Predicting apple leaf emergence from degree-day accumulation during the primary scab period
Bibliographic record
Abstract
The severity of primary infections of apple scab (Venturia inaequalis) is mainly determined by three components: the amount of primary inoculum, climatic conditions, and the amount of unprotected susceptible leaves. Prediction tools are available for the first two components but there are no tools to predict the emergence of apple leaves. The objective of this study was to develop degree-day models to predict emergence of both cluster and terminal shoot leaves during the primary scab period. The number of leaves per shoot was monitored during the primary infection period from 2003 to 2005 on three replicate apple trees of the cultivar Summerland McIntosh grafted on different rootstocks (M.9, MM.111, M.26) at one site and on trees of the cultivars Cortland, Empire, Lobo, McIntosh, Paulared and Spartan at two sites. There were no significant differences in the area under the leaf emergence curve (AULEC) for the different rootstocks and cultivars. The models were thus developed on data pooled over years, sites and cultivars. The rate of change in apple leaf emergence in response to degree-days (base temperature 5°C) accumulated from Apr. 01 was nonlinear with an initial lag phase followed by a linear response reaching a maximum after which the rate remained low or reached zero (no more new leaves). This response was well described by the Richards (R 2 = 0.91) and the Weibull (R 2 = 0.96) functions for the emergence of cluster and terminal shoot leaves, respectively. The models were tested against data not used for model development and provided an adequate prediction of the number of leaves per shoot (R 2 = 0.92 and 0.97). This new tool can be used to improve timing of fungicide applications against primary apple scab infections. Key words: Leaf development, phenologymodel, Malus domestica Borkh., Venturia inaequalis (Cke.) Wint
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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 | 0.002 | 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".