Effects of Seasonal Climate Variability and the Use of Climate Forecasts on Wheat Supply in the United States, Australia, and Canada
Bibliographic record
Abstract
Better understanding and forecasting of climate variability may impact global agricultural markets. Wheat (Triticum aestivum L.), a major food grain, may be significantly affected. The objective of this study is to determine how seasonal climate variability and wheat producers' responses to seasonal climate forecasts may affect wheat supplies in the USA, Canada, and Australia. Crop models are used to simulate yields at 31 sites under a wide range of climate conditions, management strategies, and with and without El Niño-Southern Oscillation (ENSO)-based climate forecast information. Yields and planted hectarage estimates are used to obtain estimates of each country's wheat supply with and without the use of forecasts by wheat producers. Four wheat supply curves are developed: U.S. spring and winter wheat, Australia Standard White spring wheat, and Canadian spring wheat. Results show long-run estimates of average production are within 10% of historical average production for three of the four national supply wheat curves. Results show the use of the forecasts will generate an increase in expected supply for all countries. Further work is necessary to determine the impacts on producers and consumers as a result of the use of climate forecasts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".