Managing Grapevine Trunk Diseases in California’s Southern San Joaquin Valley
Bibliographic record
Abstract
Most California vineyards are eventually attacked by one or more grapevine trunk diseases (Esca or Botryosphaeria-, Eutypa- and Phomopsis diebacks). These fungal pathogens cause chronic infections of the wood, which are slow to develop. The symptoms that follow can take years to become obvious. Prevention is an efficient approach, but requires adoption before symptoms appear. To encourage early adoption of preventative practices, economic analyses simulated their benefits in the table grape cultivar Crimson Seedless. Adoption of delayed pruning or pruning-wound protectants was compared in a young, healthy vineyard (years 3 and 5) versus in a mature, diseased vineyard (year 10). A survey of table and raisin grape growers in California’s southern San Joaquin Valley revealed their usage and perceptions of preventative practices. Also, to help growers with mature vineyards, the economic benefits of vine surgery (also referred to as “trunk renewal”) between years 11 and 15 were examined. Our economic simulations showed prevention is cost-effective, if adopted in young vineyards. However, in spite of acknowledging the impact of trunk diseases, only 25 to 30% of growers use preventative practices, and only half of such growers adopt in young vineyards. Further, growers who use prevention and adopt early also perceive preventative practices as more cost-effective. Therefore, an outreach strategy to convince non-adopters must emphasize the long-term economic benefits of early adoption of preventative practices, given the inevitable appearance of symptoms at vineyard maturity. Despite the high one-time cost of vine surgery, our economic analyses suggest its adoption is a cost-effective complement to prevention, and thus, it should be integrated into management recommendations for table grape vineyards at 11 to 15 yrs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".