Holistic Management and Adaptive Grazing: A Trainers’ View
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it