Increasing risks of crop failure and water scarcity in global breadbaskets by 2030
Bibliographic record
Abstract
Abstract As the greatest water user in the world, the agricultural sector is vulnerable to changes in climate and water resource availability. Understanding the impact of these changes on crop yield is critical in order to achieve and maintain global food security. We analyze output from an ensemble of Agricultural Model Intercomparison and Improvement Project models to project the probability of rice, soybean, maize, and wheat yield failures across global and national breadbaskets through mid-century. The probability of crop yield failures is projected to be as much as 4.5 times higher by 2030 and up to 25 times higher by 2050 across global breadbaskets. Crop failures are projected to be more likely when effects of CO 2 fertilization are ignored. We utilize the open-source Aqueduct Water Risk Atlas to create a Water Scarcity Index composed of ten hydrological variables. The index reveals high water scarcity across crop breadbaskets in India, China, and the United States. If the ability to irrigate breadbaskets was eliminated due to water scarcity, the likelihood of crop failures would increase. Shifts in breadbaskets may cross national borders as crop yields will increase in Canada and decrease in the US as a response to a changing climate. Our analysis highlights top producing agricultural regions that have historically provided the global food system with large quantities of one or more major crops, but will face challenges in continuing to do so due to climate change and growing water scarcity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".