Canadian Prairies, Agronomy Journal,92(6):1047-1053. Forecasting Spring Wheat Yield Using Time Series Analysis: A Case Study for the Canadian Prairies
Bibliographic record
Abstract
Techniques commonly used for wheat yield estimation employ weather data over the growing season. However, yield estimates are also required before wheat is sown- particularly by the grain exporting agencies to help them determine, in advance, wheat-export targets. In that case, time series techniques relying only on past yield data can be used for yield forecasting. In this paper, a procedure for applying time series analysis to forecast yield is described. A few relevant techniques (linear trend, quadratic trend, simple exponential smoothing, double exponential smoothing, simple moving averaging, and double moving averaging) are tested to model the average spring wheat yield series for Saskatchewan, Canada. Using 1975-1993, 1975-1994, and 1975-1995 spring wheat yield data, yields were forecasted for 1994, 1995, and 1996, respectively. Based on a deterministic measure (i.e., mean squared error), it was found that the quadratic model produced most accurate forecast during the model development periods (1975-93, 1975-94, and 1975-95) and model testing periods (1994, 1995, and 1996, respectively). Nonetheless, on the basis of stochastic measures (coefficient of determination, R2, Durban-Watsun statistic, and coefficient of autocorrelation), the simple moving averaging technique was
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".