Outline

Overview

One of the greatest limiting factors to the health and wellbeing of animals under our care is the nutritional value of their feed. Whether provided by nature or manufactured to meet the production and health needs of farmed animals, being able to provide suitable nutrition to animals in our care is fundamental to good animal health management. This Unit is broadly divided into three sections, namely: estimating the nutritive value of feeds; estimating the nutrient requirements of animals and diet formulation. The focus is on building up knowledge on animal nutrition by assessments of nutritional adequacy and solving of nutritional problems, with a particular emphasis on wildlife and animals used in agricultural production systems. The principles discussed in this course will be expanded in third year, in which species-specific systems will be described within the animal production major. In this unit you will develop the skills to create diets based on sound science, to meet animal requirements for a variety of purposes and under a variety of constraints and identify deficiencies, excesses and imbalances in diets and optimising nutritional health and minimising disease risk.

Unit details and rules

Academic unit	Life and Environmental Sciences Academic Operations
Credit points	6
Prerequisites ?	(BIOL1XX7 or MBLG1XXX) and 6 credit points from AVBS100X
Corequisites ?	None
Prohibitions ?	ANSC3101
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Alex Chaves, alex.chaves@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Practical exam ?	Final exam A practical skill demonstration to diet formulation (LO7). Student will be formulating a diet using the same software used during the first 7 weeks of the semester. Also the exam will consist with theoretical questions examining LO 1-6.	45%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
In-class quiz	In-class quiz In class quiz (Early Feedback Task)	5%	Week 03	10 minutes	AI allowed
Outcomes assessed:
Case studies	Problem based learning report Students will engage with the BCNRM 2016 model during practical classes from Weeks 1 to 7, which offers a structured and research-based framework for diet formulation. A report will be submitted.	25%	Week 07 Due date: 15 Sep 2025 at 23:59	14 pages max excluding references	AI allowed
Outcomes assessed:
Presentation	Individual video presentation During Weeks 8–12, students will use the Pet Diet Design software in practical classes to create a diet plan for a dog or cat, while developing both technical and presentation skills. The project will culminate in a five-minute video presentation.	25%	Week 12 Due date: 27 Oct 2025 at 23:59	5 minutes	AI allowed
Outcomes assessed:
= early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

In-Class Quiz (Early Feedback Task): The quiz will assess student ability to identify feed types based on the materials covered during Weeks 1 to 3 of the semester.

Problem-Based Learning Report: Students are required to evaluate how well each diet meets the nutritional requirements of the animals in the given scenarios. This should be done using the provided diet formulation reports.

In report, please include:

The specific feed ingredients and quantities required by the animals in each scenario/diet.
The amounts of these nutrients supplied by the formulated diets.
A scientific justification for your evaluation, referencing appropriate peer-reviewed literature or authoritative sources.
Responses to all questions provided in the report.

Important: This is a compulsory assessment. Failure to attend, attempt, or submit this task will result in the awarding of an AF (Absent Fail) grade.

Individual Video Presentation: Each student is required to produce an individual video presentation showcasing a custom-formulated diet created using the Pet Diet Designer software. The presentation should demonstrate a clear understanding of the animal's physiological needs, focusing specifically on either dogs or cats.

Your video should include the following key components:

An overview of the scenario and the animal's nutritional requirements.
A step-by-step explanation of the diet formulation process, including the selection and justification of ingredients.
A proposed feeding schedule, tailored to the animal’s life stage, health status, or activity level.
Discussion of common dietary challenges relevant to the chosen species.

To enhance clarity and engagement, incorporate visual aids such as:

Charts and tables
Feeding demonstrations
Close-up visuals of key ingredients and nutrients

The presentation may also explore the impacts of nutrition on health, reproduction, and lifespan, ensuring that the content remains scientifically grounded, educational, and species-specific.

Final Exam: The final exam will assess students' understanding of the lecture content and key concepts covered in practical classes. This is a compulsory assessment, and failure to attend, attempt, or submit will result in the award of an AF (Absent Fail) grade.

If a second replacement exam is required, it may be delivered via an alternative assessment method, such as a Viva Voce (oral exam). The alternative format will be designed to assess the same learning outcomes as the original exam. The unit coordinator will determine the format and structure of any alternative assessments.

Please note:

There are no marking adjustments and no replacement tasks available for any assessment items in this Unit of Study (UoS).
All required readings for this unit are accessible via the Library Reading List on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 20 21 (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	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.
Distinction	75 - 84	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.
Credit	65 - 74	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.
Pass	50 - 64	o be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty
Fail	0 - 49	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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:

Marking and Late Submission Policy – AVBS2004 No marking adjustments will be applied in AVBS2004. Late penalties do not apply to the In-Class Quiz (Early Feedback Task). For all other assessment tasks: Written work submitted electronically after 23:59 on the due date will be considered late. A late penalty of 5% per calendar day (based on the maximum possible marks) will apply for up to ten calendar days after the due date. The penalty will be applied after the work is marked. Submissions made more than ten calendar days late will receive a mark of zero. In such cases, the marker may provide feedback at their discretion, but is not required to do so.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Introduction to AVBS2004 (non contact); 2. Feed types and composition / Feed fractions; 3. Physically effective fibre concept.	Lecture (2 hr)
Week 01	Introduction to Canvas, Diet formulation software	Computer laboratory (1.5 hr)
Week 02	1. Digestibility; 2. Energy; 3. Metabolisable protein (MP) and lipid digestion (non contact).	Lecture (2 hr)
Week 02	Problem-based learning	Computer laboratory (1.5 hr)
Week 03	1. Rumen microbiology - bacteria, archaea, fungi and protozoa (non contact); 2. Factors to consider in the ensiling of forages; 3. Evolution and role of silage inoculants in ensiling.	Lecture (2 hr)
	Problem-based learning	Computer laboratory (1.5 hr)
	In class quiz (5%)	Computer laboratory (0.25 hr)
Week 04	1. Digestive disturbances in feedlot cattle - acidosis & bloat (non contact); 2. Pasture versus total mixed ration (TMR); 3. Diet formulation I.	Lecture (2 hr)
Week 04	Problem-based learning	Computer laboratory (1.5 hr)
Week 05	1. Macrominerals (non contact); 2. Diet formulation II; 3. Beef nutrition.	Lecture (2 hr)
Week 05	Problem-based learning	Computer laboratory (1.5 hr)
Week 06	1. Microminerals (online); 2. Camelid nutrition; 3. Use of unsalable veggies as animal feeds.	Lecture (2 hr)
Week 06	Problem-based learning	Computer laboratory (1.5 hr)
Week 07	1. Water soluble vitamins (non contact); 2. Wildlife nutrition; 3. Nutrition geometry.	Lecture (2 hr)
Week 07	Problem-based learning (PBL) report submission	Computer laboratory (1.5 hr)
Week 08	1. Fat soluble vitamins (non contact); 2. Video presentation guidelines 2025; 3. Sheep nutrition.	Lecture (2 hr)
Week 08	Design diet for video presentation	Computer laboratory (1.5 hr)
Week 09	1. Poultry nutrition (non contact); 2. Dogs and cats nutrition I; 3. Dogs and cats nutrition II.	Lecture (2 hr)
Week 09	Design diet for video presentation	Computer laboratory (1.5 hr)
Week 10	1. Horse nutrition (non contact); 2. Dairy nutrition; 3. Use of indigestible markers in animal nutrition.	Lecture (2 hr)
Week 10	Design diet for video presentation	Computer laboratory (1.5 hr)
Week 11	1. Swine nutrition (non contact); 2. In vivo experiments in animal nutrition; 3. Carbon footprint of beef from a global and Australian perspective.	Lecture (2 hr)
Week 11	Design diet for video presentation	Computer laboratory (1.5 hr)
Week 12	Submission individual video presentation.	Computer laboratory (2 hr)
Week 13	Practice final exam	Computer laboratory (2 hr)

Attendance and class requirements

Unless otherwise stated, students are expected to attend at least 80% of timetabled activities as defined in the unit of study outline. A student may fail this unit of study because of inadequate attendance.

Student attendance will be monitored using group QR codes, which link to Student Relationship Engagement System. The QR codes will be provided during the lectures and practical classes where students are required to scan the QR codes using their mobiles/iPads.

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

Book: Animal Nutrition. McDonald, P, Edwards, RA, Greenhalgh, JFD, and Morgan, CA. 7th Edition, Prentice-Hall, 2011.

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. describe feed types and composition
LO2. outline factors to consider in the ensiling process of forages
LO3. describe digestion differences between animals
LO4. examine the factors that affect the digestibility and metabolisable energy of feeds
LO5. characterise the microbes organisation in the rumen
LO6. explain the reasons for differences in nutrient requirements within and among animals
LO7. design and evaluate animal diets.

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.

UoS coordinator has engaged closely with the USYD Education Innovation Team, and we have conducted a series of productive meetings to review and respond to the feedback received from the Unit of Study Survey (USS) in 2024. - 8AM lecture will be replaced by a pre-recorded lecture (non-contact) to address students feedback. - In class quiz (Early Feedback Task) have been moved from Week 4 to 3. - The 45% final exam – secured will be run with a practical skill demonstration to meet the most important learning outcomes (LO) of AVBS2004, diet formulation (LO7). Student will be formulating a diet using the same software used during the first 7 weeks of the semester. Also, the exam will consist with theoretical questions examining LO 1-6. These changes are directly informed by student feedback and are designed to enhance engagement, promote deeper learning, and build real-world competencies. The revised assessments provide earlier feedback, more consistent practice opportunities, and authentic evaluations of student ability. Together with improved digital infrastructure, the student experience in AVBS2004 will be more coherent, supportive, and aligned with professional expectations in the field of Animal Nutrition.

Additional information

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General laboratory safety rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

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

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

AVBS2004: Animal Nutrition

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links