From genome to wheat: Emerging opportunities for modelling wheat growth and development

Ecophysiological models of crop growth and development sometimes show unrealistic responses that are attributable to our incomplete understanding of the processes the models attempt to describe. Rapid advances in plant genetics, genomics and biochemistry offer important opportunities for improving representations of key processes of growth and development. Research on incorporation of genetic information in models supports this potential, especially in modelling cultivar performance across environments. This paper reviews progress in using information from genetics, genomics and allied fields in modelling and examines approaches suitable for modelling wheat (Triticum aestivum L. and T. durum Desf.). Efforts to model wheat crops should first focus on the relatively well-understood genetic systems affecting phenology and plant height. A simple gene-based approach using linear equations to estimate cultivar-specific parameters has the advantage that it can easily be implemented in existing wheat models. One requirement is to integrate data on the genetic makeup of wheat cultivars with results from field trials that can be used to estimate genetic effects and evaluate model performance. Concomitantly, modellers should exploit findings from genomics and allied fields on wheat and other plant species in order to improve sub-models of individual processes, using more complex representations of gene action. Advances in these more mechanistic representations require much more detailed and quantitative studies on how gene action varies with specific environmental signals such as temperature and photoperiod.