Responses of soil microbial biomass, diversity and metabolic activity to biochar applications in managed poplar plantations on reclaimed coastal saline soil
Bibliographic record
Abstract
Abstract With its relatively high stability, biochar has been suggested as a means to mitigate climate change through carbon fixation and improve the physicochemical properties of soils. However, our understanding of the effects of biochar on soil microbial diversity and their metabolic activity remain unclear. In order to elucidate how the application of biochar to plantation soils influences microbial biomass and functional diversity (using Biolog EcoPlates TM ), we conducted an experiment to investigate changes in soil microbial communities at four biochar levels (0, 40, 80 and 120 Mg/ha). We found that biochar application altered the metabolic patterns of microbial communities and accelerated the utilization of amino acids, carboxylic acids, polymers and other miscellaneous plant chemical compounds by microbes. Moreover, compared to the control, soil pH increased by 0.23, 0.24, 0.28 units and microbial biomass carbon to nitrogen ratio ( MBC / MBN ) by 9.20, 20.99 and 17.74, respectively. Meanwhile, soil moisture decreased from 25.7 to 23.8%, 23.7 and 24.4%, and MBN declined by 42.2, 46.2 and 53.8%. Regression analysis showed that soil pH was the primary factor correlated with reduced MBN . Community physiological profiles revealed that high concentrated biochar (120 Mg/ha) elevated microbial metabolic activity, while biochar application did not alter microbial functional diversity represented by the Shannon diversity index ( H ′) and evenness ( E ). Furthermore, the application of biochar would affect biogeochemical cycling of carbon and nitrogen through the elevated microbial activity and utilization in different categories of carbon sources (polymers, carboxylic acids etc.) with the reduced MBN.
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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 | 0.000 | 0.000 |
| 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".