (CH) Agroscope, Zurich (Amman, Fuehrer et al. 10 papers published) Paired grassland site ( ) Cut grass/clover mixture With/without N supply Eddy flux: CO 2 Automated chambers: N 2 O Soil carbon and nitrogen Most environmental and production variables Arable crop rotation ( ) Eddy flux: CO 2 Automated chambers: N 2 O Soil carbon and nitrogen Most environmental and production variables Grignon (F) INRA, Versailles (Loubet, Cellier et al. 9 papers published)

Step 1. Benchmark an ensemble of models at GHG/ soil C measurement sites Arable models: Ceres EGC, DayCent, DNDC, mobileDNDC, ECOSSE, RothC Grassland models: DayCent, DNDC, mobileDNDC, ECOSSE, PASIM, RothC Germany:E. Haas and Klaus Butterbach-Bahl France: K. Klumpp, R. Lardy, Jean-Francois Soussana P. Cellier, Benoit Gabrielle UK-Scotland:J. Yerupalti, Pete Smith …Scotland and Germany: M. Wattenbach Pilot CN-MIP Model Intercomparison, benchmarking & imProvement

Step 2. Analyse sensitivity of models ensemble to simple and widespread mitigation options at these sites Mitigation options: N: reduce mineral N supply, reduce nitrification (inhibitors) C: reduce disturbance of primary productivity Arable: longer growth cycle Grassland: longer regrowth periods GHG fluxes: N 2 O (and CH 4 ?) emissions, SOC stock change, => GHG balance in CO 2 equivalents Production, yield => GHG balance per unit production Pilot CN-MIP Mitigation protocols

Pilot CN-MIP Nitrogen mitigation options Nitrogen Mineral N: 0, 50, 100, 200, 300 kg N ha -1 yr -1 Nitrification inhibitors (-30 % nitrification potential) Farmyard manure (or pig slurry) (to be tested)

N 2 O emission factor (EF 1 ) (Example: Oensingen site) N 2 O emission (kgN/ha) vs. N application (kgN/ha)

Pilot CN-MIP Results: N 2 O emission factor (EF1) EF 1 is the percentage fraction of N fertilizer directly emitted as N 2 O (IPCC, 2006, revised guidelines default value for EF 1 is 1%) Why are model EF 1 values higher than default values? Site measurements gave values lower than 1% (Flechard et al., 2007; …)

Pilot CN-MIP Results: Nitrification inhibitors No direct option available in these models for nitrification inhibitors ‘Quick-fix’ assumptions were used as a first sensitivity test

Pilot CN-MIP Carbon mitigation options Arable: extend growing season by 2 and 4 weeks (sowing and harvest dates) Cover crops, no-till? To be tested later Grasslands: cutting frequency (2, 4, 6 cuts yr -1 ) Grazing frequency or intensity, to be tested later

Sensitivity to nitrogen (Pasim Oensingen site) Gross Primary Productivity (GPP) Small (+7%) change in GPP (tC/ha) 30 fold increase in short-term C sequestration rate (kgC/ha)

Pilot CN-MIP C sequestration sensitivity to nitrogen fertilizer supply (Pasim Oensingen site) C sequestration factor = C storage /GPP C sequestration factor increases linearly with N fertilizer supply A 1% C sequestration factor is usual in long-term N fertilizer field experiments C sequestration rate will decline over time Note that initial SOC stock is crucial for C sequestration estimates C sequestration factor

Pilot CN-MIP Results: Carbon and GHG balance C sequestration factors differed across models e.g. from % (PASIM) up to 2.50 % for DNDC C sequestration increased over time at some sites With grasslands, increasing cutting frequency reduced SOC sequestration Changing sowing/harvest dates was not successful because of phenology parametrisation

GHG balance in CO 2e is affected by climatic variability, not by N supply (Pasim Oensingen site) Counter-intuitively, N fertilization would mitigate GHG in CO 2 equivalents, but only during first years and depending on initial SOC stock Mean GHG balance (tonsCO 2eq /ha) vs. N application (kgN/ha)

Explore ‘management space’ by changing site based practices to reduce net GHGs –Need also sites with arable monocultures Detectable effect of mitigation options? –How sensitive are they to climate? (including increased climatic variability) –They should not lead to increased GHGs (calculate risk of failure with climate variability) –While preserving yields… (GHGs per unit plant/animal) product Restrict objectives: do not include indirect land use change, lifecycle, economics (e.g. costs of cover crops, of nitrification inhibitors) –Include later: short rotation coppices, bioenergy crops, organic farming… Outputs for policies: Need to simulate consequences of policies (e.g. linked to water directives, air pollution…) C-N workshop Leuven, July 2011 Discussion: results of simulation

Rice systems –Test mitigation options, differences in soil C (eg modified RothC): eg water (flooding) management Manure management ? –is also a target for a link between arable and livestock =>Test mitigation from manure spreading –Manure is treated differently in different models –If manure is already present in the situation simulated, increase the range of application –C from manure will also play a role (humification coefficient) not only N C-N workshop Leuven, July 2011 Discussion: results of simulation

Use cover crops rather than change in duration of crop cycle We look at initial years only (changes in SOC will level off): we need also long term for deriving carbon factors Some models have a ceiling yield so increased N makes no difference C-N workshop Leuven, July 2011 Discussion: results of simulation About the simulations run Slope 1 kgC/kg N is realistic for long term field trials (Sweden) Use also a site with arable monoculture Include no-till as an option. It is often poorly described by models

Systematic differences across models in e.g. emission factors, carbon sequestration? What is the relative weight of climatic variability vs. management? Is there an interaction? Are they systems/options that result in larger(smaller) emissions from some models? Higher N 2 O emission factor from organic N compared to mineral N? C-N workshop Leuven, July 2011 Discussion: results of simulation

 To do, simulations including options: –Cover crops in the rotations –Application of manure N –Situation with arable monoculture  Circulate draft protocols that will need revision/improvements C-N workshop Leuven, July 2011 Discussion: decisions for future work  Should we include also simple models? Or should we use a limited set of ecosystem models shared between scientists and applied to a large range of situations ? Will the model specialists do the modelling only?  Which Data? : Do we need meta-analysis of data? e.g. N 2 O vs N (but site specific)

1.Revise protocols for model sensitivity 2.Finalise the first results presented today (possible paper) 3.Extended sensitivity runs with more sites/models (include e.g. rice and other grassland types) C-N workshop Leuven, July 2011 Discussion

C-N Cross-Cutting Group Thank you!