Microbial Abundance of Waste Derived Biochar Incubated Acid Soil in Bangladesh
Bibliographic record
Abstract
Due to climate change biochar is recently recommended as a control approach to increase crop productivity and global warming reduction. As biochar application changes the soil pH towards alkalinity, this effects acid soils nutrient cycles the same as microbial abundance. This research was conducted to investigate the microbial abundance as affected by waste-derived biochar application in two different rates on acidic soil of Bangladesh. Slow pyrolyzed (500±50ºC) different waste-derived biochars viz. sewage sludge, sugarcane bagasse, potato peels, water hyacinth, and organic waste were applied at 10 tons ha-1 and 15 tons ha-1 on the acidic soil. An in-vitro incubation study was conducted on experimental soil applying all the biochar to understand how nutrient availability and carbon dynamics affect the microbial abundance of the acid soil. The incubation study was divided into two stages: submerged condition (up to 60 days) followed by a dry condition (61 to 120 days) and biochars were applied in two different rates such as 10 and 15 tons ha-1. The viable count of bacteria significantly (P<0.05) increased with the variation of incubation periods and soil moisture content, although the rates did not make any difference. The beneficial Rhizobium spp. bacteria count ranged from 4.21 to 6.47 log CFU/g; Azotobacter spp. count ranged from 2.33 to 5.60 log CFU/g and Phosphate Solubilizing Bacterial count ranged from 2.30 to 3.74 log CFU/g. However, no sign of coliform bacteria or Escherichia coli was found in any sample, but also none of the biochar amended soil samples possessed Pseudomonas spp. and Trichoderma spp. In outcome, the waste-derived biochars treatments showed an insignificant impact on microbial parameters over the first 2 months after biochar incorporation which progressively increased with the course of time and the presence of oxygen.
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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) | 0.001 | 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".