Introduction Dendrocalamus brandisii is a high-yielding bamboo species valued for both its carbon-sequestration capacity and its economic and ecological benefits. Despite this, quantitative information on how its above-ground biomass (AGB) is accumulated, partitioned, and scaled across different climatic zones is still scarce. Methods We addressed this knowledge gap using a destructive sample of 45 culms (2- and 3-year old culms) harvested from a managed plantation in Sanya, Hainan, China. Two modeling strategies were compared: (i) independent models fitted by weighted non-linear least squares and (ii) compatible models based on Seemingly Unrelated Regression (SUR) that guarantee additivity among culm, branch, and leaf biomass. Predictor variables were diameter at breast height (DBH), total height (H), and a combined variable (DBH)²H. Model performance was evaluated using 1,000 bootstrap iterations with out-of-bag (OOB) predictions, and final model parameters were estimated from the full dataset. Results and discussion 2-year-old culms were roughly twice as large in DBH and H, and contained fourfold more AGB than 3-year-old culms. Independent models demonstrated that DBH was the most effective singular predictor for each component and AGB. Compatible SUR models achieved similar accuracy (R² 0.9 for AGB and culms, 0.7 for leaves, 0.5 for branches) while reducing the discrepancy between summed components and total AGB to virtually zero. We developed the first SUR-based compatible biomass models for D. brandisii in Sanya City, reducing carbon-accounting discrepancies between component and total aboveground biomass (AGB) to near-zero, which could support accurate measurement of the bamboo-forest carbon sink. The developed system could be directly applied to Tier-3 carbon monitoring and climate-adaptive management of D. brandisii plantations under similar edaphoclimatic conditions.
Wu et al. (Fri,) studied this question.