The global distribution of cultivable lands: current patterns and sensitivity to possible climate change
Bibliographic record
Abstract
Abstract Aim This study makes quantitative global estimates of land suitability for cultivation based on climate and soil constraints. It evaluates further the sensitivity of croplands to any possible changes in climate and atmospheric CO2 concentrations. Location The location is global, geographically explicit. Methods The methods used are spatial data synthesis and analysis and numerical modelling. Results There is a cropland ‘reserve’ of 120%, mainly in tropical South America and Africa. Our climate sensitivity analysis indicates that the southern provinces of Canada, north‐western and north‐central states of the United States, northern Europe, southern Former Soviet Union and the Manchurian plains of China are most sensitive to changes in temperature. The Great Plains region of the United States and north‐eastern China are most sensitive to changes in precipitation. The regions that are sensitive to precipitation change are also sensitive to changes in CO2, but the magnitude is small compared to the influence of direct climate change. We estimate that climate change, as simulated by global climate models, will expand cropland suitability by an additional 16%, mainly in the Northern Hemisphere high latitudes. However, the tropics (mainly Africa, northern South America, Mexico and Central America and Oceania) will experience a small decrease in suitability due to climate change. Main conclusions There is a large reserve of cultivable croplands, mainly in tropical South America and Africa. However, much of this land is under valuable forests or in protected areas. Furthermore, the tropical soils could potentially lose fertility very rapidly once the forest cover is removed. Regions that lie at the margins of temperature or precipitation limitation to cultivation are most sensitive to changes in climate and atmospheric CO2 concentration. It is anticipated that climate change will result in an increase in cropland suitability in the Northern Hemisphere high latitudes (mainly in developed nations), while the tropics will lose suitability (mainly in developing nations).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".