Evaluation of the Environmental Policy Integrated Climate (EPIC) Model on Predicting Crop Yield in the Canadian Prairies, a Case Study
Bibliographic record
Abstract
The Environmental Policy Integrated Climate (EPIC) model was updated with relevant weather, tillage, and crop management operations from the 1994 to 2013 Alternative Cropping Systems study to assess simulations of annual and long-term yield of wheat, barley, and canola. Linear regression and coefficients of determination (R2), root mean square error of prediction (RMSE), the d index, and paired sample t-test were used to assess the relationship between simulated and experimental values. Simulations indicated that the model captured long-term yield trends but was less accurate at predicting annual variations. These variations were due to variability of soil properties at the research field, terrain attributes, extreme weather events, and the model’s overestimation of available nitrogen (N) under low-N input systems. The R2, RMSE, and the d index values on long-term yield were R2 = 0.74, RMSE = 205 kg ha−1, and d = 0.75 for wheat; R2 = 0.90, RMSE = 226 kg ha−1, and d = 0.73 for barley; R2 = 0.98, RMSE = 238 kg ha−1, and d = 0.76 for canola, indicating good model performance. The EPIC model effectively simulated crop yields affected by agricultural inputs and cropping diversity, and may be used to assess future cropping decisions and agronomic management.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".