Result-based payments as a tool to preserve the High Nature Value of complex silvo-pastoral systems: progress toward farm-based indicators
Bibliographic record
Abstract
As shown by the Green Deal's ambition, the European Commission is progressively pushing for an environmental shift and climate action in Europe. For the Common Agricultural Policy (CAP), this involves a stronger focus on greening policy objectives. For agri-environmental schemes, this entails changes toward performance-based payments, partially replacing traditional activity-based payments. The CAP foresees greater flexibility in national programs and tailor-made solutions centered on results (i.e. environmental outcomes), benefiting farmers who go beyond the minimum environmental performance required. The environmental outcomes of farm practices must be assessed so that changes can be monitored over time and linked to payment delivery. This requires stakeholders to collaborate with researchers to identify farm-based indicators that are easily applicable, to achieve environmental results that are dependent on farm practices, and to assess and monitor changes in outcomes over time. The analysis in this paper is based on a transdisciplinary process that began in 2017 in a Natura 2000 site and its surroundings in Southern Portugal, to identify result-based measures for the Montado silvo-pastoral system. Farmers' understanding of how to adapt their practices to reach better environmental results was combined with scientific knowledge of the relevant environmental outcomes and how these can be assessed with indicators. Ten field-based visual indicators were defined, which farmers applied in the field, and validated by technical staff. These indicators are related to several aspects of the silvo-pastoral system: soil quality, pasture diversity, tree renewal, tree health, singular landscape elements, and biodiversity. The approach used in this process was innovative. We describe each step and present its advantages and drawbacks for designing and implementing result-based payments. Ultimately, their implementation is expected to lead to higher sustainability in the Montado.
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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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".