Quantification of spray drift from aerial applications of pesticide
Bibliographic record
Abstract
With widespread use of pesticides in modern agriculture, the impacts of spray drift have become a topic of considerable interest.The drifting of sprays is a highly complex process influenced by many factors.Advances in aerial application technology and in our ability to measure drift, coupled with the adoption of new technologies for regulating pesticide application have necessitated further research in the pesticide application process.Experiments were conducted to quantify spray drift and describe its movement from aerial applications of pesticide.The effects of spray quality, atomizer type and ground cover were examined.Initial airborne drift amounts were greater than downwind deposits, thus not all of the drifting spray was deposited in the measuring area.Total off-target movement of spray was significantly greater for Fine compared to Coarse sprays.Rotary and hydraulic atomizers, both producing Fine sprays, produced statistically similar off-target movement of sprays.Similarly, no significant statistical differences in spray drift between applications to bare ground and applications to a headed barley crop canopy were not identified.Contrary to expectations, aerial application to bare ground seemed to result in less off-target movement than application to a crop canopy.The vertical spray cloud profiles were similar for all applications with the greatest amount of spray present at the height of release.Spray concentrations diminished from that height upward with diffusion and downward with deposition.The empirical data disagreed with the mechanistic model AgDISP which is currently used in the Canadian regulatory process.The model over-predicted drift deposition by a factor of two to five.Variability in spray deposit values could not be attributed to average iii differences in meteorological conditions at the time of application.Experiments with appropriate protocols for increased sensitivity may be required to more accurately report subtle differences in drift at distances greater than 200 m from the target area.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".