Bacterial antagonists as a biological solution for control of potato late blight disease
Bibliographic record
Abstract
Late blight, caused by the oomycete Phytophthora infestans (Mont.) De Bary, is the most devastating disease affecting the potato, accounting for over six billion United States dollars worldwide each year due to production losses and prevention measures. For many years, synthetic fungicides have been used heavily by potato producers to minimize disease severity and prevent spread. But the incidence of new, more virulent P. infestans genotypes – many of which now fungicide-resistant – has made it increasingly difficult and costly for producers to prevent late blight epidemics. The use of biological fungicides has thus been of considerable interest among plant pathologists in recent years for their ability to produce powerful antifungal compounds and to induce systemic resistance in host plants. This M.Sc. project, in collaboration with Agriculture and Agri-Food Canada, used detached fed-leaf bioassays to test the effectiveness of six bacterial strain whole cultures against four P. infestans isolates for use as potential biocontrol agents. Data analysis revealed that the level of biocontrol differed greatly among the six bacterial strains; 189 (Pseudomonas chlororaphis) being significantly different than the control for 28 of the 32 measurements while this was true for OY3WO11 (Arthrobacter phenanthrenivorans) only once. The top two candidates 189 and WAUSV36 (Bacillus subtilis) were then compared to the commercial synthetic fungicide DithaneTM DG 75 which displayed the strongest levels of late blight control with median disease severity ratings of 0% and 3% after seven and ten days, respectively. Nonetheless, bacterial strains 189 (18% and 40%) and WAUSV36 (48% and 78%) were also significantly different from the untreated control (100% on both measurement days). Time-delay experiments were also conducted in order to determine if differences in biocontrol existed when the time between treatments and infection was increased. A significantly reduced biocontrol efficacy was detected when bacterial treatments were applied 24 hours before infection (compared to 2 and 18 hours) from measurements taken after seven days only (not ten). Integrating the use of bacterial antagonists into late blight management strategies can prove to be an effective addition to current prevention methods and help curb the heavy use of synthetic fungicides in potato production.
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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 | 0.000 | 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".