Fertilisation of agricultural soils with municipal biosolids: Glyphosate and aminomethylphosphonic acid inputs to Québec field crop soils
Bibliographic record
Abstract
Municipal biosolids (MBS) are suggested to be abundant, sustainable, inexpensive fertilisers, rich in phosphorus and nitrogen. However, MBS can also contain glyphosate and phosphonates that can degrade to AMPA. Glyphosate-based herbicides (GBH) are used in field crops all over the world. Most glyphosate generally degrades within a few weeks, mainly as aminomethylphosphonic acid (AMPA). AMPA is more persistent than glyphosate, and can accumulate from one crop year to the next. AMPA is phytotoxic even to glyphosate-resistant crops. The aims of this study were to assess whether MBS applications constitute: 1) an additional source of glyphosate and AMPA to agricultural soils with respect to GBH, 2) a significant source of trace metals, and 3) a partial replacement of mineral fertilisation while maintaining similar yields. To this end, four experimental agricultural sites were selected in Québec (Canada). Soil samples (0-20 cm) were collected to estimate the as yet unmeasured contribution of MBS application to glyphosate and AMPA inputs in agricultural soils. MBS applied in 2021 and 2022 had mean concentrations of 0.69 ± 0.53 μg glyphosate/dry g and 6.26 ± 1.93 μg AMPA/dry g. Despite the presence of glyphosate and AMPA in MBS, monitoring of these two compounds in corn and soybean crops over two years showed no significant difference between plots treated with and without MBS applications. For the same site, yields measured at harvest were similar between treatments. MBS application could thus represent a partial alternative to mineral fertilisers for field crops, while limiting the economic and environmental costs associated with their incineration and landfilling. It is also an economic advantage for agricultural producers given the possibility of using fewer mineral fertilisers and therefore reducing the environmental impact of their use.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".