Effect of Diverse Compost Products on Soilborne Diseases of Potato
Bibliographic record
Abstract
Soilborne diseases result in major economic losses for potato producers. Compost application can reduce soilborne diseases, however the effects of compost products on potato disease severity and incidence are still unclear. Diverse compost products were compared for their effects on soilborne diseases of potato in New Brunswick, Canada using field and growth room experiments. In the field, five products were applied at 45 Mg ha−1 dry weight to field plots in October of 2014 and 2015. In the growth room experiment, seven products were mixed to a 5% w/w ratio with naturally infested soil. Tubers were assessed for disease severity and incidence and compared with a no compost addition control. Severity of symptoms of silver scurf, black scurf (BS), common scab (CS), and powdery scab varied among treatments, experiments, and years. In the field experiment, BS severity was significantly greater in the control than in the poultry manure compost treatment (3.26% versus 0.90%) in 2016. Common scab severity and incidence in the field were positively related to soil pH and negatively related to soil particulate organic matter C and compost C concentrations. In the growth room experiment, CS severity was significantly greater in the control (8.98%) than in the municipal source separated organic compost and sea-waste compost treatments (1.72 and 2.47%, respectively). In this study, compost products had a significant, but inconsistent, suppressive effect on soilborne potato diseases. The quantity of compost C, rather than compost quality, was likely the most important factor in disease suppression in this study.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".