Local knowledge and relational values of Midwestern woody perennial polyculture farmers can inform tree‐crop policies
Bibliographic record
Abstract
Abstract Agricultural producers, academics and policy‐makers are increasingly interested in multifunctional tree crop systems as a solution for maintaining ecosystem services and producing food. The US Midwest is emerging as a hotbed of such systems in the temperate North in the form of farm‐scale woody perennial polyculture enterprises, but they are currently only a tiny fraction of the landscape. Understanding how such approaches might be scaled up, thus, requires learning from the farmers that are at the forefront of the transition of land to woody perennial polyculture to answer a range of questions: What unique management knowledge is being implemented by farmers to manage complexity on multiple scales? What key challenges have farmers faced? And what values and motivations underpin these fledgling efforts? From 13 interviews with 18 midwestern perennial polyculture farmers, we found that they largely used a small portion of their farm's land for their perennial enterprises, and did not earn a large portion of their income from them, though this was projected to increase as trees matured. Through experimentation, innovation and farmer networks, the farmers had amassed unique adaptive management expertise for balancing diverse crops and livestock within multifunctional tree crop systems over time and space, an area largely absent from mainstream agricultural science and policy. The barriers these farmers report facing are largely economic rather than biophysical, involving access to capital, insurance, mid‐sized markets and regional processing infrastructure, as well as government programmes mismatched with perenniality. Cross‐cutting these topics, farmers sought to fulfil values anchored in their relationships to land, to the community or to both. The values of long‐termism , learning and sharing , diversity , stewardship and care of farmland , connection to nature and wildlife , self‐sustenance , other‐sustenance and eudaimonia were embodied and expressed in farmer decisions from the practical to the personal. Economic and agrarian policy, as well as programme development for multifunctional tree crop systems, should (a) be designed to align with farmer's values and motivations and (b) take advantage of their expert management and systems knowledge to drive appropriate and successful transitions to sustainable environments and livelihoods. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".