Analyzing Temperature and Precipitation Influences on Yield Distributions of Canola and Spring Wheat in Saskatchewan
Bibliographic record
Abstract
Abstract The IPCC indicates that global mean temperature increases of 2°C or more above preindustrial levels negatively affect such crops as wheat. Canadian climate model projections show warmer temperatures and variable rainfall will likely affect Saskatchewan’s canola and spring wheat production. Drier weather will have the greatest impact. The major climate change challenges will be summer water availability, greater drought frequencies, and crop adaptation. This study investigates the impact of precipitation and temperature changes on canola and spring wheat yield distributions using Environment Canada weather data and Statistics Canada crop yield and planted area for 20 crop districts over the 1987–2010 period. The moment-based methods (full- and partial-moment-based approaches) are employed to characterize and estimate asymmetric relationships between climate variables and the higher-order moments of crop yields. A stochastic production function and the focus on crop yield’s elasticity imply choosing the natural logarithm function as the mean function transformation prior to higher-moment function estimation. Results show that average crop yields are positively associated with the growing season degree-days and pregrowing season precipitation, while they are negatively affected by extremely high temperatures in the growing season. The climate measures have asymmetric effects on the higher moments of crop yield distribution along with stronger effects of changing temperatures than precipitation on yield distribution. Higher temperatures tend to decrease wheat yields, confirming earlier Saskatchewan studies. This study finds pregrowing season precipitation and precipitation in the early plant growth stages particularly relevant in providing opportunities to develop new crop varieties and agronomic practices to mitigate climate changes.
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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".