A scoping review on the impact of rotational grazing in beef cattle systems on greenhouse gas emissions, soil health, plant diversity, and plant productivity parameters
Bibliographic record
Abstract
Recently, the government of Canada has encouraged the use of rotational grazing (RG) within its Sustainable Agriculture Strategy to improve soil health and decrease greenhouse gas (GHG) emissions from the livestock sector. However, the effectiveness of RG in improving soil health and preventing climate change remains unclear. The objective was to summarize the evidence on the impact of RG on plant productivity, GHG emissions, soil health, and plant richness and diversity in cow–calf operations in Canada and similar climate regions. This scoping review followed PRISMA-ScR reporting guidelines. Studies could be randomized controlled trials, randomized block design, controlled trials, observational, or simulation studies. Retrieved studies were screened in two stages by two independent reviewers. After screening, 15 studies were considered relevant and included in the review, and 46 outcomes were extracted. Of these, 46.5% showed a positive impact of RG, while 53.5% reported RG having neutral or no impact. There was a consistent body of evidence proving that RG benefits plant productivity. However, the evidence showing benefits on soil health and GHG emissions varied depending on the outcomes assessed. There was minimal evidence of impact on plant diversity. Rotational grazing has benefited soil surface properties, water dynamics, and nutrient availability.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".