Building Resilience through Farmers’ Experiments in Organic Agriculture: Examples from Eastern Austria
Bibliographic record
Abstract
Farmers have always lived in changing environments where uncertainty and disturbances are inevitable. Therefore, farmers need the ability to adapt to change in order to be able to maintain their farms. Experimentation is one way for farmers to learn and adapt, and may be a tool to build farm resilience. Farmers’ experiments as defined in this paper are activities where something totally or partially new is introduced at the farm and the feasibility of this introduction is evaluated. The theoretical framework applied to study farmers’ experiments is the concept of resilience. Resilience is the capacity of social-ecological systems to cope with change, and is a framework used to assess complex systems of interactions between humans and ecosystems. This paper explores to which extent farmers’ experimentation can help build farm resilience. In addition to arguments found in the literature, five organic farms in Eastern Austria are used to illustrate this potential. The farmers were interviewed in 2007 and 2008. The respective farmers all worked fulltime on their farms, were between 34 and 55 years old, and owned farms between 15 and 76 ha. These farmers experimented in ways that enhance resilience – at the farm and in the region. The outcome of experiments can be management changes, new insights, or technology that can be passed on and potentially be built into education and advisory institutions. To encourage farmers’ experiments, it is important to develop conditions that support farmers in their experimenting role.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".