Estimating Sink Parameters of Stochastic Functional-Structural Plant Models Using Organic Series-Continuous and...

Area of Science:

• Plant biology
• Computational modeling
• Ecology

Background:

• Functional-structural plant models (FSPMs) simulate plant growth at the organ level.
• Estimating unmeasurable parameters like sink strength often requires inverse fitting.
• Stochastic FSPMs introduce randomness, complicating parameter estimation due to organ variability.

Purpose of the Study:

• To develop a generic method for estimating sink parameters in stochastic FSPMs.
• To address the challenge of parameter estimation with intracanopy variability.
• To enable broader applications of FSPMs to real plants.

Main Methods:

• Disentangling plant development and growth by computing phytomer occurrence probabilities.
• Analytically computing the mean organic series from the potential structure.
• Fitting the computed organic series to organ-level target data for parameter estimation.

Main Results:

• The analytical method successfully estimated sink parameters for both continuous and rhythmic plant development.
• Results from the analytical method closely matched Monte-Carlo simulations.
• The method demonstrated effectiveness with both simulated and real plant data.

Conclusions:

• The proposed method provides a simplified approach to solving source-sink functions in stochastic plant architectures.
• This work overcomes a key limitation in applying FSPMs to real-world plant studies.
• The generic method facilitates the estimation of sink parameters and offers a novel stem-sampling technique.