Biomass burning and biochar: developments in Sub-Saharan Africa
Bibliographic record
Abstract
Abstract Biomass burning remains widespread in Sub-Saharan Africa (SSA), driven by a complex interplay of factors: technological limitations, colonial-era policies that contributed to deforestation, cultural practices, unmanaged waste disposal resulting in dumpsite fires, vegetation burning for land preparation, politically motivated fires from riots and protests, climate change-induced wildfires, and traditional cooking and heating practices rooted in poverty and insufficient land management strategies. This narrative review assesses biomass burning and biochar developments in SSA, highlighting the environmental impacts and viable mitigation strategies. Satellite data analysis reveals that Côte d’Ivoire experienced 122,014 agricultural fires from 2016 to 2019, peaking at 13,387 in February 2016. In 2019, Nigeria recorded 86,464 fires, resulting in approximately 0.019 Tg of black carbon emissions. Ghana reported 0.014 Tg of black carbon emissions, with burn scars comparable to Nigeria. Open vegetation burning in Zambia and Southern Africa during 2000 resulted in a burned area of 210,000–830,000 km 2 , emitting 18–31 Tg of carbon monoxide. SSA has a technically recoverable biomass of no less than 21,646 PJ, with approximately 1,986.5 PJ available from woody biomass, yet only 25 % of this resource is utilized, indicating significant underutilization. Biochar, derived from biomass, offers significant benefits for enhancing soil fertility, bioenergy production, carbon sequestration, and pollution control. Converting crop residues to biochar can mitigate up to 0.89 tons of CO 2 per ton of residues. In Cameroon, transforming 2,000 kg of agricultural waste into biochar could prevent 939.7 kg CO 2 eq emissions. However, SSA accounts for only 4.8 % of global biochar production, constrained by socio-economic, technological, and policy barriers. To improve biochar adoption and mitigate biomass burning impacts, this review recommends regional strategies including knowledge sharing, capacity building, policy incentives, public participation, sustainable management practices, and investment in bioenergy initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".