Effects of <i>Ganoderma lucidum</i> cultivation in forests on soil organic carbon pool and microbial community physiological profiles
Bibliographic record
Abstract
Non-timber forest products increase forests resource utilization efficiency and promote rural areas economic development. Ganoderma lucidum (Curtis) P. Karst. (Reishi) is a mushroom having great potential being cultivated as NFTPs. However, there is still a lack of effects about cultivating Reishi in forests on soil organic carbon (C) pool and microbial community, which are important for designing sustainable cultivating strategies. Therefore, this study sampled and analyzed soil from forests cultivated Reishi at 2, 4, and 6 years (LZ2, LZ4, and LZ6, respectively), and in reference natural evergreen broad-leaved forest (CK). Our results manifested that, compared with CK, LZ2 slightly increased total organic carbon (TOC), and significantly increased microbial biomass carbon (MBC) and water-soluble organic carbon (WSOC) content by 29.99% and 28.67%, respectively ( P < 0.05). Besides, compared with CK, LZ2 significantly increased the ratio of MBC/TOC and WSOC/TOC by 37.50% and 35.00%, respectively ( P < 0.05). In contrast, these parameters decreased in LZ4 and LZ6 slightly, compared with CK. Consequently, LZ2 had the highest average well-color development values and microbial functional diversity indexes, while these parameters declined in LZ4 and LZ6, compared with CK. As a result, microbial community functional structure in LZ2 was different from that in LZ4, and LZ6, while that in LZ4 and LZ6 showed similarity, according to the principal component analysis and PERMANOVA test.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".