Unbiased carbon sequestration estimation in parring bamboo (Gigantochloa atter (Hassk.) Kurz ex Munro): Integrating chemical composition and allometric models

[Geoengineering News]

https://www.sciencedirect.com/science/article/pii/S277313912600011X

Authors: Baharuddin Baharuddin, Muhammad Daud

24 February 2026

https://doi.org/10.1016/j.bamboo.2026.100230

Highlights

•Developed unbiased biomass models for Gigantochloa atter.

•Integrated chemical composition with allometric equations.

•Found species-specific carbon content of 49.17 %.

•Achieved precise estimates of carbon stock and sequestration.

•Contributes valuable data for strengthened MRV in bamboo carbon accounting.

Abstract

Bamboo species such as Gigantochloa atter play an important ecological and economic role in community-managed forests and offer substantial potential for carbon sequestration, yet accurate and unbiased estimation methods for their biomass and carbon stocks remain limited. This study integrates field-based allometric modeling with species-specific chemical composition analysis to develop a robust approach for estimating biomass and carbon stocks in G. atter stands. Data were collected from ten systematically established 20 × 50 m plots, with 30 representative culms destructively sampled for allometric model development and an additional 30 culms collected independently for model validation. Structural biomass was quantified and carbon content was determined according to ASTM standards. Five allometric models were evaluated using diameter at 0.3 m above ground level (D) and height (H) variables, with carbon fractions derived from molecular compositions of biomass constituents. The most robust model was a power equation, W = 0.348D1.830, using diameter as the sole predictor for field efficiency. The average carbon content was 49.17 %, higher than standard default values, leading to significantly improved accuracy. Mean annual carbon sequestration were estimated at 47.44 tonnes CO₂eq/ha/year. Models incorporating species-specific carbon fractions showed the lowest bias and highest precision. These findings highlight the importance of combining chemical and biometric data for species-specific carbon accounting and support the inclusion of G. atter in sustainable forest management and climate mitigation strategies.

Source: ScienceDirect