Turgor-Driven Plant Growth Inside a Soybean Functional-Structural Plant Model

Turgor pressure within a plant cell represents the key to mechanistically describe plant growth, combining the effect of both water and carbon availability. The high level of spatio-temporal variation and diurnal dynamics in turgor pressure within a single plant make it a challenge to model these on the fine spatial scale required for functional-structural plant models (FSPMs). A conceptual model for turgor-driven growth in FSPMs had been previously established, but its practical use was not yet explored.

Jonas Coussement and colleagues incorporated a turgor-driven growth model in a newly established FSPM for soybean. The FSPM simulates dynamics in photosynthesis, transpiration, and turgor pressure in direct relation to plant growth. Comparisons of simulations with field data were used to evaluate the potential and shortcomings of the modelling approach.

“Implementing the conceptual turgor-driven growth model into the soybean FSPM was relatively straightforward as the conceptual model was inherently able to deal with the added complexity of branching structures,” write Coussement and colleagues. “Similarly, from a programming standpoint, integration of transpiration and photosynthesis (P-SC-T model) based on realistic light modelling was simply a matter of replacing the theoretical inputs used in the conceptual model of Coussement et al..”

“Overall, the introduction of turgor-driven growth in an FSPM has potential to be used as a tool to better understand the performance of plant growth in terms of the internal plant properties and their response to environmental triggers. The use of this tool in combination with data from future experiments, focussed specifically on water deficit, to provide insight into the simulated model dynamics such as sap flow or stem diameter variations can alleviate the need for some model simplifications that were currently introduced. This will be the next fundamental step before an FSPM will be applicable as a predictive model to evaluate plant growth under a wide range of conditions, such as drought.”