Organic Research and Development in Denmark (1996-2010) – Effects on the Organic Sector and Society
Bibliographic record
Abstract
The ICROFS (International Centre for Research in Organic Food Systems) has conducted an analysis of the effects of organic research in Denmark (1996-2010) on the Danish organic sector and on society in general. Over these 15 years, three national programs and one program with European collaboration have been implemented in Denmark, financed via special government grants that amounted to just over 500 million DKK (approx. € 67 million—or approximately $ 80 million). The analysis itself was carried out as a compilation of information from three perspectives, each of which has been independently documented: Interviews with (representatives of) end-users of results from research and development (R&D) investigating their assessment of the challenges in the sector and solutions developed from 1996-2010Assessment of the R&D endeavours in different thematic areas (dairy/milk, pigs, crops, etc.) as they related to end-users and the stated challenges at that timeDocumentation of the dissemination of R&D results in relation to themes and challenges in the sector The results showed very good correspondence between end-users’ perceptions of the challenges overcome in the sector, the R&D initiated in the research programmes, and the dissemination of research results and other forms of knowledge transfer. The analysis documented direct effects of the research initiatives targetting the challenges in the sector such as higher yields, weed and pest control, animal health and welfare, the potential for phasing out the use of antibiotics in Danish dairy herds and reducing the problems caused by seedborne diseases. It also describes where research did not contribute as much to overcoming challenges. In contrast, the analysis showed that the effects of the research in the organic processing industry and among relevant governmental and non-governmental organisations were of a more indirect character. Research has helped stabilize the supply and quality of raw materials at a time of growing demand and sales. Organic research also generates new knowledge and leads to new opportunities that can provide inspiration for a green conversion, product diversification and growth also in conventional agriculture. The analysis showed that research under the national research programs overall have been very applied and directed at the barriers in the sector in order to support the general market and growth conditions for the organic sector. Having laid a solid foundation, the private sector has been able to take advantage of commercial opportunities when demand grew, while adhering to the organic policy objectives of the market-driven growth in the organic sector.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".