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.

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Abstract. The application of nitrogenous fertilisers to agricultural soils is a major source of anthropogenic N2O emissions. Reducing the nitrogen (N) footprint of agriculture is a global challenge that depends, among other things, on our ability to quantify the N2O emission intensity of the world's most widespread and productive agricultural systems. In this context, biogeochemistry (BGC) models are widely used to estimate soil N2O emissions in agroecosystems. The choice of spatial scale is crucial because larger-scale studies are limited by low input data precision, while smaller-scale studies lack wider relevance. The robustness of large-scale model predictions depends on preliminary and data-demanding model calibration/validation, while relevant studies often omit the performance of output uncertainty analysis and underreport model outputs that would allow a critical assessment of results. This study takes a novel approach to these aspects. The study focuses on arable eastern Scotland – a data-rich region typical of northwest Europe in terms of edaphoclimatic conditions, cropping patterns and productivity levels. We used a calibrated and locally validated BGC model to simulate direct soil N2O emissions along with NO3 leaching and crop N uptake in fields of barley, wheat and oilseed rape. We found that 0.59 % (±0.36) of the applied N is emitted as N2O while 37 % (±6) is taken up by crops and 14 % (±7) is leached as NO3. We show that crop type is a key determinant of N2O emission factors (EFs) with cereals having a low (mean EF<0.6 %), and oilseed rape a high (mean EF=2.48 %), N2O emission intensity. Fertiliser addition was the most important N2O emissions driver suggesting that appropriate actions can reduce crop N2O intensity. Finally, we estimated a 74 % relative uncertainty around N2O predictions attributable to soil data variability. However, we argue that higher-resolution soil data alone might not suffice to reduce this uncertainty.