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Related Experiment Videos

Comparing light interception with stand basal area for predicting tree growth.

Benoît Courbaud1

  • 1Cemagref, Div. Ecosystèmes et Paysages Montagnards, 2 rue de la Papeterie, B.P. 76, 38402 Saint-Martin-d'Hères, France.

Tree Physiology
|March 26, 2003
PubMed
Summary

Process-based tree growth models, incorporating foliage biomass, offer more realistic long-term simulations than empirical models, especially for silvicultural impacts like thinning. This research highlights the importance of physiological processes for accurate forest management predictions.

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Area of Science:

  • Forestry Science
  • Ecological Modeling
  • Silviculture

Background:

  • Empirical and process-based tree growth models are used concurrently but rarely compared.
  • Process-based models explicitly quantify foliage biomass, a key difference from empirical models.

Purpose of the Study:

  • To compare the growth predictions of empirical and process-based tree growth models.
  • To assess the impact of foliage biomass quantification on model behavior, particularly under silvicultural practices like thinning.
  • To evaluate model performance in simulating Norway spruce (Picea abies) growth in the French Alps.

Main Methods:

  • Developed a method to estimate leaf area and light interception from yield table data for Norway spruce.
  • Calculated light interception using interpolation between closed-stand (Beer-Lambert law) and isolated-tree models.

Related Experiment Videos

  • Constructed a process-based model using light competition and compared it with an empirical model based on stand basal area.

    • Main Results:

    • Both models accurately predicted diameter increment, with the empirical model performing slightly better.
    • Simulations revealed distinct growth responses to thinning: empirical models showed discontinuous growth, while light-based models exhibited smoother responses.
    • Heavy thinning simulations highlighted unrealistic growth predictions in the empirical model compared to the more regulated response of the process-based model.

    Conclusions:

    • Process-based models exhibit qualitative behaviors distinct from classical empirical models.
    • Explicit quantification of foliage biomass and light interception in process-based models is crucial for accurate long-term growth forecasting.
    • Physiological or ecological process-based models are essential for extrapolating predictions, especially for novel silvicultural strategies.