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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Parameter estimation for functional-structural plant models when data are scarce: using multiple patterns for

Ming Wang1,2, Neil White2,3, Jim Hanan2

  • 1Commonwealth Scientific and Industrial Research Organisation (CSIRO), Health & Biosecurity, Canberra, Australia.

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|February 1, 2020
PubMed
Summary
This summary is machine-generated.

Pattern-oriented modeling (POM) offers an efficient method for parameterizing functional-structural plant (FSP) models. This approach improves model performance and predictive power without extensive experiments.

Keywords:
Persea americanaPattern-oriented modellingagent-based modellingavocadoequifinalityfunctional–structural plant modellingindividual-based modellingmodel calibrationmodel parameterizationparameter estimationparameter identifiabilitysimulation inference

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Area of Science:

  • Plant sciences
  • Computational biology
  • Ecology

Background:

  • Functional-structural plant (FSP) models are crucial for understanding plant architecture and development.
  • Parameter estimation for FSP models presents significant challenges, often requiring extensive experimental data.

Purpose of the Study:

  • To evaluate pattern-oriented modeling (POM) as an efficient and systematic approach for FSP model parameterization.
  • To determine if POM can streamline the development of FSP models, reducing reliance on manual parameterization and experiments.

Main Methods:

  • Applied POM to an existing FSP model of avocado ('Hass').
  • Used verification patterns as filters to assess 10,000 parameter sets.
  • Validated the model using surviving parameter sets against independent validation patterns.

Main Results:

  • POM calibration yielded 22 viable parameter sets, with individual parameters showing wide variation.
  • One POM-derived parameter set closely matched manual parameterization.
  • The model, using all surviving parameter sets, accurately predicted validation patterns, demonstrating improved performance and uncertainty handling.

Conclusions:

  • POM effectively calibrates FSP models, accounting for parameter interactions and improving predictive accuracy.
  • This method accelerates FSP model development, bypassing the need for laborious experiments or manual tuning.
  • POM enhances the overall predictive power and robustness of functional-structural plant models.