| 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: C | ||||||||||||||||||
| Language of Instruction | English | ||||||||||||||||||
| No Credit Points With | 260001 Advanced Herd Management | ||||||||||||||||||
| Optional Prerequisites | 210006 210012 210014 Only one of the courses 210012 and 210014 is required. | ||||||||||||||||||
| Restrictions | None | ||||||||||||||||||
| Course Content | |||||||||||||||||||
| This course is for individuals 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. 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. Next, the course provides a comprehensive introduction to advanced quantitative herd management by combining: - Theory - Computer applications in order to illustrate theory - Practical livestock management applications developed in research The techniques covered in this course have general application 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. Specific topics include dynamic monitoring of animal performance and behavior, data filtering, use of state space models, Bayesian networks, decision graphs, linear programming, Markov decision-processes and 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. Throughout the course, t he application and limitations of the methods taught will be illustrated by larger examples presented by invited guest lecturers having used the method in question in research. 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 simple implementations of methods used to solve management problems. 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 strengths 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 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. | |||||||||||||||||||
| Course Coordinator | |||||||||||||||||||
| Anders Ringgaard Kristensen, ark@life.ku.dk, Department of Large Animal Sciences/Production and Health, Phone: 353-33091 | |||||||||||||||||||
| Study Board | |||||||||||||||||||
| Study Committee V | |||||||||||||||||||
| Work Load | |||||||||||||||||||
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