Nutrient Uptake and Fruit Quality of Pummelo as Influenced by Ammonium, Potassium, Magnesium, Zinc Application
Bibliographic record
Abstract
Pummelo-growing soils in southern Thailand are usually low concentrations of potassium, calcium, magnesium and zinc; therefore, the roles of these elements are essential and their effects on nutrient uptake and fruit quality that should be considered in this area in current and future pummelo orchard. The study was carried out during 2013 to 2014 production year on a 17 year pummelo (Citrus maxima Merr.). The objective of the study was to examine the effects of ammonium (NH4), potassium (K), magnesium (Mg) and zinc (Zn) fertilization over the farmer practice on nutrient uptake and fruit quality. The experiment comprised of 5 treatments; apply all, not apply NH4, not apply K, not apply Mg and farmer practice. Application of fertilizer increased N, Ca and Mg concentrations in the leaves, but K and Zn concentrations in leaves decreased after application. However, their concentrations were in optimum ranges. High K/Ca, K/Mg mole ratios and low exchangeable Mg in soil positively affected on K uptake. Low Ca/Mg mole ratio and low exchangeable Ca in soil negatively affected on Ca uptake. The TSS and TSS/TA tended to decline when the treatments were not applied K-fertilizer. The fruit qualities were greater in the treatments with K/Ca, K/Mg and Ca/Mg mole ratios in soil were 0.23 to 0.27, 0.63 to 0.71 and 2.35 to 3.08, respectively. This study showed that K and Zn still required for pummelo growing in this soil.
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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 | 0.000 |
| 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".