Modelling of the maize-based agroecosystem to design high-yielding ideotypes of C4 crops

Major challenges in global food security are the increase in world population and global climate change. A strategy to cope with these challenges is to breed higher-yielding, pest- and disease-resistant and climate-smart (i.e. stress tolerant) crops. The traditional breeding process is time-consuming. Recent targeted genome editing technologies provide useful tools to accelerate the crop-improvement process. To successfully exploit the targeted genome editing technologies, it is important to rationally design the ‘ideotype’ consisting of suitable target traits. However, the performance of crops depends on the complex genotype × environment × management interaction, meaning that the combination of desirable traits of an ideotype depends on the environment. System models based on the underlying mechanisms are widely considered as an effective tool for identifying desired traits in a complex system. However, many system models focus on an individual process; when the environment or other key factors in the plant are changed, the conclusions can fail. In this study, I propose a new whole-plant system model which incorporates a dynamic canopy photosynthesis model, a root system model, and a current soil-plant-atmosphere continuum model. It is expected that such an integrated system model may provide new insight into the crop’s interaction with the environment and may identify optimal breeding targets under future climates. I will use maize as an example to showcase the approach.