Time of sowing and fungicides affect blackleg (Leptosphaeria maculans) severity and yield in canola
Bibliographic record
Abstract
Three different times of sowing in conjunction with various fungicide treatments were evaluated for the management of blackleg in canola (Brassica napus L.) variety Karoo. The trials were conducted at 4 different locations in Western Australia: East Chapman, Merredin, Wongan Hills and Mt Barker, representing a range of environmental conditions. The first time of sowing was at the break of the season followed by 2 subsequent sowings about 3 and 6 weeks later. Blackleg severity was significantly reduced by 14% when sowing was delayed until the end of June or early July, however, there were yield penalties due to the shortened growing season. Yield losses from blackleg were 16, 38 and 34% for mid-May, early to mid-June and end June to early July sown crops, respectively. All the fungicide treatments substantially reduced blackleg severity and increased yields at all the locations except for East Chapman (low rainfall site). The maximum protection fungicide treatment (Jockey seed dressing at 6.6 g a.i./kg seed + Impact in-furrow at 100 g a.i./ha + 3 foliar applications of flusilazole at 100 g a.i/ha) improved seed yield by 47, 56, 46 and 16% at Merredin, Wongan Hills and Mt Barker and East Chapman, respectively, compared with the nil treatment. Averaged over time of sowing and locations, the treatments of Jockey and Impact reduced disease severities by 20 and 25% and increased seed yields by 19 and 24%, respectively. There is potential for some other fungicide treatments, such as seed dressing with Jockey in combination with foliar application of either flusilazole or prochloraz, for the control of blackleg. These investigations suggest that damage from blackleg, in some areas during some seasons, could be minimised by sowing canola crops as early as possible before the onset of maturation of pseudothecia thus avoiding major ascospore showers at the seedling stage of maximum susceptibility. However, in case of a late break of season, fungicide protection may be essential to minimise losses from blackleg, particularly if sowing moderately susceptible cultivars under moderate to high disease pressure situations.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".