I. Catalog Description
5984: Systems Approaches to Agroecology
A quantitative and qualitative approach to understanding agricultural ecosystems. Includes mathematical, statistical, and computer-based approaches to the study of insect ecology and agricultural problems. Complex systems will be studied through graphical, mathematical, and computer models, and will be analyzed using stability , sensitivity , and time-series analysis, and spatial statistics. Applications to integrated pest management through optimization and knowledge-based systems will also be discussed.
Pre: Ent 5314. Rec: Ent 6254. (2H, 3L, 3C) I.
II. Course Status
Taught once as 5984
III. Prerequisites and Corequisites
Students should be familiar with basic principles of insect ecology (Ent 5314), especially with elementary population growth, population regulation, trophic interactions, and host-plant relations. Students would also greatly benefit from a working knowledge of insect modeling techniques (Ent 6254) because this course will cover the integration of insect models with plant and crop management models, and the subsequent use of these models in analysis and decision making. First year calculus is highly recommended.
IV. Justification
Since the 1960's and particularly during the 1970's and early 1980's entomologists have been leaders in the application of the principles of systems analysis to production agriculture. Insects are herbivores (both pestilent and beneficial), predators, parasites, and hyperparasites in agricultural systems. To fully understand the entomological component of agriculture, entomologists must study everything from the complex communities that make up agricultural ecosystems to the various management practices and disturbances imposed on these systems by farmers and the weather.
Simulation modeling has been a fundamental tool used to grapple with the complexity of agroecosystems. However, helping farmers make sound management decisions requires the integration of models with optimization and other heuristic techniques to develop reliable pest, crop, and farm-level strategies and tactics.
The fields of mathematical modeling, numerical simulation, operations research, decision theory, and knowledge-based systems--here grouped under the heading, systems analysis--provide the foundation for all work in agricultural information sciences. Entomologists, because of the unique position of insects in the heart of agroecosystem dynamics, need to be familiar with these techniques in order to become leaders in the next generation of extension specialists, teachers, researchers, and administrators.
Current courses in the Entomology curriculum establish the necessary basis for students interested in pursuing the application of systems analysis to problems in production agriculture. The population modeling course (Ent 6254) teaches students how to represent biological phenomena using mathematical equations, for example, but it is limited to single-species systems in uniform environments. There is currently no course that explains how a model can be used once it exists, or that would guide students in the choice of an appropriate model type from the perspective of the problem needing to be solved.
Likewise, while there is a course in the College of Agriculture that introduces students to principles of knowledge-based systems (KBS) (AGEC 5154), no course currently examines the appropriate uses for KBS in combination with other quantitative and analytical techniques.
This course will focus on problems from production agriculture, how to decompose these complex problems into manageable components, how to use models to understand the problem, and how to select and apply appropriate problem solving approaches (involving models, KBS, and analysis).
V. Educational Objectives
Upon successful completion of this course, students will be able to:
1. Analyze the quantitative behavior of complex systems.
2. Apply structured methods to decompose difficult problems into manageable
systems.
3. Choose appropriate modeling techniques, quantitative and qualitative, with
which to synthesize
VI. Instructor
Associate Professor Nicholas D. Stone (1-6885)
VII. Texts and Special Teaching Aids
No specific text exists for this course. Readings will be taken from the following texts and may be supplemented by topical journal articles from the current literature.
Berryman, A. A., Population Systems. Plenum Press, New York, 1981.
Curry, G. L., and R. M. Feldman, Mathematical Foundations of Population Dynamics, Texas A&M University Press, College Station, 1987.
Hazell, P. B., and R. D. Norton, Mathematical Programming for Economic Analysis in Agriculture, Macmillan Publishing Company, New York, 1986.
Law, A. M., and W. D. Kelton, Simulation Modeling and Analysis. McGraw-Hill, New York, 1982.
Penning de Vries, F.W.T., and H. H. van Laar (eds.), Simulation of Plant Growth and Crop Production. McGraw-Hill, New York, 1982.
Plant, R. E., and N. D. Stone, Knowledge-Based Systems in Agriculture. McGraw-Hill, New York, 1991.
In the laboratory, students will use the Stella(TM) graphical systems analysis package on Macintosh computers (trademark of High Performance Systems, Lyme, New Hampshire).
VIII. Syllabus
Percent of Course
Lecture
1. Describing complex systems (15%)
2. Basic concepts of modeling complex systems (15%)
3. Building models with examples (30%)
4. Model refinement and validation (20%)
5. Complex problem solving (20%)
Laboratory
1. Introduction to structured representations of systems (10%)
2. Analysis of time series and spatial data (25%)
3. Systems modelling examples using Stella(TM) (25%)
4. Optimization/Decision making techniques (15%)
5. AI methodology in systems analysis (25%)
IX. Evaluation
Students will be graded based on a midterm and final exam, as well as on five laboratory exercises, and a laboratory final. Grades will be determined based on the following weighting of these evaluations:
Lecture (60% of grade) % of Section % of Total
Laboratory (40% of grade)