Bamboo biomass estimation for sustainable forest management and climate mitigation: a comprehensive review of allometric models and emerging technologies

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.