Runoff and Drainage Losses of Atrazine, Metribuzin, and Metolachlor in Three Water Management Systems
Bibliographic record
Abstract
Rainfall can transport herbicides from agricultural land to surface waters, where they become an environmental concern. Tile drainage can benefit crop production by removing excess soil water but tile drainage may also aggravate herbicide and nutrient movement into surface waters. Water management of tile drains after planting may reduce tile drainage and thereby reduce herbicide losses to surface water. To test this hypothesis we calculated the loss of three herbicides from a field with three water management systems: free drainage (D), controlled drainage (CD), and controlled drainage with subsurface irrigation (CDS). The effect of water management systems on the dissipation of atrazine (6-chloro-N2-ethyl-N4-isopropyl-1,3,5-triazine-2,4-diamine), metribuzin [4-amino-6-(1,1-dimethylethyl)-3-(methylthio)-1,2,4-triazine-5(4H)-one), and metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl)acetamide] in soil was also monitored. Less herbicide was lost by surface runoff from the D and CD treatments than from CDS. The CDS treatment increased surface runoff, which transported more herbicide than that from D or CD treatments. In one year, the time for metribuzin residue to dissipate to half its initial value was shorter for CDS (33 d) than for D (43 d) and CD (46 d). The half-life of atrazine and metolachlor were not affected by water management. Controlled drainage with subsurface irrigation may increase herbicide loss through increased surface runoff when excessive rain is received soon after herbicide application. However, increasing soil water content in CDS may decrease herbicide persistence, resulting in less residual herbicide available for aqueous transport.
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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".