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. • Irrigated agriculture is more likely to benefit from climate change than rain-fed agriculture. • Planting date changes impact rainfed fields more than irrigated ones. • Irrigation water demand is expected to increase under future scenarios. • Early planting can reduce irrigation demand of spring wheat and canola.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".