260001 Advanced Herd Management

Details
Department of Large Animal Sciences
Earliest Possible YearMSc. 1 year to MSc. 2 year
DurationOne block
 
Credits7.5 (ECTS)
Course LevelMSc
 
Examinationoral examination


All aids allowed

Description of Examination: An individual oral examination is held at the end of the course. ½ hour preparation followed by ½ hour examination. A precondition for attending exam is that at least 3 of 4 mandatory reports have been handed in and approved.

7-point scale, internal examiner
 
Organisation of TeachingLectures, theoretical exercises, practical computer exercises and report writing.
 
Block PlacementBlock 1
Week Structure: B
 
Teaching LanguageEnglish
may be conducted in Danish
 
Optional Prerequisites210003 Mathematics and Models
210004 Mathematics and Planning
210006 Statistical Data Analysis 2
260011 Animal Production
Only one of the courses 210003 and 210004 is required.
 
RestrictionsNone
 
Areas of Competence the Course Will Address
Competences obtained within basic science:
Comprehension of advanced methods for production monitoring and analysis as well as operational and tactical planning in livestock herds.

Evaluation of various methods in relation to the solution of typical management problems in livestock herds.

Make judgements concerning the choice of appropriate methods for different herd management tasks.

Competences obtained within applied science:

Apply principles and advanced methods for production monitoring based on data from specific herds.

Apply principles and advanced methods for operational and tactical planning in specific livestock herds.

Apply principles and advanced methods in development of general herd management tool.

Make judgements concerning the quality of commercially distributed general herd management tools.

Competences obtained within Ethics & Values:

Is aware of the relation between monitored production traits and the priorities defined by the farmer's utility function.


 
Course Objectives
After attending the course students should be able to participate in the development of new tools for management and control. During the course methods for solving the following tasks are studied:
- registration and filtering of data to be used in planning and control
- definition and evaluation of management decisions and strategies
- quantifying the effects of unsatisfactory results
- visualisation and presentation of plans and results
 
Course Contents
Part 1:
Initially, the necessary mathematical and statistical preconditions will be introduced 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. The duration of this part is between one and two weeks.

Part 2:
Afterwards, the lectures first focus on the tasks mentioned under "objectives". The tasks will be identified and central elements are discussed and illustrated by examples. Relevant methods are introduced and compared. The duration of this part is between one and two weeks.

Part 3:
Later on, the methods will be in focus. Each method is described and discussed in relation to the tasks. The duration of this part is 6 weeks.

The following methods are taught in the course:

Methods for data filtering and production monitoring and analysis:
- Control charts and traditional time series analysis
- Dynamic linear models
- Bayesian networks
Methods for operational and tactical planning
- Decision graphs
- Dynamic programming
- Linear programming
- Monte Carlo simulation
 
Teaching And Learning Methods
Lectures, theoretical exercises, practical computer exercises and report writing. In connection with lectures, the students are expected to participate actively in mutual discussions. In Part 3, the application and limitations of the methods will be illustrated by larger examples presented by invited guest lecturers having used the method in question in research. Part 1 and (to some degree) Part 3 will be supported by theoretical problem solving exercises. All methods presented in Part 3 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 simple implementations of methods used to solve management problems chosen by the students. Reports may be written and handed-in in minor groups of two or three students.
 
Learning Outcome
Stipulated in "Areas of Competence the Course Will Address"
 
Course Litterature
Kristensen, A.R., E. Jørgensen & N. Toft. 2006. Herd Management Science. Preliminary edition. The Royal Veterinary and Agricultural University.

West, M. & J. Harrison. 1997. Bayesian Forecasting and Dynamic Models, 2nd Ed, Springer.

Jensen, F.V. 2001. Bayesian Networks and Decision Graphs, Springer.
 
Course Coordinator
Anders Ringgaard Kristensen, ark@dina.kvl.dk, Department of Large Animal Sciences/Production and Health, Phone: 35333091
 
Study Board
Study Committee NSN
 
Course Scope
lectures54
theoretical exercises10
practicals18
project work54
supervision9
preparation60
examination1

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