Fungal Pretreatment of Lignocellulosic Feedstocks: Challenges and Opportunities in Lignin Degradation, Structural Polysaccharide Preservation, and Conversion into Ruminant Feed
Bibliographic record
Abstract
This review provides a comprehensive meta-summary of recent literature on fungal pretreatment of lignocellulosic biomass (LCB), emphasizing selective lignin degradation and polysaccharide preservation to improve the nutritive value of treated biomass for ruminant nutrition. It explores how white-rot fungal (WRF) species, substrate composition, and incubation duration influence lignin removal, carbohydrate retention, crude protein enrichment, and ruminal fermentability. Selective degraders, such as Ceriporiopsis subvermispora, Lentinula edodes, and Pleurotus eryngii, achieve extensive lignin degradation with minimal carbohydrate loss, markedly improving nutritional quality and fermentation efficiency. A minimum incubation period of 22–30 days optimizes ruminal fermentation by balancing maximal lignin breakdown with minimal carbohydrate loss. Although feeding trials demonstrate promising outcomes, large-scale adoption remains limited by lengthy incubation and pasteurization requirements. This review underscores the importance of achieving selective delignification with minimal nutrient loss and emphasizes the need for standardized, scalable, and time-efficient fungal pretreatment protocols to enhance feed value, advance sustainable livestock production, and support circular bioeconomy goals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".