• Energy Research

AbstractPast assessments report negative impacts of the climate crisis in boreal areas; but milder and shorter winters and elevated atmospheric CO2may provide opportunities for agricultural productivity potentially playing a significant role in future food security. Arable cropping systems are expanding in boreal areas, but the regional mainstay will likely continue to be livestock production. Agroecological models can when appropriately calibrated and evaluated, facilitate improved productivity while minimising environmental impacts by identifying system interactions, and quantifying greenhouse gas emissions, soil carbon stocks and fertiliser use. While models designed for temperate and tropical zones abound, few are developed specifically for boreal zones, and there is uncertainty around the performance of existing models in boreal areas. We reviewed model performance across boreal environments and management systems. We identified a dearth of modelling studies in boreal regions, with the publication of three or less papers per year since the year 2000, constituting a significant research gap. Models IFSM and BASGRA_N performed best in grassland production, DNDC best in predicting soil N2O and NH3emissions. No model outperformed all others, strengthening the case for ensemble modelling. Existing agroecological models would be worthy of further evaluation, providing model improvements designed for boreal systems.

AbstractSimulation 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 (N2O) 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 N2O 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 N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O 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 N2O emissions. Yield‐scaled N2O emissions (N2O 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 N2O emissions at field scale is discussed.