Fungicide Efficacy Guides for Foliar Diseases in Corn and Soybean: Development and Validation
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
Fungicide efficacy guides, updated annually through the Crop Protection Network, inform fungicide selection for foliar and seedling diseases of corn ( Zea mays) and soybean ( Glycine max). These guides rank fungicides based on multistate field trials across the United States and Ontario, Canada. Trials were analyzed to validate these rankings by assessing the efficacy of fungicides under varying disease severities. Under high disease severity (≥5%), fungicides with the best efficacy ratings significantly reduced gray leaf spot (GLS; caused by Cercospora zeae-maydis) and southern rust (SR; caused by Puccinia polysora) in corn when applied at the tasseling (VT) to silking (R1) growth stages and frogeye leaf spot (FLS; caused by Cercospora sojina) in soybean when applied at beginning pod (R3) growth stage. GLS severity was reduced by 8.6 to 8.8%, SR by 14.6 to 20.6%, and FLS by 15.3%. Corn yields were 420.4 kg/ha (6.7 bushels/acre) greater than the nontreated control, and yield response in 57.9 to 63.6% of the trials exceeded the economic breakeven point of 288.4 kg/ha (4.6 bushels/acre) for fungicide application. Soybean yields were 417.0 kg/ha (6.2 bushels/acre) greater than the nontreated control, with 83.3% of trials reaching the economic breakeven point of 134.5 kg/ha (2 bushels/acre). Under low disease severity (<5%), disease control and yield benefits diminished across all fungicide efficacy categories. These results validate the fungicide efficacy ratings as predictive tools for disease control and yield response, especially under high disease pressure, highlighting their importance for fungicide decisions in corn and soybean across the United States and Canada.
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.
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