Simulation-assisted crop breeding for in-silico ideotype identification

Overview

Simulation-assisted crop breeding uses sophisticated canopy biophysical simulation models to identify ideal sets of traits (ideotypes) that optimize crop growth, productivity, and resource use under projected climate conditions. This approach enables the in-silico development of optimal cultivars as breeding targets, offering a rapid and cost-effective complement to traditional field-based breeding programs. By leveraging computational modeling, breeders can explore trait combinations that would be impractical or too time-consuming to evaluate empirically, accelerating the development of climate-resilient, high-performing crop varieties.

Technical specifications

The solution is built on a vertically-resolved canopy-root-soil system biophysical model (MLCan) that simulates crop canopy CO2, water, and energy exchange. A multi-objective optimization framework quantifies ideal targets for crop structural attributes that simultaneously optimize productivity and resource use efficiencies. Key trait parameters evaluated include:

• Total canopy leaf area and its vertical profile
• Leaf angular distribution
• Shortwave radiation reflectivity (albedo)
• Plant structural and physiological traits commonly available in major crop germplasm collections

Published validation in soybean demonstrated that productivity gains of 7% are achievable with no change in water use or albedo, while water use improvements of 13% or albedo improvements of 34% are possible with no loss in productivity.

Technology readiness level

The underlying canopy biophysical simulation model and multi-objective optimization framework have been validated through peer-reviewed publication and applied to soybean canopy improvement. The next phase involves extending the approach to real breeding programs by integrating actual trait data from breeding pipelines. Collaboration with breeders possessing extensive trait datasets, particularly related to plant structure, physiology, and biochemistry, would enable validation against realistic breeding targets and accelerate translation to operational breeding programs.