Volume Estimation of Agarwood (Aquilaria malaccensis Lamk.) under Different Farming Situations

Abstract

Agarwood, also known as Gaharu, holds a prominent place in traditional medicine. Its wider adoption and increased yield will also make agarwood-based natural products more accessible to the public. This study aimed to develop reliable volume estimation models for agarwood trees under diverse farming situations. The study utilised both primary and secondary data. Primary data were collected through field measurements of height and diameter at breast height (DBH) from selected trees, while secondary data were collected with varying crop combinations and spacing arrangements, covering monoculture and intercrop systems with coffee, coconut, areca nut, cardamom, cocoa, banana, and shade trees from the Department of Silviculture and Agroforestry, College of Forestry, Ponnampet. Multiple linear regression models were fitted to predict tree volume, with model performance evaluated through coefficient of determination (R²), standard error, and F-tests. Results demonstrated that all 21 regression equations were statistically significant at the 5% level, with R² values ranging from 94.78% to 99.39%, indicating strong predictive accuracy. The most robust model was observed in coffee + silver oak + agarwood systems at Alur (R² = 99.39%, SE = 0.00003), while the least preferred was in areca nut + banana + agarwood systems at Balegadde (R² = 94.78%, SE = 0.0021). Overall, the findings confirm that volume estimation based on DBH and height provides reliable predictions across farming situations, with silver oak associations yielding particularly strong correlations. These models offer practical tools for farmers and researchers to assess agarwood growth and optimise its integration into diversified cropping systems, thereby enhancing economic sustainability in the region.