Potato Early Dying and Yield Responses to Compost, Green Manures, Seed Meal and Chemical Treatments
Bibliographic record
Abstract
Verticillium dahliae Kleb. is a soilborne fungal pathogen of many crops. In potato, it is the major causal agent of Early Dying. In Manitoba, potato fields planted with cv. Russet Burbank are infested with highly pathogenic V. dahliae isolates, which can produce up to 90 % disease severity. The objective of the study was to evaluate selected compost, green manure, and seed-meal treatments, in comparison with the soil fumigant Vapam, for their ability to reduce propagule density of V. dahliae in soil and decrease disease, and to enhance potato yield. Select green manure crops (oriental and white mustard, Canada milk vetch, sorghum-sudangrass, rye, alfalfa, oat/pea mixture), organic amendments (composted cattle manure and mustard seed-meal), and Vapam, and crop sequences that contribute to the suppression of Verticillium, or the improvement of potato yield were used in a 3-year field study initiated in 2006. Survival in soil of microsclerotia was evaluated as a measure of treatments’ success in potentially reducing Early Dying. Compost and seed-meal treatments, compared to an untreated control, reduced incidence to 30 and 40 %, respectively, but only seed-meal reduced V. dahliae propagule density. Overall, green manures over 1 or 2-years were ineffective in reducing propagule density or improving potato yield. Vapam was partially effective in reducing the propagule density only at the beginning of the potato season, but it did not reduce disease incidence compared to the control. Compost and seed-meal are promising as alternative control of V. dahliae. Only compost reduced disease and increased potato yield, which was associated with improved nutrient availability (phosphorus and sulfate) in soil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".