Key research challenges to supporting farm transitions to agroecology in advanced economies. A review
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In response to the sustainability issues that agriculture faces in advanced economies, agroecology has gained increasing relevance in scientific, political, and social debates. This has promoted discussion about transitions to agroecology, which represents a significant advancement. Accordingly, it has become a growing field of research. We reviewed the literature on and in support of farm transitions to agroecology in advanced economies in order to identify key research challenges and suggest innovative research paths. Our findings can be summarized as follows: (1) Research that supports exploration and definition of desired futures, whether based on future-oriented modeling or expert-based foresight approaches, should more explicitly include the farm level. It should stimulate the creativity and design ability of farmers and other stakeholders, and also address issues of representation and power among them. (2) Research that creates awareness and assesses farms before, during or after transition requires more holistic and dynamic assessment frameworks. These frameworks need to be more flexible to adapt to the diversity of global and local challenges. Their assessment should explicitly include uncertainty due to the feedback loops and emergent properties of transitions. (3) Research that analyzes and supports farms during transition should focus more on the dynamics of change processes by valuing what happens on the farms. Research should especially give more credence to on-farm experiments conducted by farmers and develop new tools and methods (e.g., for strategic monitoring) to support these transitions. This is the first review of scientific studies of farm transitions to agroecology. Overall, the review indicates that these transitions challenge the system boundaries, temporal horizons, and sustainability dimensions that agricultural researchers usually consider. In this context, farm transitions to agroecology require changes in the current organization and funding of research in order to encourage longer term and more adaptive configurations.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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