Estimation of Potential Greenhouse Gases from the Agriculture and Livestock Sectors
Bibliographic record
Abstract
Greenhouse gas emissions from the agricultural sector, including methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2), contribute significantly to climate change.This study aims to estimate the potential greenhouse gas emissions from the rice agriculture subsector and livestock subsector in Sigi Biromaru District, Sigi Regency, Indonesia, and identify effective mitigation measures.A descriptive survey was conducted using questionnaires administered to farmers in the district.Data on agricultural practices, including water regime, fertilizer use, and manure management, were collected.Additionally, data on the number of livestock and the average weight of livestock were obtained from the Livestock and Animal Health Office of Sigi Regency, while data on the area of rice farming land was obtained from the Horticulture and Plantation Food Crops Office of Sigi Regency.The estimated potential greenhouse gas emissions from the agricultural sector in 2022 amounted to 24,277.39 tons of CO2 eq, with the rice agriculture subsector contributing 14,538.08tons of CO2 eq (59.88%) and the livestock subsector contributing 9,739.31tons of CO2 eq (40.12%).Methane (CH4) accounted for the largest proportion of emissions, reaching 22,911.45tons of CO2 eq (94.37%), followed by nitrous oxide (N2O) at 1,303.64 tons of CO2 eq (5.37%) and carbon dioxide (CO2) at 62.30 tons of CO2 eq (0.26%).The agricultural sector in Sigi Biromaru District is a significant source of greenhouse gas emissions, with methane from rice cultivation and livestock manure management being the major contributors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".