An objective approach to model reduction: Application to the Sirius wheat model

Craigon, Jim; Cox, G.M.; Crout, Neil; Jao, Y.; Tarsitano, D.; Wood, A.T.A.; Semenov, M.

Abstract

An existing simulation model of wheat growth and development, Sirius, was evaluated through a systematic model reduction procedure. The model was automatically manipulated under software control to replace variables within the model structure with constants, individually and in combination. Predictions of the resultant models were compared to growth analysis observations of total biomass, grain yield, and canopy leaf area derived from 9 trials conducted in the UK and New Zealand under optimal, nitrogen limiting and drought conditions. Model performance in predicting these observations was compared in order to evaluate whether individual model variables contributed positively to the overall prediction. Of the 1 1 1 model variables considered 16 were identified as potentially redundant. Areas of the model where there was evidence of redundancy were: (a) translocation of biomass carbon to grain; (b) nitrogen physiology; (c) adjustment of air temperature for various modelled processes; (d) allowance for diurnal variation in temperature; (e) vernalisation (f) soil nitrogen mineralisation (g) soil surface evaporation. It is not suggested that these are not important processes in real crops, rather, that their representation in the model cannot be justified in the context of the analysis. The approach described is analogous to a detailed model inter-comparison although it would be better described as a model intra-comparison as it is based on the comparison of many simplified forms of the same model. The approach provides automation to increase the efficiency of the evaluation and a systematic means of increasing the rigour of the evaluation.

Citation

Craigon, J., Cox, G., Crout, N., Jao, Y., Tarsitano, D., Wood, A., & Semenov, M. (2014). An objective approach to model reduction: Application to the Sirius wheat model. Agricultural and Forest Meteorology, 189-190(100), 211-219. https://doi.org/10.1016/j.agrformet.2014.01.010

• Journal Article Type: Article
• Acceptance Date: Jan 13, 2014
• Publication Date: Jun 1, 2014
• Deposit Date: Mar 17, 2018
• Publicly Available Date: Nov 21, 2018
• Print ISSN: 0168-1923
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 189-190
• Issue: 100
• Pages: 211-219
• DOI: https://doi.org/10.1016/j.agrformet.2014.01.010
• Public URL: https://nottingham-repository.worktribe.com/output/1100426
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0168192314000197
• PMID: 24895470