• Introduction to Programming ( ITM 511 )
• Modelling of System Dynamics ( ITM 512 )
• Decision Support System for
  Natural Resources Management ( ITM 613 )
• Computer Simulation Modelling in Agriculture ( ITM 614 )
• Fisheries Modeling ( ITM 615 )
• Hydrological Modelling ( ITM 616 )
• Geostatistics ( ITM 617 )
• Modelling in Animal Industry ( ITM 618 )
• Modelling in Forestry ( ITM 619 )

Introduction to Programming ( ITM 511 )

A. Introduction

1. Formal problem solving
2. Programming languages

B. Program Development

1. Flow chart
2. Program construction

C. Programming Languages

1. BASIC
2. Visual Basic

Lectures/Assistants : IPB, Royal Roads

Modelling of System Dynamics ( ITM 512 )

A. Introduction

1. Definition
2. Advantages and disadvantages

B. Types of Models

1. Purpose
2. Method of implementation

C. Introduction to dynamic simulation models

1. Reasons for using models
2. Software
3. Software

D. Model building

1. Model conceptualization
2. Model building

E. Model building details

1. Model structure
2. Additional modelling techniques

F. Module testing

1. Test of model’s assumption
2. Sensitivity analysis

G. Examples of application in natural resource management

H. Using models for policy development

1. Reasons for failure
2. Steps to success

Lectures/Assistants : IPB

Decision Support System for Natural Resources Management ( ITM 613)

A. Introduction

B. Types of tools

1. Expert systems (ES)
2. Simulation models (SM)
3. Geographical Information System (GIS)

C. Integrating ES, SM, and GIS to Decision Support System (DSS)

1. Problem identification
2. Design of DSS
3. Data generation and collection
4. DSS development
5. Testing of DSS

D. DSS Case Studies

Lectures/Assistants : IPB, Royal Roads

Computer Simulation Modelling in Agriculture (ITM 614)

A. Introduction

1. Types of models used in agriculture
2. Advantages and limitations of models for agricultural planning

B. Forrester diagram

1. Boundary
2. Variables
3. Parameters
4. Flow of mass and information
5. Source and sink

C. Environmental variables and parameters

1. Soil characteristics
2. Weather variables

D. Developing agricultural simulation model

1. Goals
2. Definition of inputs and outputs
3. Model structure
4. Water balance
5. Crop phenology and growth
6. Nitrogen balance
7. Sensitivity analysis, calibration and validation

E. Case Studies

Lectures/Assistants : IPB

Fisheries Modeling ( ITM 615 )

Mathematical description and analysis of ecological systems and their relationship to fish and fisheries, system approaches using matter and energy flow models for quantifying and analyzing interdependence and dynamics in ecosystem and fisheries, linear flow models, dynamic nonlinear models, logistic growth models, and predator-prey model. Computer techniques for modelling are some of the topics to be discussed in this course. STELLA, ECOPATH, and Network Analysis will also be introduced.

Lectures/Assistants : IPB

Hydrological Modelling ( ITM 616 )

A. Introduction to hydrology modelling:

1. Hydrology cycle (at land phase)
2. Application of systems theory
3. Systems identification
4. Application of hydrology model

B. Classification of hydrology model, type of systems and process or variables; degree of causality; deterministic and stochastic models

C. Time-Space discretization; linear and non-linear system

D. Fundamental of deterministic hydrology model

1. Linear system theory
2. Response functions of linear systems convolution,
3. Main method for response function calculation.
4. Examples : SCS and HEC-1models

E. UH; derivation and application; synthetic UH

F. Conceptual models for linear time-variant system

1. Linear storage system
2. Linear cascade system
3. Complex concept model
   Examples : ANSWERS, TOPMODEL, TOPOG

G. Basic structure and requirements of deterministic catchment simulation model

H. Composition of integrated hydrology model for river catchment system

I. Assessment of human influence of land hydrology regime

J. Application of GIS in Hydrology

K. Evaluation and availability of hydrology models

L. Models for planning and management

Lectures/Assistants : IPB

Geostatistics ( ITM 617 )

A. Introduction

Geostatistics, problems, prediction with limited information, design of monitoring networks

B. Variograms and covariance functions

1. Basic concepts and quantities
2. Features of variograms
3. Determination of variograms

C. Best linear unbiased estimation (Kriging)

1. Basic concepts
2. Setting up kriging systems
3. Computing MSE estimation

D. Kriging Systems

Point, block, gradient kriging nugget variability

E. Testing model validity

1. Cross validation techniques
2. Testing residuals
3. Automated model calibration validation

F. Geostatistics and Natural Resources Management Models

1. Monte Carlo technique
2. Linearization technique
3. Geostatistics solution of inverse problem
4. Propagation of uncertainty
5. Realibility of prediction by models

H. Assignments include topics relevant to land, soil survey & mapping, and spatial modelling

Lectures/Assistants : IPB

Modelling in Animal Industry ( ITM 618 )

Modelling of animal industry at the farm level (herd) includes complex system of genetics, management and nutrition. The complexity of animal industry is modelled taking into account variables related to genetic factors and environmental components of stochastic and deterministic effects. Animal husbandry related to global ecosystem with sustainability of animal industry is discussed. The interdependency among variables is approached by linear and non-linear models; deterministic and stochastic models; and a systems approach. Discussion will be stressed on the many parameters affecting animal industry at the farm level as well as the population level.

Lectures/Assistants : IPB

Modelling in Forestry ( ITM 619 )

A. Introduction

1. Ethics in modelling
2. Theory and Concepts of modelling in forestry
3. Why use modelling for forestry management

B. Model Construction

1. Conceptualization
2. Assumptions
3. Parameteterization
4. Choice of Equation

C. Model Evaluation

1. Sensitivity analysis
2. Model validation
3. Model verification

D. Forest Models

1. Growth and Yield Models
   (COHORT, Yield, and Matrix Models)
2. Gap and Succesional Models
   (JABOWA, KLAMBRAM, FORET, MUSE)
3. Compartment Models
   (G’Day, Century, Formix)
4. Ecophysiological Models
   (N-Bals, WaNulcas)
5. Spatial/Landscape Model

Lectures/Assistants : IPB