Climate Change, Agricultural Inputs, Cropping Diversity, and Environmental Covariates in Multivariate Analysis of Future Wheat, Barley, and Canola Yield in Canadian Prairies, a Case Study
Bibliographic record
Abstract
Canada’s grain and oilseed production in the Canadian Prairies may be affected by climate change, but the impact of input and diversity has not been assessed relative to projected variability in precipitation and temperature. This study assessed wheat, canola, and barley yields simulated with the environmental policy integrated climate model for historical weather and future climate scenarios in the context of agricultural inputs and cropping diversity at Scott, SK, Canada. Variation of future yield was explored with recursive partitioning in multivariate analyses of inputs, cropping diversity, future growing season precipitation (GSP), and growing degree days (GDD). Agricultural inputs significantly affected wheat yield but not barley or canola. Wheat yield was highest under the reduced input level and lowest under the organic input level. The combination of input and diversity accounted for about one-third of variation in future wheat yield and approximately 10% for barley yield. Most of the variability in yield was correlated with GSP in May–July and GDD in April–June and August–September. Future growing season maximum and minimum temperatures increased by 1.06 and 2.03 °C, respectively, and 11% in future GSP. This study showed how input management and reduced tillage maintained or improved yield, in the context of increased temperature due to 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".