Lagged Association Between Powdery Mildew Leaf Severity, Airborne Inoculum, Weather, and Crop Losses in Strawberry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Knowledge about epidemiology and the impact of disease on yield is fundamental for establishing effective management strategies. The purpose of this study was to investigate the relationship between foliar strawberry mildew severity, Podosphaera aphanis airborne inoculum concentration, weather, and subsequent crop losses for day-neutral strawberry. The experiment was conducted at three, five, and four sites in 2006, 2007, and 2008, respectively, for a total of 12 epidemics. At each site, data were collected on 25 plants at 2-day intervals from the end of May to early October for a total of 60 to 62 samplings annually. First, seasonal crop losses were statistically described; then, a lagged regression model was developed to describe crop losses from the parameters that were significantly associated with losses. There was a strong positive linear relationship between seasonal crop losses and the area under the leaf disease progress curve (R(2) = 0.90) and daily mean airborne conidia concentration (R(2) = 0.86), and a negative linear relationship between crop losses and time to 5% loss (R(2) = 0.76) and time to 5% leaf area diseased (R(2) = 0.61). Among the 53 monitoring- and weather-based variables analyzed, percent leaf area diseased, log10-transformed airborne inoculum concentration, and weather variables related to temperature were significantly associated with crop losses. However, polynomial distributed lag regression models built with weather variables were not accurate in predicting losses, with the exception of a model based on a combined temperature and humidity variable, which provided accurate prediction of the data used to construct the model but not of independent data. Overall, the model based on log10-transformed airborne inoculum concentration did not provide accurate crop loss predictions. The model built using percent leaf area diseased with a time lag of 8 days (n = 4) and a polynomial degree of 2 provided a good description of the crop-loss data used to construct the model (r = 0.99 and 0.90) and of independent data (r = 0.92). For the 12 epidemics studied, 5% crop loss was reached when an average of 17% leaf area diseased was observed since the beginning of symptom development. These results indicate that information on foliar powdery mildew must be considered when making strawberry powdery mildew management decisions.
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.
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 it