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OCR for page 222
CHASER
Teaching Agricultural Science
as a System
Donald M. Vielor and Laurence D. Moore
C. Jerry Nelson, Rapporteur
Ten years ago, respondents to a survey by the National Higher
Education Committee ranked "food and agricultural systems analy-
sis" and "problem solving high among course areas that were not
adequately represented in agricultural curricula. The National Agri-
culture and Natural Resources Curriculum Project was organized
under the direction of Richard H. Merritt of Cook College (Rutgers
university) to respond to the National Higher Education Committee's
assessment of curriculum needs. A task force of university faculty,
the Systems Task Force, was organized in 1982 to develop curricu-
lum materials and conduct workshops that would contribute to the
teaching of systems analysis in colleges of agriculture.
The ideas about systems techniques and methodologies pre-
sented here reflect Donald Victory learning in the context of the
Systems Task Force and associated workshops and Laurence Moore's
experiences during his successful promotion of systems approaches
to agricultural production problems in Virginia. Our objective is to
present ideas and approaches to systems from our knowledge and
experience that will stimulate interaction among those who would
teach agricultural science as a system. In addition, the world view of
systems approaches presented here, particularly the Soft systems,"
provided concepts, techniques, and models of inquiry that shaped
the design of activities for involving the participants in this session.
Definitions
in order to teach agricultural science as a system, it is necessary
to define system. The term system can be used to describe a set
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TEACHING AGRICULTURAL SCIENCE AS A SYSTEM
of elements or components that are connected together to form a
whole (checkland, 1981). These components function together in
support of the objectives of the whole. This definition of system
further stipulates that the properties of the whole emerge as a func-
tion of the connections and interactions among components. The
emergent properties of the whole cannot be understood or explained
by studying the components in isolation or apart from interactions
with other components and the environment. An "agricultural sys-
tem" can be perceived as comprising interacting biological and physical
components that form a whole with emergent properties (Lowrance
et al., 1984). Connecting any group of components together does
not necessarily result in emergent properties for the whole; that is,
it does not constitute a system (Rykiel, 1984). A description of
operational units of agriculture as systems (spudding' 1979), with-
out consideration of emergent properties, is inconsistent with the
definition of system submitted here.
A "systems approach" takes a broad view that concentrates on
interactions among parts and on emergent properties of systems
that are relevant to problematic situations (checkland' 1981). The
term approach describes a way of doing. Here, doing focuses on
problems relevant to agriculture.
Models: Means or Ends?
The attention given to the development and evaluation of quanti-
tative models within agricultural disciplines and journals can con-
tribute to perceptions that techniques of simulation modeling and
linear programming equate with systems analysis and systems ap-
proaches. Rapid progress has been made in the modeling and
simulation of agricultural processes during the past 20 years. Mod-
els are available to simulate processes such as weather, hydrol-
ogy, nutrient cycling and movement, tillage, soil erosion, soil tem-
perature, and crop growth and development (Jones and Kiniry, 1986).
Models can indicate where deficiencies in current scientific knowl-
edge exist (Bawden et al., 1984). They can serve purposes of
exploration, explanation, projection, and prediction (Rykiel' 1984).
Conceptual and quantitative modeling can be useful in the prac-
tice of reductionist science and technology development in agricul-
ture. Mechanisms or technologies can be modeled apart from and
in the context of higher levels of organization in support of hypoth-
eses and experimental designs.
Since the age of Newton, reductionist science has contributed to
verification of mechanisms and models through focused inquiry
and experiments on selected parts of complex phenomena (checkland'
1981). The integration of mechanisms into biophysical models,
using the language of mathematics, can accomplish the purposes
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AGRICULTURE AND THE UNDERGRADUATE
described by Rykiel (1984). These models can represent and con-
vey the knowledge of those who built them. Yet, biophysical mod-
els may not meet the criteria set forth in the definition of system.
Emergent properties may be absent. In addition, the model may be
irrelevant to the current problems facing agriculture.
For example, models received attention during early stages of
the learning and curriculum development activities of the Systems
Task Force. Conceptual and quantitative models of different world
views of the "agricultural system" that was developed by individuals
in the group were considered among the potential materials for
teaching systems approaches. Task force discussions revealed that
each model of an agricultural system represented a simplified view
of reality that was unique to its author and to the reality it repre-
sented. Moreover, most quantitative models in the published litera-
ture have been ignored by all except the model builder and have
had relatively short lives (Rykiel, 1984). Using the definition of sys-
tem presented earlier and the experience of the Systems Task
Force, the notion equating systems approaches with modeling is
inappropriate. it may be unrealistic to expect that agricultural sci-
ence can be taught as a system through presentation and manipu-
lation of published versions of biophysical models. Whose model,
that is, whose system, will be used?
Applied Systems Analysis
The value of conceptual and quantitative models is best realized
in the context of methodologies or processes for tackling problems
and researching systems ideas. in general, agriculturalists are more
concerned with real-world applications of systems ideas to solve
problems in contexts ranging from farm to government policy levels
than they are with studies of systems ideas for their own sake.
Modeling is jUSt one stage of systems approaches to problem solv-
ing in agriculture (clayden et al., 1984). Applied systems analysis
and the associated use of computer-processed models are most
useful in settings in which goals can be specified, performance can
be monitored, and implementation can be achieved. This quantita-
tive approach evolved in the context of machine-based or hard-
ware-dominated systems. The phrase hard systems analysis has
been used to describe the approach which presupposes that a
defined need exists, in the form of a perceived difference between
a current and desired state, and that optimal solutions are both
feasible and realistic goals for an analyst working to achieve the
desired state (checkland' 1981; Naughton, 1984).
Applications of hard systems include systems engineering and
aids to decision making. Systems engineering is concerned with
conceiving, designing, evaluating, and implementing a system of
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TEACHING AGRICULTURAL SCIENCE AS A SYSTEM
interacting components that meets a specified need (Naughton, 1984).
For example, using quantitative models and simulation, a whole
system of interacting components can be designed for optimizing
the efficiency of alternative fuel production from crop biomass. Sys-
tems analysis can aid decision making through quantitative apprais-
als of the costs and consequences of alternative means of achiev-
ing the desired state or defined objective.
Systems ideas have been applied to aid producer decisions about
stocking their pastures. Computer-aided decision making can help
managers accomplish the objective of maximizing profit in an envl-
ronment of changing costs for livestock and pasture production. A
mathematical model can be constructed to describe the interdepen-
dence of stocking rate, animal- and pasture-related costs, and ani-
mal performance (victor et al., 1982). The model quantifies the
trade-off among goals for maximizing performance per animal and
per hectare and maximizing profit per hectare. What-if experiments
that use the model can assist management of the stocking rate in
support of the goal of maximizing the amount of profit per hectare
as costs change.
Clayden and colleagues (1984) have described eight stages of a
hard systems approach for achieving a desired goal. The first step
is identifying and describing the problem to be solved and the
existing system and environment. Second, the objectives of
decisionmakers and the constraints are identified in relation to the
problem or opportunity. Third, alternative routes for achieving ob-
jectives are generated and narrowed down to a set of the most
feasible options. Next, measures of performance are established
for optimization before the fifth stage of model construction. The
models serve to predict outcomes when comparing options for achiev-
ing objectives. In the sixth stage, measures of performance are
used to evaluate various routes to the objectives. in addition, the
model itself is evaluated to determine whether it is representative
of the real world. Unquantifiable objectives and constraints come
into the picture at stages seven and eight, when the best options
are chosen and implemented, respectively.
Experiential Learning
Initially, the members of the Systems Task Force lacked a com-
mon language and paradigm for learning and for thinking about and
applying systems ideas. The diverse disciplines (human ecology,
agricultural engineering, agronomy, agricultural economics, and so-
cial ecology) represented among the members were confounded
with variations in individual approaches to problems that ranged
from reductionism to holism. The fundamental epistemological and
methodological differences among disciplines and individual scien
225
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AGRICULTURE AND THE UNDERGRADUATE
fists have made it very difficult to communicate and to reach agree-
ment about the ways in which problematic situations should be
approached and students should be taught (Buttel' 1985).
A mode] of learning (Kolb et al., 1979), not teaching, was the
foundation of a common language and of paradigms for tackling
problematic situations that were learned and shared among mem-
bers of the Systems Task Force. This model of experiential learn-
ing illustrated the interplay between human experience and abstract
thinking and the roles for both reflection and action. Human activi-
ties represented in models of reductionist approaches to science
and technology development and of the steps of hard systems
analysis (Bawden et al., 1984; Wilson and Morren, l also) are analo-
gous to those of experiential learning. Similarities notwithstanding,
the activities or stages of reductionist approaches to science and
technology and of applied systems analysis are practiced at differ-
ent levels within a hierarchy of inquiry (Figure 26-1) (Bawden et al.,
1984). Differences among scientists and among students with re-
spect to the level of inquiry that each prefers within this hierarchy
are potential sources of disagreement.
Reductionist scientists may argue that knowledge and methods
for achieving the goals of agriculture will be advanced more through
studies of mechanisms that function at the cellular or biochemical
level than those that function at a systems level. Conversely, ap-
plied scientists and technologists who serve producers may view
reductionist science as too narrowly focused and discipline ori-
ented, emphasizing science without contributing to the knowledge
base of modern farming (Bradshaw and Marquart, l 990). Explora-
tion, practice, and discussion of this hierarchy of inquiry enables
learners and problem solvers to assess and compare approaches
to learning and improvement of problematic situations. The role
of biophysical and systems models at each level of inquiry is il-
lustrated above. A focus on a hierarchy of inquiry and learning
activities may be more relevant to teaching agricultural science as
a system than is a focus on a body of subject matter or on model-
ing techniques and models of agricultural systems.
Agricultural Science Under Fire
once heralded as an example of human conquest over nature.
technologies resulting from reductionist approaches to agricultural
science are now under fire from critics. The persistent search for
greater crop and livestock productivity supported by these tech-
nologies has been perceived by critics as a major source of prob-
lems facing American agriculture (Thompson, 1988). Both private
and public agents of technology transfer have been influenced by
the shift in emphasis from one of maximizing crop yields and pest
226
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TEACHING AGRICULTURAL SCIENCE AS A SYSTEM
Generate
Options
Model
System
Dentin
Objectives
Relate to
Theory
Identify
System
Validate
System
| Propose
| Solutions
Compare
Options
Implement
Option
Simplify
Problem
_.- ~
Identity
Problem
.¢
Implement
Solution
Hypothesis
Mechanism?
Unexplained I
Results
| Expenment
Publish r
_
FIGURE 2~1 Conceptual model illustrating the relationship among applied
systems analysis and reductionist approaches to science and technology
development. Source: Bawden, R. J., R. D. Macadam, R. J. Packham, and 1.
Valentine. 1984. Systems thinking and practice in the education of agricul-
turalists. Agricultural Systems 13:205-225.
227
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AGRICULTURE AND THE UNDERGRADUATE
control to one of maximizing food safety and environmental protec-
tion (Bradshaw and Marquart, 1 also). The time frame of the effort of
the Systems Task Force coincided with this shift in public concern.
As this task force considered alternative approaches to inquiry
and problem solving, the utility of reductionist approaches to sci-
ence and technology development and of hard systems analysis
were questioned, much as were the traditional goals of agricul-
ture (maximizing productivity and profitability, optimizing produc-
tion efficiency). Members of the task force, like others in agricul-
ture, were forced to examine traditional ways of teaching, learning,
and problem solving. What did agriculturalists need to do differ-
ently in practice and in the education of students to cope with the
external forces confronting agriculture? Implicit in the title of this
chapter, "Teaching Agricultural Science as a System," is the same
question.
It was the perception of the Systems Task Force that information
and technologies from levels of inquiry represented by applied sys-
tems analysis, applied science and technology development, and
reductionist science (Figure 26-1) would not satisfy critics of agricul-
ture as long as the goals and objectives of inquiry originated largely
from agricultural scientists and their clientele. To date, information
produced from these levels of inquiry has not answered accusa-
tions that the agricultural research system has failed to admit
responsibility for problems arising from agricultural technologies
and practices (Heichel ~ l 990).
The Systems Task Force was challenged to identify an approach
to inquiry and problems that would prepare agricultural graduates
to function in an environment of conflict over goals and objectives
for agriculture. The approach would need to be useful when change
was indicated, but the direction and means for change were prob-
lematic (checkland' 1981; Holt and Schoorl, 1989). The goal-seek-
ing nature of reductionist approaches and of applied systems analysis
(Figure 26-1) appeared to be an incomplete representation of the
range of human endeavor needed in agriculture. Then and now, a
systems approach that goes beyond quantifying relationships among
soil, plants, and animals is needed. Agricultural development in-
cludes relationships among people (producers, processors, and
consumers)' in addition to their natural and physical environment
(Bawden' 1 989).
Soft Systems Methodology
The ideas and methodology of soft systems offer an alternative
to goal-seeking paradigms of applied systems analysis and reduc-
tionist science and technology. Previous applications of this meth-
odology indicated that issues and concerns of participants in prob
228
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TEACHING AGRICULTURAL SCIENCE ~ A STEM
Define
System
Model
System
Assimilate
Reality
Compare
Model
with Reality
Propose and
Debate
Change
Implement
Improvement
-
Conflict
FIGURE 26-2 Conceptual model of stages of soft systems methodology.
Source: Bawden, R. J., R. D. Macadam, R. J. Packham, and 1. Valentine.
1984. Systems thinking and practice in the education of agriculturalists.
Agricultural Systems 1 3 :205-225.
lematic situations in agriculture, including critics as well as clien-
tele, could be considered in determinations of what was problem-
atic (Bawden et al., 1984; Macadam et al., l 990). During stages of
Finding out" and of Debating proposals for improvement," the soft
systems methodology facilitates self-conscious choices by partici-
pants. Those choices determine the purposes of learning and sys-
tems thinking. in contrast, an experts preconceived notion of the
agricultural system often determines what questions are asked and
what is problematic in the paradigms for applied or hard systems
analysis and reductionist approaches.
Using soft systems, the researcher and the researched, the con-
sultant and the client, and the proponent and the critic work to-
gether in a dynamic relationship to identify goals or purposes while
they collaborate to learn about the situation that they share (Bawd en '
1989). The researcher or analyst serves as a facilitator without the
pretenses of being completely objective, an expert, and detached
from the problematic situation or opportunity. The techniques and
methodology of soft systems facilitate consensus amidst the uncer-
tainty present in complex situations (checkland' 1981).
Soft systems can be modeled as a holistic approach to experien-
tial learning (Figure 26-2) (Bawden et al., 1984). This methodology
is organized into discernible stages and uses techniques that have
evolved from both practice and theory (checkland, 1981 ). The
methodology can facilitate improvements in situations where there
is conflict over what is problematic, including situations concerned
229
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AGRICULTURE AND TtlE UNDERGRADUATE
with the teaching of agricultural science. In addition, soft systems
provide a conceptual framework for researching soft and applied
systems methodologies themselves.
Mutually related judgments of reality and value can be part of the
process (vickers, 1968). While assimilating reality, a mutual appre-
ciation of values among participants from within and from the envi-
ronment of an agricultural situation can replace argument or conflict
with dialogue. Systems of human activities that "could be" are de-
fined and modeled to be relevant to the collective concerns of
participants in a problematic situation (Wilson and Morren, 1990).
A practical wisdom can arise frond the collective concerns unique
to each problematic situation as participants debate proposals for
improvement (victor and Cralle, 1990).
Soft systems provide a more holistic or higher level in the hierar-
chy of inquiry (Figures 26-1 and 26-2). This methodology provides
perspective and a clearer focus of inquiry for subtending, goal-
oriented learning at levels of applied systems analysis, applied sci-
ence and technology, and reductionist science (Bawden et al., 1984).
Subtending levels provide insights for upper levels. The learner
moves from level to level as each learning situation (i.e., problem
or opportunity) requires.
Beyon~i Lectures and Expert Advice
If each of us reflects on the way that we were taught during our
undergraduate years, we may recognize that the teacher was the
principal learner in the classroom. Teachers, like scientists, deter-
mined the focus of inquiry through their choice of subject matter
and related problems. The cognitive abilities of recall and compre-
hension were required of students to a much greater degree than
were application, analysis, synthesis, and judgment. Teachers were
similarly responsible for choosing and demonstrating those skills
for manipulating plants, animals, soil, and environment that stu-
dents should learn. Teachers were in control. Students were ex-
pected to integrate the knowledge and skills they gained from courses
in science, rhetoric, mathematics, humanities, social sciences, and
their own disciplines as they emerged into the professional environ-
ment after graduation. The relationship of student to teacher was
not unlike that of clientele to the agricultural scientist.
Recently, agricultural consultants have expressed concern that
agricultural research, the fruit of agricultural scientists, is too nar-
rowly focused and discipline oriented, often emphasizing science
and ignoring practice. Is an analogous criticism applicable to the
teaching of agricultural science? IS the subject matter of agricul-
tural science relevant to public concerns for human and environ-
mental health and agricultural sustainability Are students prepared
230
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TEACHING AGRICULTURAL SCIENCE AS A SYSTEM
to work in an environment marked by conflict among world views
represented within and outside the farm gates
The ideas and methodologies of experiential learning and soft
systems can complement the propositional (learning for knowing)
and practical (learning for doing) learning that have been empha-
sized in traditional approaches to teaching agricultural science (Bawden'
1989). Unlike propositional and practical learning, experiential learning
depends on a dynamic interplay between sensory experiences of
the world and mental abstractions (Bawden' 1989). The unique-
ness of experiences, perceptions, and conceptual thinking for each
learner suggests an approach that is learner rather than teacher
centered. Learning of agricultural science, both cognitive and conative,
will be motivated by the experiences of learners. What experiences
are currently available to undergraduates in colleges of agriculture
that motivate students to learn agricultural science?
Learner activities, both explicit and implicit, in the soft systems
methodology (Figure 26-2) illustrate what can be done to cope with
the complex issues facing agricultural science today. This holistic
approach presents a role for the learner that differs from the pur-
portedly detached and objective role of the scientist. The roles of
the values and perceptions of the learner are acknowledged. Stu-
dents conceptualize and learn with other players in agriculture in
response to problems and opportunities unique to each new situa-
tion. Practice of the soft systems methodology in today's agricul-
ture will stimulate students to seek and learn agricultural science
that is relevant. Should one goal of curriculum reform be to teach
agricultural science as a system? Or, should it be to encourage a
systemic approach to inquiry that facilitates learning of agricultural
science?
References
Bawden, R. J. 1989. Towards action researching systems. First Interna-
tional Action Research symposium, March 2~23, 1989, Bardon, Queens-
land, Australia.
Bawden, R. J., R. D. Macadam, R. J. Packham, and 1. Valentine. 1984.
systems thinking and practice in the education of agriculturalists. Agricul-
tural systems 13:205-225.
Bradshaw, D. E., and D. J. Marquart. 1sso. New age professionals for a
new agricultural age. Agrichemical Age (May):24-25.
Buttel, F. H. 1985. The land-grant system: A sociological perspective on
value conflicts and ethical issues. Agriculture and Human Values 1 1:7
95.
Chec~and, P. B. 1981. Systems Thinking, Systems Practice. Chichester,
United Kingdom: Wiley.
Clayden, D., J. Hughes, L. Jones, and J. Tait. 1984. The Hard systems
Approach: systems Models. Milton Keynes, United Kingdom: The
Open university Press.
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OCR for page 232
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Heichel, G. H. 1990. Communicating the agricultural research agenda:
Implications for policy. Journal of Production Agriculture 3:2~24.
Holt, J. E., and D. Schoorl. 1989. Putting ideas into practice. Agricultural
Systems 130: 155- 171.
Jones, C. A., and J. R. Kiniry. 1986. CORES Maize: A Simulation Model of
Maize Growth and Development. College Station: Texas A&M Univer-
sity Press.
Kolb, D. A., L. M. Rubin, and J. M. McIntyre. 1979. Organizational Psychol-
ogy. An Experiential Approach. Englewood Cliffs, N.J.: Prentice-Hall.
Lowrance, R., B. R. Stinner, and G. J. House. 1984. Agricultural Ecosys-
tems: Uniting Concepts. New York: Wiley.
Macadam, R.,1. Britton, D. Russell, and W. Potts. 1990. The use of soft
systems methodology to improve the adoption of Australian cotton grow-
ers of the Siratac Computer-Based Crop Management System. Agricul-
tural Systems 34:1-14.
Naughton, J. 1984. Soft Systems Analysis: An Introductory Guide. Milton
Keynes, United Kingdom: The Open University Press.
Rykiel, E. J., Jr. 1984. Modeling agroecosystems: Lessons from ecology.
Pp. 157- 178 in Agricultural Ecosystems: Unifying Concepts, R. Lowrance,
B. Stinner, and G. House, eds. New York: Wiley.
Spedding, C. R. W. 1979. An Introduction to Agricultural Systems. Lon-
don: Applied Science Publishers.
Thompson, P. B. 1988. Ethical dilemmas in agriculture: the need for
recognition and resolution. Agriculture and Human Values V:4-15.
Vickers, G. V. 1968. Value Systems and Social Process. New York:
Basic Books.
Victor, D. M., and H. T. Cralle. 1990. Comparison: Stage 5 of the soft
systems approach. Systems Approaches for Improvement in Agricul-
ture and Resource Management, K. Wilson and G. E. B. Morren, Jr., eds.
New York: Macmillan.
Vietor, D. M., R. M. Rouquette, Jr., B. E. Conrad, and M. E. Riewe. 1982.
Computer aided instruction: An economic analysis of pasture manage-
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Wilson, K., and G. E. B. Morren, Jr. 1990. Systems Approaches for
Improvement in Agriculture and Resource Management. New York:
Macmillan.
RAPPORTEUR'S SUMMARY
The use of a systems approach for teaching agricultural science
was appropriately introduced by Laurence Moore and Donald victor.
Moore reminded the participants of the discussion group that teach-
ing covers a spectrum from disciplinary approaches, which are usu-
ally unilateral in terms of input, to holistic approaches, which de-
pend on group interactions and problem solving. He effectively
challenged the participants to think broadly in terms of the problem-
solving method, and especially the use of a soft systems approach
to education.
Many members of the discussion group were not fully acquainted
with the hierarchy of systems approaches, and thus, it was defined
232
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TEACHING AGRICULTURAL SCIENCE AS A SYSTEM
in terms ranging from the reductionist approaches typical of those
used by the practicing researcher to the open, participatory ap-
proaches involving students from a variety of disciplines. The four
tiers of teaching or learning technology were described as follows:
1. The scientific method, which is a strongly reductionist ap-
proach. it is a well-accepted method for generating technology.
2. The application of technology, which expands the use of sys-
tems approaches. This requires an understanding of the technol-
ogy and creative insight to visualize situations for its application.
3. The hard systems approach, which is frequently associated
with mathematical models and model building. In this case, a de-
sired change can be described and inputs or outputs can be calcu-
lated or understood. This approach has a great deal of quantifiable
input and output, but the inputs and decisions are generally from
one individual.
4. The soft systems approach, which is more conceptual and
does not depend on mathematical models. It involves input from
several individuals, often in a group setting, to achieve a desired
outcome. The science base along with humanistic implications and
social values are expressed and integrated into the outcome during
the decision-making process.
One can visualize the hierarchy as (step 1) a scientist who deter-
mines that the yield of corn responds to nitrogen application be-
cause enzymes convert nitrate from the soil into ammonia and the
amino acids that are assembled into the proteins needed for me-
tabolism and growth. Others use that information (step 2) to learn
that the efficiency of the response of corn is altered, depending on
when and in what form the nitrogen fertilizer is applied, or that
wheat yield is also increased by nitrogen application. The hard
systems approach (step 3) would evaluate quantitatively the fertili-
zation practice in terms of the nitrogen cycle, plant uptake, and soil
losses as affected by crop rotation, dates of planting, and other
agronomic practices. Specific goals from nitrogen application such
as maximizing economic return, minimizing nitrate in groundwater,
or decreasing weed infestation in subsequent crops can be evalu-
ated mathematically by using the model. The soft systems ap-
proach (step 4) would add other dimensions; for example, how
would the alteration of fertilization practices influence the local economy,
the effectiveness of the school system, the quality of the public
water supply, the abundance of wildlife, or the visual appearance
of the landscaped Many of these latter outcomes are not quantifiable
determined; rather, they are value judgments often made by indi-
viduals who are not directly involved with the nitrogen decision-
making process. Thus, a soft systems analysis would be based, at
least partially, on a broad range of inputs, albeit with variable strengths
233
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AGRICULTURE AND THE UNDERGRADUATE
or impacts, regarding the correct nitrogen decision for the total
system.
To introduce systems concepts to my undergraduate students I
use the analogy of a partly cloudy day, that is, scattered cumulus
clouds floating gently overhead while being surrounded by blue
sky. Each cloud represents a cluster of knowledge or the technol-
ogy of a discipline or subdiscipline. one of the objectives of a
learner in problem solving is to read the clues or technical inputs
contained in each of the several clouds of knowledge and then to
integrate them, in effect to coalesce the clouds into a more dy-
namic set of interacting technologies. A major effort in learning is
to be able to anticipate, determine, and evaluate the linkages, that
is, the relationships between steps 1 and 2 above. The application
of mathematical formulas to quantify the relationships moves us
to step 3.
The other example I use is the spider web, with its intricate
interwoven network of slender threads (or a set of elements that
are connected together) that forms a whole. Pressing on one inter-
section of the web causes it to move, but every other intersection
also moves, with the actual movement (impact) being dependent
on the distance from the pressure point. The challenge for the
students is to define the factors that are affected by a given deci-
sion (nitrogen fertilizer rate) and assigning each factor to a location
relative to the pressure point. in a limited soft systems manner,
the backgrounds and perspective of the individuals in the class
are reflected through the selected (defined) input and outputs for
the "decision" and, especially, the distance (relative strength) that
each one is placed from the origin or pressure point within the
web.
The steps in systems analysis are to analyze, synthesize, judge,
and apply. On the basis of this sequence, the participants in the
discussion session were divided into six groups for a discussion of
the issues raised by Donald Victor and Laurence Moore and their
concerns about the systems approach, that is, analysis, or step 1.
The factors reported back by one or more groups included the
following:
1. The systems approach adds relevance to the reasons for why
things are learned and, in fact, helps to define science in a broader
perspective.
2. The systems approach provides a sense of input and leader-
ship for students, and students develop confidence in problem
solving, with more of a focus on group rather than individual deci-
sion making.
3. The systems approach helps to bridge the training and educa-
tion relationship, especially the balance between gaining knowl-
edge and understanding the approach to knowledge.
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TEACHING AGRICULTURAL SCIENCE AS A SYSTEM
4. The systems approach may cause students to act more as
generalists in their approach rather than as specialists, which
would occur when they are focused through a specific discipline.
5. Less emphasis on a discipline may reduce the amount of
cognitive material that can be covered in the curriculum, but not all
courses would need to use systems approaches.
6. Students may be more prepared for the systems approach
than are the faculty. More faculty time would be required to de-
velop objectives and a format for teaching the systems approach
than for traditional lectures.
7. Many classes are large, which may lead to compromises in
teaching approaches, but most groups acknowledged that there
are probably some innovative ways around this problem.
8. Faculty, in general, are reductionist. A challenge will be to
find receptive faculty who can be motivated and rewarded for refocus-
ing on the systems approach.
The groups then discussed what could be done to accomplish
more systems approaches in student learning, that is, synthesis
and application, or step 2. The factors reported back included the
following:
1. Identify faculty who will be pioneers in teaching innovation
and who will do it well. There will be needs for special training and
opportunities for faculty to gain experience. Faculty determine the
content and format of the curriculum, but administration needs to
persuade, facilitate, and reward innovative and effective ways of
presenting the curriculum.
2. Administrators and faculty need to recognize that the student
body is changing and that there is a need to define in the curricu-
lum the amount of effort to be devoted to systems approaches.
Also, graduate programs or other advanced technical programs
traditionally build strength in specific disciplinary areas.
3. Professional societies need to be involved and can provide
leadership. Although they are discipline oriented, the societies di-
vide the infrastructure of individual institutions into a matrix
to allow communication among faculty with common missions
and perspectives. Societies also constitute a critical peer group
beyond an individual campus.
4. Faculties must openly address the relationships among gen-
eral education, professional education, and disciplinary specializa-
tion, especially with the goals of teaching students to think and
interact in the process of lifelong learning.
5. Curricula and pedagogic approaches need to facilitate and
effectively move more disciplines together so that they can be-
come adopted by faculty and so that faculty can have a sense of
ownership or belonging to a broader-based curriculum.
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AGRICULTURE AND THE UNDERGRADUATE
6. Faculty development is critical. There is a need to develop
and support multidisciplinary efforts or retreats to gain faculty, stu-
dent, and administrative perspectives on innovative approaches to
the importance of teaching. Then, support mechanisms need to
exist for experimentation and implementation of new methodolo-
gies.
7. Team teaching may help to develop the transition to systems
approaches and solve short-range problems, but it adds complexity
and does not address many of the real issues involved.
In a subtle way, the groups responded in a systems methodol-
ogy through steps 1 and 2. Time limitations prevented comprehen-
sive input and evaluation, however, and the groups were too large
to have the proper discussion needed for systems evaluation. As
in true student learning situations, some groups were dominated
by strong individuals, and some individuals did not actively partici-
pate or offer input. Despite these limitations, the group reports
contained a considerable overlap of outcomes, yet each report had
a distinctive personality that reflected the makeup and background
of the individuals who participated. One group, perhaps largely
unknowingly, even treaded into developing a model that could be
judged or evaluated, that is, step 3 of the systems approach.
In summary, the groups were able to use the initial steps of a
systems approach in considering the use of a systems approach.
The exercise helped the individuals to recognize the strengths and
weaknesses of systems approaches and gave them a glimpse of
how students may respond or interact in the analysis and synthesis
settings. Above all, however, the presentation and discussion helped
the audience gain a deeper appreciation for systems technologies
and methodologies and how they can facilitate the teaching of agri-
cultural science.
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Representative terms from entire chapter:
teaching agricultural
