Allometric equations for estimating above and belowground biomass of Colophospermum mopane in Mozambique
Bibliographic record
Abstract
Seasonally dry tropical woodlands are vital for climate change mitigation, yet their full potential in carbon storage remains poorly understood. This is largely due to the lack of species-specific allometric models tailored to these ecosystems. To address this knowledge gap, this study aimed to develop species-specific biomass allometric equations (BAEs) for accurately estimating both above- and below-ground biomass of Colophospermum mopane (J.Kirk ex Benth.) J.Kirk ex J. Léonard, and to assess the feasibility of incorporating total height, estimated using height-diameter ( H-D ) equations, to improve the accuracy of biomass estimation in Mozambique. We applied a destructive method and felled 120 C. mopane trees in Mabalane and Tambara Districts. We measured breast height diameter ( D ), total height ( H ) and biomass of each component (roots, stem, branches and leaves). We fitted three BAE models using Ordinary Least Squares (OLS) regression and fifteen H-D models using nonlinear regression. A validation procedure checked the applicability of all models. We conducted a forest inventory with 78 temporary clusters, each consisting of four 100 m x 20 m plots, to estimate the average AGB and BGB of C. mopane using the best-fitting BAE and H - D model. We also compared the best BAE of C. mopane with existing locally developed species-specific BAEs. The study found that the model including the interaction of D 2 and H, performed better than the model with separate D and H . However, models with D and H performed similarly to models with D alone. The Power model with two parameters and the HossfeldIV model with three parameters exhibited the best performance among the H-D models. Integrating the H from the H-D model into the BAE for AGB estimation can improve the efficiency and reliability of biomass estimation from forest inventory data. This research is timely as there is an urgent need to improve the accuracy of carbon counting to support climate change goals and sustainable management of Mopane woodland given their spatial extent in southern Africa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".