Global Benchmarking of Beef Cattle's Climate Impact: A Meta-Analysis of GWP Values
Bibliographic record
Abstract
Studies have reported significant variations in global warming potential (GWP) values associated with beef cattle production.This study was aimed to benchmark the GWP values of beef cattle production globally using meta-analysis.A total of 52 research papers, 89 studies, and 1,258 samples were included in this meta-analysis.This metaanalysis includes only peer-reviewed publications with empirical GWP values for the beef cattle industry, expressed in quantities comparable to CO2eq/kgLW (carbon dioxide equivalent/kilogram liveweight).The aggregated GWP value of beef cattle, as derived by combining data from other research, is 15.69 CO2eq/kgLW.This number serves as a benchmark for carbon footprint associated with the beef cattle production.Several subgroup analyses were conducted to identify significant discoveries that contributed to the range of greenhouse gas emission levels in beef cattle research.he utilisation of different feed types revealed that organic feed exhibits a greater GWP value (P<0.01)than inorganic feed.The extensive farming system exhibited a larger (P<0.01)GWP compared to intensive systems, but the intensive system displayed a lower (P<0.01)GWP than semi-intensive systems.Large-scale cow farming, defined as operations with more than 251 head of cattle, had a reduced (P<0.01)GWP compared to medium-scale (50-250 head) and small-scale (<100 head) activities.The last stages of cattle production exhibited reduced (P<0.01)GWP values compared to the cow-calf and cow-calffinishing stages.The benchmark GWP value from this meta-analysis is a critical reference point for stakeholders to reach emissions reduction targets and improve the sustainability practices.It also allows stakeholders to track progress, compare carbon footprints, and encourage innovations that aim to reduce the environmental impact of beef production.
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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 | 0.044 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".