A novel strategy for synthetic microbial community conversion of liquid pig manure into microbial fertilizer
Bibliographic record
Abstract
The pig industry generates copious amounts of liquid pig manure (LPM), which poses a great challenge to the environment; conventional treatment of the manure is often time-consuming and inefficient. We developed a novel technique for converting LPM into liquid microbial fertilizer with two steps: (1) screened ammonia nitrogen-degrading strains and synthetic microbial communities (SynCom) were used in combination with H2O2 for the biological deodorization of LPM; and (2) the bio-deodorized and autoclaved LPM was then inoculated with screened SynCom2 to produce liquid microbial fertilizers. The results of bio-deodorization demonstrated that both single ammonia nitrogen-degrading strains and SynCom1 reduced over 90% of the odor and GHGs emissions (NH3, H2S, CO2 and CH4) from LPM, and decreased the pH, chemical oxygen demand (COD), ammoniacal nitrogen (NH4+-N) and EC to varying extents. The SynCom1 was able to completely eliminate malodorous volatile organic compounds (p-methyl phenol) from LPM, outperforming single ammonia nitrogen-degrading strains. The deodorized LPM treated with SynCom2 exhibited high levels of nutrient concentration (31.58 g/L of total carbon and 3.81 g/L of total nitrogen), microbial biomass (up to 1.92 × 1010 CFU/mL), plant safety and maturity (germination index > 100%), indicating suitability as microbial fertilizer. Cultivation experiments demonstrated a significant increase of over 70% in shoot length, root length, fresh weight and dry weight of rice seedlings when microbial fertilizers were applied in black soil. Overall, our study provided a valuable direction for the development of potential industrial applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".