A commentary on strategic community-led directions for water quality modelling in Australia and New Zealand - Danlu Guo; Anna Lintern; Alexander H. Elliott; Melanie E. Roberts; Susan M. Cuddy; Ulrike Bende-Michl; Baihua Fu; Klaus Joehnk; Robert Sargent; Danswell Starrs; Anthony J. Jakeman; Vaughn Grey; et al. (Journal of Hydrology, 2025)

Review of APSIM's soil nitrogen modelling capability for agricultural systems analyses - Kirsten Verburg; Heather R. Pasley; Jody S. Biggs; Iris Vogeler; Enli Wang; Henrike Mielenz; Val O. Snow; Chris J. Smith; Chiara Pasut; Andrea D. Basche; Di He; Sotirios V. Archontoulis; et al. (Agricultural Systems, 2025)

Pasture growth simulation and sensitivity analysis using APSIM-related models in a tropical silvopastoral system - Lucas Fillietaz Balcão; Mariana Andreucci; Val Snow; Dean Holzworth; Henrique Bauab Brunetti; Mariely Lopes dos Santos; Cristiam Bosi; Jose Ricardo Macedo Pezzopane; Patricia Menezes Santos (European Journal of Agronomy, 2024)

Residual correlation and ensemble modelling to improve crop and grassland models - Renáta Sándor; Fiona Ehrhardt; Peter Grace; Sylvie Recous; Pete Smith; Val Snow; Jean-François Soussana; Bruno Basso; Arti Bhatia; Lorenzo Brilli; Jordi Doltra; Christopher D. Dorich; et al. (Environmental Modelling & Software, 2023)

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N 2 O emissions - Fiona Ehrhardt; Jean‐François Soussana; Gianni Bellocchi; Peter Grace; Russel McAuliffe; Sylvie Recous; Renáta Sándor; Pete Smith; Val Snow; Massimiliano de Antoni Migliorati; Bruno Basso; Arti Bhatia; et al. (Global Change Biology, 2018)
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 2 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 2 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 2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N 2 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 2 O emissions. Yield‐scaled N 2 O emissions (N 2 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 2 O emissions at field scale is discussed.