Biotechnological development of Trichoderma-based formulations for biological control
Bibliographic record
Abstract
Trichoderma spp. are a genus of well-known fungi that promote healthy growth and modulate different functions in plants, as well as protect against various plant pathogens. The application of Trichoderma and its propagules as a biological control method can therefore help to reduce the use of chemical pesticides and fertilizers in agriculture. This review critically discusses and analyzes groundbreaking innovations over the past few decades of biotechnological approaches to prepare active formulations containing Trichoderma. The use of various carrier substances is covered, emphasizing their effects on enhancing the shelf life, viability, and efficacy of the final product formulation. Furthermore, the use of processing techniques such as freeze drying, fluidized bed drying, and spray drying are highlighted, enabling the development of stable, light-weight formulations. Finally, promising microencapsulation techniques for maximizing the performance of Trichoderma spp. during application processes are discussed, leading to the next-generation of multi-functional biological control formulations. KEY POINTS: • The development of carrier substances to encapsulate Trichoderma propagules is highlighted. • Advances in biotechnological processes to prepare Trichoderma-containing formulations are critically discussed. • Current challenges and future outlook of Trichoderma-based formulations in the context of biological control are presented.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".