Advanced Quantitative Methods in Herd Management · • Evaluate methods, models and software tools for herd management. • Transfer methods to other herd management problems than

Department of Veterinary and Animal Sciences

Advanced Quantitative Methods in Herd ManagementCourse 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


Slide 2

Preconditions

Courses

• Mathematics: ”Matematik ogmodeller”/”Matematik og planlægning”

• Statistics ”Statistisk dataanalyse 2”

• Mandatory first year (economics, statistics etc)


Slide 3

Brush-up courses …

The course will start up with brush-up courses of• Probability calculus and statistics• Linear algebra


Slide 4

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.


Slide 5

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.


Slide 6

Outcome - 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.


Slide 7

Outcome: 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.


Slide 8

The management cycle: Classical theories

UtilityTheory,Ch. 3.

Neo-classicalProductionTheory,Ch. 4.

(ScarceResources)

(Animal science,Production function)

BasicProductionMonitoring,Ch. 5.


Slide 9

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!


Slide 10

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.


Slide 11

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


Slide 12

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

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Total revenue, f(x)py

Average revenue, f(x)py/x

Marginal revenue, f’(x)py

How much to produce, logical bounds

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How much to produce, optimum

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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 observations.

• No updating of knowledge.


Slide 17

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


Slide 18

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


Slide 19

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 knowledgefoundation


Slide 20

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


Slide 21

Teachers

Anders Ringgaard Kristensen, professor, course responsibleDan Børge Jensen, assistant professorJeff Hindsborg, research assistant


Slide 22

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 examThe 4 reports are distributed over the following methods:

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


Slide 23

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.


Slide 24

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

Documents

Advanced Quantitative Methods in Herd Management · • Evaluate methods, models and software tools for herd management. • Transfer methods to other herd management problems than