Residual correlation and ensemble modelling to improve crop and grassland models
journal contribution
posted on 2023-05-21, 15:59authored bySandor, R, Ehrhardt, F, Grace, P, Recous, S, Smith, V, Snow, V, Soussana, J-F, Basso, B, Bhatia, A, Brilli, A, Doltra, J, Dorich, CD, Doro, L, Fitton, L, Grant, BB, Matthew HarrisonMatthew Harrison, Skiba, U, Kirschbaum, MUF, Klumpp, K, Laville, P, Leonard, J, Martin, R, Massad, RS, Moore, A, Myrgiotis, V, Pattey, E, Rolinski, S, Sharp, J, Smith, W, Wu, L, Zhang, Q, Bellocchi, G
Multi-model ensembles are becoming increasingly accepted for the estimation of agricultural carbon-nitrogen fluxes, productivity and sustainability. There is mounting evidence that with some site-specific observations available for model calibration (with vegetation data as a minimum requirement), median outputs assimilated from biogeochemical models (multi-model medians) provide more accurate simulations than individual models. Here, we evaluate potential deficiencies in how model ensembles represent (in relation to climatic factors) the processes underlying biogeochemical outputs in complex agricultural systems such as grassland and crop rotations including fallow periods. We do that by exploring the correlation of model residuals. We restricted the distinction between partial and full calibration to the two most relevant calibration stages, i.e. with plant data only (partial) and with a combination of plant, soil physical and biogeochemical data (full). It introduces and evaluates the trade-off between (1) what is practical to apply for model users and beneficiaries, and (2) what constitutes best modelling practice. The lower correlations obtained overall with fully calibrated models highlight the centrality of the full calibration scenario for identifying areas of model structures that require further development.
Funding
Meat and Livestock Australia
Integrity Ag & Environment
History
Publication title
Environmental Modelling and Software
Volume
161
Article number
105625
Number
105625
Pagination
1-12
ISSN
1364-8152
Department/School
Tasmanian Institute of Agriculture (TIA)
Publisher
Elsevier Sci Ltd
Place of publication
The Boulevard, Langford Lane, Kidlington, Oxford, England, Oxon, Ox5 1Gb
Climate change adaptation measures (excl. ecosystem); Climate change mitigation strategies; Management of greenhouse gas emissions from plant production