Advancing the Science of Modeling:
Industry Perspectives
Dave Gustafson
29 March 2011
• Importance of high quality input data
• Use best available modeling technology
• Follow Good Modeling Practices (“GMPs”)
• Increasing importance of buffers
• Multiple ecosystem services provided
• Agreed methods for quantifying benefits
• Closing comments about the future
Federal Agencies
• Al Barefoot, DuPont
• Dave Archer – USDA-ARS
• Paul Hendley, Syngenta
• Nancy Baker – USGS
• Scott Jackson, BASF
• Jeff Frey – USGS
• Russell Jones, Bayer
• Jerry Hatfield – USDA-ARS
• Iain Kelly, Bayer
• Doug Karlen – USDA-ARS
• Mike Legget, CropLife
• Cristina Negri – DOE-ANL
• Nick Poletika, Dow
• John Prueger – USDA-ARS
Importance of High Quality Input Data
• “GIGO”: a cliché, but still very true
• USDA-NASS data collection must be supported and
should be greatly expanded
• More frequent collection of more extensive nutrient
input data (including timing info, etc.)
• New collection of data on tillage practices
• Standardized, enhanced hydrology (NHDPlus)
• New, higher resolution NEXRAD data should be utilized
whenever possible and appropriate
Use Best Available Modeling Technology
• Pesticide screening tools – OK for Tier 1 only
• Ensure underlying mathematics of the
simulation model is actually correct
• Pesticide dissipation
• Dispersion (leaching and in rivers)
• Modeling for landscape management
• HIT (Jon Bartholic, Michigan State University)
• SWAT & APEX (Claire Baffaut, USDA-ARS)
GUS: Example Tier 1 Screening Tool
• Initially proposed as
“Groundwater Ubiquity Score: A Simple Method for Assessing
Pesticide Leachability,” J. Environ. Toxic. & Chem., 8:339-357 (1989).
a joke to colleagues
at Monsanto
• Ended up getting
published and “going
viral” in the early 1990s (pre-Internet)
• Not appropriate for exposure analysis
• Only useful for the purpose of determining when
higher tier modeling techniques are needed
Getting the Underlying Mathematics Right
• Pesticide dissipation is
“Nonlinear Pesticide Dissipation in Soil: A New Model Based on
Spatial Variability,” Environ. Sci. & Technol., 24:1032-1038
nearly always nonlinear,
yet many models still
assume linear, 1st-order
dissipation kinetics
“Modeling Root Zone
Dispersion: A Comedy of
Error Functions,” Chem.
Eng. Comm., 73:77-94
“Fractal-Based Scaling and
Scale-Invariant Dispersion
of Peak Concentrations of
Crop Protection Chemicals
in Rivers,” Environ. Sci. &
Technol., 38:2995-3003
• Dispersion coefficient
increases linearly with
mean distance traveled,
yet nearly all models
assume constant DL
Modeling Challenge: Predicting Peak
Concentrations in Surface Water
• A key regulatory question is the following:
• What is the “peak” pesticide concentration to
which humans and aquatic organisms are
exposed via surface water?
• The answer depends largely on scale
• Need a proper model for scale effects
• Exploit scaling properties of fractals to provide
such a model
One Possible Modeling Approach
• Determine daily edge-of-field concentrations and
flows using an existing regulatory model
• Feed these into a simple analytical model to
simulate scale effects
MACRO, etc.
A Fractal-Based, Scale
Dependent Analytical Solution
to Convective-Dispersion Eq.
Method Validated Using Heidelberg
College (WQL) Monitoring Data
Temporal Intensity of Heidelberg
Pesticide Monitoring Data
Surface water monitoring results from the Water Quality Laboratory. Each plot shows daily streamflow per unit area (Q/A) and
concentrations of four herbicides: acetochlor (AC), alachlor (AL), atrazine (AT), and metolachlor (ME) during 1996, a high runoff year.
Excellent Fits Achieved to Shape of
Hydrograph and Chemograph
Hydrograph following large
upstream runoff event in
June 1996
Atrazine chemograph
following the same
runoff event
Additional Modeling Science Issues
• Challenges of modeling water and contaminant
transport at edge-of-field water exit points
• Agree appropriate scales for watershed
modeling, particularly in Regulatory contexts
• Alternatives to Nash-Sutcliffe (accuracy metric
for hydrological models), such as Ehret & Zehe†
• Data needed for parameterization of buffer
performance (more on this later in the talk)
Hydrol. Earth Syst. Sci., 15, 877–896, 2011
Good Modeling Practices (“GMPs”)
• Modeling results should be reproducible and
able to be compared with alternative models
• All assumptions and methods clearly stated
• Input data and model source code available
• Guidance concerning applicability of results
• Clearly state any limits on valid extrapolation of
results (in space or time, especially the future)
• What weaknesses of the model or modeling
report should be known by the user/reader?
Landscape-Scale Management
Riparian Herbaceous
slide: Doug Karlen (USDA-ARS)
Buffers: Increasingly Important, &
Increasing Challenged ($7 corn)
• Conservation buffers are areas or strips of land maintained in
permanent vegetation to help control pollutants and manage
other environmental problems (USDA definition)
• Used for many years to
reduce transport of eroded soil
• Also provide other benefits,
such as reduction of runoff and
nutrient entry into surface
waters, wildlife habitat
improvement, streambank
protection, and mitigation of
drift (if placed around entire field)
VFSMOD: Mechanistic Modeling of
Vegetative Filter Strips
• VFSMOD developed for regulatory
modeling of buffer effectiveness
• Improved understanding of
pesticide retention processes
• Nonlinear, complex relationship,
relating pesticide retention to:
– Rainfall/run-on event size
– VFS length
• Availability of this new, useful
model drives new data needs
New Concept: “Bioenergy Buffers”
• Plant a nonfood perennial bioenergy crop
(switchgrass, Miscanthus, etc.) as a buffer strip
around all sides of all row crop fields
• Width is negotiable,
but probably try to fit
1 or 2 passes of harvest
equipment (~15-30’)
• 7.5M acres for all US
corn and soybean fields
• Assuming 20’ width and
80 acre average field size (40’ for adjoining fields)
Bioenergy Buffers Provide
Multiple Ecosystem Services
• Improved water quality
• Additional wildlife habitat
Miscanthus giganteus
• Enhanced “C-questration”
• Sustainable energy source
• Endangered species protection
source: Jeff Volenec (Purdue)
• Mitigation of spray drift
Reed Warbler nest
in Miscanthus (UK)
source: Doug Karlen (USDA-ARS)
source: DEFRA
Elephant Grass
Bioenergy Buffer Collaborations
• Minnesota: Don Wyse (Univ MN), Xcel Energy
• White Paper on pesticide drift mitigation
• USDA-ARS (Jerry Hatfield, et al.)
• Ceres, Dow, DuPont/Danisco, Mendel, Monsanto
• Field study demonstration
• Location: Indian Creek
watershed near Fairbury IL
• Key collaborators:
CTIC, DOE Argonne
New CropLife America Initiative on
Buffers and Pesticide Mitigation
• Buffers now required on many pesticide labels to
reduce potential impacts on aquatic organisms
• Need for agreed modeling methods on quantifying
the degree of mitigation provided by buffers
• Need to further develop and refine practical solutions
for positioning, introducing and maintaining buffers
• Success will require a broad collaboration among
Grower Groups, EPA, USDA, State Agencies, etc.
• Utilize appropriate, standardized label language
Closing Comments about the Future
• Bioenergy Buffers likely to become widespread
• Either through BCAP-type incentives or by
modifying existing conservation programs
• Continued increases in Nitrogen Use Efficiency
• Step-changes coming through new Biotech Traits
• Better input data through Remote Sensing
• GMPs essential if good science is to prevail

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