This workflow automates a critical R&D bottleneck: manually correlating genomic data with long-term climate models to forecast which traits will be most valuable. It ingests future climate projection grids (temperature, precipitation) from sources like CMIP6, aligns them with georeferenced genomic and phenomic databases, and runs spatial modeling to match genetic adaptations with predicted environmental stress. The operational upside is a data-driven, forward-looking R&D portfolio that reduces the risk of investing in traits that become obsolete, focusing resources on genetic solutions for tomorrow's climate reality.




