Climate change will exacerbate land conflict between agriculture and timber production
Bibliographic record
Abstract
Abstract Timber and agricultural production must both increase throughout this century to meet rising demand. Understanding how climate-induced shifts in agricultural suitability will trigger competition with timber for productive land is crucial. Here, we combine predictions of agricultural suitability under different climate change scenarios (representative concentration pathways RCP 2.6 and RCP 8.5) with timber-production maps to show that 240–320 Mha (20–26%) of current forestry land will become more suitable for agriculture by 2100. Forestry land contributes 21–27% of new agricultural productivity frontiers (67–105 Mha) despite only occupying 10% of the surface of the land. Agricultural frontiers in forestry land occur disproportionately in key timber-producing nations (Russia, the USA, Canada and China) and are closer to population centres and existing cropland than frontiers outside forestry land. To minimize crop expansion into forestry land and prevent shifting timber harvests into old-growth tropical and boreal forests to meet timber demand, emissions must be reduced, agricultural efficiency improved and sustainable intensification invested in.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".