Magnesium fertilization promotes flavor balance and phenolic accumulation in Guanxi honey pomelo (Citrus maxima (Burm.) Merr.) on acidic soils
Bibliographic record
Abstract
Magnesium (Mg) deficiency poses a major challenge to the sustainable cultivation of Guanxi honey pomelo ( Citrus maxima (Burm.) Merr.) in the acidic soils of South China, severely impairing fruit quality and restricting the high-value development of the pomelo industry. To address this issue, soil Mg status was assessed across the main pomelo-producing region in Pinghe County, and Mg removal through fruit harvest was further estimated to evaluate the potential imbalance between soil Mg supply and fruit demand for Mg. Potential relationships were subsequently examined between Mg availability (in soils and leaves) and fruit yield and quality. Based on these assessments, preliminary validation was conducted through multi-site, multi-year field trials, followed by two years of multi-gradient fertilization at a long-term experimental site, involving two fertilization strategies: soil application and foliar spraying. Results showed that 65.5 % of surveyed orchards had soil Mg concentrations below 30 mg kg⁻¹. Fruit harvest removed approximately 13.64 kg ha⁻¹ yr -1 of Mg annually, indicating substantial depletion pressure. Mg fertilization significantly increased pomelo yield, by 23.9 % under soil application and 31.7 % under foliar spraying. It also improved key fruit quality parameters, with the TSS/TA (total soluble solids/titratable acidity) ratio increasing by 19.51 % (soil) and by 42.98 % (foliar), alongside notable enhancements in vitamin C (Vc), total phenolics, and flavonoids. After 90 days of ambient storage, Mg-treated fruits showed better retention of peel and pulp moisture content. In particular, foliar application resulted in the highest TSS/TA ratio during storage. Across both application methods, the most effective rates were 45 kg ha⁻¹ MgO for soil fertilization and 4 % MgSO₄ for foliar spraying.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".