Farmer perceptions of regenerative agriculture in the Corn Belt: exploring motivations and barriers to adoption
Bibliographic record
Abstract
Regenerative agriculture has been proposed as a sustainable approach that balances environmental and economic trade-offs in farming. However, regenerative agriculture lacks a consistent definition and implementation, and there is a need for context-specific information on adoption. In our study, we evaluated farmer perceptions in an economically depressed region on the Indiana-Ohio border. Guided by diffusion theory, we explored definitions of regenerative agriculture and motivations and barriers to adoption using an online pre-survey (n = 49) and exploratory, in-depth interviews with early adopters (n = 16) who identified themselves as using regenerative agriculture. Early adopters defined regenerative agriculture as principles and practices that support healthier soils, with an emphasis on livestock and cover cropping. Interviewees noted that environmental and economic priorities were more strongly linked in regenerative agriculture versus conventional agriculture. Motivations were primarily environmental (e.g., soil, water, biodiversity), whereas barriers were primarily economic (e.g., start-up costs, marketing). However, community benefits, such as healthier food and farmer wellbeing, were other motivators. Regenerative practices were perceived as highly observable but lacking in support from the broader community. Further, in economically depressed communities, costs were seen as limiting, especially for livestock integration, which was perceived to have lower trialability versus practices like cover crops. Our analysis reveals that although many farmers would not say they use regenerative agriculture, there is increasing engagement with some associated practices. Financial and marketing support and facilitating information sharing between early adopters and other farmers may increase regenerative practices in economically depressed regions of the Corn Belt.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".