Enhancing micronutrient bioavailability in wheat grain through organic fertilizer substitution
Bibliographic record
Abstract
The effect of organic fertilizer substitution (OFS) on crop micronutrients often varies due to differences in environmental conditions, soil types, and nutrient status. This study aims to evaluate the effects of OFS on wheat grain micronutrients and bioavailability across five sites in Shandong Province from 2021 to 2022. All experimental sites included five common treatments: control, traditional farming, optimized practices, and 15 and 30% OFS for chemical nitrogen. The results revealed regional variation in wheat yield; the average wheat yield was 9.06 Mg ha −1 , and the highest yield was 9.58 Mg ha −1 in the 15%OF treatment. No significant differences in soil micronutrient availability were observed. Compared to the control, the OFS treatments exhibited significant increases in grain Fe (24.69%) and Zn (19.19%) contents. The OFS treatments significantly increased Fe and Zn bioavailability by reducing the PA/Fe and PA/Zn molar ratios. Organic fertilizer substitution also increased micronutrient nutritional yields and reduced the current health burden of Fe and Zn. Under the pessimistic scenario, the OFS treatment reduced health burdens of Zn and Fe deficiencies by 2.38 and 1.31%, respectively, whereas these mitigation efficiencies substantially increased to 7.15 and 3.94% under the optimistic scenario. In conclusion, OFS improved the content and bioavailability of Fe and Zn without affecting yield, which enhanced the nutritional quality of these nutrients, and alleviate the health burden of Fe and Zn deficiency. The findings demonstrate that a 15% organic fertilizer substitution (OFS) optimally enhances wheat grain Fe and Zn bioavailability and nutritional quality while maintaining crop yield, offering region-specific evidence for sustainable agricultural practices to mitigate micronutrient deficiencies and improve human health outcomes.
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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".