Response of soybean sudden death syndrome to tillage and cultivar
Bibliographic record
Abstract
Sudden death syndrome (SDS), caused by Fusarium solani f. sp. glycines, is a season-long root disease of soybean (Glycine max) with foliar symptoms beginning in late vegetative and early reproductive stages of plant growth. The disease has been reported in most soybean-growing regions of the United States of America and the world. Previous research found that SDS was less severe in areas that were subsoiled when compared with those with no-till. The objectives of the current study were to determine (i) if cost-effective tillage practices, i.e., chisel plowing and disk tillage, can be efficient in reducing SDS and (ii) if cultivars with a range of SDS resistance or susceptibility respond differently in SDS expression across various tillage practices. In the fall of 2000 and 2001, plots were established in a field with a history of severe SDS and with a soil type consisting of an Armiesburg silty clay loam. The experiment was organized in a split-plot design with four replications. Main plots were tillage (chisel tillage, disk tillage, and no tillage), and subplots were cultivars randomized in main plots. Twelve cultivars were selected based on resistance or susceptibility to SDS. In 2000, chisel tillage reduced the area under the foliar disease progress curve (AUFDPC) when compared with no-till or disk tillage. In 2001, both chisel and disk tillage reduced AUFDPC similarly when compared with no-till. In 2000, the rate of root infection by the fungus, that is, the area under the root colonization progress curve (AURCPC), was higher in both disk- and chisel-tillage treatments than in no-tillage treatments. In 2001, higher rates of root infection occurred with chisel tillage compared with disk tillage and no-till. Among the cultivars, foliar disease ratings, AUFDPC, and soybean yield differed, but there were no differences in the AURCPC.
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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 | 0.001 |
| 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".