Climate change impacts on hard red spring wheat yield and production risk: evidence from Manitoba, Canada
Bibliographic record
Abstract
A Just–Pope production function is employed to investigate the effects of historic weather changes on hard red spring wheat yield variability in Manitoba. Field-level data on wheat yield, proportion of wheat seeded area, and fertilizer inputs from the Manitoba Agricultural Services Corporation were employed to determine how temperature and precipitation affect mean wheat yield and production risk, and how projected climate scenarios impact yield variability in heterogeneous risk areas of Manitoba. Variety richness increases average yield and reduces yield variance while varieties protected by plant breeders’ rights increase yield variance. Phosphorus fertilizer is positively associated with average wheat yield while total precipitation is shown to negatively affect mean yield and positively impact yield variability. June precipitation matters while June and July temperatures negatively affect yield. Projected climate change is expected to increase yield variability in both the medium (2034–2050) and long term (2079–2095), both under low- and high-carbon scenarios with production variance effects differing across crop districts. Adaptation strategies may be required to mitigate yield risk effects of climate change resulting in late seeding decisions from increased spring precipitation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".