Empirical allometric models for estimating aboveground biomass of Bambusa teres and Bambusa tulda in non-forest areas of Nepal
Bibliographic record
Abstract
Abstract The ability of bamboo to store carbon in its biomass varies depending on species, site conditions, and management practices. In Nepal, bamboo is widely distributed outside forest areas, often with little or no management, making it essential to develop biomass models to quantify its carbon stock potential in such settings. Therefore, this study aims to develop species-specific aboveground biomass models for Bambusa teres Buch.-Ham. ex Munro and Bambusa tulda Roxb. in non-forest areas of Nepal. A total of 104 culms (54 B. teres, 50 B. tulda) were sampled; diameter at breast height (DBH) and height ranged 4.0–9.4 cm and 8.3–22.4 cm in B. teres, 4.3–10.5 cm and 7.0–20.7 cm in B. tulda. Various regression models (linear, power, and exponential) were tested using DBH and height as independent variables and biomass components (foliage, branch, culm, and total aboveground biomass) as dependent variables. Due to the small sample size, the leave-one-out cross-validation method was used for model validation. Our findings indicate that B. tulda had significantly higher mean DBH, foliage, and branch biomass than B. teres. The power model incorporating both DBH and height (M9) performed best (adj. R2 > 0.80) for predicting culm biomass and total aboveground biomass in both species. However, none of the models accurately predicted foliage biomass and branch biomass (adj. R2 < 0.55), suggesting that allometric models may not be suitable for these components. This study aids in quantifying bamboo carbon and establishing a database for studied species, facilitating Nepal’s entry into the carbon credit market. We recommend development of species- and age-specific allometric models for other bamboo species along with belowground biomass models to enhance bamboo carbon quantification in non-forest settings in Nepal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".