Soilborne Inoculum Density and Environmental Parameters Influence the Development of Pythium Stunt Caused by <i>Pythium tracheiphilum</i> in Head Lettuce Crops
Bibliographic record
Abstract
Matta) is responsible for important yield losses in head lettuce crops each year, which can reach up to 50% in certain cases. Despite the significance of the disease, factors influencing its development remain poorly documented, and no disease risk indicators are available, which makes the development of management strategies difficult. Hence, growers systematically use chemical fungicides throughout the growing season to reduce crop losses. However, it is known that soilborne disease incidence or severity may be influenced by soil inoculum density and environmental parameters. Therefore, the objectives of this study were to investigate the influence of inoculum density on lettuce growth under controlled conditions and evaluate the influence of soil inoculum density, air temperature, relative humidity, and rainfall on disease incidence under field conditions. In particular, this study aims to develop accurate predictors for Pythium stunt incidence. Results showed that, under controlled environment, thresholds of inoculum density of 97 and 46 propagules per gram of dry soil were needed to reduce lettuce dry weight by one-half for cultivars Estival and Prestige, respectively. These results were confirmed under field conditions, where a soil inoculum density >132 propagules per gram of dry soil combined with air temperatures <18°C for the first 2 weeks and rain accumulation >64 mm for the first 3 weeks after transplanting accurately predicted disease incidence 79% of the time. These relationships improve understanding of seasonal Pythium stunt development and will provide useful tools to develop sustainable 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".