Risk assessment of possible impacts of climate change and irrigation on wheat yield and quality with a modified CERES-Wheat model
Bibliographic record
Abstract
Abstract The effects of climate change on yield and quality in different climate regions have high uncertainty. Risk assessment is an effective measure to assess the seriousness of the projected impacts for decision-makers. A modified quality model was used to simulate integrated impacts of climate change, environment, and management on wheat yield and quality. Then, the Canadian Earth System Model version 5 (CanESM5) was used to forecast the daily meteorological data, and the Statistical Downscaling Model (SDSM V5.2) was used for downscaling. The modified CERES-Wheat was combined with the forecasted meteorological data to simulate the future wheat yield and grain protein concentration (GPC). The risk to wheat yield and quality in three climatic regions in Northwest China under two climate change scenarios of the CanESM5 was assessed. The average temperature increased by 0.22–3.34 °C, and precipitation increased by 10–60 mm from 2018 to 2100. Elevated temperature and precipitation had positive effects on the yields. The risk to yield in most regions with climate change decreased by 3.8–25.1%. The risk to GPC in all regions with climate change decreased by 7.3–27.2%. Irrigation decreased the risk to yield greatly but had different effects in the three climatic regions. The risk to yield with irrigation decreased by 37.7–52.1%. In contrast to previous studies, in this study, the risk to GPC with irrigation substantially increased by 25.8–28.9% in humid regions and 3.9–8.8% in subhumid regions and decreased by 37.7–52.1% in semiarid regions. The irrigation should be discreetly applied for different climatic regions to combat climate change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".