Bamboo biomass estimation for sustainable forest management and climate mitigation: a comprehensive review of allometric models and emerging technologies
Bibliographic record
Abstract
This review synthesizes global research on allometric models for estimating bamboo biomass across a wide range of species and ecological regions. A systematic search of four major scientific databases Scopus, Web of Science, Science Direct, and Google Scholar covering the period 2000–2025 identified 55 peer-reviewed studies that met defined inclusion criteria. The review evaluates the effectiveness, limitations, and applications of these models in supporting forest management, carbon sequestration, sustainable agriculture, and bioenergy production. Representative case studies from Asia, Africa, Latin America, and other regions reveal key methodological trends, including species-specific modeling, regional adaptation, and the use of standardized biometric parameters. Persistent challenges include limited data availability, restricted model transferability across regions, and the influence of structural variation among bamboo species on model accuracy. Recent innovations highlight the integration of remote sensing, LiDAR (Light Detection and Ranging), machine learning, and GIS (Geographic Information Systems) to improve model precision, scalability, and operational efficiency. The review underscores the importance of regionally calibrated models and proposes a hybrid framework that combines field-based measurements with advanced analytical tools to capture spatial and temporal variability in bamboo biomass. Finally, future research directions are outlined, focusing on enhancing model robustness, expanding geographic and taxonomic coverage, and improving policy relevance in the context of climate change mitigation and sustainable land-use planning.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".