A new growth curve and fit to the National Forest Inventory data of Finland

Lauri Mehtätalo, Minna Räty and Juho Mehtätalo

Ecological Modelling, 2025, vol. 501, issue C

Abstract: Predictive models for stem volume increment are needed for many purposes, including the analysis of cutting potential and carbon balance. Motivated by a simple simulator based on age distribution and growth curve of a certain forest type within region, we present a new growth function for the volume increment as a function of stand age. The function is inspired by a mechanistic description of tree crowns and growth process, where the annual gross primary production is proportional to the theoretical area of the forest canopy when projected to the ground, and respiration is proportional to the accumulated growing stock volume. The function has four parameters with biologically meaningful interpretations. The function treats the annual increment as an instant event associated with ages that are non-negative integers. We also show how such a model for annual increment can be estimated based on National Forest Inventory (NFI) data of past 5 year’s increment in the context of nonlinear regression modelling. The model showed extremely good fit in a data set of 34000 remeasured NFI plots from Finland. Comparison with the widely used Richard’s function further indicates that the new function may provide a significant improvement to forest growth modelling. The new function and the approach to estimate current annual increment based on the periodic annual increments of past 5 years opens interesting new opportunities to the use of NFI data sets in scenario analyses of the growth, removals and carbon sinks in long term, which are also illustrated.

Keywords: Tree growth; National Forest Inventory; Growth process; Mechanistic; Nonlinear regression; GNLS; Difference equation; Scenario analysis; Growth and yield; Boolean model (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:501:y:2025:i:c:s0304380024003946

DOI: 10.1016/j.ecolmodel.2024.111006

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