Prairie crop insect pests: how can we improve our economic impact estimate?
Bibliographic record
Abstract
There is no agreement on which insects are the most important Prairie pests or an estimate of their approximate total economic cost to Canadian agriculture. There are multiple reasons for this: (1) thresholds (2) and surveys are lacking for key pests, (3) there is no repository of acreages impacted, (4) or record of the land area where a control action was taken along with costs, (5) or estimated efficiency of control actions, (6) or records of resulting values of the grain and quality losses. In this review, we estimate these costs by assigning nominal values based on annual pest reports published in the minutes of the Western Committee on Crop Pests (WCCP) from 2015 to 2024. We concluded that flea beetles, grasshoppers, cutworms, and lygus bugs were the dominant pests and along with other pests they cost Prairie farmers around 204 million dollars in an average year. We suggest that future standardization of the qualitative pest reports can improve estimates of economic impacts and allow stakeholders to prioritize research funding. Furthermore, we provide an extensive commentary and review of the potential use of artificial intelligence and machine learning to aid us in developing future predictive models to refine tools to improve the accuracy of control decisions. We conclude that to aid in this endeavour, Canada needs to maintain a curated long-term data repository to capture and integrate multiple data on biotic stressors, climate, abiotic conditions, and agronomic practices across the Prairies.
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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 | 0.036 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".