Abstract
The development of a model is highly crucial in cases where there are intricate geographical features, and conducting a forest inventory is both time-consuming and expensive, requiring significant manual effort for measurement. Acquiring reliable data regarding the forest's condition and future progression is essential for making informed decisions about its management. Therefore, this research aimed to create an individual tree diameter growth model specifically for Terminalia alata (B. Heyne. ex Roth). This study was conducted in Terai Arc Landscape of Nepal, encompassing 14 districts in the Terai and Chure regions of Nepal. Individual tree data (diameter at breast height, tree height, crown height, crown cover, longitude, and latitude) from three different time periods (2011, 2017, and 2022) were obtained with 673 sample plots maintained for forest research assessment by Government of Nepal, and annual diameter growth was estimated. Multiple linear, linear mixed, and generalized additive models were employed to fit the growth modeling for individual tree diameter growth of T. alata. We observed higher mean diameter growth rates in 0-25 cm and 101-125 cm tree diameter classes (0.318 cm·yr(-1)). There were significant differences in diameter growth across tree quality classes, but no significant differences due to crown classes were observed. Although the generalized additive model (Adj. R (2) = 0.32) performed better than the linear mixed model (adj. R (2) = 0.23) and the multiple linear model (adj. R (2) = 0.03), it still explained only a small proportion of the variance in diameter growth. This suggests that other factors, such as unmeasured environmental variables, biotic interactions, or complex nonlinear relationships, may play a significant role in explaining the variation. In addition, the low R (2) values indicate that the models may need further refinement, possibly by incorporating interaction terms, random effects, or other possible nonlinear approaches. Future research should also consider the potential influence of spatial or temporal heterogeneity on the growth dynamics.