Fluxes of CH<sub>4</sub> and CO<sub>2</sub> from soil and termite mounds in south Sudanian savanna of Burkina Faso (West Africa)
Bibliographic record
Abstract
The contribution of West African savanna ecosystems to global greenhouse gas budgets is highly uncertain. In this study we quantified soil‐atmosphere CH4 and CO2 fluxes in the southwest of Burkina Faso from June to September 2005 and from April to September 2006 at four different agricultural fields planted with sorghum (n = 2), cotton, and peanut and at a natural savanna site with termite (Cubitermes fungifaber) mounds. During the rainy season both CH4 uptake and CH4 emission were observed in the savanna, which was on average a CH4 source of 2.79 and 2.28 kg CH4‐C ha−1 a−1 in 2005 and 2006, respectively. The crop sites were an average CH4 sink of −0.67 and −0.70 kg CH4‐C ha−1 a−1 in the 2 years, without significant seasonal variation. Mean annual soil respiration ranged between 3.86 and 5.82 t CO2‐C ha−1 a−1 in the savanna and between 2.50 and 4.51 t CO2‐C ha−1 a−1 at the crop sites. CH4 emission from termite mounds was 2 orders of magnitude higher than soil CH4 emissions, whereas termite CO2 emissions were of the same order of magnitude as soil CO2 emissions. Termite CH4 and CO2 release in the savanna contributed 8.8% and 0.4% to the total soil CH4 and CO2 emissions, respectively. At the crop sites, where termite mounds had been almost completely removed because of land use change, termite fluxes were insignificant. Mound density‐based upscaling of termite CH4 fluxes resulted in a global termite CH4 source of 0.9 Tg a−1, which corresponds to 0.15% of the total global CH4 budget of 582 Tg a−1, hence significantly lower than those obtained previously by biomass‐based calculations. This study emphasizes that land use change, which is of high relevance in this region, has particularly affected soil CH4 fluxes in the past and might still do so in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".