Global warming presents new challenges for maize pest management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It has been conjectured that global warming will increase the prevalence of insect pests in many agro-ecosystems. In this paper, we quantitatively assess four of the key pests of maize, one of the most important systems in North American grain production. Using empirically generated estimates of pest overwintering thresholds and degree-day requirements, along with climate change projections from a high-resolution climate model, we project potential future ranges for each of these pests in the United States. Our analysis suggests the possibility of increased winter survival and greater degree-day accumulations for each of the pests surveyed. We find that relaxed cold limitation could expand the range of all four pest taxa, including a substantial range expansion in the case of corn earworm (H. zea), a migratory, cold-intolerant pest. Because the corn earworm is a cosmopolitan pest that has shown resistance to insecticides, our results suggest that this expansion could also threaten other crops, including those in high-value areas of the western United States. Because managing significant additional pressure from this suite of established pests would require additional pest management inputs, the projected decreases in cold limitation and increases in heat accumulation have the potential to significantly alter the pest management landscape for North American maize production. Further, these range expansions could have substantial economic impacts through increased seed and insecticide costs, decreased yields, and the downstream effects of changes in crop yield variability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it