Effect of Biofumigation on Population Densities of Pratylenchus spp. and Verticillium spp. and Potato Yield in Eastern Canada
Bibliographic record
Abstract
Abstract Biofumigation has been proposed as an alternative to soil fumigation to manage soil-borne diseases including potato early dying disease complex (PED). This study examined the potential of using brown mustard (Mustard juncea) biofumigation to manage PED under rain-fed potato production in New Brunswick, Canada in two trials between 2017 and 2020 in comparison with chloropicrin fumigation and a conventional barley rotation. Biofumigation increased yield in one trial, but not in a second trial where the potato crop experienced severe drought, whereas chloropicrin fumigation increased yield in both trials. Biofumigation was effective in suppressing root-lesion nematode (RLN, Pratylenchus spp.) counts in both trials, but was ineffective in suppressing V. dahliae population density. Chloropicrin fumigation was effective in suppressing RLN counts and V. dahliae population density only in the hill where injected, but the effect was short-lived as the population density of V. dahliae in the hill increased to the level of the control in one potato growing season. Biofumigation may be an alternative to chloropicrin fumigation in managing PED, particularly in fields with high RLN population but relatively low Verticillium population density. However, neither biofumigation nor fumigation used alone may be sustainable in the short-term potato rotations commonly used in New Brunswick, and additional beneficial practices are required to sustain productivity in the long-term.
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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 | 0.001 | 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".