Asian Journal of Research in Agriculture and Forestry

This study examined the diameter at breast height (DBH), total tree height (THT) of trees and modelled the height-diameter across nonlinear regression (NLR) and artificial intelligence (AI) approaches in the sacred grove forest, Benue State, Nigeria. The study reported a mean DBH of 40.92 cm and THT of 18.76 m, indicating a structurally diverse and uneven-aged forest. The DBH distribution was positively skewed (1.08) with moderate kurtosis (0.41), while tree height was nearly symmetric (skewness = 0.08) and slightly platykurtic (–0.34). DBH and THT correlated with each other in a curvilinear manner, where the height increment was decreasing after a point of about 50 cm DBH. Out of the seven fitted NLR models, the Weibull model (M2) best overall statistical model with a coefficient of determination (R ² = 0.89), a minimum Akaike Information Criterion (AIC = 453.3), and an acceptable residual distribution, followed by the Chapman Richards model (R ² = 0.857, AIC = 453.9). The Michaelis-Menten model was also competitive and exhibited low residual bias and biological realism. The XGBoost algorithm was the most predictive and least biased in the AI models, with R ² = 0.865, RMSE = 1.623 m, MAE = 1.210 m, and MAPE = 9.37%. Thus, this study shows that AI and the classic regression methods can efficiently estimate the height of trees using their diameter; however, the XGBoost model AI-based gave a better performance in the conditions experienced in the heterogeneous forest.