Effect of Glyphosate Application on Sudden Death Syndrome of Glyphosate-Resistant Soybean Under Field Conditions
Bibliographic record
Abstract
Sudden death syndrome (SDS), caused by Fusarium virguliforme, is an important yield limiting disease of soybean. Glyphosate is used to control weeds in soybean; however, its effect on SDS is not clearly understood. The objective of this study was to examine the impact of glyphosate on SDS, yield, and plant nutrition under field conditions. Fourteen field experiments were conducted in Iowa, Illinois, Indiana, Michigan, Wisconsin, and Ontario, Canada during 2011 to 2013. The experiment consisted of six treatment combinations of glyphosate and herbicides not containing glyphosate. Disease index was significantly different across the location-years, ranging from 0 to 65. The highest disease was noted in locations with irrigation, indicating that high soil moisture favors development of SDS. There were no effects of herbicide treatments or interactions on disease. The foliar disease index among the treatments over all years ranged from 9 to 13. Glyphosate-treatments also tended to yield more than treatments of herbicides not containing glyphosate. There were no interactions between glyphosate-treatments and total manganese in plant tissue. The interaction of glyphosate with other nutrients in plant tissue was inconclusive. This 14 location-year study demonstrated that glyphosate application did not increase SDS severity or adversely affect soybean yield under field conditions.
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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 | 0.000 | 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) | 0.001 | 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".