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Dynamic simulation models

Dynamic simulation models

Dynamic simulation models

We develop and use dynamic simulation models. Behind every bit of research stands an idea how a particular system works. We try to simulate these ideas. This helps us to understand how different processes or traits might function together. The emphasis is thereby on model coupling. The various methods mentioned on this website, yield large amounts of data. The models help us to interpret the data and integrate different measurements into an integrated understanding of how the plant works or functions. The environment plays thereby an important role, and much of our time goes towards simulating the spatio-temporal dynamics of various important environmental factors. We would like to know how plants respond to different environmental conditions, and to understand to what extend those response represent stress or an adaptive response to cope with stress. Example questions are

How do different root architectural traits combined result in a specific root length distribution measured in the field?
What is the utility of plant trait in a specific environment?
How do traits interact?
What are the important tradeoffs, costs, or negative feedbacks?

We use OpenSimRoot (OSR), an opensource platform for dynamic simulation and model coupling. OSR originally started as a root architectural model named SimRoot, 20 years ago in the plant nutrition lab of Jonathan Lynch. OSR is being developed on linux, but compiles on both windows and mac. More info you will find on the OSR website.

Examples of models:

SimRoot_01OSR has a simple, lintul like, crop model implemented.


SimRoot_02Evapotranspiration depends on radiation, wind, VPD etc an can be estimated with the Pennman Monteith equation. We extended this equation in order to differentiate between evaporation and transpiration. Here is a result of a random example.


SimRoot_03Soil temperature model. Deeper down the soil profile, temperature is closer to the yearly average. The daily oscillations in surface temperature dampen more quickly than the yearly oscillations. There is also a delay, the maximum temperature at 50 cm depth is later in the year than the max temperature at the surface.


SimRoot_04Root architectural model of maize. Pseudo colors show nitrate concentrations around the roots. Older roots have depleted most of the nitrate in their vicinity.


SimRoot_05Different plants species have different root architectures. We simulated how maize, bean, and squash are complementary in their strategies for acquiring N.


SimRoot_06When we couple the root architectural model to a 3D finite element model which simulates water and solute transport we can start simulating and studying nutrient uptake by a given root architecture.


SimRoot_07The Barber-Cushman model simulates uptake of nutrients by plant roots. It is especially useful for simulating phosphorus uptake as 3D finite element models struggle with the steep gradients in the concentration of P around the roots (first graph).


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