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May 11, 2025Keywords:
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Main Article Content
Diameter distribution, maximum likelihood estimation, probability distribution function, generalized Weibull, 3-parameter gamma, parameter estimation
Abstract
Different diameter distributions are suited to various forests, influenced by factors such as species composition, site conditions, and estimation methods. Given this variability, it is crucial to fit and identify models that can most accurately characterize the secondary forest at IITA, Ibadan, Oyo State, Nigeria, where such functions are nonexistent. To address this gap, diameter distribution models were fitted for the IITA forest, using six probability distribution functions: 3-parameter Weibull, 2-parameter Weibull, generalized Weibull, Logit-Logistic, 3-parameter Gamma, and 2-parameter Gamma distribution functions. A straightforward systematic line technique was employed for data collection in the study area. Eight plots, each 25 m by 25 m, were sampled and alternately laid out with four sample plots on each transverse line at 50 m intervals. 241 trees with diameter at breast height (DBH) ≥ 5 cm were measured in total. The six probability distribution functions' parameters were calculated using maximum likelihood estimation (MLE), and models were fitted appropriately. The accuracy and consistency of the distribution functions were assessed using three goodness-of-fit statistics: The Kolmogorov-Smirnov statistic (Dn), the Anderson-Darling statistic (A2), and the Cramér-von Mises statistic (W2), to fit the diameter data. The results indicated that the highest proportions of the trees fell within the small diameter classes (20–29.9 cm, 30–39.9 cm, and 10–19.9 cm) and the distribution exhibited the expected inverse J-shaped pattern of natural forests. The generalized Weibull distribution function was identified as the model that is best suited for describing and projecting the DBH distribution of trees in the natural forest of IITA, as it exhibited the lowest relative rank sum.
