Methane Emissions from Beef Cattle in South Sulawesi, Indonesia: An Inventory and Trend Analysis
Bibliographic record
Abstract
Methane (CH4) emissions from cattle are significant but they can be reduced. The beef cattle industry is vital for providing protein to humans. However, the higher number of cattle populations influence the higher amount of methane emissions. This study was conducted to establish an inventory of methane emissions from cattle in the districts of the Regency of Bone (27 districts) and Barru (7 districts), Province of South Sulawesi, Indonesia. The estimation of CH4 emissions (Gg/year) was calculated using Tier 1 method (IPCC). Tier 1 method is a simplified approach that is typically used when more detailed data and resources are not available. The location of the study was in the regency of Bone and Barru because Bone is the center of cattle fattening and Barru is the breeding center of local cattle in South Sulawesi. Methane emissions from enteric fermentation in the regency of Barru decreased by 14.5% similarly from manure, in 2019-2020 compared to the previous year. Of all the districts in Barru regency, the lowest contributor to CH4 emissions was Balusu district. The trend of methane emissions from enteric fermentation similar to manure in the regency of Bone showed a gradual increase (around 31%) from 2013 to 2020. District Tanete Riattang and Amali in Bone Regency produced the least amount of enteric and manure CH4 emissions. It requires more data on the age group categories and body weight to establish inventory of the correlation of emissions to the age group from cattle's industry in those regencies to further decide the actions of mitigation, because the latest Tier method requires the detail record of age group and body weight which may reflects closest to the real amount of the emissions. This study supports the policy of establishing more complete record from the regency and the strategy of reducing the amount of methane emissions whilst increasing the number of cattle population in Indonesia particularly in South Sulawesi.
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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 | 0.002 | 0.002 |
| 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 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".