Harnessing Trichoderma in Agriculture for Productivity and Sustainability
Bibliographic record
Abstract
Increased agricultural activities driven by rising food demand have led to environmental problems mostly arising from the high levels of external inputs and resources that are required. Additionally, environmental changes, such as global warming, can lead to various biotic and abiotic stresses, which have negative impacts on crop production. Numerous solutions and agricultural strategies have been introduced to overcome these problems. One of the ways to improve plant production as well as to increase resistance towards biotic and abiotic stresses is by utilizing beneficial microbes as soil inoculants. A better understanding of the ability of Trichoderma to enhance crop production and the mechanisms that are involved are important for deriving maximum benefits from their exploitation. These versatile fungi hold great promise for the development of viable commercial products that can be used widely in agriculture for increasing crop productivity in a more sustainable way. Many previous reviews on Trichoderma have tended to focus on the mechanisms of Trichoderma in enhancing plant growth and yield. This current review discusses the sustainability aspect of using Trichoderma as plant growth regulators, the impact on plant growth and yield as well as their effects in regulating biotic and abiotic stresses.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".