Understanding the phases and tensions of regenerative agriculture for better health outcomes for farmers
Bibliographic record
Abstract
Abstract Australian farmers face an array of challenges impacting mental health, such as extreme climatic events, market uncertainties, technological dependence, regulatory demands, and social isolation. Regenerative agriculture (RA) has been suggested as a way for farmers to improve wellbeing by integrating natural systems, continuous evaluation, and adaptation—while benefitting from the socio-cultural aspects of farmer relations—for sustainable food production. This rapid review of the literature aims to synthesise evidence on the relationship between RA practices and farmer mental health and wellbeing. The review encompassed 9 databases (n = 13795 articles) and 3 sources of grey literature (n = 209 studies). The final 44 items included in the review demonstrated that regenerative agricultural practices have gained recognition for environmental benefits and that the impact on farmers’ mental health has started to be explored. Findings show underlying tensions in the transition process from conventional farming practices to RA—including notable phases of triggering, accepting alternatives, adopting, and adapting. Although evidence is still scarce and limited in its scope, tailored mental health intervention and prevention strategies need to consider farmers’ vulnerability during these RA transition phases. Importantly, farmers need different supports at different phases of the system.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".