Recurring Challenges from a Necrotrophic Fungal Plant Pathogen: a Case Study with Leptosphaeria maculans (Causal Agent of Blackleg Disease in Brassicas) in Western Australia
Bibliographic record
Abstract
BACKGROUND: Blackleg disease of Brassica napus, caused by the necrotrophic fungus Leptosphaeria maculans, causes severe yield losses in Australia, Europe and Canada. In Western Australia, it nearly destroyed the oilseed rape industry in 1972 when host genotypes and conducive environmental conditions favoured severe epidemics. The introduction of cultivars with polygenic resistance and the adoption of sound cultural practices two decades later helped to manage the disease. These were abandoned by many farmers in recent years in favour of the effective but ephemeral resistance conferred by the single dominant gene-based resistance derived from B. rapa ssp. sylvestris. Recently, several cultivars carrying this gene have collapsed widely within a period of 3 years after their commercial release. An environment conducive to the disease and the association of the pathogen with susceptible hosts in Western Australia for over 80 years together have led to the proliferation of L. maculans races, amounting to half of all races delineated to date from Europe, including the United Kingdom, Canada and Australia. SCOPE: This review demonstrates the problems that emerge when traditional cultural practices employed, along with cultivars containing polygenic resistance to a serious necrotrophic pathogen, are discarded in preference to the exclusive deployment of effective but ephemeral single dominant gene-based resistance to the disease across Southern Australia. CONCLUSIONS: Single dominant gene-based resistance currently available, on its own, will not confer durable resistance to blackleg disease in oilseed rape. Return to earlier management practices, including reliance upon polygenic resistance and induced resistance, may be the best currently available options to maintain production in regions across Southern Australia predisposed to severe epidemics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".