An Analysis of Miscible Displacement and Numerical Modelling of Glyphosate Transport in Three Different Agricultural Soils
Bibliographic record
Abstract
Since the introduction of genetically modified (GM) glyphosate-resistant crops, especially in Australia, the United States, and Canada, the use of glyphosate has exploded, raising concerns about its environmental effects both in terrestrial and aquatic environments. There are several factors that can affect the transport of glyphosate in soil, including the pH of the soil, the iron and aluminium oxides in the soil, and the structure of the soil, as well as the application time and microbial biodegradation in the soil. Furthermore, some field studies have shown that glyphosate, along with its degradation products, can be found deep in the aquatic environment and can contaminate groundwater by leaching, which implies that studying glyphosate leaching through agricultural soils is very crucial. The research in this study involves column-leaching experiments on glyphosate-dosed soils using application and flow rates representative of field conditions with bromide as a non-reactive tracer. To determine whether the observed behaviour of glyphosate is consistent with commonly recognized transport processes, the results obtained were incorporated into a one-dimensional transport model (HYDRUS 1D). Initially, physical transport parameters were determined by fitting experimental bromide breakthrough curves (BTCs) with analytical solutions to advection–dispersion equations (ADEs) for pulse boundary conditions at the upper end and zero-gradient conditions at the lower end. Then, these parameters and those from the sorption experiments were used in HYDRUS 1D to describe glyphosate transport behaviour. After three different glyphosate applications, the columns with soils C and A showed the highest glyphosate leaching rates, which is closely related to their macropore structures since bromide also leached at higher rates. A similar lower glyphosate leaching rate was found for soil B as for bromide BTC, indicating that competition between phosphorus and glyphosate for sorption sites did not result in higher rates of leaching.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".