The Efficiency of Arbuscular Mycorrhizal Fungi in Promoting Alfalfa Growth in Acid Soils
Bibliographic record
Abstract
High concentrations of soil Al3+ in acid soil severely influence the growth of Medicago sativa (alfalfa). The objective of the current study was to analyze whether Arbuscular Mycorrhizal Fungi (AMF) inoculation could improve alfalfa growth in acid soils. A two-way completely randomized factorial design was employed for M. sativa and M. lupulina (black medick) with two inoculations (rhizobia and AMF) and three Al3+ levels, and replicated four times. The soil Al3+ levels were adjusted to 900 mg/kg, 1000 mg/kg and 1100 mg/kg. Spores of AMF were isolated directly from rhizosphere soils of black medick. The rhizobia were isolated from root nodules in fields separately from two plant species. At each Al3+ level, there were four inoculations, non-inoculation, AMF solely, rhizobia solely and dual-inoculation with AMF and rhizobia. Soil Al3+ concentration significantly limited above- and below-ground growth of both alfalfa and black medick, reducing plant height, branching number, shoot and root weight, and root length, surface area and volume. Compared to rhizobia, AMF showed a higher tolerance to soil Al3+. AMF inoculation increased the shoot and root weight of both plant species under most circumstances. Overall, AMF colonization had a trend in increasing the contents of phosphorus in both plant species at all Al3+ concentrations but not nitrogen and potassium. Dual inoculation significantly increased nodulation ability, enabling both plant species to form nodules at 900 and 1000 mg/kg Al3+. Though the soil Al3+ concentration influenced the efficiency of AMF inoculation, AMF inoculation improved nodulation, increased plant growth and nutrient uptake, suggesting that it was an alternative way in improving alfalfa growth in acid soils.
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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) | 0.000 | 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 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".