Maize whole-plant system model

Background

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. 

Project description

In this study, crop modelling, remote sensing, imaging processing, environmental monitoring sensors and manual measurement will be combined. Remote sensing data will be used to estimate N spatial information and reconstruct the 3D canopy and root morphologies. The proposed plant system model will be built at first as an integration of different sub-models, and then model parameters will be estimated using literature data and measured data; the proposed model will be calibrated and validated by actual measured data. The validated plant system model will be used to study the interaction between the environment and plant and find optimal breeding targets under future climates.