Tracing the contribution of cattle farms to methane emissions through bibliometric analyses
Bibliographic record
Abstract
Methane contributes to global warming, and livestock is one of the sources of methane production. However, methane emission studies using bibliometric tools in livestock are lacking. Given the negative impact of climate change on the ecosystem and the rise in methane emissions, it is essential to conduct a bibliometrics study to provide an overview and research trends. We used the Bibliometrix package and VOSviewer to decipher bibliometric indices for methane emissions in cattle farms (MECF). Current dataset were collected from the Web of Science (Core Collection) database, and 8,998 publications were analyzed. The most co-occurring keywords scientists preferred were methane (1528), greenhouse gas (443), methane emissions (440), and cattle (369). Methane was the most frequently used keyword in the published scientific literature. Thematic evolution of research themes and trend results highlighted carbon dioxide, methane, dairy cattle, cattle, and risk factors during 1999-2017. Chinese Academy of Sciences ranked on top with 485 publications, followed by Agriculture & Agri-Food Canada, University of Colorado, National Oceanic and Atmospheric Administration, and Aarhus University. Chinese Academy of Sciences was also the most cited organization, followed by the University of Colorado, Agriculture & Agri-Food Canada, National Oceanic and Atmospheric Administration, and United States Geological Survey. Source analysis showed that the Science of the Total Environment was cited with the highest total link strength. “Science of the Total Environment” ranked first in source core 1 with 290 citation frequencies, followed by “Journal of Dairy Science” with 223 citation frequencies. Currently, no bibliometric study has been conducted on MECF, and to fill this knowledge gap, we carried out this study to highlight methane emissions in cattle farms, aiming at a climate change perspective. In this regard, we focused on the research productivity of countries authors, journals and institutions, co-occurrence of keywords, evolution of research trends, and collaborative networking. Based on relevance degree of centrality, methane emissions and greenhouse gases appeared as basic themes, cattle, and dairy cattle appeared as emerging/declining themes, whereas, methane, greenhouse gas and nitrous oxide appeared to fall amongst basic and motor themes. On the other hand, beef cattle, rumen and dairy cow seem to be between motor and niche themes, and risk factors lie in niche themes. The present bibliometric analysis provides research progress on methane emissions in cattle farms. Current findings may provide a framework for understanding research trends and themes in methane emissions in cattle farms (MECF) research.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.162 | 0.209 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".