Tillage and Controlled Drainage‐Subirrigated Management Effects on Soil Persistence of Atrazine, Metolachlor, and Metribuzin in Corn
Bibliographic record
Abstract
Abstract The occurrence of herbicides in surface waters necessitates the development of management practices to reduce herbicide loss through tile drainage and surface runoff. Four tillage‐intercrop systems: moldboard plow (MB), moldboard plow with rye grass ( Lolium multiflorum Lam.) intercrop (MB+IC), soil saver (SS), and soil saver with rye grass intercrop (SS+IC), and two water table management treatments: controlled drainage‐subirrigation (CDS) and no control drainage (D) were investigated for their effect on herbicide persistence. Atrazine [2‐chloro‐4‐ethylamino‐6‐isopropylamino‐ s ‐triazine] (1.1 kg ha −1 ), metribuzin [4‐amino‐6‐(1,1‐dimethylethyl)‐3‐(methylthio)‐1,2,4‐triazin‐5( 4H )‐one] (0.5 kg ha −1 ), and metolachlor [2‐chloro‐ N ‐(2‐ethyl‐6‐methylphenyl)‐ N ‐(2‐methoxy‐1‐methylethyl) acetamide] (1.68 kg ha −1 ) were strip applied in a corn ( Zea mays L.) management system to reduce herbicide inputs 50%. Tillage‐intercrop system had little consistent effect on soil residues of the herbicides at 0‐ to 10‐cm depth. Control drainage‐subirrigation decreased haif‐life of atrazine and metolachlor in one of two years. Half‐life for atrazine ranged from 34 to 56 d, metribuzin 24 to 35 d, and metolachlor 40 to 79 d, with longer half‐life in dry years. Des‐ethyl atrazine [2‐chloro‐4‐amino‐6‐isopropylamino‐ s ‐triazine], the major metabolite of atrazine, persisted along with atrazine and metolachlor to the next planting season. Less than 10% of the original herbicide application was recovered the year following application. It was concluded that environmental factors such as rain affect herbicide residues more than cultural practices.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".