Effect of arbuscular mycorrhizal fungi on the physiological functioning of maize under zinc-deficient soils
Bibliographic record
Abstract
Abstract Zinc (Zn) deficiency can severely inhibit plant growth, yield, and enzymatic activities. Zn plays a vital role in various enzymatic activities in plants. Arbuscular mycorrhizal fungi (AMF) play a crucial role in improving the plant’s Zn nutrition and mitigating Zn stress effects on plants. The current study was conducted to compare the response of inoculated and non-inoculated maize (YH 1898) in the presence of different levels of zinc under greenhouse conditions under a Zn deficient condition. There were two mycorrhizal levels (i.e., M + with mycorrhizae, M- without mycorrhizae) and five Zn levels (i.e., 0, 1.5, 3, 6, and 12 mg kg-1), with three replicates following completely randomized design. At the vegetative stage (before tillering), biochemical, physiological, and agronomic attributes were measured. The results showed that maize plants previously inoculated with AMF had higher gaseous exchange traits, i.e., a higher stomatal conductance rate, favoring an increased photosynthetic rate. Improvement in antioxidant enzyme activity was also observed in inoculated compared to non-inoculated maize plants. Moreover, AMF inoculation also played a beneficial role in nutrients availability and its uptake by plants. Higher Zn12 (12 mg Zn kg-1 soil) treatment accumulated a higher Zn concentration in soil, root, and shoot in AMF-inoculated than in non-inoculated maize plants. These results are consistent with mycorrhizal symbiosis beneficial role for maize physiological functioning in Zn deficient soil conditions. Additionally, AMF inoculation mitigated the stress conditions and assisted nutrient uptake by maize.
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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".