Holistic Management and Adaptive Grazing: A Trainers’ View
Bibliographic record
Abstract
Holistic Management (HM) is a grazing practice that typically uses high-intensity rotation of animals through many paddocks, continually adapted through planning and monitoring. Despite widespread disagreement about the environmental and production benefits of HM, researchers from both sides of that debate seem to agree that its emphasis on goal-setting, complexity, adaptivity and strategic decision-making are valuable. These ideas are shared by systems thinking, which has long been foundational in agroecology and recognized as a valuable tool for dealing with agricultural complexity. The transmission of such skills is thus important to understand. Here, twenty-five Canadian and American adaptive grazing trainers were interviewed to learn more about how they teach such systems thinking, and how they reflect upon their trainees as learners and potential adopters. Every trainer considered decision-making to be a major component of their lessons. That training was described as tackling both the “paradigm” level—changing the way participants see the world, themselves or their farm—and the “concept/skill” level. Paradigm shifts were perceived as the biggest challenge for participants. Trainers had difficulty estimating adoption rates because there was little consensus on what constituted an HM-practitioner: to what level must one adopt the practices? We conclude that: (1) trainers’ emphasis on paradigms and decision-making confirms that HM is systems thinking in practice; (2) the planning and decision-making components of HM are distinct from the grazing methods; and (3) HM is a fluid and heterogeneous concept that is difficult to define and evaluate.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| 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".