Integrating oscillator-based circadian clocks with crop growth simulations

Authors: LOCHOCKI, EDWARD - University Of Illinois; McGrath, Justin

Submitted to: in silico Plants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/11/2021
Publication Date: 3/19/2021
Citation: Lochocki, E.B., McGrath, J.M. 2021. Integrating oscillator-based circadian clocks with crop growth simulations. in silico Plants. 3(1). Article diab016. https://doi.org/10.1093/insilicoplants/diab016.
DOI: https://doi.org/10.1093/insilicoplants/diab016

Interpretive Summary: Computer programs to simulate crop growth are commonly used to estimate productivity of crops in different regions, determine impacts of climate on crop growth, and identify ways to improve productivity. Timing various biological events, such as flowering, are required to properly predict yield, but most models use simplistic timing methods that are not accurate in all conditions. There are models that simulate the complex biological processes that regulate real biological systems, but these are too complicated for use in crop models. Here we developed a relatively simple clock model that simulates most of the complexity of the real biological clock. This allows for more precise timing of events in crop models with little added complexity.

Technical Abstract: Circadian rhythms play critical roles in plant physiology, growth, development, and survival, and their inclusion in crop growth models is essential for high fidelity results, especially when considering climate change. Commonly used circadian clock models are often inflexible or result in complex outputs, limiting their use in general simulations. Here we present a new circadian clock model based on mathematical oscillators that easily adapts to different environmental conditions and produces intuitive outputs. We then demonstrate its utility as an input to Glycine max development models. This oscillator clock model has the power to simplify the inclusion of circadian cycles and photoperiodic effects in crop growth models and to unify experimental data from field and controlled environment observations.