Assessing greenhouse gas emissions from outdoor cattle sleeping areas in Cameroon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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 it