Development and evaluation of robust tree biomass equations for rubber tree (Hevea brasiliensis) plantations in India

Background: In India, rubber (Hevea brasiliensis) plantations cover similar to 0.8 million ha of land, emphasizing its significant role in the Earth's carbon dynamics. Therefore, it is important to estimate the biomass stocks of plantations precisely in the context of carbon management. Previous studies in India have focused on development of allometric equations for estimating aboveground biomass (AGB) through harvesting younger trees (up to 14 yr) only or on studies with small sample sizes without assessing model bias. The objective of this study was to develop biomass estimation models for different tree components in rubber plantations and assess model predictive performance at the stand level. Methods: A total of 67 trees were harvested from plantations of different ages (6, 15, 27 and 34 yr) in North East India and their diameter at 200 cm (D), height and dry weights of different tree components were recorded. The data were used for evaluation of H-D and biomass estimation models at the stand level. Results: The Michaelis-Menten function was found to be the most appropriate model for estimating tree height among 10 commonly used H-D models. For estimation of AGB and coarse root biomass, a model that involves tree volume (i.e. (DH)-H-2) was found to provide better prediction than either D or H alone or a model that combines H, D and stand density. The estimated AGB varied from 28 Mg.ha(-1) in 6 yr. old plantation to 169 Mg.ha(-1) in 34 yr. old plantations. The coarse root biomass was estimated at 4 Mg.ha(-1) for 6 yr. old plantation and 12 Mg.ha(-1) for 34 yr. old stands. Conclusions: It is concluded that models involving tree volume are more appropriate for regional level biomass estimation than simple power-law models for individual stands. We recommend that the power-law model should not be used for estimation of AGB in plantations at different growth stages because power-law parameters can be biased due to data truncation.