Pesticide Runoff from Conventional, Minimum, and No-Tillage Cropping Systems: Meta-Analysis of Published North American Data
Bibliographic record
Abstract
Pesticide applications may soon be regulated by laws predicated on the presupposition that reducing tillage, and thereby increasing soil surface crop residue cover, decreases sorbed and soluble agrochemical losses in surface runoff and erosion. This analysis was conducted to determine whether pesticide transport via surface runoff and erosion could be manipulated by tillage practices. Estimates of the amounts of crop residue cover within each tillage practice were averaged from data reported in the original articles. Response ratios of the paired means of runoff, erosion, and pesticide losses and concentrations from the effects of tillage practices were meta-analyzed as paired t-tests using inverse-variance weighted least-square means from data reported from research experiments conducted in the United States of America and Canada and published between 1984 and 2006. Transitioning from conventional-tillage to minimum-tillage increased crop residue cover 5.4-fold while concurrently reducing runoff, sediment, and soluble and sorbed pesticide losses 26%, 64%, and 15%, respectively, despite an 11% increase in pesticide concentrations in runoff. Conversely, converting from conventional- to no-tillage increased crop residue cover 15.3-fold, reduced runoff 43%, and decreased sediment loss 87%, yet had no effect on pesticide losses because eliminating tillage increased pesticide concentrations in runoff 77%. Soil, environmental, time, and physiochemical factors were not included in the analysis due to lack of data. Consequently, minimizing rather than eliminating tillage may be effective at decreasing agrochemical losses in surface runoff and erosion, but more research is needed to examine the potential effects of co-factors to make recommendations to reduce pesticide runoff.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.035 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".