Greenhouse gas emissions from sheep excreta deposited onto tropical pastures in Kenya
Bibliographic record
Abstract
To improve the estimate of greenhouse gas emissions (GHG) from tropical rangelands in sub-Saharan Africa, we measured GHG emissions from sheep excreta over two periods of 51 days on a Kenya rangeland. In addition, we measured GHG emissions from potential hotspots in the landscape linked to sheep grazing: overnight enclosures (“bomas”), where sheep are kept at night to protect them from theft and predators, the areas surrounding sheep bomas, and areas surrounding watering troughs. Results showed a short pulse of CO2 fluxes after sheep urine application and a rapid increase of CH4 fluxes following sheep dung application in both rainy and dry season. However, only small increases of N2O fluxes were observed after dung and urine applications compared to controls without excreta. Elevated N2O fluxes mainly coincided with heavy rainfall. Overall, N2O emission factors (EFs) did not vary across excreta type or seasons, but mean N2O EFs for dung (0.01%) and urine patches (0.02%) were only one tenth of the default EFs from the 2019 IPCC Refinement for dry climate. We did, however, find that bomas and watering troughs are sites of herd concentration that are important sources of GHG emissions in the landscape, and that emissions in these locations can remain elevated for months to years, especially when soil moisture is high. This study contributes to more robust estimates of GHG emissions from African livestock systems, which are fundamental to develop targeted mitigation strategies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".