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Unit outline_

AREC3005: Agricultural Finance and Risk

Semester 1, 2022 [Normal day] - Remote

Agricultural production is typically risky, adding complexity to decision analysis and increasing need of risk consideration in agricultural policy design. This unit explores this theme, and has two related components: risk and risk management in agriculture, and issues of agricultural producer finance. These two components cover a broad range of topics that incorporate production risk and other sources of risk in agriculture.

Unit details and rules

Academic unit Economics
Credit points 6
Prerequisites
? 
AREC2005 or AREC2001 or AREC2002 or AGEC2101 or ECOS2001 or ECOS2901 or ECOS2040
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Shauna Phillips, shauna.phillips@sydney.edu.au
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final exam
Written take home exam. Short answer questions.
55% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
In-semester test (Open book) Type C in-semester exam Mid-semester exam (take home exam)
Take home written exam
20% Week 08
Due date: 12 Apr 2022 at 10:00
1 hour
Outcomes assessed: LO1 LO3 LO4 LO5
Assignment Assignment
Written assignment
25% Week 13
Due date: 27 May 2022 at 23:00
1500 words
Outcomes assessed: LO1 LO2 LO3 LO5
Type C final exam = Type C final exam ?
Type C in-semester exam = Type C in-semester exam ?

Assessment summary

The mid-semester exam will include both multiple-choice and short-answer questions.

The assignment will be a written essay.

The final exam will include True/False and Explain questions and short-answer questions.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a High distinction indicates work of an exceptional standard, a Distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Work not submitted on or before the due date is subject to a penalty of 5% per calendar day late. If work is submitted more than 10 days after the due date the mark will be 0.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction; no tutorial Lecture (2 hr) LO1
Week 02 Decision analysis; quantifying uncertainty(I); Tutorial 1 Lecture and tutorial (3 hr) LO1 LO3 LO4
Week 03 Quantifying uncertainty(II); quantifying uncertainty from data(I); Tutorial 2 Lecture and tutorial (3 hr) LO1 LO3 LO4
Week 04 Quantifying uncertainty from data(II); incorporating attitudes to risk; Tutorial 3 Lecture and tutorial (3 hr) LO1 LO3 LO4
Week 05 E,V analysis; quadratic programming; Tutorial 4 Lecture and tutorial (3 hr) LO1 LO3 LO4
Week 06 Sequential stochastic programming; stochastic dominance; Tutorial 5 Lecture and tutorial (3 hr) LO1 LO3 LO4
Week 07 Introduction to real options Lecture and tutorial (3 hr) LO1 LO3 LO4
Week 08 Mid-semester exam Lecture (1 hr) LO1 LO3 LO4 LO5
Week 09 Real options; Tutorial 6 Lecture and tutorial (3 hr) LO1 LO3 LO4
Week 10 Tools for mitigating risk(I); Tutorial 7 Lecture and tutorial (3 hr) LO2 LO3 LO4
Week 11 Tools for mitigating risk(II); Tutorial 8 Lecture and tutorial (3 hr) LO1 LO2 LO4
Week 12 Diversification and hedging; Tutorial 9 Lecture and tutorial (3 hr) LO1 LO2 LO4
Week 13 Drought policy; Tutorial 10 Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5

Attendance and class requirements

  • Attendance: According to Faculty Board Resolutions, students in the Faculty of Arts and Social Sciences are expected to attend 90% of their classes. If you attend less than 50% of classes, regardless of the reasons, you may be referred to the Examiner’s Board. The Examiner’s Board will decide whether you should pass or fail the unit of study if your attendance falls below this threshold.
  • Lecture recording: Most lectures (in recording-equipped venues) will be recorded and may be made available to students on the LMS. However, you should not rely on lecture recording to substitute your classroom learning experience.
  • Preparation: Students should commit to spend approximately three hours’ preparation time (reading, studying, homework, essays, etc.) for every hour of scheduled instruction.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed on the Library link available on Canvas.

  • Recommended textbook: Hardaker, J.B., Huirne, R.B.M., Anderson, J.R. and G. Lien (1997) Coping with Risk in Agriculture, CAB International.
  • Recommended textbook: Dixit, R. and R. Pindyck (1994) Investment Under Uncertainty, Princeton University Press.
  • Recommended textbook: Moss, C.B.  (2010) Risk, Uncertainty and the Agricultural Firm, World Scientific.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. demonstrate a sound understanding of theoretical knowledge of decision making under risk and uncertainty in agriculture
  • LO2. demonstrate a sound understanding of the methods and tools for dealing with risk and uncertainty that are available to agricultural producers
  • LO3. demonstrate a sound understanding of different representations of risk and uncertainty
  • LO4. demonstrate an ability to use data to calculate different measures of risk and uncertainty
  • LO5. demonstrate an ability to clearly communicate results and implications of applied economic analysis.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.