Estimation of potential changes in cereals production under climate change scenarios
Bibliographic record
Abstract
Abstract This survey proposed a new methodology ‐ iGAEZ (improved GAEZ), developed based on the GAEZ (Global Agro‐Ecological Zones) model, capable of simulating crop yields on a global scale for wheat, potato, cassava, soybean, rice, sweet potato, maize, green beans. iGAEZ determines the optimum criteria of crop parameter of growth cycles to ensure best realistic crop yield combinations under comprehensive consideration of climate and crop condition. Global‐scale crop yields were calculated using iGAEZ model for the period of 1990‐1999. Through comparing simulated yields and FAO statistics, iGAEZ has demonstrated a very good ability to reproduce realistic crop yields on a global scale. We also predicted the impact of global warming on crop yields from the 1990s to 2090s by projecting five GCM outputs for AR4 under SRES A1B scenarios. According to the result, temperature rise will make many cultivated areas (eastern part of USA, India, eastern China, Africa) less productive. On the other hand, the regions with cold weather under current climate condition (Canada, northern Europe, northeastern China) become suitable for crop productivity under future climate scenario. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".