Seasonal Leaching and Biodegradation of Dicamba in Turfgrass
Bibliographic record
Abstract
The leaching of surface-applied herbicides, such as dicamba (2methoxy-3,6-dichlorobenzoic acid), to ground water is an environmental concern. Seasonal changes in soil temperature and water content, affecting infiltration and biodegradation, may control leaching. The objectives of this study were to (i) investigate the leaching of dicamba applied to turfgrass, (ii) measure the degradation rate of dicamba in soil and thatch in the laboratory under simulated field conditions, and (iii) test the ability of the model EXPRES (containing LEACHM) to simulate the field transport and degradation processes. Four field lysimeters, packed with sandy loam soil and topped with Kentucky bluegrass (Poa pratensis L.) sod, were monitored after receiving three applications (May, September, November) of dicamba. Concentrations of dicamba greater than 1 mg L(-1) were detected in soil water. Although drying of the soil during the summer prevented deep transport, greater leaching occurred in late autumn due to increased infiltration. From the batch experiment, the degradation rate for dicamba in thatch was 5.9 to 8.4 times greater than for soil, with a calculated half-life as low as 5.5 d. Computer modeling indicated that the soil and climatic conditions would influence the effectiveness of greater degradation in thatch for reducing dicamba leaching. In general, EXPRES predictions were similar to observed concentration profiles, though peak dicamba concentrations at the 10-cm depth tended to be higher than predicted in May and November. Differences between predictions and observations are probably a result of minor inaccuracies in the water-flow simulation and the model's inability to modify degradation rates with changing climatic 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 | 0.000 |
| 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 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".