<i>Bremia lactucae</i> Infection Efficiency in Lettuce is Modulated by Temperature and Leaf Wetness Duration Under Quebec Field Conditions
Bibliographic record
Abstract
More than 80% of Canadian lettuce production is located in the province of Quebec. Yet most of our knowledge on the epidemiology of lettuce downy mildew (Bremia lactucae) is derived from controlled-condition experiments or field experiments conducted in subtropical climates and, thus, cannot readily be applied to Quebec lettuce production. The influence of temperature and leaf wetness duration on the infection efficiency (IE) of B. lactucae was studied for 4 years (2003, 2004, 2012, and 2013) under field and growth-chamber conditions. IE was defined as the ratio of the number of lesions/leaf to the airborne conidia concentration (ACC). B. lactucae ACC was measured with rotating-arm samplers three times/week. In addition, 72 lettuce trap plants/sampling day were exposed to the potential airborne B. lactucae inoculum and disease intensity was assessed after 7 days of incubation in greenhouse. Under growth-chamber conditions, an ACC of 1 conidium/m 3 was sufficient to cause 1 lesion/leaf, and IE ranged from 0.25 to 1.00. Under field conditions, an ACC of 10 to 14 conidia/m 3 was required to cause 1 lesion/leaf, and IE ranged from 0.02 to 0.10, except in 2004, when IE ranged from 0.03 to 1.00. IE increased with increasing leaf wetness duration but decreased with increasing temperature. Also, considering an observed average temperature range from 10 to 20°C in the area of Quebec, 2 h of leaf wetness was sufficient for infection by B. lactucae. Therefore, under Quebec lettuce production conditions, a leaf wetness period of 2 h and an ACC of 10 to 14 conidia/m 3 can be used as risk indicators to facilitate disease management decisions. Also, under typical Quebec weather conditions, measuring both morning and evening leaf wetness events could be used to improve the reliability of leaf wetness duration as a downy mildew risk indicator. Further research is needed to validate these risk indicators for integration into management strategies.
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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) | 0.000 | 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 | 0.000 |
| 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".