Jingjing Liang 08/2006
NRM 410 Numerical Methods for Natural Resources Management
Course Syllabus
Course Information:
NRM 410 Contemporary Natural Resources Management Methods, 4 credits
Prerequisites: none
Location: to be determined
Time: Lectures 10:00am to 12:00pm, Tuesday and Thursday
Laboratories 2:00pm to 5:00pm, Thursday
Instructor:
Dr. Jingjing Liang, 317 O’Neill, 474-1831, j.liang@uaf.edu
Supplementary Readings:
Buongiorno, J; Gilless J.K. 2003. Decision Methods for Forest Resource Management.
Academic Press, Oxford, UK.
Course Description:
This course teaches the most up-to-date numerical methods for natural resources
managers and researchers. Lab sessions (1 credit) cover important computer skills to help
students excel in modern natural resources management.
Course Objective:
The course is directed towards senior undergraduate or graduate students who are
interested in natural resources studies, including students in forest sciences, soil sciences,
plant and animal sciences, and geography. This course covers three essential methods for
modern natural resources studies: modeling, programming, and optimization. To make it
more practical and less difficult for students, NRM 410 focuses on the application of
those numerical methods instead of theories.
Outcomes:
1. Formulate research questions, refine topics, develop hypothesis
2. Evaluate sources critically, discern the strength of evidence and arguments, determine
credibility, and identify potential bias and overall quality.
3. Use statistics, simulation, and optimization as tools to address various natural
resources management problems and challenges.
Grading:
Students’ level of performance will be evaluated with letter grades:
A+(99+), A(95-98), A-(90-95), B+(85-89), B(80-84), B-(75-79), C+(70-74), C(65-69),
C-(60-64), and F(59-).
Lab reports (20%)
Three In-lecture quizzes (5% each, 15% in total)
Midterm exam (30%)
Final exam (35%)
Jingjing Liang 08/2006
Tentative Course Calendar:
1. Introduction to system optimization with linear programming (Chapter 1-3)

Raising cattle or growing trees?
Lab 1: Introduction to linear programming and Solver software
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
Graphic solution of linear programs.
Duality and interpretation of shadow prices for resources valuation.
Lab 2: Graphic analysis of multiple-use problem, and Solver solution.
2. Application of linear programming to even-aged forests (Chapter 4-9)

Converting southern hardwoods to regulated pine plantations.
Lab 3: Wildland restoration planning with linear programming

Insuring sustainability with area and volume control.
Lab 4: Forest area and volume control.

A dynamic model of forest management.
Lab 5: Managing for a diverse landscape and economic implications.
Lab 6: Maximizing old growth while maintaining community stability.
Midterm Exam
3. Multiple-objective resource management with goal programming (Chapter 10)


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Goal versus linear programming.
Choice of cardinal and ordinal weights in goal programming.
Application to a multiple-use forest management problem.
Lab 7: Weighting timber production against erosion, fire, and cost.

Approximating sustained yield with goal programming.
Lab 8: Creating the forest you want, or nearly so.
4. Planning with integer variables (Chapter 11)
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
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Minimum-spanning tree: building a minimum-disturbance road network.
Assignment models.
Forest planning involving multiple-use and road building.
Jingjing Liang 08/2006
Lab 9: Building just enough roads, economically.
5. Scheduling projects with PERT/CPM methods (Chapter 12)
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
Scheduling the purchase of a marsh with a network model.
Calculation of starting and completing dates with PERT.
Lab 10: National Park planning with PERT.
6. Simulation models for resources managers (Chapter 15)


Deterministic simulation of plant and animal populations.
Simulation with random events.
Lab 11: Effect of fire risk on the economic rotation of plantations.
Exam Week: Final Exam
Jingjing Liang 08/2006
Notes and Advice:
1. Attendance is not mandatory, but highly recommended. Quiz might be given during a
lecture and its outcome will influence the final score.
2. To receive full-credit on an exam or homework problem you must justify your answer.
A simple conclusion or numerical answer may receive no credit.
3. Notes or the text are not allowed for exams. Exam computations are to be done using
definitions (some “calculation formulas” exist - these will not be used on exams).
4. Take lecture notes and complete the details.
5. Write down what you’ve learned and do problems. For some people working with one
or two others is helpful. Reading lecture or text material over and over is not of much use.
6. Study regularly. Statistics is best learned in smaller doses (it is also more palatable this
way). Not sleeping for nights before an exam is an inefficient way to learn Statistics.
7. Exam problems will resemble homework problems. To gain experience on working on
problems with a real time constraint practice on 5 or 6 homework problems you haven’t
done for awhile, using the same time limit as the real exam.
8. The instructor will work with the Office of Disabilities Services (203 WHIT, 474-7043)
to provide reasonable accommodation to students with disabilities.