No abstract

No abstract

This paper presents a new algorithm, Nitrous Oxide Emission (NOE) for simulating the emission of the greenhouse gas N 2 O from agricultural soils. N 2 O fluxes are calculated as the result of production through denitrification and nitrification and reduction through the last step of denitrification. Actual denitrification and nitrification rates are calculated from biological parameters and soil water-filled pore space, temperature and mineral nitrogen contents. New suggestions in NOE consisted in introducing (1) biological sitespecific parameters of soil N 2 O reduction and (2) reduction of the N 2 O produced through nitrification to N 2 through denitrification. This paper includes a database of 64 N 2 O fluxes measured on the field scale with corresponding environmental parameters collected from five agricultural situations in France. This database was used to test the validity of this algorithm. Site per site comparison of simulated N 2 O fluxes against observed data leads to mixed results. For 80% of the tested points, measured and simulated fluxes are in accordance whereas the others resulted in an important discrepancy. The origin of this discrepancy is discussed. On the other hand, mean annual fluxes measured on each site were strongly correlated to mean simulated annual fluxes. The biological site-specific parameter of soil N 2 O reduction introduced into NOE appeared particularly useful to discriminate the general level of N 2 O emissions from site to site. Furthermore, the relevance of NOE was confirmed by comparing measured and simulated N 2 O fluxes using some data from the US TRAGNET database. We suggest the use of NOE on a regional scale in order to predict mean annual N 2 O emissions.

No abstract

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N O emissions. Yield-scaled N O emissions (N O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N O emissions at field scale is discussed.