Use of Lysimeters to Evaluate the Atrazine Dynamics in Soil Cultivated With Maize
Bibliographic record
Abstract
Despite the numerous studies reporting about pesticide interactions in inumerous environmental conditions, there is insufficient information relating their dynamics to the various textural classes of Brazilian soils and, consequently, the environmental problems caused by the use and application of these compounds.In this way, the objective of this study was to evaluate the atrazine dynamics through determination of the surface runoff and percolation in a Red Latosol cultivated with maize, through studies delineated in drainage lysimeter.Applications of atrazine at the recommended doses were performed weekly up to 44 days after emergence of the maize.Rainfall simulations (150 mm) were performed 24 and 48 h after each application, collecting samples of runoff and percolated water at intervals of 5 min.The samples were sent to the laboratory for analysis of physical and chemical attributes and determination of atrazine concentrations by GC-ECD.Concentrations above the maximum values allowed by the regulatory agencies were found in approximately 99.16% of the obtained samples.The presence of atrazine in runoff and percolated water was recorded.Until 30 days after emergence (DAE) of the crop, higher concentrations of the pesticide were observed in the runoff 24 h after application, mainly in the initial collection minutes.In the percolated samples high concentrations of the pesticide were found even with the development of the crop, however, usually being smaller to those observed for surface runoff.There was a good correlation between the GUS (Groundwater Ubiquity Score) index and the GOSS model and the results obtained.It is concluded that there is the possibility of transporting atrazine in surface runoff and percolation in the different phenological stages of the maize crop when submitted to sequential applications and under high precipitation conditions.
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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 | 0.001 |
| 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 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".