Assessing greenhouse gas emissions from outdoor cattle sleeping areas in Cameroon
Bibliographic record
Abstract
Cattle production is an important source of greenhouse gas (GHG) emissions which affects the environment. While emissions have mostly been quantified from barns and manure storage facilities, little information is available on emissions from outdoor sleeping areas, especially in Africa. This project was carried out in two beef cattle farms (Banshe and Menteh) in Cameroon, with the aim of quantifying GHG emissions from the outdoor sleeping areas. The sleeping areas were fenced with planks and the floor was bare soil covered mainly with cattle manure. Gas emission rates were measured when the cattle were on pasture using 2 non-steady state flux chambers during the wet season for 1 week in each farm. Manure dry matter, determined using method 1648 of the U.S. Environmental Protection Agency, was in the range of 28–38% while the volatile solid content was in the range of 41–57%. Emission hotspots and hot moments were observed with large variations in time and location. The methane (CH4) emissions were 4.04 ± 4.3 and 1.85 ± 1.7 mg m−2 min−1 in Banshe and Menteh, respectively. The nitrous oxide (N2O) emissions were 0.008 ± 0.02 and 0.049 ± 0.06 mg m−2 min−1 in Banshe and Menteh, respectively. The sleeping area with high CH4 emissions was associated with low N2O emissions and vice versa. The carbon dioxide (CO2) to CH4 emission ratios were high; ∼7 for Banshe and ∼15 for Menteh, indicating more aerobic conditions. The total GHG (CH4 + N2O) emission rates were 139.7 and 77.5 mg CO2e m−2 min−1 in Banshe and Menteh, respectively. This indicated that CH4 contributed 98 and 81% of the total GHG in Banshe and Menteh, respectively. This shows that mitigation strategies should be geared more towards CH4 in the sleeping areas in this study during the wet season. The GHG emission factors estimated in this research were the first of its kind in Cameroon, and can be used as a basis for planning management practices that mitigate emissions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".