An agro-economic model comparison of cropland change until 2050
Bibliographic record
Abstract
The future development of land under agricultural production has important implications for environment and climate. Different methods to project future agricultural land use have been published indicating large uncertainty due to different model assumptions and methodologies. In this paper we present a first comparison of global agro-economic models, which have been harmonized on drivers like future population, GDP growth and biophysical yields. The comparison includes four partial and six general equilibrium models, which differ largely according to their modelled land supply and amount of available land. We analyse results of four scenarios: The reference scenario assumes no climate change and a medium pathway of economic growth and population development. The second scenario assumes higher economic growth and population, whereas scenario three and four assume the impacts of climate change on crop yields (HadGEM2, RCP 8.5) and differ according to the used crop model to project the yield changes (DSSAT and LPJmL). Most models (7 out of 10) project an increase of cropland of around 10 to 25% by 2050 compared to 2005, whereas one model projects a decrease. Across all models most of the cropland expansion takes place in South America and Sub-Saharan Africa but also in North America (especially Canada), if the impacts of climate change are considered. In general, the strongest differences in model results are related to differences in the costs or substitution elasticities of land expansion, the endogenous productivity responses and the assumed development of bioenergy demand.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".