Spatial–Temporal Variation of Grain Magnesium, Calcium and Their Response to Phosphorus Nutrition in Sweet Corn
Bibliographic record
Abstract
ABSTRACT Sweet corn (Zea mays L. saccharata) is gaining global popularity as a staple crop and a vegetable due to its high nutritional value. However, information on grain magnesium (Mg) and calcium (Ca) status and their response to phosphorus (P) fertilization in sweet corn is still insufficient. In the current study, we combined the genotype evaluation and P gradient experiments to investigate how P supply influences the spatio‐temporal variation of grain mineral nutrition (Mg and Ca) and their bioavailabilities in sweet corn. Increasing P decreased grain Ca by 16.7%, from 0.18 to 0.15 g kg−1, but increased phytic acid phosphorus (PAP) concentration by 7.4%. Optimal P levels increased grain Mg concentration by 14.7%, from 1.36 to a peak of 1.56 g kg−1; however, excessive P reduced it by 3.8% to 1.50 g kg−1. Spatially, optimal P increased the grain Mg concentration across all cob positions and in the germ, with the largest variation observed at the upside position (Coefficient of variation (cv) = 11.88%). Conversely, high P decreased Mg concentration in middle‐cob grains and the germ. High P also reduced Ca in the upper/middle‐cob grain and in both germ and endosperm. P consistently increased grain PAP but reduced Mg and Ca bioavailability across all growth stages, all grain fractions, and the middle/bottom cob positions. Notably, PAP showed the largest variation at the late growth stage in bottom cob grains (CV = 8.75%). Mg and Ca bioavailabilities exhibited significant variations, primarily in upper grains during the early filling stage, with CVs reaching 14.5% and 43.23%, respectively. Temporally, early grain growth stage was more sensitive to P‐induced changes in Mg, Ca and their bioavailabilities, while later growth stage was more responsive to grain PAP alterations. These findings suggest that reducing P input can be advantageous for achieving high Mg and Ca biofortified sweet corn grain.
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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 | 0.001 | 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 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".