Enhancing iron and zinc uptake in spring wheat through commercial arbuscular mycorrhizal fungi inoculation under different soil phosphorus addition levels
Bibliographic record
Abstract
Improving the concentrations and bioavailability of micronutrients, especially iron (Fe) and zinc (Zn), in crop grains is important to alleviate their deficiencies in humans. Inoculating crops with arbuscular mycorrhizal fungi (AMF) can potentially enhance soil nutrient supply and crop yield, but the effectiveness is influenced by soil factors, particularly soil phosphorus (P) availability. A greenhouse pot experiment was conducted to evaluate the effect of a commercial AMF product on spring wheat ( Triticum aestivum L.) yield and grain concentrations of Zn and Fe under different soil P addition levels (0, 5, and 20 mg P kg–1 dry soil). Results showed that AMF inoculation significantly increased root colonization rate of wheat across all P addition levels. Wheat growth, as evidenced by dry weights of shoot and grain, was significantly enhanced by AMF and high P addition treatments. AMF inoculation did not affect grain Zn concentration, but significantly increased grain Fe concentration compared to the un-inoculated control. As expected, P addition resulted in a significant reduction in grain concentrations of Fe and Zn, primarily due to a growth dilution effect. An integrated analysis using the radar plot concludes that AMF inoculation is most effective in increasing crop yield and grain micronutrient concentrations when soil P levels are low. Importantly, while adequate P supply is crucial for maintaining crop productivity, it may decrease grain micronutrient availability without complementary strategies.
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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) | 0.001 | 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 | 0.001 |
| 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".