Nonstructural Carbohydrate Concentrations in Dormant Grapevine Scionwood and Rootstock Impact Propagation Success and Vine Growth
Bibliographic record
Abstract
Objectives of this study were to quantify starch and soluble sugar concentrations in wine grape ( Vitis sp . ) scionwood and rootstock material, and to examine relationships between carbohydrate (CHO) metrics and both grafting success and shoot growth. CHOs of three wine grape scionwood cultivars [Merlot and Riesling ( Vitis vinifera ) and Vidal blanc ( Vitis sp.)] harvested from four separate vineyards in the Niagara Peninsula in Ontario were analyzed for starch, total CHOs, total sugars, sucrose, monosaccharides (glucose, fructose), and oligosaccharides (raffinose, stachyose) to determine if CHO differences existed between scionwood cultivar and site, and whether these impacted propagation success when grafted to two different rootstocks [‘3309 Couderc’ (3309) and ‘101-14 Millardet et de Grasset’ (101-14) ( V. riparia × V. rupestris )]. Differences in CHOs existed between vineyards for all cultivars, and their relationship with propagation success was most evident with ‘Vidal blanc’. Differences were also observed between sites for some cultivars in terms of grafting success and shoot length of grafted vines. Hot water treatment (HWT) of rootstock increased total sugars, glucose, fructose, and stachyose in 3309, and led to lower starch, total CHOs, and higher sucrose in 101-14 when measured immediately following HWT. At time of grafting, HWT 3309 had lower starch sucrose and raffinose, and higher glucose, fructose, and total sugars compared with non-HWT material, whereas HWT 101-14 contained lower total sugars, raffinose, and sucrose and higher stachyose and glucose. Stachyose, raffinose, sucrose, glucose, fructose, and total sugars in scionwood at time of grafting were correlated with propagation success. However, CHOs at time of scionwood collection in February did not correlate to successful propagation. Relationships between scion viability of all cultivars vs. stachyose and total sugars for both rootstocks suggested a possible relationship between these CHO metrics and propagation success. Differences likewise existed between propagation success on rootstocks 3309 and 101-14 on most occasions with 3309 displaying higher percent scion viability and shoot growth. This may be of particular importance in grafting difficult-to-propagate rootstock cultivars.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".