Threats posed by green water change to rainfed crop production globally and in Finland.
Bibliographic record
Abstract
Globally, rainfed agriculture accounts for 75% of total cropland and is crucial for food production. However, green water changes (soil moisture accessible to plants), driven by climate change and other factors, pose notable threats to these systems. While many studies focus on the immediate impacts of green water changes on rainfed crop yields, the cumulative threats over multiple years remain underexplored. This study introduces an innovative approach by adapting the global water risk framework to evaluate the impact of green water changes on rainfed crop production over the past 60 years, both globally and in Finland. The findings highlight hotspots for rainfed maize, wheat, and soybean in the USA, Canada, Brazil, Argentina, Serbia, China, and India, largely due to high production levels and substantial green water changes, except for India, where production density is lower despite notable changes in green water. In Eastern Europe, countries like Ukraine, Romania, Bulgaria, and Hungary are identified as hotspots for maize and wheat, while African countries such as Nigeria, Ethiopia, and Uganda emerge as hotspots for maize production. In Finland, hotspot regions are found in Päijänne Tavastia, South Karelia, and Kymenlaakso, though the country does not face the highest threat on a global scale. This study underscores the urgency of addressing vulnerabilities in these hotspot regions to safeguard rainfed agriculture and enhance global food security in the face of green water challenges.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".