Geospatial database of global agricultural lands in the year 2015
Bibliographic record
Abstract
Description: This project is a continuation and update in methodology of the work from Ramankutty et al. (2008). We combine subnational level census data and national level FAOSTAT data to develop a global spatial dataset of croplands and pastures on a graticule of 5 arcminutes (~10 km at the equator). These maps support a wide variety of research topics, from land use and food security to climate change and biodiversity loss. This Zenodo bucket includes a full set of replicable code, intermediate and final outputs, experiment results, and source files. Final Data Products: Cropland Area fraction: Fraction of five-minute gridcell area Pasture Area fraction: Fraction of five-minute gridcell area Resolution: Spatial: Five minute by five minute resolution (~10km x 10km at equator) Map Projection: Data presented as five-arc-minute, 4320 x 2160 cell grid Spatial Reference: GCS_WGS_1984 Datum: D_WGS_1984 Cell size: 0.083333 degrees Layer extent: Top : 90 Left: -180 Right: 180 Bottom: -90 ***For a simplified version of this repository, containing only the output maps of cropland, pasture and other land cover, see 10.5281/zenodo.15363700.***
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.058 |
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".