Continuous Four‐Year Biogas Slurry Application Regulates the Soil Fertility, Microbial Communities, and Nitrogen Cycling Functions in a Farmland Ecosystem
Bibliographic record
Abstract
ABSTRACT Biogas slurry (BS) is widely applied as a crop fertilizer due to its high available nitrogen content. However, the effects of continuous BS application on soil fertility and nitrogen cycling in the farmland ecosystem remain unclear. In this study, we investigated the continuous four‐year BS application on soil properties, enzyme activity, microbial communities, and nitrogen cycling functions in a farmland field. The results showed that BS could increase soil alkali‐hydrolyzable nitrogen, total and available phosphorus, available potassium, organic matter, and the soil NH 4 + ‐N and NO 3 − ‐N concentrations over time. Moreover, BS application also changed the activities of key soil enzymes (e.g., urease, nitrate reductase, catalase, and phosphatase). Furthermore, the high‐throughput sequencing revealed an increase in the relative abundance of Proteobacteria , and decreases in the relative abundance of Bacteroidetes , Acidobacteria , Actinobacteria , Planctomycetes , and Nitrospiraceae with continuous BS application. With the BS application years increasing, the abundance of amoA ‐AOB initially increased and then declined, while the nitrogen cycling genes (e.g., narG , nirS , norB , and nosZ ) exhibited significant alterations. Overall, these shifts indicate that continuous BS improves soil nutrient status and enzyme‐mediated processes while reshaping bacterial community structure and the expression of key nitrogen‐functional genes. This work provides new insights into the fertility benefits and the microbial‐ecological trade‐offs of repeated BS fertilization.
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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".