Aligning research with policy and practice for sustainable agricultural land systems in Europe
Bibliographic record
Abstract
Agriculture is widely recognized as critical to achieving the Sustainable Development Goals (SDGs), but researchers, policymakers, and practitioners have multiple, often conflicting yet poorly documented priorities on how agriculture could or should support achieving the SDGs. Here, we assess consensus and divergence in priorities for agricultural systems among research, policy, and practice perspectives and discuss the implications for research on trade-offs among competing goals. We analyzed the priorities given to 239 environmental and social drivers, management choices, and outcomes of agricultural systems from 69 research articles, the SDGs and four EU policies, and seven agricultural sustainability assessment tools aimed at farmers. We found all three perspectives recognize 32 variables as key to agricultural systems, providing a shared area of focus for agriculture's contribution to the SDGs. However, 207 variables appear in only one or two perspectives, implying that potential trade-offs may be overlooked if evaluated from only one perspective. We identified four approaches to agricultural land systems research in Europe that omit most of the variables considered important from policy and practice perspectives. We posit that the four approaches reflect prevailing paradigms of research design and data analysis and suggest future research design should consider including the 32 shared variables as a starting point for more policy- and practice-relevant research. Our identification of shared priorities from different perspectives and attention to environmental and social domains and the functional role of system components provide a concrete basis to encourage codesigned and systems-based research approaches to guide agriculture's contribution to the SDGs.
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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 | 0.258 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.036 | 0.021 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".