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Unit outline_

AREC3006: Agricultural Production Economics

Semester 1, 2022 [Normal day] - Remote

This unit of study is concerned with the application of microeconomic principles to management decisions in agricultural production systems. It builds on the theoretical knowledge acquired in previous studies and introduces the methods of applied economic analysis through a range of topics including: production, cost and profit functions; methods for the measurement of productivity; optimisation in biological production systems; and production under risk. The unit introduces the linear programming technique to solve decision making problems encountered by agribusiness and natural resource firms and managers in public agencies.

Unit details and rules

Academic unit Economics
Credit points 6
Prerequisites
? 
AREC2005 or ECOS2001 or ECOS2901 or AREC2003
Corequisites
? 
None
Prohibitions
? 
AREC2001 or AREC3001
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Tiho Ancev, tiho.ancev@sydney.edu.au
Lecturer(s) Tiho Ancev, tiho.ancev@sydney.edu.au
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final exam
Two-hour written exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
In-semester test (Open book) Type C in-semester exam Mid-semester exam
One-hour written exam
35% Week 07
Due date: 05 Apr 2022 at 14:00
1 hour
Outcomes assessed: LO1 LO2 LO3
Assignment Tutorial report
Written report on work completed in all tutorials during the semester
15% Week 13
Due date: 27 May 2022 at 23:00
1500-word equivalent report on tute work
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Type C final exam = Type C final exam ?
Type C in-semester exam = Type C in-semester exam ?

Assessment summary

Detailed information for each assessment can be found on Canvas.

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.

High Distinction

85-100

Attains the learning outcomes with outstanding proficiency

Distinction

75 - 84

Attains the learning outcomes with superior proficiency

Credit

65 - 74

Attains the learning outcomes with good proficiency

Pass

50 - 64

Attains the learning outcomes

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:

The Assessment Procedures 2011 provide that any written work submitted after 11:59pm on the due date will be penalised by 5% of the maximum awardable mark for each calendar day after the due date. If the assessment is submitted more than ten calendar days late, a mark of zero will be awarded. However, a unit of study may prohibit late submission or waive late penalties only if expressly stated below.

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 and housekeeping Lecture and tutorial (3 hr)  
Week 02 Production function and optimisation Lecture and tutorial (3 hr)  
Week 03 Inputs (including fixed inputs), costs, and input demand functions Lecture and tutorial (3 hr)  
Week 04 Land and land rents Lecture and tutorial (3 hr)  
Week 05 Productivity, efficiency and technological change Lecture and tutorial (3 hr)  
Week 06 Supply curve and optimal production under restrictions Lecture and tutorial (3 hr)  
Week 07 Economies of scale and size Lecture and tutorial (3 hr)  
Week 08 Dynamic production decisions Lecture and tutorial (3 hr)  
Week 09 Risk and Uncertainty Lecture and tutorial (3 hr)  
Week 10 Multiple outputs and multistage production Lecture and tutorial (3 hr)  
Week 11 Production planning using linear programming (LP) Lecture and tutorial (3 hr)  
Week 12 Applications of LP models Lecture and tutorial (3 hr)  
Week 13 Revision and focus on assessment Lecture and tutorial (3 hr)  

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. 

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

Prescribed reading for this Unit is:

Rasmussen, Svend, 2013. Production Economics: The Basic Theory of Production Optimisation, 2nd Edition, Springer

This book is available as an e-book via the University Library.

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 ability to conceptualise production decisions made by producers
  • LO2. Apply production theory to empirical economic problems in agricultural, environmental and natural resource contexts
  • LO3. Perform basic evaluation of productivity and efficiency
  • LO4. Conceptualise and analyse risk and uncertainty in production economics context
  • LO5. Analyse production behaviour in a dynamic context
  • LO6. Use linear programming techniques to analyse and optimise production decisions

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.

In response to student feedback some changes in weights of assessments have been made, and in the format of lectures and tutorials.

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.