Site properties, environmental factors, and crop identify influence soil bacterial communities more than municipal biosolid application
Bibliographic record
Abstract
Reducing the environmental impact of Canadian field crop agriculture, including the reliance on conventional synthesised fertilisers, are key societal targets for establishing long-term sustainable practices. Municipal biosolids (MSB) are an abundant, residual organic material, rich in phosphate, nitrogen and other oligo-nutrients, that could be used in conjunction with conventional fertilisers to decrease their use. Though MBS have previously been shown to be an effective fertiliser substitute for different crops, including corn and soybean, there remain key knowledge gaps concerning the impact of MBS on the resident soil bacterial communities in agro-ecosystems. We hypothesised that the MBS fertiliser amendment would not significantly impact the structure or function of the soil bacterial communities, nor contribute to the spread of human pathogenic bacteria, in corn or soybean agricultural systems. In field experiments, fertiliser regimes for both crops were amended with MBS, and compared to corn and soybean plots with standard fertiliser treatments. We repeated this across four different agricultural sites in Quebec, over 2021 and 2022. We sampled MBS-treated, and untreated soils, and identified the composition of the soil bacterial communities via 16S rRNA metabarcoding. We found no indication that the MBS fertiliser amendment altered the structure of the soil bacterial communities, but rather that the soil type and crop identities were the most significant factors in structuring the bacterial communities. Moreover, there was no evidence that the MBS-treated soils were enriched in potential human bacterial pathogens over the two years of our study. Our analysis indicates that not only can MBS function as substitutes for conventional, synthesised fertilisers, but that they also do not disrupt the structure of the resident soil bacterial communities in the short term. Finally, we suggest that the use of MBS in agro-ecosystems poses no greater concern to the public than existing soil bacterial communities. This highlights the significant role MBS could potentially have in reducing the use of conventional industrial fertilisers and improving agricultural production, without risking environmental contamination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".