Response of Broad‐Spectrum and Target‐Specific Seed Treatments and Seeding Rate on Soybean Seed Yield, Profitability, and Economic Risk
Bibliographic record
Abstract
Seed‐applied fungicides and insecticides have become common components in modern soybean [ Glycine max (L.) Merr.] production for their broad‐spectrum activity. However, adding a target‐specific seed treatment (fluopyram) to these seed treatment packages in light of increased costs and declining grain sale prices has not been evaluated. Reducing seeding rates (SRs) is possibly one avenue to maximize the economic benefit of seed treatments. Three seed treatments and six SRs were evaluated to determine yield, profitability, and economic risk benefits across 26 environments. Seed treatment effects on plant stand and yield were environment specific. Commercial base (CB) and CB plus fluopyram (ILeVO) seed treatments increased plant stand over the untreated control (UTC) and across all environments, the addition of fluopyram in ILeVO increased yield by 2.8% over CB. In environments where sudden death syndrome (SDS) symptoms were present, yield response of ILeVO over CB was 5.3 and 6.1%. The CB treatment, and more so, ILeVO, lowered farmer risk (>70%) and increased profit (9–78 US$ ha −1 ) at currently recommended and reduced SRs regardless of grain sale prices. The lowest risk and largest average profit increase always occurred at the economically optimal SR (EOSR), which decreased with the grain sale price and differed between seed treatments by as much as 17,000 seed ha −1 . This study reinforces the profit and economic risk benefits of broad spectrum and target specific seed treatments across diverse environments. These benefits may be amplified by targeting fields with a history of early‐season insect and disease pressure.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".