Exposure of global agricultural lands to extreme weather using CMIP6 projections of future climate
Bibliographic record
Abstract
Abstract As climate change intensifies, extreme weather is becoming a major threat to global food security. Yet we still lack a good understanding of how these extremes will be distributed across the world’s agricultural lands—particularly across small versus large croplands and pasturelands. In this study, we assess their exposure to extreme weather in a warming world. In a world that is 2 °C warmer than today, 25% (11 million km 2 ) of present-day agricultural lands will face over two months of extreme heat, up from 16% today (7 million km 2 ), and another ∼2% (5 million km 2 ) will be exposed to a combination of two or more extremes, up from 10% (4 million km 2 ). The total area exposed to prolonged dry conditions and extreme precipitation will remain unchanged (less than 1% or 5 million km 2 , with increases in some regions balanced by decreases elsewhere), while ∼2% less area (2 million km 2 ), down from 7% (3 million km 2 ), will experience a month of frost. Future exposure to extreme weather varies by land use type. Pasturelands will experience prolonged exposure to heat stress, whereas croplands will be exposed to higher excessive rains and heat stress combined. Spatial correlations between farm size and geography indicate potential differences in exposure. Exposure to extreme precipitation and heat stress is highest in small (1–2 ha) and medium (2–4 ha) cropland and pastureland, respectively. These findings offer a preliminary global assessment of how exposure to extreme weather varies by farm size and land use, underscoring the need for tailored adaptation strategies to safeguard food security in a warming world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".