The cost of heat waves and droughts for global crop production
Bibliographic record
Abstract
Abstract Heat waves and droughts are a key risk to global crop production and quantifying the extent of this risk is essential for insurance assessment and disaster risk reduction. Here we estimate the cumulative production losses of six major commodity groups under both extreme heat and drought events, across 131 countries, over the time period of 1961-2014. Our results show substantial variation in national disaster risks that have hitherto gone unrecognised in regional and global average estimates. The most severe losses are represented by cereal losses in Angola (4.1%), Botswana (5.7%), USA (4.4%) and Australia (4.4%), oilcrop losses in Paraguay (5.5%), pulse losses in Angola (4.7%) and Nigeria (4.8%), and root and tuber losses in Thailand (3.2%). In monetary terms we estimate the global production loss over this period to be $237 billion US Dollars (2004-2006 baseline). The nations that incurred the largest financial hits were the USA ($116 billion), the former Soviet Union ($37 billion), India ($28 billion), China ($10.7 billion) and Australia ($8.5 billion USD). Our analysis closes an important gap in our understanding of the impacts of extreme weather events on global crop production and provides the basis for country relevant disaster risk reduction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".