A Method for the Development of Dynamic Site Index Models Using Height–Age Data from Temporal Sample Plots

Abstract

Knowledge of the potential productivity of forest sites is fundamental for making strategic decisions in forest management. Site productivity is usually evaluated using the site index, and therefore the development of site index models is one of the crucial tasks in forest research and forest management. This research aims to develop an effective method for building top-growth and site index models using data from temporary sample plots (TSP). Exploiting the advantages of the generalised algebraic difference approach (GADA), the proposed method overcomes the limitations of the guide curve method that has been to date used in site index modelling using TSPs data and allows to obtain only a set of anamorphic site index curves. The proposed approach enables the construction of dynamic site index models with polymorphism and variable asymptotes. Such models better reflect local, site-specific height growth trajectories and therefore allow more appropriate site index estimation. We tested the proposed method using data collected from 5105 temporary sample plots in Poland. Our results indicate that growth trend estimates using height–age measurements of TSPs may be valuable data for modelling top height growth. For these reasons, the proposed method can be very useful in forest management.

Height growth rate of Scots pine in Central Europe increased by 29% between 1900 and 2000 due to changes in site productivity

“…The "static SI" proposed by Brandl et al (Brandl et al, 2018) is based on a similar notion, where pseudo-age-height trajectories are used from momentary data of different ages. The use of false age-height trajectories was first described as the von Baur's original strip method (Brandl et al, 2018) and is currently referred to as the guide curve method (Mathiasen et al, 2006;Socha and Tymińska-Czabańska, 2019). The examples cited and the analyses performed in our research show that the use of the SI as a measure of site productivity necessitates an amendment in top height growth models to account for the age trend in the SI resulting from changes in site productivity.…”

Estimating polymorphic growth curve sets with nonchronological data

“…Edminster et al (1991) first applied the guide curve method, which can essentially be applied for only the anamorphic growth curve set, and then nonaverage growth curves were roughly estimated given the variation of the real data of each age class. Socha and Tymińska-Czabańska (2019) assumed that a series of percentile values of each age in a given data set can be treated as virtual chronological data of virtual individuals. With the virtual chronological data, the estimation method for chronological data was applied.…”

“…The assumptions of the proposed method presented in Section 2.2 are similar to those of the estimation techniques proposed by Edminster et al (1991) and Socha and Tymińska-Czabańska (2019). Edminster et al (1991) first applied the guide curve method, which can essentially be applied for only the anamorphic growth curve set, and then nonaverage growth curves were roughly estimated given the variation of the real data of each age class.…”

“…Furthermore, nonaverage growth curves are determined using a rough estimate without the assumption of a model of growth curve set. Socha and Tymińska‐Czabańska (2019) proposed a method that evokes chronological data with the assumption that a series of percentile values of each age in numerous nonchronological data can be assumed to be the chronological data of virtual individuals. However, the chronological data of the virtual individuals reveal quite jagged shapes that do not resemble actual growth curves, even using 5,105 data.…”

Observation of children's age to predict maximum height

“…Nutrition received by the body can shape the child's height and provide health (Vyas et al, 2016). Therefore, height measurement can be valuable data shaping a child's height growth model (Haile et al, 2016;Socha & Tymińska-Czabańska, 2019). Human physical form is an important measure and is the main capital of health (Spears, 2020).…”