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

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Department of Veterinary and Animal Sciences

Advanced Quantitative Methods in Herd Management

Course introduction

Anders Ringgaard Kristensen

Outline

Preconditions Outcome: What are you supposed to learn? The framework and definition of herd management The management cycle Classical production theory Limitation of classical theories Outline of the course Teachers

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Preconditions Courses

• Mathematics: ”Matematik og

modeller”/”Matematik og planlægning”

• Statistics ”Statistisk dataanalyse 2”
• Mandatory first year (economics, statistics

etc)

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Brush-up courses …

The course will start up with brush-up courses of

• Probability calculus and statistics
• Linear algebra

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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

• bservation uncertainty into account.

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Outcome - knowledge

After completing the course the student should be able to:

• 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.

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Outcome - skills

After completing the course the student should be able to:

• Construct models to be used for monitoring and decision

support in animal production at herd level.

• Apply the software tools used in the course.

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Outcome: Competencies:

After completing the course the student should be able to:

• 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.

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The management cycle: Classical theories

Utility Theory,

• Ch. 3.

Neo-classical Production Theory,

• Ch. 4.

(Scarce Resources) (Animal science, Production function) Basic Production Monitoring,

• Ch. 5.

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Herd Management Science

Basic level:

• As we define the basic level, it consists of
• Utility theory
• Neo-classical production theory
• Basic production monitoring
• (Animal nutrition, animal breeding, ethology,

farm buildings)

• What any animal scientist should know about

management

• The starting level of this course
• Volume I of the textbook!

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Neo-classical production theory

Answers 3 basic questions:

• What to produce.
• How to produce.
• How much to produce.

Marginal considerations Basic principle: Continue as long as the marginal revenue, MR, exceeds marginal costs, MC. At optimum we have MR = MC.

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How much to produce One factor x and one product y Prices px and py A production function y = f(x). Profit u(x) = ypy – xpx = f(x)py – xpx Problem:

• Find the factor level that maximizes the profit

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How much to produce Maximum profit where u’(x) = 0. u(x) = f(x)py – xpx u’(x) = f’(x)py – px u’(x) = 0 ⇔ f’(x)py = px Maximum profit where:

• Marginal revenue = Marginal cost!

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How much to produce

,2 0,2 0,4 0,6 0,8 1

Total revenue, f(x)py Average revenue, f(x)py/x Marginal revenue, f’(x)py How much to produce, logical bounds

,2 0,2 0,4 0,6 0,8 1

Total revenue, f(x)py Average revenue, f(x)py/x Marginal revenue, f’(x)py How much to produce, optimum

,2 0,2 0,4 0,6 0,8 1

Total revenue, f(x)py Average revenue, f(x)py/x Marginal revenue, f’(x)py

Price of factor px

Limitations of neo-classical theory

Static approach:

• Immediate adjustment
• Only one time stage

Deterministic approach

• Ignores risk
• ”Biological variation”
• Price uncertainty

Knowledge representation (knowledge considered as certain):

• Unobservable traits
• ”Production functions”
• Detached from production: No information flow from
• bservations.
• No updating of knowledge.

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Background for course

Structural development in the sector

• Increasing herd sizes
• Decreasing labour input

Technological development

• Sensors, automatic registrations
• Computer power
• Networks

Methodological development

• Statistical methods
• Operations Research

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Outline of course - I

Part I:

• Brush-up course on
• Probability calculus and statistics
• Linear algebra
• ”Advanced” topics from statistics
• Basic production monitoring
• Registrations and key figures
• Analysis of production results

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Outline of course - II Part II: The problems to be solved

• From registrations to information, value of

information, information as a factor, sources of information

• Decisions and strategies, definition and knowledge

foundation

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Outline of course - III Part III: The methods to be used

• State of factors
• Monitoring and data filtering
• Bayesian networks
• Decision support
• Decision graphs
• Simulation (Monte Carlo)
• Linear programming (low priority)
• Markov decision processes (dynamic programming)
• Mandatory reports

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Teachers

Anders Ringgaard Kristensen, professor, course responsible Dan Børge Jensen, assistant professor Jeff Hindsborg, research assistant

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Mandatory reports 4 minor reports must be handed in

• Based on the exercises

At least 3 must be approved in order to attend the oral exam The 4 reports are distributed over the following methods:

• Bayesian networks
• Monitoring and data filtering
• Linear programming
• Markov decision processes

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The web Absalon Home page of the course

• http://www.prodstyr.ihh.kvl.dk/vp/
• Course description
• Plan
• Pages for each lesson with a description of the

contents, literature, exercises, software to use etc.

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Master’s thesis

Plenty of opportunities for Master’s theses in relation to the course (almost all methods discussed):

• Pig data
• Dairy cow data

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