Resolving the Foliar Calcium Mobility Paradox: Enhancing Foliar Calcium Transport in Tomato Using Osmotic Regulators
Bibliographic record
Abstract
Calcium (Ca) deficiency can impair fruit development even under optimal soil Ca levels due to its transpiration-dependent transport. Reduced fruit transpiration may limit Ca delivery to fruits, leading to lower Ca content in the fruit and diminished quality. Foliar application of Ca offers a potential strategy to mitigate these effects; however, its low mobility in the phloem often limits treatment efficacy. To address this, we employed X-ray fluorescence spectroscopy (XRF) to investigate the penetration and transport of foliar-applied Ca, using strontium (Sr) as a physiological tracer. Additionally, we evaluated the influence of osmotic regulators, sucrose, mannitol, glycerol, and potassium, on Ca transport. Results showed that Sr was effectively translocated to distal tissues. While potassium and mannitol had no significant impact on transport kinetics, sucrose and glycerol enhanced Sr movement. XRF imaging of leaf tissue revealed that Sr was primarily transported through the apoplast toward the leaf margin. Moreover, foliar application of Sr combined with sucrose significantly increased Sr accumulation in seeds and in the apical portion of tomato fruits. These findings suggest that, contrary to the common assumption of limited foliar Ca mobility, sucrose, can acts as an effective osmotic regulator, enhancing both short-range movement within leaf tissue and long-distance translocation to fruit.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".