Evolving the Common Agricultural Policy for Tomorrow's Challenges
Bibliographic record
Abstract
Since its creation, the evolution of the Common Agricultural Policy (CAP) has entailed continuous adaptation to the challenges of agriculture and food.This is why throughout its history the CAP has attempted above all to respond to the threat of scarcity by pooling the risks between Member States (MS), and then to respond to the conditions of abundance.Other challenges have been subsequently added; for example, the volatility of agricultural prices, which, as they rise, penalise the most modest populations and, as they decline, penalise the incomes of farmers.Indeed, it has remained essential to ensure the sustainability of this abundance above all other considerations.New questions were therefore addressed to European decision-makers: How do we adapt agriculture to climate change and enable it to cope with the growing world population?How do we reduce the environmental impacts of agriculture and livestock farming?How do we consume less energy and water, alongside a reduced loss of raw materials?How do we encourage the improvement of nutritional, taste, and health quality of the food supply?These questions became all the more relevant as new "consumer-citizens" have emerged, urging the agricultural and agri-food sectors to take their expectations into account in terms of quality, transparency, greenhouse gas emissions, and damage to biodiversity.By placing the fight against climate change at the heart of the new European Commission's action through the implementation of the European Green Deal, President of the European Commission, Ursula von der Leyen, seeks to breathe new life into the European project.This ambition is separated into eight major objectives, one of which explicitly targets agricultural and food issues within the framework of both the Farm to Fork Strategy and the European Biodiversity Strategy by 2030.European decision-makers have a powerful tool at their disposal for this strategy: the CAP.Thus, it will be necessary to once again consider how to adapt the CAP to meet the challenges of the transition in agriculture and food without overlooking the question of its financing, which has become increasingly problematic with public budgets stretched over several competing priorities.Both the COVID-19 crisis and the war in Ukraine reinforce to us the strategic nature of food sovereignty for Europe and for all countries around the world.Within this context, this book is the result of a cycle of seminars that I initiated, in agreement with the French Ministry of Agriculture and Food, and led by Cécile Detang-Dessendre and Hervé Guyomard in 2017-2018.The seminars brought together many scientists from the French National Institute for Agriculture, Food and Environment (INRAE), but also academic partners who provided useful input into the discussions, and ministerial stakeholders who made it possible to include the scientists' work in the ongoing debates on the shape of the future CAP.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".