30-08-2018 Department of Veterinary and Animal Sciences Department - PDF document

30-08-2018 Department of Veterinary and Animal Sciences Department of Veterinary and Animal Sciences Outline Preconditions Outcome: What are you supposed to learn? The framework and definition of herd management Advanced Quantitative Methods in The management cycle Herd Management Classical production theory Course introduction Limitation of classical theories Outline of the course Anders Ringgaard Kristensen Teachers Slide 2 Department of Veterinary and Animal Sciences Department of Veterinary and Animal Sciences Preconditions Brush-up courses … Courses • Mathematics: ”Matematik og The course will start up with brush-up courses of modeller”/”Matematik og planlægning” • Probability calculus and statistics • Statistics ”Statistisk dataanalyse 2” • Linear algebra • Mandatory first year (economics, statistics etc) Slide 3 Slide 4 Department of Veterinary and Animal Sciences Department of Veterinary and Animal Sciences Learning outcome Outcome - knowledge After attending the course students should be able to After completing the course the student should be able to: participate in the development and evaluation of new tools for • Describe the methods taught in the course management and control taking biological variation and • Explain the limitations and strengths of the methods in observation uncertainty into account. relation to herd management problems. • Give an overview of typical application areas of the methods. Slide 5 Slide 6 1

30-08-2018 Department of Veterinary and Animal Sciences Department of Veterinary and Animal Sciences Outcome - skills Outcome: Competencies: After completing the course the student should be able to: After completing the course the student should be able to: • Construct models to be used for monitoring and decision • Evaluate methods, models and software tools for herd support in animal production at herd level. management. • Apply the software tools used in the course. • Transfer methods to other herd management problems than those discussed in the course. • Interpret results produced by models and software tools. Slide 7 Slide 8 Department of Veterinary and Animal Sciences Department of Veterinary and Animal Sciences Herd Management Science The management cycle: Classical theories Basic level: (Scarce Utility Resources) Theory, • As we define the basic level, it consists of Ch. 3. • Utility theory • Neo-classical production theory • Basic production monitoring • (Animal nutrition, animal breeding, ethology, Basic farm buildings) Neo-classical Production Production Monitoring, • What any animal scientist should know about Theory, Ch. 5. management Ch. 4. • The starting level of this course • Volume I of the textbook! (Animal science, Production function) Slide 9 Slide 10 Department of Veterinary and Animal Sciences Department of Veterinary and Animal Sciences Neo-classical production theory How much to produce Answers 3 basic questions: One factor x and one product y • What to produce. Prices p x and p y • How to produce. A production function y = f( x ). • How much to produce. Profit u ( x ) = yp y – xp x = f(x) p y – xp x Marginal considerations Basic principle: Continue as long as the Problem: marginal revenue, MR, exceeds marginal • Find the factor level that maximizes the profit costs, MC. At optimum we have MR = MC. Slide 11 Slide 12 2

30-08-2018 Department of Veterinary and Animal Sciences How much to produce How much to produce Maximum profit where u’ ( x ) = 0. u ( x ) = f(x) p y – xp x u’ ( x ) = f’( x ) p y – p x 1 Total revenue, f( x ) p y u’ ( x ) = 0 ⇔ f’( x ) p y = p x 0,8 Maximum profit where: 0,6 • Marginal revenue = Marginal cost! 0,4 Average revenue, f( x ) p y / x 0,2 0 Marginal revenue, f’( x ) p y -0 ,2 Slide 13 How much to produce, optimum How much to produce, logical bounds 1 1 Total revenue, f( x ) p y Total revenue, f( x ) p y 0,8 0,8 0,6 0,6 0,4 0,4 Average revenue, f( x ) p y / x Average revenue, f( x ) p y / x 0,2 0,2 Price of factor p x 0 0 Marginal revenue, f’( x ) p y Marginal revenue, f’( x ) p y -0 ,2 -0 ,2 Department of Veterinary and Animal Sciences Department of Veterinary and Animal Sciences Limitations of neo-classical theory Background for course Static approach: Structural development in the sector • Immediate adjustment • Increasing herd sizes • Only one time stage • Decreasing labour input Deterministic approach Technological development • Ignores risk • Sensors, automatic registrations • ”Biological variation” • Computer power • Price uncertainty • Networks Methodological development Knowledge representation (knowledge considered as certain): • Statistical methods • Unobservable traits • Operations Research • ”Production functions” • Detached from production: No information flow from observations. • No updating of knowledge. Slide 17 Slide 18 3

30-08-2018 Department of Veterinary and Animal Sciences Department of Veterinary and Animal Sciences Outline of course - I Outline of course - II Part I: Part II: The problems to be solved • Brush-up course on • From registrations to information, value of information, information as a factor, sources of • Probability calculus and statistics information • Linear algebra • Decisions and strategies, definition and knowledge • ”Advanced” topics from statistics foundation • Basic production monitoring • Registrations and key figures • Analysis of production results Slide 19 Slide 20 Department of Veterinary and Animal Sciences Department of Veterinary and Animal Sciences Outline of course - III Teachers Part III: The methods to be used • State of factors Anders Ringgaard Kristensen, professor, course responsible • Monitoring and data filtering Dan Børge Jensen, assistant professor • Bayesian networks Jeff Hindsborg, research assistant • Decision support • Decision graphs • Simulation (Monte Carlo) • Linear programming (low priority) • Markov decision processes (dynamic programming) • Mandatory reports Slide 21 Slide 22 Department of Veterinary and Animal Sciences Department of Veterinary and Animal Sciences Mandatory reports The web 4 minor reports must be handed in Absalon • Based on the exercises Home page of the course At least 3 must be approved in order to attend the oral • http://www.prodstyr.ihh.kvl.dk/vp/ exam The 4 reports are distributed over the following • Course description methods: • Plan • Bayesian networks • Pages for each lesson with a description of the • Monitoring and data filtering contents, literature, exercises, software to use • Linear programming etc. • Markov decision processes Slide 23 Slide 24 4

30-08-2018 Department of Veterinary and Animal Sciences Master’s thesis Plenty of opportunities for Master’s theses in relation to the course (almost all methods discussed): • Pig data • Dairy cow data Slide 25 5