Impact assessment of EC 2030 green deal targets for sustainable crop protection in potato production
Bibliographic record
Abstract
Dit rapport presenteert een effectbeoordeling van de twee doelstellingen van de Farm to Fork-strategie met betrekking tot vermindering van het gebruik en risico van gewasbeschermingsmiddelen voor de aardappelteelt en -verwerkende industrie.Aan de hand van zes cases in Noordwest-Europa zijn de agronomische en bedrijfseconomische gevolgen verkend van drie scenario's op bedrijfsniveau.Aan de hand van de resultaten van deze scenario's zijn de economische gevolgen verkend voor de teelt en de verwerkende industrie.Vervolgens is nagegaan welke duurzame alternatieve gewasbeschermingsmethoden perspectief hebben om de negatieve gevolgen te verminderen.Op basis van de analyses worden aanbevelingen gegeven voor alle betrokken stakeholders.In this report, an impact assessment is given of the two objectives of the Farm to Fork strategy with regard to reducing the use and risk of pesticides for the potato growing and processing industry.Based on six cases in Northwestern Europe, the agronomic and economic consequences of three scenarios at farm level were explored.Based on the results of these scenarios, the economic consequences for cultivation and the processing industry have been elaborated.Then it is examined which sustainable alternative crop protection methods have prospects for reducing the negative consequences.Based on the analyses, recommendations are given for all stakeholders involved.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".