Effect of crop rotation and tillage system on sclerotinia stem rot on soybean
Bibliographic record
Abstract
Sclerotinia stem rot (SSR) of soybean is a major disease in the North Central region of the United States. A 3-year study was done to determine if crop rotation and tillage, moldboard plowing then mulch tillage (MP + MT), mulch tillage (MT), and no-till (NT), affected the population density of sclerotia and apothecia, incidence of SSR, and soybean yield. Crop rotation did not significantly affect the number or distribution of sclerotia or SSR incidence, but did affect the number of apothecia and the yield. The highest number of apothecia per square metre was observed in the plots in the continuous soybean rotation. Tillage affected both the number and distribution of sclerotia in the soil profile. Moldboard plowing lowered the number of sclerotia per litre of soil, compared to MT and NT plots, and buried the sclerotia deeper than 10 cm into the soil. Tillage did not affect the total number of apothecia observed, but moldboard plowing did delay emergence of apothecia compared to no-till. The MP + MT plots had the lowest disease incidence and the highest yield, while the NT plots had the highest disease incidence and the lowest yields. One year of moldboard plowing will bury sclerotia at least 10 cm in soil and delay the production of apothecia. How this affects SSR development depends on the other factors involved with disease development.
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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 | 0.001 |
| 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".