Silver lining to a climate crisis: multiple prospects for alleviating crop waterlogging under future climates
Bibliographic record
Abstract
Abstract Extreme weather events threaten food security, yet global assessments of crop waterlogging are rare. Here, we make three important contributions to the literature. First, we develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a contemporary farming systems model to discern changes in global crop waterlogging under future climates. Third, we elicit viable systems adaptations to waterlogging. Using projections from 27 global circulation models, we show that yield penalties caused by waterlogging increased from 3–11% historically to 10–20% by 2080. Altering sowing time and adopting waterlogging tolerant genotypes reduced yield penalties by up to 18%, while earlier sowing of winter genotypes alleviated waterlogging risk by 8%. We show that future stress patterns caused by waterlogging are likely to be similar to those occurring historically, suggesting that adaptations for future climates could be successfully designed using current stress patterns.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".