Mathematical models developed to describe processes in soils should enable scientists and policy makers to identify agricultural practices that contribute to CO2, N2O and CH4 mitigation for grasslands and annual crops in various climates.
The objective of the CN-MIP project was to assess and improve predictions of the effects of agricultural practices on soil greenhouse gas (GHG) emissions. The project used crop models jointly simulating crop production and these emissions (CO2, N2O, CH4) in a «model ensemble« approach to reduce uncertainty in estimating emissions from agricultural systems (field crops, grasslands and livestock systems). The models were also used to quantify the effect of management options (nitrogen fertilization, irrigation, pasture intensity) on production and GHGs, in order to develop credible mitigation strategies adapted to diverse agrosystems in various climates. This work contributes directly to improving GHG inventory methods, notably the certified TIER3 method implementing mathematical models for quantifying emissions at the regional scale.
The project was conducted with 24 models and 10 data sets in grasslands and field crops from four continents. The analysis focused on productivity and GHGs, using five-step modeling, using an incremental approach starting with blind simulation and then allowing progressive access by modellers to experimental data until the models were fully calibrated. The calibrated models were then used to test mitigation options (mineral fertilization, irrigation, grazing intensity, crop residue management) on grasslands and field crops and their effects on productivity, N2O emissions and soil carbon on a large scale. The potential of all these models was evaluated by reference to the experimental uncertainties of the observed yields and N2O emissions.
None of the models tested performed better under all circumstances. The median value simulated by the ensemble of model is therefore a plausible estimator of yields, N2O emissions and C storage for the main crops (maize, wheat, rice) and grasslands. This set of models, used to assess the effectiveness of management options on GHG mitigation, quantified the potential reduction in nitrogen fertilization, irrigation or grazing intensity, without compromising the productivity of the agrosystems studied.
This work has implications at regional and global scales : (i) ability of the proposed modelling approach to supply a reliable and generalizable method to estimate GHG; (ii) opportunity to develop a TIER 3 methodology to estimate the N2O emissions at regional scale through the construction of a meta-model. This work contributes to the international initiative 4 pour 1000 by estimating the performances of the models to predict the evolution of the C stocks in the soil.
Ehrhardt F., Soussana J.-F., Bellocchi G., et al. . (2018). Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions. Global Change Biology, 24 (2), e603-e616. , DOI : 10.1111/gcb.13965.
Brilli L.,et al. (2017). Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes. Science of the Total Environment, 598, 445-470. , DOI : 10.1016/j.scitotenv.2017.03.208.
Sandor R., et al. (2016). C and N models Intercomparison - benchmark and ensemble model estimates for grassland production. Advances in Animal Biosciences, 7 (3), 245-247.
Ehrhardt F., Soussana J.-F., Grace P., Recous S., Snow V., Bellocchi G. et al. (2015) An international intercomparison & benchmarking of crop and pasture models simulating GHG emissions and C sequestration. Climate Smart Agriculture conference, 16-18 March 2015, Montpellier, p. 41.
Madame Sylvie RECOUS (Laboratoire public)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
Queensland University of Technology Queensland University of Technology
Woods Hole Research Center Woods Hole Research Center
Colorado State University Colorado State University
The New Zealand Institute for Plant and The New Zealand Institute for Plant and Food Research
Università degli studi di Sassari, Nucle Università degli studi di Sassari, Nucleo di ricerca sulla desertificazione
Universita degli Studi di Milano Universita degli Studi di Milano
CRA- Consiglio per la Ricerca e la Speri CRA- Consiglio per la Ricerca e la Sperimentazione in Agricoltura
University of Florence University of Florence
Helmholtz-Zentrum Potsdam Deutsches GeoF Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZenterum
University Court of the University of Ab University Court of the University of Aberdeen
Help of the ANR 105,974 euros
Beginning and duration of the scientific project: December 2013 - 36 Months