Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Data-constrained Modelling of Plant Growth

CEES project

Data-constrained Modelling of Plant Growth

Plants are complex reactive systems. They process water and nutrients, fix carbon dioxide (CO2) into new plant material, decide where to allocate this new carbon (e.g. leaves, roots, stems), and decide when to flower and produce seeds.

Accurate models of this intelligent behaviour -- and how it depends on climate and other factors -- could revolutionize our understanding of natural plant communities, whether natural or agricultural. We hope to arrive at such models for select plant communities, by using an integrated approach encompassing model design, experimental design, and computational statistics. We have a suite of candidate models of plant growth, which we are now parameterizing and selecting between using experimental designs optimized for this purpose. The result should be a new understanding of growth in our selected communities -- e.g. European dune annuals, Bornean raindforests -- but perhaps more importantly, new models and statistical routines that can be used to address the process of plant growth more generally. This could help with anything from understanding biodiversity and ecosystem function, to designing new water- and nutrient-efficient agricultural systems.

Relevant Publications