Understanding transitions in agriculture: pathways from conventional to sustainable production systems
Bibliographic record
Abstract
Practical, affordable, climatic, and environmentally conscious; sustainable Best Management Practices (BMPs) represent a growing alternative to dominant conventional methods of agricultural production. As BMP adoption decisions are made at the farm-level, they are influenced by the system that they are embedded within, and they are diverse and context-specific. Through a systematic review of literature this paper aims to examine the drivers and barriers influencing farmer’s motivations and uptake of sustainable practices. In an effort to understand how to increase the prevalence of BMP use and trigger a transition from the current agri-food system‚ dominated by a productivist paradigm, and the rationale of economic growth over ecological stability‚ this paper proposes the use of the Multi-Level Perspective (MLP). Highlighting the relationship between niche-, regime, and landscape-dynamics, the MLP provides a framework to examine the methods by which small, dynamic segments of society or system can disrupt and destabilize existing and powerful regimes. Applying the MLP to our review of literature, this paper argues that much of the existing scholarship on factors impacting farmer’s adoption of BMPs currently lacks critical examination of the relationships which support transitions to sustainability, and future research could be strengthened through the consideration of interaction and interdependence across levels in the system. Additionally, we propose the use of the Positive Deviance approach to help in identifying and recognizing niche-innovations and actors, deepening understandings of the existing structural/institutional barriers faced by farmers, and the means by which these are being overcome. This research study is financed by OMAFRA. Funding: OMAFRA through the Ontario Agri-food Innovation Alliance
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.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".