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Plant Science Today (PST)

Review Articles

Vol. 13 No. 3 (2026)

Current breakthroughs in biomass and carbon modelling of rubber plantations: A bibliometric analysis and literature review

DOI
https://doi.org/10.14719/pst.15821
Submitted
3 June 2026
Published
28-09-2026 — Updated on 30-09-2026
Versions

Abstract

Rubber plantations (Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg.) are expanding tropical carbon (C) stores, storing an estimated 160–300 mg C ha-1 of total ecosystem C at maturity, but precise biomass and soil organic carbon (SOC) assessment remains difficult due to age disparities, historical land-use variations and data gaps. This review systematically analyses trends and research gaps in biomass accumulation of rubber systems. PRISMA 2020 guidelines were used to examine 104 studies in Scopus published between 1999 and 2025. The literature revealed a marked increase in research studies in China and Thailand post-2013 using a bibliometric approach presented by Biblioshiny. The most important methods are species-specific allometry (coefficient of determination (R2) > 0.9), age-based growth curves, remote sensing (RS) regressions (R2 0.2–0.7) and machine learning (ML) models like Random Forest/XGBoost (R2 0.8–0.97, root mean square error (RMSE) 7–40 mg/ha), which outperform simpler parametric models. Deep learning (DL) yields the highest accuracies (RMSE ~6 mg ha-1). Gaps persist in standardised protocols, South Indian datasets (e.g., Tamil Nadu/Western Ghats) and integrated lifecycle carbon accounting, including latex/SOC. Forest-to-rubber conversion incurs net losses, while cropland-to-rubber/agroforestry offers gains. The authors recommend adopting harmonised Tier 2/3 IPCC models, multi-decadal RS archives for reducing emissions from deforestation and forest degradation (REDD+) and ML extension to Indian smallholders. Policy integration with offset markets requires avoiding deforestation to realise climate-smart benefits.

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