Assessing the impacts of shifting planting dates on crop yields and irrigation demand under warming scenarios in Alberta, Canada
Bibliographic record
Abstract
Understanding the impacts of climate change on crop production and irrigation water demand is crucial for adapting to global warming. This study evaluated the effects of shifting planting dates on irrigated and rainfed crop yields and irrigation water demand under the latest Shared Socio-economic Pathways (SSPs) climate scenarios using the AquaCrop-OS model in Alberta, Canada. The results indicate: (1) climate change generally benefits irrigated crop yields while reducing rainfed yields under low mitigation scenarios (SSP585 and SSP370). (2) The impacts of planting date shifts on crop yields vary spatially and temporally across different SSPs. Early planting improves both rainfed and irrigated crop yields and reduces irrigation water demand under SSP585 in the latter half of the 21st Century, suggesting it is a viable strategy for mitigating heat and water stress in agricultural systems. However, this strategy does not guarantee yield increases under other SSPs. (3) The irrigated yields of spring wheat and canola are expected to increase under all scenarios, while rainfed yields decline under SSP585 and SSP370, with only marginal increases under SSP126. Annual irrigation demand will increase in the future, with the monthly irrigation peak occurring earlier. The most irrigation demand is under SSP585, followed by SSP370 and SSP126. (4) Early planting results in reduced irrigation water demand.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".