Use of Electrical Conductivity to Assess Irrigation Impacts on Grapevine Winter Hardiness
Bibliographic record
Abstract
A simple method involving measurement of electrical conductivity of grapevine cane tissue was evaluated. ‘Sovereign Coronation’ vines on two sites were subjected to five irrigation treatments based upon reference evapotranspiration (ET0) and various crop coefficients (Kc): non-irrigated control; 100% ET0 × Kc = 0.75; 150% ET0 × Kc = 0.75 (ET0 × 1.12); 100% ET0 × Kc = 0.2 to 0.8; 150% ET0 × Kc = 0.2 to 0.8. ‘Chardonnay’ vines were likewise subjected to several irrigation treatments: non-irrigated control, early season deficit (irrigation until berry set), midseason deficit (irrigation until lag phase of berry growth), late season deficit (irrigation until veraison), and full season irrigation (irrigation until harvest). Cane samples were collected from December to March inclusive, and cane segments were subjected to multiple temperature treatments (−24, −26, −28, −30, −32°C) in addition to a non-treated control. Treated cane segments were thinly sliced and incubated in distilled water (t1) at room temperature and at 100°C (t2), after which the electrical conductivity (EC) was read. Two indices calculated from both EC values (ItA, ItB) were responsive to irrigation. However, there was little relationship between either ItA and ItB versus irrigation in ‘Sovereign Coronation’, likely because it is a very winter-hardy cultivar. Nonetheless, there was a relationship between both ItA and ItB versus irrigation in ‘Chardonnay’, whereby treatments to which high irrigation volumes were applied also had high ItA and ItB values, suggesting higher potential winter damage. In summary, the technique described herein is relatively inexpensive and not time-consuming and, therefore, may constitute a rapid method for assessment of grapevine winter injury.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".