Farmers’ Own Research: Organic Farmers’ Experiments in Austria and Implications for Agricultural Innovation Systems
Bibliographic record
Abstract
Farmers’ experiments can be defined as the autonomous activities of farmers to try or introduce something new at the farm, and include evaluation of success or failure with farmers’ own methods. Experiments enable farmers to adapt their farms to changing circumstances, build up local knowledge, and have resulted in countless agricultural innovations. Most research on the topic has been conducted in countries of the south. In this paper, however, we present experiments of randomly sampled organic farmers in Austria, and we discuss implications for agricultural innovation systems. In 76 structured questionnaire interviews we investigated topics, motives, methods and outcomes of farmers’ experiments, and factors related to the frequency of experimentation. From the interviewed farmers, 90% reported experiments, and the majority of experiments (94%) involved monitoring and evaluation strategies. Farmers who reported a high frequency of experimentation showed a significantly higher propensity to plan, document and repeat their experiments, and had a more positive attitude towards experimenting than farmers that rarely experimented. We conclude that experimenting is a common activity among organic farmers in Austria, and that farmers have their own methods to conduct and assess their experiments. The most significant outcome is the creation of new knowledge, stressing the importance of experimentation for learning and adaptive farm management. Farmers’ experiments are significant on two levels, i.e. at individual farm level and at the level of agricultural innovation systems. Taking full advantage of this innovative potential requires a better involvement of farmers as co-researchers into the development of agricultural innovations.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".