Adaptive multi-paddock grazing and wellbeing: uptake, management practices and mindset among Canadian beef producers
Bibliographic record
Abstract
Adaptive multi-paddock (AMP) grazing practices have been debated based on production, environmental and workload impacts, but farmer wellbeing is only beginning to be explored. A panel-based online survey of 200 Canadian beef producers was undertaken in early 2020 with a descriptive aim to explore the uptake, management, mindsets, and wellbeing implications associated with AMP grazing. AMP practices were more common than expected (29%) as well as distinct in grazing regime, featuring fast rotation, and long rests. AMP ranchers reported high physical wellbeing, as well as systems thinking, nontraditional values, a priority for enjoying life and tendency to use a wide range of modes to learn about grazing. Other dimensions of wellbeing, environmental motivations, and gender dimensions suggested by smaller-n studies were not associated with AMP grazing in this work. These insights are important as the federal government begins to promote AMP grazing and its variants as strategies to combat climate change. More nuanced understanding of adaptive grazing and its trajectory would be possible via consistent, longitudinal surveys with improved operationalization of wellbeing concepts, more detailed exploration of educational background, inclusion of religious beliefs, and elucidation of management characteristics beyond grazing regime variables.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".