Effects of co-applied biochar and plant growth-promoting bacteria on soil carbon mineralization and nutrient availability under two nitrogen addition rates
Bibliographic record
Abstract
In the background of climate warming, the demand for improving soil quality and carbon (C) sequestration is increasing. The application of biochar to soil has been considered as a method for mitigating climate change and enhancing soil fertility. However, it is uncertain whether the effects of biochar application on C-mineralization and N transformation are influenced by the presence or absence of plant growth-promoting bacteria (PGPB) and soil nitrogen (N) level. An incubation study was conducted to investigate whether the effects of biochar application (0 %, 1 %, 2 % and 4 % of soil mass) on soil respiration, N status, and microbial attributes were altered by the presence or absence of PGPB (i.e., Sphingobium yanoikuyae BJ1) under two soil N levels (N0 and N1 soils as created by the addition of 0 and 0.2 g kg−1 urea- N, respectively). The results showed that biochar, BJ1 strain and their interactive effects on cumulative CO2 emissions were not significant in N0 soils, while the effects of biochar on the cumulative CO2 emissions were dependent on the presence or absence of BJ1 in N1 soils. In N1 soils, applying biochar at 2 % and 4 % increased the cumulative CO2 emissions by 141.0 % and 166.9 %, respectively, when BJ1 was absent. However, applying biochar did not affect CO2 emissions when BJ1 was present. In addition, the presence of BJ1 generally increased ammonium contents in N0 soils, but decreased nitrate contents in N1 soils relative to the absence of BJ1, which indicates that the combination of biochar and BJ1 is beneficial to play the N fixation function of BJ1 in N0 soils. Our results highlight that biochar addition influences not only soil C mineralization but also soil available N, and the direction and magnitude of these effects are highly dependent on the presence of PGPB and the soil N level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".