Outline

Overview

Our ever-changing world requires knowledge that extends across multiple disciplines. The ability to identify and explore interdisciplinary links in livestock production is a crucial skill for emerging professionals and researchers alike. This unit presents the opportunity to bring together the concepts and skills you have learnt in livestock production and agriculture, and apply them to a real-world problem. For example, you will work on a project analysing a selected livestock production system following a whole system approach, and understanding drivers and trends shaping the business. This will allow you to identify limitations to livestock productivity, sustainability and profitability which can then be put in a business plan to reduce or overcome the impact of these limitations. Working on a multidisciplinary team will allow you to solve these limitations with a holistic approach proposing improvements across the system's components (soils, pastures, crops, animals, economics, technological advances, amongst others). In this unit, you will continue to understand and explore disciplinary knowledge around Animal Production, while also meeting and collaborating with students from across the University through project-based learning; identifying and solving problems of livestock production systems, collecting and analysing data and communicating your findings to a diverse audience. All of these skills are highly valued by employers. This unit will foster the ability to work in interdisciplinary teams, and this is essential for both professional and research pathways in the future.

Unit details and rules

Academic unit	Life and Environmental Sciences Academic Operations
Credit points	6
Prerequisites ?	AVBS2004 and GEGE2X01
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Familiarity with data analysis and animal handling.
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Luciano Gonzalez, luciano.gonzalez@sydney.edu.au
Lecturer(s)	Wendy Muir, wendy.muir@sydney.edu.au
	Daniel Tan, daniel.tan@sydney.edu.au
	Janine Analie Chang Fung Martel, janine.changfungmartel@sydney.edu.au
	Sergio Garcia, sergio.garcia@sydney.edu.au
	Sonia Liu, sonia.liu@sydney.edu.au
	Jeffrey Downing, jeff.downing@sydney.edu.au

Type	Description	Weight	Due	Length
Oral exam ?	Viva voice exam Viva voice exam	30%	Formal exam period	15 minutes (oral)
Outcomes assessed:
Assignment	Problem/project conceptualisation Status of the problem report: conceptualisation of project	20%	Week 05	500-1000 words
Outcomes assessed:
Presentation	Consultant's presentation Oral presentation	20%	Week 12	30 minutes
Outcomes assessed:
Assignment	Consultant's report Written assignment, final consultant's report	30%	Week 12	2000-3000 words
Outcomes assessed:
= group assignment ?

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.

Result code	Result name	Mark range	Description
HD	High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
DI	Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
CR	Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
PS	Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
FA	Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.
AF	Absent fail	0 - 49	When you haven’t completed all assessment tasks or met the attendance requirements.
CN	Cancelled	No mark	When your enrolment has been cancelled.
DC	Discontinued not to count as failure	No mark	When you discontinue a unit after the relevant census date but before the DC deadline.
DF	Discontinue – fail	No mark	When you discontinue a unit after the DC deadline but before the DF deadline
FR	Failed requirements	No mark	When you don’t meet the learning outcomes to a satisfactory standard, for units which are marked as either Satisfied requirements or Failed requirements.
SR	Satisfied requirements	No mark	When you meet the learning outcomes to a satisfactory standard, for units which are marked as either Satisfied requirements or Failed requirements.
WD	Withdrawn	No mark	When you discontinue a unit before the relevant census date. WD grades do not appear on your academic transcript

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.

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.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
Mid-semester break	Virtual field trip	Online class (8 hr)
Week 01	Holistic farm business management	Online class (2 hr)
Week 02	KPI’s for the dairy industry	Online class (2 hr)
Week 03	Key performance indicators for the beef industry	Online class (2 hr)
Week 03	Understanding the research question and Developing a consultant report	Online class (3 hr)
Week 04	Key performance indicators for crop production	Online class (2 hr)
Week 04	Team formation and building	Online class (3 hr)
Week 05	Key performance indicators for the pork industry	Online class (2 hr)
Week 05	Pork production system analysis	Online class (3 hr)
Week 06	Key performance indicators for the sheep and wool industry	Online class (2 hr)
Week 06	Project work – assembling data	Tutorial (3 hr)
Week 07	Key perfromance indicators for the poultry and egg industry	Lecture (2 hr)
Week 09	Project work - Property mapping and data record keeping	Independent study (5 hr)
Week 10	Project work - Data analysis	Independent study (5 hr)
Week 11	Project work - Data interpretation and planning	Independent study (5 hr)
Week 12	Project work - Developing new strategies	Independent study (5 hr)
Week 13	Consultant's presentation	Online class (5 hr)

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.

Learning outcomes

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.

Outcomes
Graduate qualities

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

LO1. apply disciplinary knowledge to find and define problems in an interdisciplinary context
LO2. analyse data using modern information technologies and digital skills
LO3. create an investigation strategy, explore solutions, discuss approaches and predict outcomes
LO4. solve problems related to production systems based on evidence-based analysis
LO5. demonstrate integrity, confidence, personal resilience and the capacity to manage challenges, both individually and in teams
LO6. collaborate with diverse groups and across cultural and disciplinary boundaries
LO7. communicate project outcomes effectively to a broad audience

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
Learning outcomes

Responding to student feedback

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

This unit is the multidisciplinary project for the major in Animal Production

Additional information

Science student portal

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

ANSC3888: Production Systems Analysis

Semester 2, 2021 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect