No abstract

Acacia catechu (Khair) is one of the significant tree species evolved together with Nepali communities. The tree is widely used for medicinal purposes, feeding the livestock, fulfilling the structural needs, and satisfying religious and spiritual needs. Despite the wide use and importance of this tree, the available publications have failed to address the risks the tree is vulnerable to, and develop a management design to overcome these threats. Due to these reasons, the people are growing the Khair trees without any robust health management plan. The abundance and overall importance of this tree in the South Asian region strongly demands an interpretative and comprehensive way of its cultivation. This study is aimed towards bringing together the available information on Khair and finally coming up with an advantageous management plan that can deal with all the hazards the tree is prone to, and help in the production of healthy and of economically high-value timber. This study only deals with two of the several prevalent fungal stresses-Ganoderma lucidum and Fomes badius, causing root rot and heart rot, respectively, putting the tree under risk. The heart rot and root rot are capable of destroying the whole site rendering the trees useless for consumption. The findings from this study can help cultivators know the nature of the diseases and their occurrences and improve the way of cultivating the tree and take prompt actions on the syndromes.

No abstract

Recent advancements in laser scanning technology have demonstrated great potential for the precise characterization of forests. However, a major challenge in utilizing metrics derived from lidar data for the forest attribute prediction is the high degree of correlation between these metrics, leading to multicollinearity issues when developing multivariate linear regression models. To address this challenge, this study compared the performance of four different modeling methods for predicting various forest attributes using aerial lidar data: (1) Least Squares Regression (LSR), (2) Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO), (3) Random Forest (RF), and (4) Generalized Additive Modeling Selection (GAMSEL). The study used three primary plot-level forest attributes (volume, basal area, and dominant height) as response variables and thirty-nine plot-level lidar metrics as explanatory variables. A k-fold cross-validation approach was used, with consistent folds to assess the performance of each method. Our results revealed that no single method demonstrated a significant advantage over the others. Nonetheless, the highest R2 values of 0.88, 0.83, and 0.87 for volume, basal area, and dominant height, respectively, were achieved using the ALASSO method. This method was also found to be less biased, followed by GAMSEL and LSR.