Towards sustainable and circular farming in the Netherlands: Lessons from the socio-economic perspective
Bibliographic record
Abstract
The dutch dairy sector has shown a strong development in the last decades, resulting in fewer but larger and more specialised farms. Larger farms and more intensive ways of production have raised concerns about environmental impacts. This paper shows that there is a clear economic incentive to increase the scale of production. Larger farms tend to show better economic results in terms of lower cost prices and higher incomes. The environmental results are more diverse and depend on the chosen indicators. Larger farms are able to include environmental objectives in their farm management when there are clear incentives to do so. These incentives can be provided by policies, but also by private sector initiatives. Several sustainability initiatives have been developed to monitor and improve the sustainability performance of farms. Our current way of agricultural production is faced with several sustainability challenges. Circular food systems are expected to contribute to the solution of these challenges. In the Netherlands, policy measures and sector initiatives are developed to increase sustainability and to implement and experiment with the concept of Circular Agriculture. This concept is deliberately broadly defined. However, to guide development towards more sustainable production systems, it requires objective parameters and goals at different levels of scale. This would allow all stakeholders to develop solutions in their own circumstances and objectively evaluate progress. One of the bottlenecks of the transition towards more circular food systems is the search for new business models for farmers. Some frontrunners are currently developing new circular farming businesses. These innovative (social) entrepreneurs are experimenting with new business models that contribute to the realisation of circular agriculture. This paper describes methods that have been developed to assist farmers but also regional governments in the transition to a more sustainable agriculture and the development of new business models. Developing new business models together with frontrunners is just a first step. Questions like ’How to broaden these initiatives to sector level?’, ‘How to provide effective incentives?’, ‘How to incorporate external effects in prices?’ and ‘What are the costs of farm investments or practices to improve sustainability?’ and ‘Who should pay for a more circular agriculture?’ are still very much unanswered.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".