Articles >
ESTIMATION OF FIXED EFFECT MODELS ON TREE GROWTH
VARIABLE PREDICTION IN A MIXED FOREST STAND, EDO STATE,
NIGERIA
Author(s): Egonmwan Y. I., Izekor D.N. & jimoh H.
Volume/Issue: Volume 6 , Issue 1 (2026)
ABSTRACT:
This study evaluated the performance of six fixed-effect growth models, Logistic, Chapman-Richards, Mitscherlich, Gompertz, Polynomial, and Hossfeld IV for predicting tree growth dynamics in a mixed forest stand in Edo State, Nigeria. Accurate growth modeling is essential for sustainable forest management, particularly in heterogeneous stands where species interactions and site variability influence growth trajectories. Data were collected from established permanent sample plots representing the mixed forest conditions of the study area. Each model was fitted using fixed-effect estimation techniques, and their predictive accuracy was assessed using the coefficient of determination (R²), root mean square error (RMSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Results revealed notable differences in the models’ ability to describe the growth patterns of trees within the mixed stand. Among the six candidate models, the Chapman-Richards function consistently outperformed the others, exhibiting the highest R² (76.19%) and the lowest RMSE (4.498), AIC (330.78), and BIC (334.28) values respectively, indicating superior goodness-of-fit and greater predictive reliability. The Logistic and Gompertz models provided moderately accurate estimates but fell short compared to the Chapman-Richards model, while the Polynomial, Mitscherlich, and Hossfeld IV (worst) models showed comparatively weaker performance and higher information criteria values. The findings underscore the suitability of the ChapmanRichards model for describing tree growth in mixed forest ecosystems in Edo State, emphasizing its ability to capture asymptotic growth behavior and structural complexity. This study provides a robust empirical basis for selecting appropriate growth models in tropical mixed forests and contributes to improved forest planning, yield prediction, and management decision-making in Nigeria. Future studies should incorporate longer-term monitoring data and additional ecological variables to enhance model robustness and predictive accuracy.
KEYWORDS:
Chapman-Richard, forest, fixed effect, Terminalia superba, model
