Climate change impacts on corn heat unit for the Canadian Prairie provinces
Bibliographic record
Abstract
Abstract corn heat unit (CHU) are used in Canada to rank corn (Zea mays L.) hybrids for maturity; however, maps showing expected CHU during the growing season have not been updated since 1976. The objective of this study was to: (a) examine historical weather data for the Canadian Prairie provinces and determine if there has been a change in the annual accumulation of CHU (ΣCHU), and (b) use updated technology and improved access to weather data to revise ΣCHU maps. Daily temperature data, recorded over the past century at 8,750 weather stations across Canada and consolidated into 1,413 sites were downloaded. There were 466 sites in Alberta, Saskatchewan, and Manitoba. The daily CHU were summed over the growing season for each site by year. The annual growing season for corn in the Prairies increased by 3–12 d over the past century, with greatest increases in southern and western locations. The change coincided with warmer springtime temperatures, allowing for earlier seeding, and warmer autumn temperatures, allowing for delayed harvest. During the past century, ΣCHU increased by 200–400 depending upon location, and the area with ΣCHU suitable for growing corn for silage has more than doubled to 915 thousand km2 (46% of the total area). These results provide tangible proof of climate change and its impacts on the potential for growing corn on the Prairies. The new ΣCHU maps can help farmers select regionally adapted corn hybrids. This study demonstrates the need for adaptive strategies for climate change in agriculture.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| 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".