Rootstocks with Different Vigor Influenced Scion–Water Relations and Stress Responses in AmbrosiaTM Apple Trees (Malus Domestica var. Ambrosia)
Bibliographic record
Abstract
In recent years, awareness has been raised around the benefits of diversifying rootstocks, in order to enhance tree health and sustain apple fruit production under the influence of climate change. However, performances of many rootstocks under stresses remain unclear. This study aimed to set the first step towards a much-needed comprehensive evaluation on water relationships and stress responses of scion–rootstock systems for the popular apple cultivar AmbrosiaTM (Malus domestica var. Ambrosia), because its production and horticultural knowledge had been largely limited to the Malling 9 rootstock (M.9). Five rootstocks were evaluated in a greenhouse water deficit experiment and at the onset of heat stress in a field trial in Summerland, British Columbia, Canada. Multiple stress indicators of leaves and fruits were analyzed to elucidate water use strategies and drought resistance mechanisms. The rootstocks led to differences in scion vigor, and stomatal and photosynthetic characteristics. The largest semi-dwarfing Geneva 202 (G.202) demonstrated more water use and higher stress susceptibility. Large dwarfing Geneva 935 (G.935) and Malling 26 (M.26) showed more stringent stomatal control and reduced water use under stresses, typical of a drought-avoidance strategy. The smallest large dwarfing M.9NIC29® and the small dwarfing Budagovsky 9 (B.9) led to smaller and denser stomata. B.9 demonstrated the most stable water status and drought tolerance. The study suggested that scion stress responses were influenced by rootstock vigor and tree water use strategies. It implied the necessity of vigor-specific irrigation management for alleviating stresses and achieving production goals of different rootstocks.
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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 | 0.000 | 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".