Advancing the Science of Modeling: Industry Perspectives

Dave Gustafson29 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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• Dave Archer – USDA-ARS

• Nancy Baker – USGS

• Jeff Frey – USGS

• Jerry Hatfield – USDA-ARS

• Doug Karlen – USDA-ARS

• Cristina Negri – DOE-ANL

• John Prueger – USDA-ARS

• Al Barefoot, DuPont

• Paul Hendley, Syngenta

• Scott Jackson, BASF

• Russell Jones, Bayer

• Iain Kelly, Bayer

• Mike Legget, CropLife

• Nick Poletika, Dow

Industry Federal Agencies

Importance of High Quality Input Data

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• “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)5

GUS: Example Tier 1 Screening Tool

• Initially proposed asa joke to colleaguesat Monsanto

• Ended up gettingpublished and “goingviral” in the early 1990s (pre-Internet)

• Not appropriate for exposure analysis• Only useful for the purpose of determining when

higher tier modeling techniques are needed6

“Groundwater Ubiquity Score: A Simple Method for Assessing Pesticide Leachability,” J. Environ. Toxic. & Chem., 8:339-357 (1989).

• Pesticide dissipation isnearly always nonlinear,yet many models still assume linear, 1st-order dissipation kinetics

• Dispersion coefficientincreases linearly with mean distance traveled, yet nearly all models assume constant DL

Getting the Underlying Mathematics Right

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“Nonlinear Pesticide Dissipation in Soil: A New Model Based on Spatial Variability,” Environ. Sci. & Technol., 24:1032-1038 (1990).

“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).

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

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• Determine daily edge-of-field concentrations and flows using an existing regulatory model

• Feed these into a simple analytical model to simulate scale effects

A Fractal-Based, Scale Dependent Analytical Solutionto Convective-Dispersion Eq.

PRZMorMACRO, etc.

Method Validated Using Heidelberg College (WQL) Monitoring Data

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Temporal Intensity of Heidelberg Pesticide Monitoring Data

11Surface 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

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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)

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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 report should be known by the user/reader?

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Windbreak

Landscape-Scale Management

Riparian Herbaceous 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 ofdrift (if placed around entire field)

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VFSMOD: Mechanistic Modeling of Vegetative Filter Strips

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• VFSMOD developed for regulatorymodeling 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, usefulmodel drives new data needs

• 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 fit1 or 2 passes of harvestequipment (~15-30’)

• 7.5M acres for all UScorn and soybean fields

• Assuming 20’ width and80 acre average field size (40’ for adjoining fields)

New Concept: “Bioenergy Buffers”

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Bioenergy Buffers Provide Multiple Ecosystem Services• Improved water quality

• Additional wildlife habitat

• Enhanced “C-questration”

• Sustainable energy source

• Endangered species protection

• Mitigation of spray driftsource: Jeff Volenec (Purdue)

source: Doug Karlen (USDA-ARS)source: DEFRA

SwitchgrassElephant Grass

Miscanthus giganteus

Reed Warbler nest in Miscanthus (UK)

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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

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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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