Advancing the Science of Modeling:
Industry Perspectives
Dave Gustafson
29 March 2011
Outline
• 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
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Acknowledgements
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Industry
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
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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)
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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
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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
(1990).
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
(1988).
“Fractal-Based Scaling and
Scale-Invariant Dispersion
of Peak Concentrations of
Crop Protection Chemicals
in Rivers,” Environ. Sci. &
Technol., 38:2995-3003
(2004).
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• 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
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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
PRZM
or
MACRO, etc.
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A Fractal-Based, Scale
Dependent Analytical Solution
to Convective-Dispersion Eq.
Method Validated Using Heidelberg
College (WQL) Monitoring Data
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Temporal Intensity of Heidelberg
Pesticide Monitoring Data
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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
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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)
†
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Hydrol. Earth Syst. Sci., 15, 877–896, 2011 www.hydrol-earth-systsci.net/15/877/2011/doi:10.5194/hess-15-877-2011
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
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report should be known by the user/reader?
Landscape-Scale Management
Riparian Herbaceous
Windbreak
Buffer
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)
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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
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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)
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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)
Switchgrass
source: Doug Karlen (USDA-ARS)
source: DEFRA
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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:
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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
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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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