Use of technology for sustainable livestock processes: a bibliometric review
Bibliographic record
Abstract
To this date, livestock activity continues to constitute one of the main bastions of the world economy and global food security. Still, just as it is vital for the subsistence of humanity, it also generates environmental and health effects that deserve attention, and that forge the irrevocable need to look for all possible alternatives to guarantee the sustainability of animal production processes. Therefore, this research has been developed under the framework of a review of the scientific literature related to the use of technology to develop sustainable livestock production processes. This review consisted of a bibliometric analysis developed within the Scopus database, delimiting all the documents published between 1987 and 2023, based on the keywords: "Sustainability", "Livestock" and "Technology", from which the data was obtained, using the search equation (TITLE-ABS-KEY ( "sustainability" ) AND TITLE-ABS-KEY ( "livestock" ) OR TITLE-ABS-KEY ( "cattle breeding" ) OR TITLE-ABS-KEY ( "cattle raising" ) OR TITLE-ABS -KEY ( "cattle" ) AND TITLE-ABS-KEY ( "technology" ) ). A total of 887 papers in all were discovered as a consequence, with journal articles accounting for 59% of them, reviews for 19%, conference articles for 11%, and other formats for the remaining 11%. The scientific output examined between 1987 and 2023 demonstrates an increasing tendency in the study area, with the years 2019 to 2022 exhibiting the biggest publishing peaks (47% of all published papers). The findings indicate that 60% of the papers produced for the study subject were published in the United States, the United Kingdom, India, Australia and Italy. Furthermore, Sustainability (Switzerland), Animal, Journal of Animal Science, Journal of Cleaner Production and IOP Conference Series: Earth and Environmental Science, were the journals that published the most on the topic, accounting for 13% of the total publications. The remaining publications are distributed among various journals. Considering that 92% of researchers in this subject are temporary, Koziel JA is the author with the most publications with seven. Similarly, Wageningen University and Research, Iowa State University, Empresa Brasileira de Pesquisa Agropecuária - Embrapa, University of Guelph and Università Degli Studi di Milano were the institutions that conducted the most research on the study's subject, accounting for 11% of the publications. Key words: livestock, sustainability, cattle raising, cattle breeding, cattle, technology, health, environment
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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 | 0.016 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.188 | 0.242 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".