Reduction of Crop Diversity Does Not Drive Insecticide Use
Bibliographic record
Abstract
Reduction of crop diversity, on farm and landscape levels, has been suggested as a factor that leads to lower biodiversity in agricultural systems, and thus makes them more prone to pest damage. To determine whether the relationship between increase of corn production and reduction of crop diversity and the proportion of cropland treated with insecticide found previously in the Midwestern states is universally applicable, we applied spatial panel model analysis to USDA Agricultural Census data for 1997, 2002, 2007, and 2012 using county-level data for the entire continental USA. The 7 Midwestern states and the remaining 41 states were analyzed separately. Simpson’s diversity index was used as the metric for crop diversity. We also examined the effect of temperature, the proportion of all cropland that is corn and average crop market value as additional predictor variables. The results show that expansion of corn production, together with the market value of crop products, and accumulated amount of heat could be key factors driving the increase of acres treated with insecticide. This phenomenon is observed in the entire continental USA, indicating that it was not a reduction of crop diversity in general, but specifically the predominance of corn that is likely driving increased insecticide use in the Midwestern states in recent years.
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 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) | 0.000 | 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) | 0.002 | 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".