Farmland abandonment in Europe: an overview of drivers, consequences, and assessment of the sustainability implications
Bibliographic record
Abstract
In the last decades, there have been large areas of agricultural land that were abandoned in Europe, producing significant social and environmental impacts. Land abandonment is a dynamic process, which is influenced by a complex range of drivers that vary over time and space. This process is driven by a combination of socio-economic, political, and environmental factors by which formerly cultivated fields are no longer economically viable under existing land-use and socio-economic conditions. The implications of land abandonment on biodiversity and other ecosystem services can be positive or negative depending on the conservation status of the area, agro-climatic conditions, and local factors. Therefore, the scope and extent of environmental impacts vary over time and location. Considering that land abandonment is a contentious issue in Europe, there is still growing need for research on this topic. This paper reviews (i) drivers and consequences of farmland abandonment in Europe, (ii) policy measures and tools developed by the European Union in relation to land abandonment process, (iii) the impacts and indicators that are used to assess ecosystem services that are related to land abandonment, and (iv) the methods by which socio-economic, environmental, and cultural values can be assessed. An overview of key impacts and indicators and the impact assessment methodologies will guide policy-making and planning processes that focus on sustainability impact assessment of land abandonment related to ecosystem services in Europe.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".