Beyond imperfect maps: Evidence for <scp>EUDR</scp>‐compliant agroforestry
Bibliographic record
Abstract
Abstract Not all good intentions lead to effective and fair policy designs, as their implementation creates new problems. The European Union Deforestation Regulation (EUDR) may be an example. In targeting ‘deforestation‐free’ trade, it forces a complex social–ecological reality into an oversimplified forest–non‐forest representation. The forest definition used refers to tree cover but excludes farmer‐managed agroforestry (AF). Not all tree cover indicates forest, as forest‐like forms of agriculture (AF) exist, for example producing much of the worlds' cacao, coffee and rubber. The EUDR design trusts maps and relies on detailed spatial data to verify the deforestation‐free claims needed for access to EU markets. Tree cover is observable in remote sensing; the intended exclusion of AF is not. No map is perfect but for global forest maps prepared for EUDR use there is 18% chance a forest pixel is considered non‐forest in other data, all supposedly based on the same forest definition and cut‐off date. Map errors imply two types of risk: non‐compliant imports to the EU (that ‘fraud prevention’ tries to avoid) or unjustified exclusion (collateral damage). Globally, the EUDR maps claim 12% more forest in 2020 than national data compiled by FAO suggests; in specific countries, the gap is wider. The probability that an AF garden producing coffee cocoa or rubber is (erroneously) mapped as forest is two‐thirds for a study in Indonesia. Elsewhere similar problems have been noted. Data sources beyond direct earth observation will be needed to legally establish pre‐2021 agroforestry as a source of EUDR‐compliant commodity trade. We present a typology for such evidence. Evidence can be based on direct observations on the ground or remotely, based on what people say and on accounts of what they did. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.052 | 0.456 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".