ENSO and Sea Surface Temperature Anomalies in Association with Canadian Wheat Yield Variability
Bibliographic record
Abstract
Interannual variations of spring wheat yields in Canadian agricultural regions are analyzed, together with the associated sea surface temperature (SST) anomalies in the northern hemisphere tropics and extratropics, from 1961 to 2015. The cubic trend is calculated and used to represent the trend related to advances in agricultural technology over this time period. The correlations between Canadian wheat yields at regional scales and the tropical El Niño–Southern Oscillation (ENSO) variability are not robust at any stage of the evolution of ENSO. Based on the power spectrum and cross-spectrum analysis, the most prominent yield variance is found in the Canadian Prairies, with a significant power peak of 4.5 years but does not co-vary significantly with interannual ENSO variability. ENSO weakly affects temperature and precipitation anomalies in the Canadian Prairie Region in summer—two important agroclimatic conditions for crop growth—and hence insignificantly impacts wheat yields. This indicates that there would be little benefit to including tropical ENSO indices in the operational wheat yield forecasting system. For Canadian wheat yield forecasting, attention should be paid to the preceding winter and spring SST anomalies in the northern extratropics. The SST anomalies associated with yields in the Canadian Prairie region and Central Region are generally stronger than those associated with yields in the Canadian Pacific Coast Region and eastern Maritime Region. In association with the Prairie Region and Central Region yields, SST shows pronounced anomalies in the mid-high latitudes of the North Pacific from winter to summer. The non-linearity of the SST anomalies associated with the Canadian yields is also clearly evident. Stronger (weaker) SST anomalies in the extratropical North Pacific correspond to low wheat yields in the Prairie (Central) Region, while weaker (stronger) SST anomalies correspond to high yields in the Prairie (Central) Region.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".