Mineral Biofortification and Growth Stimulation of Lentil Plants Inoculated with Trichoderma Strains and Metabolites
Bibliographic record
Abstract
Biofortification of crops via agricultural interventions represents an excellent way to supply micronutrients in poor rural populations, who highly suffer from these deficiencies. Soil microbes can directly influence plant growth and productivity, e.g., by contrasting plant pathogens or facilitating micronutrient assimilation in harvested crop-food products. Among these microbial communities, Trichoderma fungi are well-known examples of plant symbionts widely used in agriculture as biofertilizers or biocontrol agents. In this work, eleven Trichoderma strains and/or their bioactive metabolites (BAMs) were applied to lentil plants to evaluate their effects on plant growth and mineral content in greenhouse or field experiments. Our results indicated that, depending upon the different combinations of fungal strain and/or BAM, the mode of treatment (seed and/or watering), as well as the supplementary watering with solutions of iron (Fe) and zinc (Zn), the mineral absorption was differentially affected in treated plants compared with the water controls. In greenhouse conditions, the largest increase in Fe and Zn contents occurred when the compounds were applied to the seeds and the strains (in particular, T. afroharzianum T22, T. harzianum TH1, and T. virens GV41) to the soil. In field experiments, Fe and Zn contents increased in plants treated with T. asperellum strain KV906 or the hydrophobin HYTLO1 compared with controls. Both selected fungal strains and BAMs applications improved seed germination and crop yield. This biotechnology may represent an important challenge for natural biofortification of crops, thus reducing the risk of nutrient deficiencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".