Stephen R. Heller and Stephen L. Rawlins
Model and Database Coordination Laboratory
Agricultural Research Institute
USDA, ARS, BARC-W, Bldg. 007
Beltsville, MD 20705 USA
Despite its enormous productivity, American agriculture is
suffering serious economic problems. Many feel that these
problems require better ways to bring science information and
knowledge to bear on the solution of these problems. Computer
facilitated simulation models and expert systems with their
supporting databases represent one approach to accomplish this
goal. But carrying out this new interdisciplinary research to
deal with holistic systems is easier said than done. Some of the
most complex biological systems exist in agriculture. This paper
examines the question of what needs to be done, and suggests
possible system research approaches for reaching the goal of
solving the agriculture of today and the future.
Keywords: Agriculture, systems research. computer models,
interdisciplinary research, collaborative research, team
research. technology transfer.
This paper deals with the organizational aspects of creating the
necessary environment, support and structure to restructure
biological information and knowledge to solve important
agricultural problems. It presents possible approaches to
developing, testing, and validating models, in the context of the
new initiative of USDA's Agricultural Research Service to emphasize
systems research using large interdisciplinary teams.
In spite of the magnitude of new data, new results, and new theories which scientists create every day, real progress is in many ways quite slow. Many think this is a consequence of the very conservative nature of the scientific establishment. It was Max Planck  who once wrote that
'New scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.'
This thought is often referred to as Planck's Law (as opposed to
Planck's constant, although his Law is probably more fundamental
than his constant).
Scientists have traditionally confined their attention primarily
to a particular discipline, be it chemistry, biology, physics,
agronomy, or other areas. Research is conducted in relatively
narrow fields, and papers were published in a few journals in
that field. The need for a change to a more interdisciplinary
approach is increasingly evident. One major reason for this
needed change is the vast accumulation of data and information in
the scientific literature, making it difficult' to keep abreast
of all research work related to one's area. This new attitude and
outlook toward broader scientific activities on the part of some
researchers is due to their realizing that the solution of real
problems does not lie within narrow disciplines. Decreasing
research budgets also tend to place increased emphasis on
research that solves relevant problems. They realize they need to
broaden their viewpoint and coordinate and integrate their
research with that of others in other areas so as to be able to
make the critical breakthroughs in truly new and innovative
Communications within disciplines have tended to be of the 'old
boy network' type. Breaking into this network was mostly a non-technical matter. This too is changing. More journals end '
societies are being established to accommodate the larger number
of researchers in scientific fields. Disciplines are being
subdivided into sub-disciplines and science specialty areas. As
this is done, new groups and networks are established. Some of
these groups are clearly of the narrow focus kind, while a few of
the others are moving in the direction of broadening their
outlook and becoming the prototypes of interdisciplinary research
As new technologies have developed, specifically the computer and telecommunications, new and vastly different channels of information dissemination have evolved. No longer is the printed journal the only route of 'publication'. Computer programs, computer models, expert systems, and computer readable databases are all new products of research that can be shared. These new products and mechanisms of information transfer are causing problems for those accustomed to counting journal papers to evaluate a scientist's productivity and stature. Part of the problem is, no doubt, due to difficulty in establishing who the author of such a product is, or what percentage of the research product each contributor can rightfully claim. Clearly for legal as well as professional reasons scientists should be recognized and acknowledged for their endeavors. A study (U.S. Congress ) released in April 1986 by the Office of Technology Assessment (OTA), the research arm of the US Congress, recognizes this problem. The report very clearly explains the problem of identifying authorship in the statement:
'A group of authors using personal computers connected by a telephone network can collaborate in writing an article, a piece of software, or a database. This work can exist in various forms, in different places, and can be modified by anyone having access to the network. This networking technology provides new opportunities to combine talents, resources, and knowledge. Also, using satellite technology, authors, scholars, or other creators from all over the world can work together simultaneously on the same project. However, this same networking capability might create problems for the intellectual property system. Copyright, for example, is granted to "original" works of "authorship". In a world where there are many authors of one work, worldwide collaboration, and ever-changing materials, a law based on the concepts of originality and authorship may become too unwieldy to administer.'
While no clear policy to deal with the problem was proposed by
OTA, the fact that it is being examined at the highest levels of
Government is encouraging. The first step to solving a problem is
admitting or recognizing it as a problem.
The OTA study is not the only recent recognition of the different
nature of scientific activities which make use of the computer as
a tool. Abelson , writing in the American Scientist on computers and instrumentation, notes:
'The evolution of instrumentation and computers is already affecting the ways in which scientists work and the ways in which they interact. We have come a long distance from the days of sealing wax and string, when scientists constructed their own apparatus and mainly worked alone and were sole authors of papers. Industrial scientists fairly early found it desirable to work in interdiscplinary teams to reach specific goals. Such teamwork was likely to be more agreeable to gregarious people than to independent spirits, who tended to stay at universities. The large and costly facilities of big science tend to involve many people. It is not uncommon for a paper describing work in high-energy physics to have 100 or more coauthors. What has each contributed?'
This growing concern about proper recognition, both in a legal
as well as a professional sense will be addressed again later
in this presentation.
New organizational requirements
The recent rapid growth of science is slowly leading the
scientific establishment and managers of science to realize
that, in many ways, things are getting out of hand. All of the
millions and millions of bits and pieces of scientific research
that have been discovered and created have finally reached the
point where many of us feel we are about to produce the straw
that will break the camel's back. There is little or no
organization to much of this information. More importantly, information in the form of data alone is not knowledge. Knowledge
requires the existence of a theory to interrelate the
interactions of separate parts of a whole organism or other
system. The need to link or connect things (data and
information) and make sense (knowledge) of these things is
becoming clearer. The need for a matrix of such data was
reported by the NIH-funded NAS study on 'Models for biomedical
research - A new perspective' . A similar study in the
agriculture field entitled 'Enabling interdisciplinary
research: Perspectives from agriculture, forestry, and home
economics'  comes to the same conclusion. That conclusion
is very simple. Components of real systems are clearly
interlinked. When you act on one part of a system, you affect
the other parts. The only way to manage the whole system is to
understand the processes and linkages among its components.
A number of years ago a colleague who was funding a computer
program to make mass spectrometry library comparisons wanted to
provide additional funding to a research group to add infrared
spectroscopy as a complimentary and confirming identification
tool. I understand that the reason the research group gave for
refusing the additional funding was that mass spectrometry was all that was
needed for chemical identification. While it has taken over a
decade to show that we need every clue and piece of information
we can accurately obtain to identify a chemical [16,17], such
narrow thinking (i.e., tunnel vision) probably still does exist
in other areas. In agriculture I have found scientists who
believe weeds are just another sort of plant, and don't need to
be handled as an independent entity. In a purely scientific
sense this is true, but as we all know, grass is preferable to
dandelions in our lawns. Thus, it seems appropriate for any
plant growth model to have at least two parts, one module which
has information and characteristics on the plants we want to
grow, and one module in which there is information and
characteristics for plants (i.e., weeds) we don't want to grow.
Time will tell if this is correct.
Two examples from agriculture that Illustrates the need for
interdisciplinary research are conservation tillage and range
management. In the case of conservation tillage management of
both crop and soil systems are required to leave sufficient
crop residue on the soil surface to control erosion. In the
case of range management the interdisciplinary team needs to
include at least the fields of plant, soil, and livestock
sciences, as well as meteorology and economics to maximize
output and minimize inputs.
The fact that we need interdisciplinary research does not mean
that single discipline or multi-discipline research are not
important parts of the problem-solving process. But these
research activities need to be focused on filling specific
knowledge gaps - not producing what amounts to random or non-coordinated findings. We need a new perspective that permits
us to organize the vast amount of research dealing with the
complex biological systems. This perspective is modeling, and
it is the goal of the NIH sponsored NAS study  which has
led to this AAAS symposium.
While it is one thing to identify the need for systems
approaches, interdisciplinary research and team activities, it
is another to actually do something about it. Fortunately, the
Agricultural Research Service (ARS) in this need has been both
identified and acted on. ARS is the in-house research arm of
the US Department of Agriculture,
consisting of over 8000 employees in some 140 locations,
primarily in the USA. As stated : 'The mission of the ARS is
to plan, develop, and implement research that is designed to
produce the new knowledge and technologies required to assure
the continuing vitality of the Nation's food and agriculture
enterprise'. ARS is clearly a problem-solving agency. The
products of its research are knowledge and technology to solve
The problem of organizing research to deliver products that
solve problems was explicitly addressed by the senior management
of the Agricultural Research Service in the 1983 publication of
the ARS Program Plan, a strategic plan for the agency for the late
1980's . This plan has six objectives, including managing and
conserving soil and water, maintaining and increasing crop and
animal quality and productivity, maximizing domestic and export
markets for agricultural products, and promoting public health and
welfare through improved nutrition and family resource management.
Objective 6 called for the agency to
'Develop the means for integrating scientific knowledge of
agricultural production, processing, and marketing into systems
that optimize resource management and facilitate transfer of
technology to users.'
Central to Objective 6 is the need for modeling as stated in
this ARS document:
'The modeling approach has several advantages. First, it is
efficient. Time frames of many years can be simulated quickly and
inexpensively for many locations and management strategies.
Second, an unlimited number of management strategies can be
considered, whereas in field experiments only a few can be
considered. Third, modeling is a research tool that provides
knowledge about agricultural processes and identifies the research
that is needed to fill the gaps in knowledge. The development of
models that deal with specific components of the agricultural
system is described in other objectives of the ARS Program Plan.
The intent of Objective 6 is to integrate those component models
into models that can simulate even larger agricultural systems.
Multidisciplinary teams of scientists and engineers will be needed
to plan and carry out the modeling and validate the results.'
An example of the use of modeling to develop technologies that are
transferrable to those who need to solve problems in agriculture
is the recently established International Benchmark Sites Network
for Agrotechnology (IBSNAT), funded by the US Agency for
International Development(AID)  IBSNAT is designed to address
the question of how agrotechnology can be transferred from
locations where research has been performed to new locations. The
desire is to integrate new locations. The desire is to integrate
new crops, new products, and new agricultural practices into
existing farming systems more productive without duplicating the
research performed at other locations. To transfer this technology
the researchers are using simulation models based on fundamental
physical, chemical and biological processes. The assumption is
that the processes are the same everywhere - only the environment
differs. If the appropriate environmental conditions are
incorporated, the model would predict equally well regardless of
ARS is not the only organization interested in modeling, as
evidenced in a news article from Science magazine in March 1986
 which indicates that at NIH the National Institute for Environmental Health Sciences has been working for over five years
to lay a basis for validation of in vitro tests, and the National
Cancer Institute is also developing in vitro models for
carcinogenesis. While some of this is in response to the Animal
Rights movement, much of the motivation has come from the same
needs identified by ARS.
It is interesting to note that the two major federal agencies
involved in this effort of computer modeling of biological systems
are ARS and NIH, both research agencies and problem-solving
agencies. While the NSF is a research agency also, its goal is
fundamental research and scientific knowledge for its own sake,
not to feed the world, or have a healthy population. There is a
need for both the market pulled research of problem-solving
agencies, and the technology push of fundamental research
agencies. One also should note that much of the technology push
fundamental research stemming from technology push is generally
oriented along disciplinary lines which follows from the fact that
it is not designed to solve problems.
Following the publication of the ARS Program Plan in 1983, the initial response of researchers joining together to work towards Objective 6 was limited. Much to their credit, to give Objective 6 the required nudge, ARS management put their money where their report was and in October 1985, established the Agricultural Systems Research Institute (ASRI) and within the institute a new laboratory, the Model and Database Coordination Laboratory (MDCL).
Concurrent with the establishment of a systems research institute in ARS was the publication of a new six-year implementation plan  which identifies the strategies ARS intends to employ in reaching the goals stated in its Program Plan [31 The four strategies identified are shown in table 1.
All of these involve coordination and interdisciplinary research. Strategy 1, which involves technical problems critical to the agricultural sector, states that
'We have underutilized the approach of using interdisciplinary teams for problem solving. Such teams are needed to develop the agricultural management systems that can reduce production costs, maintain or improve product quality, reduce losses of products, and conserve soil and water resources.'
Strategy 2, which calls for the most effective utilization of the limited resources which are available at the Federal level, states that ARS must
'Assure coordination of similar or interrelated research projects both within ARS and between ARS and other research organizations to avoid unnecessary duplication, ensure complementarity, and speed progress.'
Further this second strategy states that ARS must
'Identify needs and allocate resources for new research approaches or new expertise (for example, computers and systems
science, new techniques from biotechnology, and improved
equipment and facilities) to increase the rate of progress or
probability of success.'
Strategy 3 is an explicit statement of placing 'increased
emphasis on using interdisciplinary teams for problem solving'.
This strategy is a further realization that the problems of today
and those of tomorrow are complex, and the days of uncoordinated
individual research activities that do not specifically
contribute to the solution of problems are, for the most part,
numbered. This strategy recognizes that interdisciplinary
research and coordination, while not a guarantee to success, is
likely to improve the odds considerably.
Lastly, Strategy 4 is a statement of policy to
'Continue to foster the use and development of computer-based
information and data-management systems and networks in ARS.
Communications networks allow scientists of different disciplines
and at different locations to readily share their knowledge and
their research ideas and results. These same networks are used
to transfer new technology to (agricultural) Extension and other
user agencies and will eventually deliver information and
decision support services directly to farmers and ranchers.'
Data-management systems now support the development of both
modeling and expert systems in agriculture. Computer models of
crop and animal production, soil erosion, and other systems are
becoming useful vehicles for expressing the state of the art of
many areas for scientific research. Expert systems can combine
models and data from many different disciplines and then
integrate the massive quantities of knowledge into manageable
decision support systems for users of research. Agriculture is
rapidly moving toward using both models and expert systems on the
farm, in Government, and in business transactions.
The Model and Database Coordination Laboratory (MDCL) has a
number of goals in the systems research area that follow directly
from these four strategies. The first is to act as a national
focal point in the agriculture research community to facilitate
the development of interdisciplinary research teams to work on
the solution of high priority problems. As part of this function,
the MDCL lab will foster more and better communications within
the agricultural community. To this end, the lab has become a
satellite of the NIH-sponsored BIONET system 16], and has
established electronic mail and bulletin board communication to
improve the interaction and communication in the agriculture
research community. The computer system being made available is
on the Telenet, and soon will be available on the ARPAnet, and
BITnet networks for ease of access.
The second goal is to evaluate and validate models, databases and
expert systems in agriculture. As part of this activity, the lab
is starting to develop a compute! accessible inventory database
of models, databases, and expert systems to provide a clear,
complete, accurate and up-to-date picture of existing, developing
and planned activities in these areas. The laboratory will also
work with agricultural experts to develop processes for capturing
their expertise in the form of expert systems.
The third goal of the laboratory will be to include economics
within the systems analyses, to provide a rational basis for
deciding which new technologies the agency should pursue to have
the greatest impact in solving critical national problems with
With the laboratory only a few months old, it is too soon to
tell how well these goals will be met and what adjustments will
be needed to reach them. But, we have started, and hope to have
as short a learning curve as possible.
To date we have initiated a number of efforts. To help MDCL serve
as a national focal point for the development of interdisciplinary
teams to work on high priority national needs and problems, we are
focusing on two areas. One is to facilitate the efforts of
existing Objective-6 teams, dealing primarily with national
resources conservation and cropping systems to maximize farmer's
profit while conserving resources. The other is fundamental
biology using the BIONET system to link individual scientists into
teams through the mechanism of shared database and software
In the second area, evaluation and validation of agricultural
research models and databases, the laboratory has started the
laborious task of finding, organizing, and creating a computer
readable inventory. The goal of this project is to collect the
knowledge that already exists to help researchers, as well as
administrators in determining what needs to be produced. It is
hoped that as a result of this effort resources now being spent
on duplicate activities can be allocated to more productive
projects that fill critical knowledge gaps. Further we hope to be
able to find common elements in models that can be modularized.
Some modules could then be used in more than one model. For
example, if an irrigation module for crop X is developed, tested
and made operational, it would be very useful if it could be used
for the irrigation portion of the model for crop Y. Achieving the
goal of modularized models requires a common basic structure with
clearly defined interface parameters. It also requires that
modules be based on sound physical, chemical, or biological
processes. Modularization assumes that systems are sufficiently
similar and that the same processes act. Only the environment,
system dimensions, and other factors which can be quantitatively
measured can differ between systems having the same module. As a
further party this evaluation and validation activity we hope to
develop standard sensitivity analysis tools to help determine
where more effort is required on the modules being developed and
used. Sensitivity analysis allows one to vary each parameter in
the model, one parameter at a time, and determine the relative
sensitivity of model output to incremental changes in values of
Aiding the general communication for agriculture scientists is
essential to everything the lab does to facilitate the development
of interdisciplinary teams. We plan to proceed first by establishing an electronic bulletin board, electronic mail, and a
computer conferencing capability to improve existing
communications. To this end we have initiated access to our
computer systems via a number of networks, including Telenet,
ARPAnet, and BITnet, working with the University of Maryland
Computer Center to access the latter two networks. As we all know,
communications are fundamental and critical to scientific
progress. In the old days, when there was less published, less to
learn and less to know, the classical means of communications were
generally adequate. Those included journals, presentations and
informal talks with colleagues at meetings, letter writing, and
giving seminars at universities and laboratories around the
country and around the world. Today the information explosion
makes it impractical to cope with all of the relevant information
through these means. Fortunately new technology is available to
help cope with these changes. Online information in the forms of
databases are readily (although perhaps not so inexpensively)
available. Electronic mail, electronic bulletin boards, and
computer (electronic) conferencing are being used, although not
as widely as originally expected . The reasons for this include
the proliferation of electronic mail systems which don't talk with
one another, and the lack of a critical mass of scientists in a
particular area. Some of us have been using electronic mail for
almost 20 years, while others have yet to use it. One clear major
benefit of this new direction of organizing biological information
and developing models (all of which must be computer based owing
to the vast size of the project) is that those scientists who
don't join the crowd are going to be left behind. Way behind!
Staying at the forefront of science today requires more up-to-date
information, discussions, and contacts than ever before. This need
for modern communications between scientists has led the NSF to
create still another network, the NSFnet . The need for such
a network was eloquently stated in the abstract of this multi-author (five) article:
'Scientific research has always relied on communication for
gathering and providing access to data; for exchanging information; for holding discussions and meetings, and seminars; for
collaborating with widely dispersed researchers; and for
disseminating results. The pace and complexity of modern research,
especially collaborations of researchers in different
institutions, has dramatically increased scientists' communications needs. Scientists now need immediate access to data and
information, to colleagues and collaborators, and to advanced
computing and information services. Furthermore, to be really
useful, communication facilities must be integrated with the
scientists' normal day-to-day working environment. Scientists
depend on computing and communication tools and are handicapped
Before leaving this topic it should be noted that one must be
careful not to push too much high technology. New and strong
medicine must be taken in carefully measured doses over a proper
period of time. What is needed is substance, not hype. The idea
that computer networks can replace existing forms of
communication, collaboration, and interaction must be soundly
dispelled. The personal contacts and 'office' politics that take
place at meetings cannot be replaced by a computer terminal. A
proper combination of computer networking and communications,
along with 'classical' meetings and interactions are likely to
be the best solution.
Areas of opportunities (i.e., problems)
Besides the human factors of changing attitudes and ways of
conducting research, the biggest problem we see is the lack of
databases and knowledge bases to develop, test, and validate
expert systems Bledsoe, a pioneer in the Al field, has
recently stressed this need in his presidential lecture to the
AAAI community . After talking about all the important Al
research areas of the future, Bledsoe says 'Knowledge is still
He believes that 'a large-structured knowledge base would not
only allow sharing of knowledge by numerous systems, but, if
structured correctly, could provide much more robustness and
functionality than is possible from number of distinct smaller
knowledge bases'. He also believes that such a knowledge base
must contain common sense knowledge as well as expert and encyclopedic knowledge.
The need for a 'matrix data base' as mentioned in the NlH/NAS
study will take a good deal of time and effort to develop and
maintain, and hopefully would be a step in the direction needed
to develop the master knowledge base which Bledsoe dreams about.
But almost everyone would rather develop models and expert
systems, hoping the foundation and groundwork will be done by
others. To change this view will require the proper management
understanding of the needs of this new area of endeavor. The
development of theories for whole systems requires both
interdisciplinary teams as well as interdisciplinary data and
knowledge. While models can and will help organize information
and lead to new developments, the lack of the information and
knowledge will clearly hinder practical applications of such
model development. The next big step, as NIH is planning, is to
refine the thoughts in the NIH/ NAS study, through a workshop and
define a solid segment that establishes what is needed and how
much it will cost. Then a properly funded activity to actually
initiate the research and development work necessary to carry out
a solid piece of information/knowledge base building must be
undertaken. From there we would hope the basic framework and list
of pieces of the pie that need to be put together will evolve and
be accepted for further work. However, one must be careful not
to just provide facilities. In a recent guest editorial in
Science  entitled 'Computers and interdisciplinary research',
Abelson lauds the material successes of the University of
Illinois at Urbana-Champaign. Our question to this editorial is
why is the computer resource being stressed rather than the human
resource? Computers are tools to be used, and when put first they
can distort the process of modeling or whatever the scientific
research endeavor is. One must be very concerned about how to
bring the human resource up to the speed of the computer
resource. Without proper management and direction this will not
occur in the near future.
To summarize, it is hoped that this presentation has both explained the position of, and plans of, the Agricultural Research Service in initiating new perspectives which incorporate interdisciplinary research. What is needed is the right combination of people and the right environment! It certainly promises to be an interesting couple of years ahead as ARS works toward this new area of organizing activities and undertaking research in the area of the development of new models and systems.
 Abelson, P.H., Computers and interdisciplinary research, Science, 230, Editorial (November 22,1985).
 Abelson, P.H., Instrumentation and Computers, American Scientist, 74 (1986)182.
 The mission of the Agricultural Research Service, Agricultural Research Service, US Department of Agriculture, Issued July (1982).
 Agricultural Research Service Program Plan, Miscellaneous publications number 1429, Agricultural Research Service, US Department of Agriculture (1983).
 Agricultural Research Service Program Plan, 6-year implementation plan, 1986-1992. Agricultural Research Service, US Department of Agriculture (September 1985).
 BIONET - National Computer Resource for Molecular Biology, NIH cooperative agreement #1-U41 RR01685 (3/1/84-2/28-89), IntelliGencties division, Intellicorp Inc., 1975 El Camino Real, Mountain View, CA 940402216 (415-%5-5575).
 Bledsoe, W., I had a dream: AAAI presidential address, 19 August 1985, AI Magazine 7 (1986) 57.
 Edersheim, P., Electronic mail hasn't delivered, but Backers say it is on the way, Wall Street Journal, July 23 (1985) 35 (E).
 Holden, C., A pivotal year for lab animal welfare, Science
232 (1986) 147.
 International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT), Contract #DAN-4054-C-207100, University of Hawaii, College of Tropical Agriculture and Human Resources, Department of Agronomy and Soil Science, Honolulu, HA 96822 (1985).
 Jennings, D.M., L.H. Landweber, J. H. Fuchs, D.J. Farber and W.R. Adrion, Computer networking for scientists, Science 231 (1986) 543.
 NAS, Models for biomedical research - A new perspective (National Academy Press, Washington, DC, 1985).
 Planck, M., Scientific autobiography and other papers (Williams and Norgate, London, 1950) 33-34.
 Russell, M.G., R.J. Sauer and J.M. Barnes, Eds., Enabling interdisciplinary research: perspectives from agriculture, forestry, and home economies, Miscellaneous publications 19-1982, Agriculture Experiment Station, University of Minnesota (1982).
 U.S. Congress, Office of Technology Assessment, Intellectual property rights in an age of electronics and information OTA-CIT-302, (US Government Printing Office, Washington, DC, April 1986).
 Wilkins, C.L., Hyphenated techniques for analysis of complex organic mixtures, Science, 222 (1983) 291.
 Wilkins, C.L., G. N. Giss, R. L. White, G.M. Brissey and E. Onyirinka, Mixture analysis by gas chromatography/ Fourier transform infrared spectrometry/mass spectrometry, Anal. Chem. 54 (1982) 2260.