TITLE:
Modelling Height-Diameter Allometry of Tectona grandis under Varying Site Quality in Tanzania
AUTHORS:
Wilson Ancelm Mugasha, Eliakim Zahabu
KEYWORDS:
Site Quality, Site Indices, Height-Diameter Allometry, Tectona grandis, Mixed Effect Modelling
JOURNAL NAME:
Open Journal of Ecology,
Vol.16 No.4,
April
7,
2026
ABSTRACT: Single tree parameters, such as tree height (H) and diameter at breast height (D), are essential for the prediction of difficult-to-measure parameters, such as volume and biomass, which are critical for yield prediction and forest management planning. Yet direct H measurement is often demanding and challenging. While various H-D models have been developed, there is insufficient information on how site quality influences H-D allometry. This study developed generalized and site-quality-specific H-D models for Tectona grandis (teak) plantations in Longuza and Mtibwa forest plantations, Tanzania, using a large dataset comprising 7253 observations. The dataset was separated by site class (quality) using dominant height and stand age. Models were fitted using two commonly used non-linear model forms, i.e., the Weibull and Richards. The general H-D models included site classes as random effects. The model performance was assessed using goodness-of-fit statistics, bias, and repeated random holdout validation. Findings showed that site quality strongly influenced H-D allometry, with distinct model trajectories and asymptotic H values across site classes. Although the generalised mixed-effects model improved performance relative to the fixed-effects model, it exhibited increased bias at the extremes of site quality (site class I and III). In contrast, site-quality-specific models consistently produced lower bias when applied to their corresponding site classes. These findings demonstrate that having H-D models by site quality improves H prediction accuracy. It is recommended that site-quality-specific H-D models be used in operational inventories where site class information is available, while the general model provides a robust alternative for data-limited or preliminary assessments.