Identification of Verticillium species and control methods for Verticillium wilt of potato in Manitoba.
Bibliographic record
Abstract
Manitoba is the second largest potato-producing province in Canada, after Prince Edward Island. Although the Manitoba average yield is slightly higher than the Canadian average, there are commercial fields with lower yield as a result of the pressure of diseases like Verticillium wilt, caused by Verticillium dahliae. In Manitoba and elsewhere, there is increased interest in the use of soil fumigation and application of composted manures to reduce Verticillium wilt. However, accurate quantification of V. dahliae inoculum in soil is needed for disease control decisions as well as to determine success of practices to reduce pathogen levels in soil. The traditional wet plating method for determination of Verticillium levels in soil is often prone to errors, laborious and costly. Therefore, the objectives of this thesis research were to: (i) evaluate control measures including compost addition and soil fumigation on Verticillium wilt and yield of potato, cv. Norland and cv. Russet Burbank; (ii) investigate the presence and quantity of microsclerotia-forming Verticillium species and its relation to Verticillium wilt in potato; and (iii) evaluate pathogenicity of V. tricorpus on potato, cv. Russet Burbank. In a replicated field study, composted manure did not prove to consistently reduce Verticillium wilt. Fumigation with metam sodium resulted in a reduction of V. dahliae levels in soil; however, only the lowest rate at 374 L ha-1 reduced Verticillium inoculum at planting by up to 40%, and increased marketable yield in cv. Russet Burbank by up to 24%. This result could potentially help growers to reduce environmental impact on organisms not targeted and the costs associated to the use of soil fumigant for the control of Verticillium wilt. Examination of soils and plants from 17 commercial potato fields determined the presence of the microsclerotia-forming Verticillium species V. dahliae as well as Verticillium tricorpus and Verticillium klebahnii in Manitoba. Nevertheless, the study did not focus on the presence of other non-producing microsclerotia species. The study optimized a real-time PCR method to identify and quantify V. dahliae, V. tricorpus and V. longisporum in soil and plant. A subsequent pathogenicity study of selected isolates of the Verticillium species demonstrated that only those of V. dahliae were pathogenic to potato.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".