Spray Drift Estimation for 

Establishing an Action Area for 

ESA Assessments

April 22nd, 2014

Mike Leggett

On Behalf of the CLA Spray Drift Issue Management Team
Overview

• Factors influencing Spray drift

• AgDRIFT overview

• Application of Drift Models for Risk Assessment 

and Defining an Action Area for ESA 

Assessment

• Reality Check

• Suggested Options for Improvement
ESA – Proximity Analysis:

Modeled exposure estimates

• Drift

• Run-off
Effective Filtering

4

• An accurate estimation of exposure potential 

increases the efficiency in identifying 

species/chemical interactions of concern. 

• Screening level estimates of exposure: 

• avoid overlooking consideration of effects on a 

species where exposure is possible (but less likely)

• expand the scope of risk assessment, making it less 

manageable

• The proposed use of AgDRIFT to estimate Spray Drift 

exposure grossly increases the estimation of the Action Area 

and should be reconsidered. 
Factors Influencing Spray Drift

• Weather, etc.

 Air movement (direction and velocity)

 Temperature & humidity

 Air stability/inversions

• Equipment & Application

 Nozzle type, size, orientation

 Nozzle pressure

 Height of release

• Spray Characteristics

 Drop size

 Chemical / Formulation / Adjuvants
Droplet Size and Drift

Wind 10 mph

18” below nozzle

<1 ft 4.6 ft 17.3 ft

300 micron

200 micron 150 micron 100 micron

50 micron

(evaporates)

From Ozkan OSU Extension Fact Sheet AEX-525-98.

Modeled using ambient temperature 85 F, relative humidity 80 %
AgDRIFT Ground model

• Based on Spray Drift Task-force data

 Mechanistic aerial model

 48 unique SDTF Deposition Datasets for Ground Application

 Excellent quality - GLP

 Getting older, (1992,1993), spray application technology 

has advanced

• Issues with AgDRIFT:

 Predictive capability declines with distance from field edge

 Averaging of the data limits its utility

 Multipliers built into the model

 shown to be incorrect

• Better Options Available
AgDRIFT Estimates for Ground Applications

120 m

AAFC data

400 m

SDTF data

• SDTF data is combined - 90th centile curve 

approximates the distribution of the data

• Estimates are ‘scaled-up’ to account for 

additional deposition near field from 

application to multiple adjacent ‘swaths’ or 

passes by a sprayer

• Model never goes to zero, predictive capability 

wanes with increased distance from the field 

edge 
Comparison Between AgDrift Outputs vs. Field Data

AgDrift Assumes 20 Spray Swaths, 90th% tile 

Distance (ft)

0 200 400 600 800 1000

%

o

f

A

p

plie

d

0.0

0.2

0.4

0.6

0.8

1.0

SDTF 1992

SDTF 1993

Wolf (PMRA)

Ganzelemier

AgDrift (20 swaths)

Figure Courtesy J. Wright
Influence of Swath Addition 

Increases for Far Field Estimates

• There is greater over-estimate 

the further you get from the 

edge of the field

• The addition of curves leads to 

multiplication of the error 
Considering Best Management Practices

1) Boom Height

2) Nozzle Class

3) Wind Speed

AgDRIFT default parameters are inconsistent with typical 

Agricultural applications 
Eg. Release Height

From Teejet – Flat fan

Lowering the release height can greatly reduce drift potential

• dropping the boom height 20” can decrease drift by 50%

Nozzle manufacturers recommend height setting to:

• optimize spray overlap 

• Minimize drift 

AgDrift default is 50 inches

Manufacturer recommendations are 20” to 30”
Selection of Endpoint to Define Action Area

Summary of Terrestrial Toxicity Studies Plant Species for Low Dose Herbicide 

Effect Endpoint (lbs a.e./acre) 

EC 25 EC 05/NOAEC 

Terrestrial Monocots 

Wheat Emergence 0.0046 0.00099 

Vegetative Vigor 0.012 0.0039 

Terrestrial Dicots 

Sugar Beet Emergence 0.0024 0.00017 

Cucumber Vegetative Vigor 0.0009 0.000064 (7 ug/m2)

• Typically use one of two non-target plant guideline studies. 

• Seedling emergence

• Vegetative Vigor

• Plants - Lowest of NOAEC or EC05 

• Most Sensitive Species

• Eg., Herbicide
Non-Crop Rate 0.9 lbs/ac

AgDrift Estimate of Required Tier 1 Buffer 

Courtesy Scott Jackson, BASF
300 and 1000 foot drift buffer Streams and Ditches

Linda Abbott, Robert Johansson and James Schaub

USDA Office of the Chief Economist

Assumed that drift exposure at levels of 

concern encompasses whole fields.
Reality Check

Photo courtesy of Dr. Tom Wolf

Plots showing drift damage

• 10s of feet, not 100s of feet
Spray Coverage Influences Efficacy

Wind

Droplet Dispersal

Lower effect

Concentration having an effect following application 

with thorough coverage in a small plot or laboratory 

setting, unlikely to have the same effect in the field 

in a drift exposure situation

Droplets with 

higher 

concentration 

more dispersed

Droplets with lower 

concentration and more 

complete coverage

Same 

Concentration per 

unit area 

Photo courtesy of Dr. Don Obert, 

Limagrain Cereal Seeds
Comparison Between AgDrift Outputs vs. Field Data

AgDrift Assumes 20 Spray Swaths, 90th% tile 

Distance (ft)

0 200 400 600 800 1000

%

o

f

A

p

plie

d

0.0

0.2

0.4

0.6

0.8

1.0

SDTF 1992

SDTF 1993

Wolf (PMRA)

Ganzelemier

AgDrift (20 swaths)

Figure Courtesy J. Wright

Drift studies indicate that exposure potential is largely 

contained to within 200 ft of the field 

Mostly Non-detects

Assuming relevant 

exposure out here 

because the model 

predicts it, is not very 

useful
REGDISP

20

• Model built on AGDISP (AGDISP Version 8.26) 

• USFS made code available to Industry 

• the current EPA accepted version of AgDISP

• desire to keep the mechanistic ground model 

• No changes to existing AGDISP code existing 

• Better interface for AGDISP calculators

• Enables parsing of data/ addition of data

• Log-log regression to far field prediction errors
REGDISP Includes Canadian Data

21

 Agricultural Agri Food Canada (AAFC) ‘2000

 Agricultural Agri Food Canada (AAFC) ‘2004

 Agricultural Agri Food Canada (AAFC) ‘2011

 49 unique AAFC Deposition Datasets

 Spray Drift Task Force ‘1992

 Spray Drift Task Force ‘1993

 48 unique SDTF Deposition Datasets

 Total in the model: 97 unique deposition 

datasets
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Conclusions

• Conservative estimates of what is a relevant 

concentration for toxicity lead to a perceived need to 

estimate very low levels of exposure from drift 

• The AgDRIFT and AGDISP models are inadequate 

for the task

 Not designed/intended for defining an ‘Action Area’

 The underlying data provide a bias estimate of exposure

 Model accuracy declines with distance from the edge of the 

field

– tend toward increasing over-estimate of exposure relative to 

all available data

• Better tools more suitable for this purpose are 

available
Thank you