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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 222
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 223
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 224
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
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
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
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
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
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
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. 231
AGRICULTURE AND THE UNDERGRADUATE 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- ment. Journal of Agronomic Education 11: 17-21. 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
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
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. 234
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. 235
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. 236
