Pest Management Science

Pest Manag Sci 59:691–698 (online: 2003)
DOI: 10.1002/ps.682

Software for pest-management science:computer models and databases from theUnited States Department of Agriculture—
Agricultural Research Service†‡
R Don Wauchope,1∗ Lajpat R Ahuja,2 Jeffrey G Arnold,3 Ron Bingner,4Richard Lowrance,1 Martinus T van Genuchten5 and Larry D Adams6
1 Southeast Watershed Research Laboratory, USDA-ARS Tifton, GA, USA
2 Great Plains Systems Research, USDA-ARS Ft Collins, CO, USA
3 Grassland Soil and Water Research Laboratory, USDA-ARS Temple, TX, USA
4 National Sedimentation Laboratory, USDA-ARS Oxford, MS, USA
5 George E Brown Jr Salinity Laboratory, USDA-ARS Riverside, CA, USA
6 National Program Staff, USDA-ARS Beltsville, MD, USA

Abstract: We present an overview of USDA Agricultural Research Service (ARS) computer models anddatabases related to pest-management science, emphasizing current developments in environmental
risk assessment and management simulation models. The ARS has a unique national interdisciplinary
team of researchers in surface and sub-surface hydrology, soil and plant science, systems analysis
and pesticide science, who have networked to develop empirical and mechanistic computer models
describing the behavior of pests, pest responses to controls and the environmental impact of pestcontrol methods. Historically, much of this work has been in support of production agriculture and insupport of the conservation programs of our ‘action agency’ sister, the Natural Resources Conservation
Service (formerly the Soil Conservation Service). Because we are a public agency, our software/databaseproducts are generally offered without cost, unless they are developed in cooperation with a private-sector
cooperator. Because ARS is a basic and applied research organization, with development of new scienceas our highest priority, these products tend to be offered on an ‘as-is’ basis with limited user support
except for cooperating R&D relationship with other scientists. However, rapid changes in the technology
for information analysis and communication continually challenge our way of doing business.
Published in 2003 for SCI by John Wiley & Sons, Ltd.

Keywords: simulation model; database; pesticide transport; pesticide fate; non-point pollution; risk assessment;
runoff; leaching; erosion; riparian buffer; watershed

1 INTRODUCTION
Modern simulation and empirical model and databasedevelopment are a product of the enormous andfriendly computer power available to individual
scientists today. We build models and databases
because we can do so, more and more easily, sitting at
gigahertz PCs wired for almost instantaneous global
communication and exchange of information. We
can easily store, transform, describe and graphicallysummarize breathtaking quantities of information.
This is fortunate because real agricultural andenvironmental systems are typically complex and must
be analyzed using a systems approach.
Within the past two decades computer models
of agricultural soil/plant/water systems have evolved

from objects of suspicion among field scientists
and regulators to a mainstream technique for pest
management and risk assessment of pest-management
technology to ecological systems and humans.1 – 5
Models are simply a continuation of the classicalscientific goal of expressing observed behavior inmathematical terms, and of doing it precisely enoughto make useful predictions. A validated model also
serves as an extremely efficient way to communicate
scientific knowledge or, to use the more pragmatic
phrase, to achieve technology transfer.
The US Department of Agriculture’s Agricultural
Research Service (ARS) has a long history of computer
analysis applied to agricultural problems. Wischmeierand Smith’s ‘Universal Soil Loss Equation’ or USLE,6∗ Correspondence to: R Don Wauchope, US Department of Agriculture, Agricultural Research Service, PO Box 748, Tifton, GA 41794, USA
E-mail: don@tifton.usda.gov† One of a collection of papers on various aspects of agrochemicals research contributed by staff of the Agricultural Research Service of
the United States Department of Agriculture, and collected and organized by Drs RD Wauchope, NN Ragsdale and SO Duke
‡ This article is a US Government work and is in the public domain in the USA(Received 2 September 2002; accepted 12 November 2002)

Published in 2003 for SCI by John Wiley & Sons, Ltd.

691

RD Wauchope et ala regression equation used for erosion prediction,
based on thousands of erosion plot experiments,
dates back to the 1950s and is perhaps the mostused product ever created by ARS. CREAMS7 andits successor GLEAMS8 have been important tools
for conservation and water quality programs.9 For
this overview, we will restrict ourselves to current
research on computer applications related to pest
management and to environmental impacts of pest
management, most from the Natural Resources andSustainable Agricultural Systems National Programsof ARS. Consult the ARS National Programs web page
http://www.nps.ars.usda.gov/ to scan our research
program structure.
A listing of models and databases relevant to theinterests of workers in pest management is given in

Table 1. For more information on a particular modelor database, contact the ARS scientist listed or visit
the web page if available. We will discuss a selectionof some of the larger ARS multi-disciplinary projects,
all of which involve environmental simulations which
include pesticide pollution predictions.

2 ENVIRONMENTAL SIMULATION MODELS
2.1 Groundwater Loading Effects ofAgricultural Management Systems (GLEAMS)GLEAMS8 is based on CREAMS,7 ARS first nonpoint pollution model, which was designed to predict
surface water runoff from fields and resulting nonpoint pollution by sediment, nutrients and pesticides.
GLEAMS can be used to predict surface water runoff

Table 1. Active USDA-Agricultural Research Service model and database projects related to pest management science

Model/database description
Environmental simulation models: field scale
GLEAMS† (Groundwater Loading Effects ofAgricultural Management Systems): field-scalemodel of sediment, nutrient, pesticide leaching
and runoffRZWQM† (Root Zone Water Quality Model):field-scale hydrology and nutrient/pesticideleaching and runoff prediction model (see text)
HYDRUS† : simulating one- and multi-dimensionaltransport of water, heat and dissolved
agricultural contaminants in soils andgroundwater

Maintenance and support

By ARS research unit (limited) Version 2.1: Daren Harmel,
dharmel@brc.tamus.edu
http://arsserv0tamu.edu/nrsu/glmsfact.htm
Version 3.0:
http://www.cpes.peachnet.edu/sewrl/
Gleams/gleams y2k update.htm
By ARS research unit
Laj Ahuja: laj.ahuja@ars.usda.gov
Great Plains Systems Research,
Ft. Collins, CO
By ARS research unit and the Rien van Genuchten:
rvang@ussl.ars.usda.gov
International Ground Water
http://www.ussl.ars.usda.gov/MODELS/
Modeling Center, Golden,
HYDR1D1.HTM
CO
http://www.ussl.ars.usda.gov/models/
hydrus2d.HTM
George E Brown Jr Salinity Laboratory,
Riverside, CA

Envronmental simulation models: watershed scale
AnnAGNPS† (Annualized Agricultural Non-Point
By ARS research Unit
Source model): watershed-scale nonpoint
pollution model for chemicals, nutrients andsediments
SWAT† (Soil and Water Assessment Tool): models By ARS research Unit EPA
water, sediment, chemical movement in a large
supports SWAT as part ofwatershed
BASINSEAHM (Everglades Agro-Hydrology Model): uses In development by ARS Unit
WEPP model hydrology and GLEAMS pesticide
algorithms to describe south Florida Agricultural
nonpoint pollution
Buffer zone models
REMM† Riparian Ecosystem Management Model: By ARS research unit
simulation of movement and fate of pollutants instreamside soil/water/plant systems
Decision support models
GPFARM† (Great Plains Framework for
Agricultural Resource Management): decision
support system for Great Plains farmers
MARIA† (Managing Agricultural Resources
through Integrated Assessment): decision
support system for conservation planning

692

Contact

Ron Bingner: rbingner@ars.usda.gov
http://www.sedlab.olemiss.edu/AGNPS.html
National Sedimentation Lab, Oxford, MS
Jeff Arnold: arnold@brc.tamus.edu
http://www.brc.tamus.edu/swat/index.html
EPA: http://www.epa.gov/OST/BASINS/
MR Savabi: rsavabi@saa.ars.usda.gov
Subtropical Horticultural Research Unit, Miami,
FL

Richard Lowrance:
LORENZ@tifton.cpes.peachnet.edu
http://sacs.cpes.peachnet.edu/remm/
Southeast Watershed Res Lab, Tifton, GA

By ARS research unit

Laj Ahuja: laj.ahuja@ars.usda.gov
Great Plains Systems Research, Ft Collins, CO

In design stage

Laj Ahuja: laj.ahuja@ars.usda.gov
Great Plains Systems Research, Ft Collins, CO

Pest Manag Sci 59:691–698 (online: 2003)

Software for pest-management science
Table 1. Continued

Model/database description

Maintenance and support

CQT-STATS: statistics for calculating risk of
By ARS research unit
pest species survivability and reproduction incommercial shipment of stored fruit
No day-to-day support
MZA (Management Zone Analyst): sub-fieldanticipatedmanagement zone delineation forsite-specific crop management (precision
agriculture)Stored Grain Advisor(s): stored grain insect
By ARS research unit
protection decision support systems forfarm-stored and commercial elevators
Pest and pathogen biology models
MARYBLYTTM : predicts fire blight infections ofapple and pear trees: assists in schedulingbactericide sprays

Not supported

By ARS Research Unit
PMP (USDA Pathogen Modeling Program):
empirical foodborne bacterial pathogen
population model: estimates growth, survival
and inactivation.
By ARS research unit
SWFSIM (Screwworm Fly Simulation Model):computer model that simulates the influenceof weather on growth of the screwworm in awide geographical range.
Weedcast: site-specific prediction of weed
By ARS Research Unit
seedling emergence and growth for 17
weed species
Spray drift models
Aerial Spray nozzle atomization models:
By ARS research Unit, coop.
spreadsheets estimate spray droplet
With National Ag Aviation
parameters for a given set of application
Association and Spray Driftconditions. Used by applicators to comply
Task Forcewith droplet spectrum requirements53,54
Drift Model: spraydrift and evaporation, droplet By ARS research unit
temperature changes and droplet impact
energy of droplets from irrigation sprinklers
and spray heads
Databases
ARS-PPD (ARS Pesticide Properties
Database): critical compilation ofenvironmental parameters for pesticides

Not supported; most newer (<5years) compounds missing;
update in progress

MedHost: bibliographic database of hosts ofMediterranean fruit fly

By ARS research unit

Plant Disease Databases: rust, karnal bunt,fusarium—bibliography, variety
susceptibility, resistance genes, index offungi, annual surveys

By ARS research unit

Modeling support
OMS† (Object Modeling System): a framework
for archiving and configuring modular
simulation models
StormGen: generates synthetic
storm-occurrence, depth, duration andwithin-storm intensity data with seasonal(monthly) dependence

Contact
Peter Follett pfollet@pbarc.ars.usda.gov PacificBasin Agricultural Research Center, Hilo, HI
Newell Kitchen: kitchenn@missouri.edu
http://www.fse.missouri.edu/ars/decision−
aids.htm ARS Cropping Systems & Water
Quality Research, Columbia MO
Paul W Flinn: flinn@gmprc.ksu.edu
http://bru.usgmrl.ksu.edu/proj/sga/index.
html US Grain Marketing and Production Res
Unit, Manhattan, KS
Jay Norelli: jnorelli@afrs.ars.usda.gov
Appalachian Fruit Research Station,
Kearneysville, WV
Afrsweb.usda.gov/fireblight/fb8.htm
Mike Tamplin: mtamplin@arserrc.gov
http://www.arserrc.gov/mfs/PATHOGEN.htm
Microbial Food Safety Research, Wyndmoor
PA
Dan Haile:HAILED@tivoli.si.edu Screwworm
Research Unit, Panama City, Panama
http://www.screwworm.ars.usda.gov/
David Archer: Archer@morris.ars.usda.gov North
Central Soil Conservation Research Lab,
Morris, MN http://www.morris.ars.usda.gov/
morris/products/weedcast/weed2.htm
Ivan Kirk: i-kirk@tamu.edu
http://apmru.usda.gov/downloads/
downloads.htm Areawide Pest Management
Res Unit, College Station, TX
Dennis Kincaid: Kincaid@nwisrl.ars.usda.gov
Northwest Irrigation and Soils Research,
Kimberly, ID

Don Wauchope: don@tifton.usda.gov
http://wizard.arsusda.gov/acsl/ppdb.html
Southeast Watershed Research Laboratory,
Tifton, GA
Grant McQuate gmcquate@pbarc.ars.usda.gov
Pacific Basin Agricultural Research Center,
Hilo, HIMark Hughes: markh@umn.edu
Cereal Disease Laboratory, St. Paul, MN

In development

Laj Ahuja: laj.ahuja@ars.usda.gov Great Plains
Systems Research, Ft Collins, CO

In development

Jim Bonta: bonta@coshocton.ars.usda.gov
North Appalachian Experimental Watershed
Research, Coshocton, OH† See text for a more detailed description of model.

Pest Manag Sci 59:691–698 (online: 2003)

693

RD Wauchope et al

from fields, infiltration through the root-zone, andresulting non-point pollution by sediment, nutrientsand pesticides both in surface and subsurface flow.
GLEAMS added a ‘tipping bucket’ (layer draining)leaching model and an improved foliar washoff modelto CREAMS’ algorithms for pesticide runoff and firstorder degradation in crop foliage and soil. Thesealgorithms have since been incorporated in most of
the ARS environmental models, including SWAT,
the Everglades Hydroecosystem version of WEPP,AnnAGNPS and EPIC (see below). GLEAMS is
supported by ARS to a limited extent (see Table 1).
It has a large user base world-wide and has been
applied to several significant national non-point
pollution assessment (pesticides, sediment, nutrients)
projects.9 – 11 A recently updated version of the model(version 3.0), which is available on the web, but
not supported by ARS, includes a turf harvesting
scenario, ability to simulate many pesticides, twophase pesticide degradation, and metabolite tracking.
2.2 The Root Zone Water Quality Model(RZWQM)
Development of RZWQM started at the height ofagricultural groundwater pollution concerns. The
software was initially designed to function as amore theoretical ‘physical’ field-scale hydrologic
model for leaching and to simulate the effects ofagricultural management practices on water quality.
However, RZWQM has evolved into a comprehensive
one-dimensional field process model for tracking
the dynamics of water, pesticides, metabolites andnutrients in a climate/soil/crop system. The modelutilizes time steps from minutes to days dependingon the process to be described, and may be used toestimate long-term (decades) effects.12,13 Applicationsof the model to a variety of pesticide problems is being
reviewed and presented in a series of papers for this
Journal.14 – 19 RZWQM is quite complex, but a full
Windows-based user interface has greatly improved
its friendliness over the original version. The pesticide
model is also reasonably balanced in sophistication incomparison with the rest of the model.
RZWQM currently does not have phosphate anderosion components, but these are under construction.
The model may be downloaded from the Web andis currently supported by the developers. A small
cadre of researchers is exploring its application topesticides and testing RZWQM’s features against
field data, including pesticide soil residue ‘aging’,soil sorption of weak acid–base pesticides, variable
degradation rates in time and depth, volatilization fromthe soil surface, and controlled-release formulations.
It is currently being ‘modularized’ as a test case for
the Object Modeling System modeling environment
discussed below.
2.3 The HYDRUS subsurface flow/transportmodelsHYDRUS-1D20 and HYDRUS-2D21 are Windowsbased modular modeling environments for addressing
694

one- and two-dimensional sub-surface flow andcontaminant transport problems. The codes have been
applied to a variety of pesticide and other contaminanttransport problems.22 – 25 The HYDRUS codes use
the Richards equation for variably saturated flow
and Fickian-based convection–dispersion equations
for both heat and solute transport. The solutetransport equations include provisions for nonlinear sorption, one-site and two-site non-equilibriumtransport, physical non-equilibrium and degradate
formation and transport. Optimization modules allow
estimation of soil hydraulic and solute transport andreaction/degradation parameters from experimental
data. A hierarchical set of pedotransfer functions
based on a combined bootstrap–neural network
procedure predict water retention parameters andsaturated and unsaturated hydraulic conductivity,
as well as their probability distributions, from soil
texture and related data.26 The HYDRUS models aresupported by interactive graphics-based interfaces fordata pre-processing, generation of structured finite
element grids, and graphical interpretation of theresults (including animation). The two-dimensional
package includes an option of a mesh generator for
unstructured finite element grids.
Agricultural applications include pesticide leaching and volatilization, irrigation and drainage design,
salinization of irrigated lands, transport of toxic traceelements, non-point source pollution, virus and bacteria transport, and analyses of riparian systems. Futureplans22 are to include (a) preferential flow using a
range of dual-porosity and dual-permeability flow
modules, (b) the fate and transport of colloids, bacteria
and pharmaceuticals, (c) colloid-facilitated transport(d) improved geochemistry to account for the transport of organic contaminants, toxic trace elements,or mixtures thereof, (e) surface runoff and sedimenttransport, (f) constructed wetlands, (g) improved pesticide chemistry, including soil fumigants, and (i) athree-dimensional version.
2.4 Annualized AGricultural Non-Point Source
model (AnnAGNPS)
AnnAGNPS is a continuous-simulation (in time)
version of the single-rainfall-event AGNPS model.27,28
AnnAGNPS is designed to analyze the impact
of non-point source pollutants from mixed-landuse (but predominately agricultural) watersheds on
the aquatic environment. It consists of a suiteof integrated models developed in cooperation
with the Natural Resources Conservation Service(NRCS). The modules in addition to AGNPS
include: (1) the Conservational Channel Evolution
and Pollutant Transport System (CONCEPTS),29
a set of stream network, corridor and water quality
computer models designed to predict the effectsof bank erosion, bed aggradation and degradation,
burial and re-entrainment of contaminants, andstreamside riparian vegetation on channel morphology
and pollutant loadings; (2) the Stream Network
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Software for pest-management science

TEMPerature model (SNTEMP),30 a watershedscale, stream network, water temperature computermodel to predict daily average, minimum andmaximum water temperatures; (3) the Sediment
Intrusion & Dissolved Oxygen (SIDO) model,31 a
set of salmonid life-cycle models designed specificallyto quantify the impact of pollutant loadings on theirspawning and rearing habitats, as well as to include
other important life-threatening obstacles; and (4) an
economic model that determines the net economic
value of Pacific Northwest salmonids restored to either
the commercial or recreational catch.
There are a number of additional modules that
support the user in developing the needed AnnAGNPS databases. These include: (1) the TOpographic
PArameteriZation program32 (TOPAZ), to generate cell and stream network information from awatershed digital elevation model (DEM); (2) the
AGricultural watershed FLOWnet generation program (AGFLOW)33,34 to format TOPAZ output for
AnnAGNPS; (3) the Generation of weather Elements
for Multiple applications (GEM) program35 to generate the climate information for AnnAGNPS. Additional programs format and exchange data and provide
a graphical user interface including GIS I/O.
2.5 Soil and Water Assessment Tool (SWAT)
SWAT36 – 38 is designed to simulate watershed processes (including watersheds so large that they require
longer than one day to respond to storms) and theimpact of land and water management on water
quality. The model operates on a daily time-stepand allows a basin to be subdivided into grid cells
or natural sub-watersheds. Model sub-basin components include hydrology, weather, sedimentation, soil
temperature, crop growth, nutrients, pesticides andagricultural management. The primary considerations
in model development were to stress (1) land management, (2) water quality loadings, (3) flexibility in basin
discretization, and (4) continuous time simulation.
The model integrates hydrology, soil erosion, plant
growth and nutrient cycling with off-site processes
such as channel erosion/deposition, pond and reservoir
processes, groundwater flow and climate variability.
Numerous user interfaces, including geographic information systems (GIS) and web-based tools, have beendeveloped. The model has been validated against measured stream flow and water quality parameters acrossthe USA and in numerous foreign countries.
SWAT has been applied extensively for policy
planning and in developing best management practicealternatives. Major projects include: (1) Hydrologic
Model of the United States (HUMUS),39 a project
between ARS, NRCS and Texas A&M University
to simulate stream flow and quality of all major
US river basins; (2) integration of SWAT into the
US Environmental Protection Agency’s BASINS
interface and use of SWAT by several EPA programs;(3) providing non-point source pollution estimatesalong US coastlines for the National Oceanic
Pest Manag Sci 59:691–698 (online: 2003)

and Atmospheric Administration National Coastal
Pollutant Discharge Inventory.
The equations used to model the movement ofpesticide in the land phase of the hydrologic cycle
were adopted from GLEAMS. While an unlimited
number of pesticides may be applied to individual
sub-watersheds, only one pesticide may be routed
through the channel network of the watershed dueto the complexity of the processes simulated. The
total pesticide load in the channel is partitioned
into dissolved and sediment-attached components.
While the dissolved pesticide is transported withwater, pesticide attached to sediment is affected by
sediment transport and deposition processes. The
major in-stream processes simulated by the model aresettling, burial, resuspension, volatilization, diffusion
and transformation.
2.6 Riparian Ecosystem Management Model(REMM)
Riparian buffer systems—streamside areas with natural or planted vegetation that can act as sinks anddegradation zones for non-point pollutants, keeping
them out of the adjacent water bodies—are a critical agricultural pollution research area in the USA.40
REMM41 is designed to analyze the capabilities ofvariations of a three-zone buffer design42 consisting of(1) an herbaceous filter strip adjacent to the delivering
agricultural or other area; (2) a managed (harvested)
forest, and (3) a permanent, undisturbed forest adjacent to the water body. Thus, a detailed descriptionof plant species types and their growth and effects on
the hydrology and nutrient cycling is required, basedon the vegetation used in the three zones. REMM
simulates the fate and behavior of nutrients and sediment delivered to such buffers by runoff and lateralsub-surface water flow from upslope agricultural orforest areas. A daily time-step hydrologic model tracks
soil water movement in two dimensions (downslope
runoff and sub-surface flow in a layered system andleaching between layers) as affected by precipitation,
plant interception and evapotranspiration. Channel
and overland erosion and deposition are calculated aswell as enrichment of fines compared to the parent
soil and/or incoming sediment. Carbon, nitrogen andphosphorus pools and processes are modeled in somedetail, based on a variety of previous models especially
CENTURY, NLEAP and EPIC.43 – 45
Currently REMM does not have pesticide processdescriptions, but building this is a current project of
the ARS Laboratory at Tifton.
2.7 Great Plains Framework for Agricultural
Resource Management (GPFARM)‘Sustainability’ is a complex concept involving management and economic decisions that are impacted
by many interrelated variables. GPFARM46 is a ‘decision support system’ which integrates some aspectsof artificial intelligence (rule-based systems)47 with
databases and environmental simulation models, all695

RD Wauchope et al

put together using object-oriented programming.48
The project is a joint effort between ARS and Colorado
State University, and recognizes that the developmentof sustainable farming practices requires a long-term
strategic whole-farm systems analysis. GPFARM supports decision-making for a combined crop/livestock
operation and attempts to optimize water, nitrogenand pesticide uses, while protecting natural resources.
Currently the system is operational, and databases andparameter values have been developed and tested withcooperating Eastern Colorado dryland farm/ranch
operations.

2.8 MARIA: Managing Agricultural Resources
through Integrated Assessment
Resource conservation planning software used by local
conservation agency personnel is primitive comparedto current science and information technology, andrecent developments in agricultural system models,decision support software, and computer technologies
have the potential to provide much-improved tools.49
However, use of most complex models still requires
significant user expertise. The objective of the MARIA
project is to develop a research-information databaseas a decision support system to help NRCS personnel
help producers make timely and more accurate
decisions for water quality management. The productwill bridge the gap between current science/technology
and NRCS water quality conservation planning, so as
to address agricultural impacts on water quality. The
effects of management from the ARS MSEA projects50
and other experimental sites will initially be quantified
and evaluated with the Root Zone Water Quality
Model (RZWQM) or its hybrid with components
from other models. The calibrated/validated models
will then be used to extend the database forother soils, climates and potential BMPs in the
Midwest. An expert panel from NRCS and ARS
will define the range of potential BMPs for model
simulation and evaluate simulation results afterwards.
An economic package, Procosts, will be used todetermine economic impacts of simulation results.
The information database will be distributed to NRCS
personnel through the Internet, based on site-specificsoil/weather/management practices. Decision variables
for conservation planning and water quality protectionin the database will include crop yield and net return,nutrient, pesticides and soil erosion. Uncertainty ofsimulation results for each endpoint will be provided tohelp users understand the impact of natural variability
and to assess risks of alternatives. The information anddecision support system will be developed for a partof Iowa, tested and modified. In a second five-year
cycle of the project, the information database will be
expanded to include environmental quality concerns
besides water quality (eg soil and air quality) andextended to other states (eg in the Great Plains andSouthwest).
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3 LINKING A LIBRARY OF MODELS: THE
OBJECT MODELING SYSTEM OMS
Object-based modeling makes use of the modular
architecture of modern software and the power ofthe graphics interface to provide the user with avisual way to construct collections of interacting piecesof software. The concept has enormous potential
to provide archival storage of simulation algorithms
which may be integrated to describe complex systems.
Accessing a library of tested and validated modules
avoids rewriting the code for an equation—or trying
to understand someone else’s code. OMS is being
developed in a collaboration between ARS, NRCS
and the US Geological Survey, and is one of several
ongoing US Government Agency attempts to harness
this concept. A foundation for the OMS project wasprovided by the work of Leavesley et al.51,52
The objectives of the OMS project are to:(1) identify modeling library parts (modules orcomponents) and glean them from existing nonmodular simulation models; (2) formalize the linkages
between these components to support model building;(3) develop generic software tools to support models
and modeling; and finally (4) develop the framework
which supports these objectives. The following
functional components will be part of the framework:
1 A module-building component that will facilitate
the integration of existing (legacy) code into theframework. This adaptation support will simplify
the technical procedure for module implementation.
2 A module repository that will contain modules
that can be readily utilized to assemble a working
model. Types of modules included in the library are:science-, control-, utility-, assessment- and systemmodules.
3 A model builder that will assemble modules fromthe module library into executable models and verify
data connectivity and compatibility in scale andcomprehensiveness.
4 A dictionary framework that will manage extended
modeling data type information and provide
extended semantics checking for module connectivity verification.
5 An extensible user interface that will facilitate
an appropriate user interaction for general model
development and application. It will be supported by a number of contributing software packages for database management, visualization andmodel deployment.
For scientists, this means that their specific code
can achieve a measure of immortality, because theircode becomes reusable as part of other systems, but
retains its identity (and attribution). This solves many
intransigent problems associated with the interface
between research scientists and their agencies’ supportof their models.
OMS as a framework is operational, and several
models (including RZWQM), have been or are
being ‘modularized’ for testing within the framework.
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Software for pest-management science

Importantly, the system and its library will (must)
be developed in a completely ‘open’ development
environment using the internet: the library will be
the product of a global community of scientists.

4 CONCLUSION
We have focussed mostly on environmental, hydrologic, soil- and water-models because these tend to
be our agency’s emphasis in the systems analysis
area. Table 1 lists other software products related topest management, and these powerful tools are being
applied by ARS researchers in many other areas ofagriculture, such as land use planning and GIS, crop
productivity and climate change. These are extraordinary times for those fortunate enough to have theopportunity to apply the almost unbelievable powerof computer technology to one of the most difficultand critical problems facing mankind: the sustainable production of a safe and adequate food supply.
Computers and modeling systems will not solve theproblem by themselves, but they will certainly be an
essential part of the solution.

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