Wheat growth and grain yield is strongly modulated by the complex genotype (G) by environment (E) and management (M) (GxExM) interactions. Process-based crop modelling has been considered as an effective means to disentangle the GxExM interactions and assist in developing breeding and management synergies to increase crop performance for target environments. However, most current crop models lack the ability to establish the gene-trait and trait yield relationships. They cannot effectively capture the genetic control of physiological traits and often assume uniformity of many important traits (e.g. leaf size, tillering, resource use efficiency and partition of resources to different organs) across genotypes, limiting their applications to evaluate the impact of elite traits of modern cultivars to optimize GxExM.

We present our recent efforts in modelling gene-trait-yield relationships in wheat. We incorporated the newest understanding in genetics to develop a molecular-physiology model to simulate wheat flowering time, which enabled simplification of phenotyping of phenology and prediction of flowering time and grain yield of new wheat cultivars from genotyping data and at time of release. In addition, we developed gene-based approaches for simulation of leaf development and grain yield of wheat with the new genetic traits of early vigour and long coleoptiles.

We demonstrate how incorporation of genetic understanding and data into farming systems modelling can enable gene-trait-yield simulations across environments to assist in the design of ideotypes and management strategies to optimise GxExM for increased productivity and resilience of wheat under climate change.