Quantitative Methods in Herd Management
Course content
This course is for people seeking to improve their abilities to
assemble and use data of animal performance to enhance rational,
quantitative decision making in animal production. It is a
computer intensive course, where the students will need to install
specific programs (R and R studio) as well as specific R packages
on their computers. We will be using real-world data sets
collected in commercial herds and in research herds.
The students will write and submit weekly reports. In these
reports, they will demonstrate their abilities to use the methods
and explain the concepts introduced in a given week. When making
these reports, the students will work together in groups of 2-4
people.
The necessary mathematical and statistical preconditions will be
introduced at appropriate times, relative to the topics being
covered in a given week. This is done in order to make sure that
all students have a basic understanding of topics like vector and
matrix operations (i.e. linear algebra), random numbers,
Bayes’ theorem, distributions (including multivariate and
conditional), etc.
Next, the course provides a comprehensive introduction
to quantitative herd management by combining:
- Theory
- Computer applications in order to illustrate theory
- Presentations of practical livestock management applications developed in research
- Presentations from relevant guest lecturers
The techniques covered in this course have general applications in
diverse animal production systems. These techniques are applied to
an array of management decisions including culling, breeding and
mating; feed allocation and slaughter timing; and medical
treatment.
The course will cover the following specific topics:
- Linear algebra and R-programming
- Monitoring of time series data using classical methods (i.e. moving average, exponentially weighted moving average)
- Monitoring of time series data using state space models
- Random forests
- Artificial neural networks
- Markov decision processes
- Monte Carlo Simulation
MSc Programme in Animal Science - restictive elective
After attending the course, students should be able to
participate in the development and evaluation of new tools for
management and control, while taking biological variation and
observation uncertainty into account.
After completing the course, the students should be able to:
Knowledge:
- Describe the methods taught in the course.
- Explain the common pitfalls, and how to avoid them, when using specific methods taught in the course.
- Explain the limitations and strengths of the methods in relation to various herd management problems.
- Give an overview of typical application areas of the methods.
Skills:
- Write and apply their own software tools in R.
- Apply the other software tools used in the course.
- Train and validate machine learning methods for classification and regression tasks.
- Construct models to be used for monitoring and decision support in animal production.
Competencies:
- Combine the different methods in meaningful ways in response to a given herd management problem.
- Transfer methods to other herd management problems than those discussed in the course.
- Evaluate the validity of machine learning models in terms of architecture, training, and validation strategies.
- Evaluate methods, models, and software tools for herd management.
- Interpret results produced by models and software tools.
Lectures, theoretical exercises, computer exercises, and report
writing.
In general, each week will cover one specific topic. The students
will make a mandatory report relating to the topic of each week.
The students can work on the reports in groups of 2-4 people.
The submitted reports will be assessed regularly. If a report is
not accepted in the first attempt (including if the report is not
submitted on time), the students have one more chance to submit a
corrected report, utilizing the feedback provided in the
assessment.
In connection with lectures, the students are expected to
participate actively in discussions. Throughout the course, the
application and limitations of the methods taught will be
illustrated by larger examples presented by relevant guest
lecturers.
Lectures will be supported by theoretical problem solving
exercises, and all methods presented will be supported by computer
exercises where the application of the methods is illustrated. The
reports will consist of answers to selected exercises, evaluation
of methods, and one or more implementations of methods used to
solve management problems.
Evaluation model: Survey-based model
Kristensen, A.R., E. Jørgensen and N. Toft. 2010. Herd
Management Science I. Basic concepts. 2010 Edition, University of
Copenhagen, Faculty of Life Sciences.
Kristensen, A.R., E. Jørgensen and N. Toft. 2010. Herd Management
Science II. Advanced topics. 2010 Edition, University of
Copenhagen, Faculty of Life Sciences
Kristensen, A.R. 2010. Herd Management Science. Exercises and
supplementary reading. 2010 edition.
Additional scientific papers and other materials, which will be relevant to the topic of a given week.
It will be an advantage for the student to have taken one or
more of the following courses (or similiar) before taking this
course:
LMAB10066U Matematik og databehandling
LMAB10069U Statistisk dataanalyse 1
A Bachelor's degree in Animal Science (Danish: Husdyrvidenskab) or similar.
Open for credit transfer students and other external students. Apply here:
Credit transfer students:
Credit transfer student at SUND – University of Copenhagen (ku.dk)
Other external students:
https://healthsciences.ku.dk/education/student-mobility/guest-students/
- ECTS
- 15 ECTS
- Type of assessment
-
Oral examination, 25 minutes
- Type of assessment details
- An individual oral examination is held at the end of the
course.
The student randomly draws an exam question related to one of the topics taught in the course.
The student then randomly draws one out of 5 reports. If the student has succesfully completed 6 or 7 reports, the student can choose one or two reports, respectively, to omit from the random draw.
The student has 30 minutes of preparation time, including the drawing of the exam question and report. This is followed by 25 minutes of examination, followed by 5 minutes of deliberation and grading.
Students in the same group cannot participate in their fellow student’s individual oral exam unless they have already had their own individual oral exam. - Exam registration requirements
-
A precondition for attending exam is that at least 5 of 7 mandatory reports have been submitted and approved.
If the student has succesfully completed 6 or 7 reports, the student can choose one or two reports, respectively, to omit from the exam.
The student may still draw an exam question related to the topic of the omitted reports, so it will be advantageous to the student to complete all reports, even though only 5 completed reports are strictly required.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
More than one internal examiner.
Criteria for exam assessment
To achieve the maximum grade of 12, the
student must be able to:
Knowledge:
- Describe the methods taught in the course.
- Explain the common pitfalls, and how to avoid them, when using specific methods taught in the cause.
- Explain the limitations and strengths of the methods in relation to herd management problems.
- Give an overview of typical application areas of the methods.
Skills:
(Only tested in mandatory reports)
Competencies:
- Evaluate the utility and validity of the methods, models, and software tools for herd management problems
- Interpret results produced by models and software tools
- Describe meaningful combinations of various methods for specific herd management problems
Single subject courses (day)
- Category
- Hours
- Lectures
- 42
- Preparation
- 108
- Theory exercises
- 42
- Project work
- 200
- Guidance
- 19
- Exam
- 1
- English
- 412
Kursusinformation
- Language
- English
- Course number
- SASK22001U
- ECTS
- 15 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 2
- Schedulegroup
-
Teaching will be concentrated mainly on Mondays and Tuesdays, while the rest of the week will mainly be dedicated to the writing of the weekly reports.
- Capacity
- 30 students.
- Studyboard
- The Study Board for Veterinary Medicine and Animal Science
Contracting department
- Department of Veterinary and Animal Sciences
Contracting faculty
- Faculty of Health and Medical Sciences
Course Coordinator
- Dan Børge Jensen (3-6b6871477a7c756b35727c356b72)
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Kursusinformation for indskrevne studerende