1. Overview + Introduction to systems IPM

· The role of systems analysis and models in IPM, historical trends, objectives of IPM models, example applications

· Types of models

1. Conceptual, soft systems and qualitative models

2. Database models

3. Heuristic models, expert systems and other rule-based models

4. Statistical (e.g. multivariate regression) models

5. Analytical/mathematical models

6. Simulation models

7. Optimization models

· Fields of study related to system modeling for IPM

Entomology, Horticulture, Crop Science, Plant Pathology & Nematology

Quantitative Ecology

Statistics, Numerical analysis methods

Computer science

Mathematical modeling

Simulation science

Agricultural Economics

Operations Research

Systems Analysis

General Systems Theory

· Approaches to modeling

While other approaches will be introduced briefly, a strategy of model construction will be detailed that includes emphasis on meeting specified objectives, including feasibility design and project cost estimation, involvement of end-users in the modeling process, and appropriate implementation technologies.

· A simple modeling paradigm: cause and effect relationships

· A basic IPM decision model

· Descriptive properties of models:

1. realism

2. generality

3. precision

4. accuracy

5. adequacy vs. correctness

6. deterministic vs. stochastic models

7. static vs. dynamic models

8. simple vs. complex models
 

· Typical questions generally asked of IPM models:

- Will the pest occur here this year? (climatological forecasting, dispersal, landscape ecology)

- Is the pest organism really a problem? (crop loss assessment)

- When will a pest reach a certain developmental stage? (phenology, meteorology)

- If and when will a pest attain some critical population level? (population dynamics, biological control)

- When should management tactics be applied? (toxicology, economic thresholds)

- How should insecticide resistance be managed? (toxicology, resistance monitoring)

- Can the pest be eradicated at least locally until re-invasion occurs? (quarantine technology, total population control, areawide control)
 

2. Systems concepts for IPM

· System definition

General systems theory concepts

Definitions:

system, subsystem: a set of related entities, a subset of related entities that can be examined partly in isolation from other subsystems and from the entire system.

model, submodel: a simplification or abstraction of the real world system, a portion of a larger model that has value or some reason to consider in isolation.

modular: a property of systems: they can be decomposed into subsystem.

hierarchical: a property of systems: they are multi-layered and multi-leveled, such that systems have subsystems which themselves have subsystems, ad infinatum.

resolution (spatial and temporal): usually varies with subsystem level; often higher level subsystems treated at a coarser resolution than lower level system.

variables:

input - entities not affected by the subsystem of study (usually set by the higher level subsystem).

state - entities affected within the subsystem of study.

output - entities resulting from subsystem processing, usually impact other subsystems or one of objective questions.

independant (also cause or stimulus) - see input variables

dependant (also effect or response) - see output variables

coupling - variables identified that link or associate two or more subsystems.

parameters: constants (usually) or variables used in equations that determine state or output variables; same as coefficients.

Systems model properties:

1. the model will be hierarchical and modular

2. each system and subsystem is conceptualized and modeled in two forms:

a) according to its holistic behavior "more than the sum of the parts"

b) according to its structure as a coupled collection of subsystems "sum of the parts"

1. Modularity of subsystems can be achieved by careful identification and description of the coupling (linking) variables between the subsystems. With this in mind, a particular subsystem can be expanded or reduced in resolution without changing the resolution of the rest of the model, or the coupling variables with other subsystems.

2. Because of modularity, a subsystem can be studied in isolation or in combination with other compatible parts.

· Strategies of model construction

Definitions:

Strategies vs. tactics - generally, strategies are long range approaches and tactics are short-term approaches to solving problems.

Algorithm - a finite process for solving a problem, a specified set of steps to perform some task; pseudocode is an algorithm that can be understood by non-programmers and used by programmers. Pseudocode, flowcharts, outlines, and Warnier-Orr diagrams are all ways to help organize problems and express algorithms.

Decomposition - the process of structuring a problem by reducing a model and its objectives into submodels and their associated sub-objectives. Each objective (and sub-objective) must have some important function that contributes to solving the problem, and be clearly associated with specifications that assure that that function can be performed, in order to satisfy that objective. In other words, objectives must be both achievable and provide a role in the model.

Stepwise Refinement - a principle technique used for structured thinking/programming, could be called "divide and conquer", similar to pure reductionism. It is the process of decomposing a problem by:

1) splitting the problem into a reasonable number of steps (3 to 7),

2) working on each step (specify or describe the step), and

3) if a step is too complex, go to 1).

Top-down design - starting with the higher-level more general concepts and proceeding to more detail afterwards. Design is usually best done top-down.

Bottom-up design - starting with the detailed parts and generalizing afterwards. Execution is usually best done bottom-up.

Warnier-Orr diagram - a diagrammatic method for performing top-down stepwise refinement and developing algorithms, where brackets are used to reduce a single complex action into a set of simpler actions.

Equation - a statement of equality, used to describe a function. Equations have input variables (arguments) and output variables (values).

Paradigm - a body of definition, belief, and fact associated with some area of science or metascience. A supermodel.

Strategy of Model Construction Outline:

1. Specify the model objectives as a list of model specifications

2. Identify submodels and subobjectives

3. Construct and validate submodels

4. Assemble the submodels into the complete model, and validate

5. Seek answers to the objective question

6. Examine the general behavior of the model: identify the structure

and parameters that are causal for the behaviors of interest

7. Validate those causal structures and parameters

Developing models - Specifics:

1. Objectives

-Identify context of the target system

-Goals vs. objectives vs. purpose vs. tasks, etc.

-Making objectives explicit, clear, achievable specifications

2. Subsystem structure

-Relation between submodels and sub-objectives

-Decomposition is the identification of parts needed to make up higher level parts

-How to determine when further decomposition is needed

3. Construction and Validation

-Iterative nature of model construction:

    1. Construct a model and generate output. Go to 3.

    2. Revise the model and generate output. Go to 3.

    3. Test against the validity criteria. Stop or return to 2.

-Validation: standards were set up during model specification

-Validation: region of prescribed vs. predicted regions

4. Summary points

-The model will be hierarchical and modular

-Each system and subsystem are conceptualized and modeled in two ways, according to holistic behavior and according to its structure as a collection of interrelated subsystems

-Modularity is achieved by identification of the coupling variables and the functional relationships between them.

-Any subsystem should have independent resolution versus other subsystems.

-So any subsystem should be readily modeled and studied independent to other subsystems, or as coupled to another subsystem.

5. Four ways to define and view data (each dependent on previous)

1) By knowing what variables are involved - input, output and state variables, and their resolution (analogy: database format) question asked: What are the kinds of data?

2) By the variations or activities of the variables (analogy: database filled with data; the dataset) question asked: What are the values of the data over time?

3) By the behavior of the variables; rules describing the activities (analogy: validated regression model; knowing the functional relationships between the variables) i.e. Why do the data behave in the ways they do?

4) By the structure of the subsystems; how they are linked (analogy: validated systems model) i.e. How does the system really work?

References: Overton 1981, handouts

3. Problem Solving Techniques:

·  Scientific method, relation to modeling

· Systems theory

· Flow charts and other conceptual modeling techniques

Classroom exercise 1: flow charts used in conceptual modeling (a target problem area to be individually selected by each student).

· Top-down design

· Stepwise refinement

· Warnier-Orr Diagrams

Classroom exercise 2: Warnier-Orr diagrams (examples plus a target problem area to be individually selected by each student).

References: Orr 1977, Warnier 1976, various handouts.
 

General Class/related References:

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Overton, W.S. 1981. A strategy of model construction. In: Hall, C.A.S. and J.W. Day. Ecosystem Modeling in Theory and Practice. J. Wiley and Sons. NY. pp50-73.

Warnier, J.D. 1976. Logical construction of programs. Van Nostrand Reinhold Co. NY.

Arlinghaus, S.L. 1994. Pracitical Handbook of Curve Fitting. CRC Press, Baton Raton, FL. 272 pp.

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