Zn uptake behavior of rice genotypes and its implication on grain Zn biofortification
Bibliographic record
Abstract
Abstract Understanding Zn uptake dynamics is critical to rice grain Zn biofortification. Here we examined soil Zn availability and Zn uptake pathways as affected by genotype (high-grain Zn varieties IR69428 and IR68144), Zn fertilization and water management in two pot experiments. Results showed significant interactions ( P < 0.05) between genotypes and Zn fertilization on DTPA (diethylenetriaminepentaacetic acid)-extractable soil Zn from early tillering to flowering. DTPA-extractable Zn in soils grown with IR69428 was positively correlated with stem ( r = 0.78, P < 0.01), flagleaf ( r = 0.60, P < 0.01) and g r ain ( r = 0.67, P < 0.01) Zn concentrations, suggesting improved soil Zn availability and continued soil Zn uptake by IR69428 even at maturity. Conversely for IR68144, DTPA-extractable Zn was positively correlated only with leaf Zn uptake ( r = 0.60, P < 0.01) at active tillering, indicating dependence on remobilization for grain Zn loading. Furthermore, the highest grain Zn concentration ( P < 0.05) was produced by a combination of IR69428 and Zn fertilization applied at panicle initiation (38.5 μg g −1 ) compared with other treatments ( P < 0.05). The results highlight that Zn uptake behavior of a rice genotype determines the fate of Zn from the soil to the grain. This has implications on overcoming Zn translocation barriers between vegetative parts and grains, and achieving grain Zn biofortification targets (30.0 μg g −1 ).
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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 | 0.000 |
| 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) | 0.000 | 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".