Balancing Bees and Pest Management: Projected Costs of Proposed Bee-Protective Neonicotinoid Regulation in California
Bibliographic record
Abstract
Neonicotinoid insecticides are widely used in agriculture, including in many California specialty crops. With mounting evidence that these insecticides are harmful to bees, state and national governments have increasingly regulated their use. The European Union, Canada, and United States have imposed use restrictions on several neonicotinoids, such as on the timing of applications. In 2020, California proposed a draft regulation to mitigate harm to managed pollinators from four nitroguanidine-substituted neonicotinoids (NGNs): clothianidin, dinotefuran, imidacloprid, and thiamethoxam. We use data on California pesticide use from 2015 to 2017 to analyze the economic and pest management implications of the 2020 draft proposed regulation for seven crops: almond, cherry, citrus, cotton, grape, strawberry, and tomato. From 2015 to 2017, these crops accounted for approximately 85% of total hectares treated with NGNs and 87% of NGN use by kilograms of active ingredient applied in treatments that would have been affected by the proposed regulation. These insecticides often primarily target Hemipteran insect pests. In most cases there are alternatives; however, these are often more expensive per hectare and do not have the same residual effectiveness as the NGNs, which are systemic insecticides. Overall, we estimate that pest management costs for these crops would have increased an estimated $13.6 million in 2015, $12.8 million in 2016, and $11.1 million in 2017 if the 2020 draft proposed regulation had been in effect, representing a 61% to 72% increase in the cost of managing the target pests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".