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 111 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.
Highlights
Simulation models that predict the yield of agricultural crops from weather, soil and management data have provided a focus for crop physiological research over the last three decades and have contributed to current understanding of crop-environment interactions
Many such models have been developed for a wide range of crops, for example, STICS (Brisson et al, 2003), APSIM (Keating et al, 2003), and DSSAT (Jones et al, 2003)
The complexity of crop models typically arises from the inter-relationships between modelled mechanisms rather than the sophistication of individual process representation
Summary
Simulation models that predict the yield of agricultural crops from weather, soil and management data have provided a focus for crop physiological research over the last three decades and have contributed to current understanding of crop-environment interactions. Many such models have been developed for a wide range of crops, for example, STICS (Brisson et al, 2003), APSIM (Keating et al, 2003), and DSSAT (Jones et al, 2003).
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