Regulation of Phosphorus and Zinc Uptake in Relation to Arbuscular Mycorrhizal Fungi for Better Maize Growth
Bibliographic record
Abstract
Zinc (Zn) is an important micronutrient for plants, whose deficiency in alkaline soils creates hurdles in the achievement of optimum crop growth. Moreover, overuse of phosphorus (P) fertilizers often causes Zn immobilization in the soil. The employment of arbuscular mycorrhizal fungi (AMF) could be potentially environmentally friendly technology in this regard. Therefore, a pot experiment was conducted to assess the beneficial role of AMF (Glomus species) on maize under low and high P and Zn levels. Seven levels of Zn (0, 20, 40, 60, 80, 100 and 120 mg Zn kg−1 soil ZnSO4·7H2O) and three levels of P (0, 14.5, 29 and 58 kg ac−1 as single superphosphate) were applied with (M+) and without AMF (M−). The results showed that a high application rate of Zn (100 and 120 mg Zn kg−1 soil) restricted P translocation in plants and vice versa. Moreover, the nutritional status of mycorrhizal plants (AM) was better than non-mycorrhizal (NM) plants. AM plants showed a maximum positive response at 20 mg Zn kg−1 soil, or 29 kg P ac−1. In response to 20 mg Zn kg−1 soil, root colonization was maximum, which enhanced the maize nutrient concentration in shoots. In conclusion, AMF inoculation (M+) with P (29 kg ac−1) and Zn (20 mg kg−1) is efficacious for improving maize’s growth and nutrition. More investigations are suggested at the field level under different agroclimatic zones to ascertain whether P (29 kg ac−1) or Zn (20 mg kg−1) with AMF is the best treatment for maize growth optimization.
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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 | 0.000 | 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) | 0.001 | 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".