Analysis of Arbuscular Mycorrhizal Fungal Inoculant Benchmarks
Bibliographic record
Abstract
Growing evidence showed that efficient acquisition and use of nutrients by crops is controlled by root-associated microbiomes. Efficient management of this system is essential to improving crop yield, while reducing the environmental footprint of crop production. Both endophytic and rhizospheric microorganisms can directly promote crop growth, increasing crop yield per unit of soil nutrients. A variety of plant symbionts, most notably the arbuscular mycorrhizal fungi (AMF), nitrogen-fixing bacteria, and phosphate-potassium-solubilizing microorganisms entered the era of large-scale applications in agriculture, horticulture, and forestry. The purpose of this study is to compile data to give a complete and comprehensive assessment and an update of mycorrhizal-based inoculant uses in agriculture in the past, present, and future. Based on available data, 68 mycorrhizal products from 28 manufacturers across Europe, America, and Asia were examined on varying properties such as physical forms, arbuscular mycorrhizal fungal composition, number of active ingredients, claims of purpose served, mode of application, and recommendation. Results show that 90% of the products studied are in solid formula—powder (65%) and granular (25%), while only 10% occur in liquid formula. We found that 100% of the products are based on the Glomeraceae of which three species dominate among all the products in the order of Rhizophagus irregularis (39%), Funneliformis mosseae (21%), Claroideoglomus etunicatum (16%). Rhizophagus clarus is the least common among all the benchmark products. One third of the products is single species AMF and only 19% include other beneficial microbes. Of the sampled products, 44% contain AMF only while the rest are combined with varying active ingredients. Most of the products (84%) claimed to provide plant nutrient benefits. Soil application dominates agricultural practices of the products and represents 47%. A substantial amount of the inoculants were applied in cereal production. Recommended application doses varied extensively per plant, seed and hectare. AMF inoculant seed coating accounted for 26% of the products’ application and has great potential for increased inoculation efficiency over large-scale production due to minimum inoculum use. More applied research should also be conducted on the possible combination of AMF with other beneficial microbes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".