Weather, Microclimate, Canopy Density and Neighbouring Non-Host Crop Impacts on Sclerotinia Stem Rot Disease in Canola
Bibliographic record
Abstract
Sclerotinia stem rot (SSR) disease is one of the most devastating diseases of canola in the Canadian prairies caused by the fungus Sclerotinia sclerotiorum (Lib.) de Bary. Yield losses ranging between 5 to 100 percent can be experienced as a result of this disease. This study evaluated the impacts of weather and microclimate on SSR development in canola with varying canopy density. Ascospore dispersal and disease incidence were compared under modified canopy densities and misting regimes to alter microclimate. The effectiveness of crop rotation and the influence of neighbouring non host crops were also analyzed in this study. A randomized complete block design was used to compare values for canopy density, microclimate and disease development under 3 seeding rates and 3 fertilizer treatments. This design was implemented over 4 site-years, in Winnipeg and Carman during 2011 and 2012. Weather stations were installed to monitor environmental conditions at each site and compare these to disease. At each site, a wheat plot was created to examine ascospore release under a non-host crop to determine the influence such a crop may have on neighbouring canola fields. Results of this study showed that peaks in ascospore concentrations occurred simultaneously between Winnipeg and Carman fields during both years indicating that regional weather conditions are important for ascospore release. Disease development in canola fields occurred where adequate precipitation and relative humidity were present prior to ascospore release and dispersal. A decrease in relative humidity and an increase in temperature were required for spore release from apothecia. Disease development was greater in Carman, where relative humidity values overall were higher and temperatures remained lower compared to those in Winnipeg in 2011 and 2012. Ascospore release did occur under the wheat canopy and ascospores were dispersed to a distance of at least 7 meters from the plot.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".