# Spray Drift Estimation for Establishing an Action Area for ESA Assessments April 22 nd , 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 - of

f

# Effective Filtering

4 

### • An accurate estimation of exposure potential

increases the ef

ficiency in identifying species/chemical interactions of concern. • Screening level estimates of exposure: • avoid overlooking consideration of ef fects 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 ty pe, 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 - 90 th centile curve approximates the distribution of the data • Estimates are ‘scaled - up’ t o account for additional deposition near field from application to multiple adjacent ‘swaths’ o r passes by a sprayer • Model never goes to zero, predictive capability wanes with increased distance from the field edgeComparison Between AgDrift Outputs vs. Field Data

AgDrift Assumes 20 Spray Swaths, 90th% tile

Distance (ft) 0 200 400 600 800 1000%ofApplied

0.0 0.2 0.4 0.6 0.8 1.0

SDT F 1992 SDT F 1993 Wolf (PMRA) Ganzelemier AgDr ift (20 swaths)

Figure Courtesy J. W right

# Influence of Swath Addition Increases for Far Field Estimates

• T here is greater over- estimate the further you get from the edge of the field
• T he 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 Te ejet – 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 Cucum ber Vegetative Vigor 0.0009 **0.000064 (7**  **ug /m**  **2 )**  • T ypically use one of two non - target plant guideline studies. • Seedling emergence • Ve getative Vi gor • Plants - Lowest of NOAEC or EC05 

### • Most Sensitive Species • Eg ., HerbicideNon - Crop Rate 0.9 lbs/ac 

### AgDrift Estimate of Required T ier 1 Buf fer

Courtesy Scott Jackson, BASF

### 300 and 1000 foot **drift buffer Streams and Ditches** 

**Linda A bbott, Robert Johansson and James**  **Schaub USDA**  **O ffice**  **of the Chief Economist** 

### Assumed that drift exposure at levels of **concern encompasses wh**  **ole fields.** 

# Re ality Check

Photo courtesy of Dr . T om W olf Plots showing drift damage • 10s of feet, not 100s of feet

# Spr ayCov er ag e Influences E ff ic acy Wind Dr oplet Disper sal L ower effect

Concentr ation having an effect following application with thor ough cover age in a small plot or labor atory set ting , unlik ely to have the same effect in the field in a drift exposur e 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 SeedsComparison Between AgDrift Outputs vs. Field Data

AgDrift Assumes 20 Spray Swaths, 90th% tile

Distance (ft) 0 200 400 600 800 1000%ofApplied

0.0 0.2 0.4 0.6 0.8 1.0

SDT F 1992 SDT F 1993 Wolf (PMRA) Ganzelemier AgDr ift (20 swaths)

Figure Courtesy J. W right Drift studies indicate that exposure potential is largely contained to w ithin 200 ft of the field

Mostly Non - detects

Assuming relevant exposure out here because the model predicts it, is not very useful

# REGDISP

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### • Model built on AGDISP(AGDISPV ersion 8.26) • USFS made code available to Industry • the current EP

A accepted version of AgDISP • desire to keep the mechanistic ground model
• No changes to existing AGDISPcode existing • Better interface for AGDISPcalculators • 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) ‘201 1  **49 unique AAFC**  **Deposition Datasets**  Spray Drift Ta sk Force ‘1992  Spray Drift Ta sk Force ‘1993  **48 unique SDTF Deposition Datasets**  **To tal in the model: 97 unique deposition**  **datasets**22 

# Conclusions

• C onservative 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