| Responsible Department | Department of Large Animal Sciences | ||||||||||||||||||
| Earliest Possible Year | MSc. 1 year | ||||||||||||||||||
| Duration | One block | ||||||||||||||||||
| Credits | 7.5 (ECTS) | ||||||||||||||||||
| Level of Course | MSc | ||||||||||||||||||
| Examination | Final Examination oral 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. Weight: 100% 7-point scale, internal examiner | ||||||||||||||||||
| Requirement for Attending Exam | A precondition for attending exam is that at least 3 of 4 mandatory reports have been handed in and approved. | ||||||||||||||||||
| Organisation of Teaching | Lectures, theoretical exercises, practical computer exercises and report writing. | ||||||||||||||||||
| Block Placement | Block 1 Week Structure: B | ||||||||||||||||||
| Language of Instruction | English | ||||||||||||||||||
| Optional Prerequisites | 210004 210012 Mathematics and Models 210006 Statistical Data Analysis 2 260024 Animal Production Only one of the courses 210004 and 210012 is required. | ||||||||||||||||||
| Restrictions | None | ||||||||||||||||||
| Course Content | |||||||||||||||||||
| 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 herd management tasks like: - registration and filtering of data to be used in planning and control - assessing the value of information - definition and evaluation of management decisions and strategies - quantifying the effects of unsatisfactory results 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 | |||||||||||||||||||
| After attending the course students should be able to participate in the development and evaluation of new tools for management and control taking biological variation and observation uncertainty into account. After completing the course the student should be able to: Knowledge: - Describe the methods taught in the course - Explain the limitations and stregths of the methods in relation to herd management problems. - Give an overview of typical application areas of the methods. Skills: - Construct models to be used for monitoring and decision support in animal production at herd level. - Apply the software tools used in the course. Competencies: - Evaluate methods, models and software tools for herd management. - Transfer methods to other herd management problems than those discussed in the course. - Interpret results produced by models and software tools. | |||||||||||||||||||
| Course Literature | |||||||||||||||||||
| Kristensen, A.R., E. Jørgensen & N. Toft. 2009. Herd Management Science. I. Basic concepts. University of Copenhagen, Faculty of Life Sciences. Kristensen, A.R., E. Jørgensen & N. Toft. 2009. Herd Management Science. II. Advanced topics. University of Copenhagen, Faculty of Life Sciences. Jensen, F.V. 2001. Bayesian Networks and Decision Graphs, Springer. | |||||||||||||||||||
| Course Coordinator | |||||||||||||||||||
| Anders Ringgaard Kristensen, ark@life.ku.dk, Department of Large Animal Sciences/Production and Health, Phone: 353-33091 | |||||||||||||||||||
| Study Board | |||||||||||||||||||
| Study Committee NSN | |||||||||||||||||||
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