Advanced Herd Management Course introduction Anders Ringgaard - - PDF document

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Slide 1

Advanced Herd Management

Course introduction

Anders Ringgaard Kristensen

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Outline

Preconditions Competences: What are you supposed to learn? The framework and definition of herd management The management cycle Objectives of production, utility theory Classical production theory Classical replacement theory Limitation of classical theories Outline of the course Teachers The concept of uncertainty

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Preconditions Courses

• ”Husdyrproduktion”
• ”Matematik og modeller”/”Matematik og

planlægning”

• ”Statistisk dataanalyse 2”
• Mandatory first year (economics etc)

Slide 4

Preconditions UR 2005

Not all of you have had a chance to fulfill the preconditions defined in UR 2005. My guess is that very few have had those courses. As a consequence of this assumption, the course will start up with a brush-up course of

• Probability calculus and statistics
• Linear algebra

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Competencies I

Competencies obtained within basic science:

• Comprehension of advanced methods for

production monitoring and analysis as well as

• perational and tactical planning in livestock

herds.

• Evaluation of various methods in relation to the

solution of typical management problems in livestock herds.

• Make judgements concerning the choice of

appropriate methods for different herd management tasks.

Slide 6

Competencies II

Competencies obtained within applied science:

• Apply principles and advanced methods for production

monitoring based on data from specific herds.

• Apply principles and advanced methods for operational and

tactical planning in specific livestock herds.

• Apply principles and advanced methods in development of

general herd management tools.

• Make judgements concerning the quality of commercially

distributed general herd management tools.

Competencies obtained within Ethics & Values:

• Is aware of the relation between monitored production traits

and the priorities defined by the farmer’s utility function.

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A pig (an animal)

Feed Medicine Meat Manure Piglets Milk

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Pig production = N × a pig?

Feed Medicine Meat Manure Piglets Milk

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Feed Medicine Meat Manure Piglets Milk

Pig production = N × a pig?

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Pigs:

• Ages
• Groups
• Individuals

Feed Buildings Fields Farm hands Owner Neighbors, society, consumers

Pig production

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Elements of production I

The factors (input to production)

• Animals
• Feed
• Buildings, inventory
• Labor
• Management
• Veterinary services
• Energy
• …

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Elements of production II

Objectives: Maximization of the farmer’s welfare:

• Income (personal)
• Leisure time (personal)
• Animal welfare (animals)
• Working conditions (farm hands)
• Environmental preservation (future generations)
• Prestige (personal)
• Product quality (consumers)

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Elements of production III

Constraints limiting production

• Physical (land, housing capacity, storage capacity)
• Economical (capital, prices)
• Legal (laws)
• Personal (skills, education)

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Definition of herd management

Having discussed the three key elements:

• Factors (input to production)
• Objectives (farmer’s welfare)
• Constraints (limitations)

we are now able to define what we mean by Herd Management: Herd management is a discipline serving the purpose of concurrently ensuring that the factors are combined in such a way that the welfare of the individual farmer is maximized subject to the constraints imposed on his production. A dynamic optimization problem under constraints. Decisions! We decide how to combine the factors.

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The management cycle: A never ending story

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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
• Briefly revised today

Slide 18

Utility theory

We need a criterion for comparison of plans (“ways” to produce). Several concerns:

• The farmer
• The staff
• Consumers
• The animals
• Environment
• …

Who decides the weighting? My answer: The farmer!

Slide 6

Animals Owner Farm hands Feed Farm buildings Land Neighbors, society, consumers

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Farmer’s preferences

The farmer has/defines a list of concerns:

• Own direct concerns:
• Income, u1
• Leisure time, u2
• Prestige, u3
• ...
• Indirect concerns (because he cares for others)
• Animal welfare, u4
• Working conditions, u5
• Environment, u6
• Product quality, u7
• …

The farmer knows/decides the weighting The “items” on the list (u1, u2, … , uk) are called attributes of the farmer’s utility.

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When is ”something” an attribute?

When it directly influences the subjective welfare of the farmer. May NOT be an attribute:

• Average milk yield of cows
• Average daily gain of slaughter pigs
• Animal welfare, ”because animals at a high level of

welfare also produce at a higher level”.

May be an attribute:

• Monetary gain
• Leisure time
• Animal welfare, if the farmer is willing to accept that

it to some extent decreases the levels of other attributes.

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Consequences measured by attributes

ukT … uk2 u11 k … … … … … u2T … u22 u11 2 u1T … u12 u11 1 T … 2 1 Stage Attribute

At any stage, the attributes will depend on the production Yt and the factors xt. The relation is given by the attribute function h: ut = h(Yt , xt)

Slide 22

Aggregation of attributes: Utility function

The utility function

• Aggregation over time
• Monetary gain
• Animal welfare
• …
• Aggregation over attributes

Expected Utility Theorem: Maximization of U is all we need to care about! Refer to Chapter 3 for details!

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Production function

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Production function In classical production theory, the uncertainty represented by the e’s is ignored.

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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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What to produce Two products y1 and y2 Prices p1 and p2. Fixed factor allotment Value of production: u = p1y1 + p2y2 Fixed value u’:

• y2 = u’/p2 – (p1/p2) y1

The price ratio p1/p2 determines the optimal combination! The general principle: Continue producing one more unit of Product 1 (reducing the output of Product 2 accordingly) as long as the marginal revenue exceeds the marginal costs.

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What to produce: Two products

y2 y1

p1/p2 = 1/2 p1/p2 = 2

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How to produce Two factors x1 and x2 Prices p1 and p2. Fixed production Cost of production: c = p1x1 + p2x2 Fixed value c’:

• x2 = c’/p2 – (p1/p2) x1

The price ratio p1/p2 determines the optimal combination! The general principle: Continue adding one more unit of Factor 1 (reducing the output of Factor 2 accordingly) as long as the marginal revenue exceeds the marginal costs.

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How to produce: Two factors

x2 x1

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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

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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

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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

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Classical replacement theory

The replacement problem in a broad sense is one of the most important decision problems in animal production. Dynamics: What we decide at this stage (keep/replace) may influence production in many future stages. Many other decision problems relate to the replacement problem:

• Insemination
• Treatment for diseases
• Feeding level
• …

A correct handling of the other problems implies that the question of replacement must be taken into account.

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Definition

Replacement:

• When an existing asset is substituted by a new
• ne with (more or less) the same function.
• Examples:
• Light bulbs
• Cars
• Sows
• Milking robots

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Female production animals:

• (Ewes, mink, goats)
• Sows
• Dairy cows
• Two levels:
• Optimal lactation to replace
• Optimal stage of lactation to replace
• Repeatability of milk yield over lactation rather high

(as opposed to litter size in sows).

Replacement problems in animal production

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Replacement problems in animal production

Technical:

• Examples:
• Farm buildings
• Equipment & machinery
• Very similar to the sow replacement problem, except for the

”biological” variation

• Technological improvements probably more important than

the corresponding genetic improvement in cows.

• Marginal/average considerations apply well

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Slaughter calves:

• If housing capacity is limited and replacements are

available, the problem is in agreement with classical theory.

• Marginal/average considerations

Slaughter pigs:

• Two levels:
• When to deliver individual pigs (animal level)
• When to deliver the remaining pigs (batch level)

Broilers:

• At batch level (no animal level) in agreement with classical

theory.

• Contracts may limit the decisions of the farmer

Replacement problems in animal production

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A chain of assets Asset 1 Asset n Asset 3 Asset 2

tr

How do we determine an optimal value for tr - the length of the period to keep each asset in the chain?

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Optimal time for replacement Assume that the price of a new asset is S The salvage value of the asset at time t is st The net returns from the asset in stage (time step) t is rt The total net revenue T(t) from the asset if it is replaced at stage t is then

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Optimal time for replacement Average net revenue, if replaced at time t: Marginal net revenue at time t Optimal replacement time where Replace where: marginal revenue = average revenue

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Graphical illustration

The replacement problem

• 200
• 100

100 200 300 5 10 15 20 Time Revenue Marginal Average

Slide 44

Marginal revenue

Typically decreasing because of decreasing productivity and increasing maintenance costs. The net returns adjusted for change in salvage value. The marginal curve crosses the average curve where the average is maximal.

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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.

Slide 46

Limitations of classical replacement theory

Uncertainty: The classical replacement theory assumes full certainty about the marginal profit function, the investment costs and all prices. As discussed in details in Chapter 2, uncertainty is an inherit property

• f the decision making process in herd management.

The uncertainty is partly a consequence of imperfect knowledge, and partly of random variation. Uniqueness: The general theory implicitly assumes that the marginal and average profit functions are as shown in Figure 4.5 with a uniquely determined

• intersection. For several applications the intersection

is not unique. This is, for instance, the situation in dairy cows, where the average and marginal profits are as shown in Figure 4.7. Availability: The theory assumes that a new asset for replacement is always available.

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Background

Structural development in the sector

• Increasing herd sizes
• Decreasing labour input

Technological development

• Sensors, automatic registrations
• Computer power
• Networking

Methodological development

• Statistical methods
• Operations Research

Slide 48

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

• Consequences of decisions and states
• Assessing the utility value of tools

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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

Part I:

• Anders Ringgaard Kristensen

Part II:

• Anders Ringgaard Kristensen

Part III:

• Anders Ringgaard Kristensen
• Cécile Cornou (Post Doc IPH)
• (Jehan Frans Ettema PhD student of herd management)
• Guests:
• Thomas Algot Søllested (Egebjerg)
• Thomas Nejsum Madsen (TNM Consult)
• Bea Nielsen (NorFEED)
• Others …

Slide 52

Mandatory reports 4 minor reports must be handed in

• Based on the exercises

At least 3 must be approved in order to attend the

• ral exam

The 4 reports are distributed over the following methods:

• Linear programming
• Monitoring and data filtering
• Markov decision processes
• Bayesian networks (including decision graphs)

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The web Herd Management at KU LIFE:

• http://www.prodstyr.ihh.kvl.dk

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 54

Exercise, uncertainty

Production function:

• - milk yield given energy, protein and

fat

Adding uncertainty, the actual milk yield is

f x x x c x c x c x c x c x c x c x x c x x c x x ( , , )

1 2 3 11 1 2 22 2 2 33 3 2 1 1 2 2 3 3 12 1 2 13 1 3 23 2 3

= + + + + + + + +

Y = f(x1,x2,x3) + e

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Uncertainty, II Adding uncertainty to production function:

• Considerable improvement, BUT
• Significant uncertainty about true energy, protein

and fat content still ignored

• Example, only considering energy

Slide 56

Uncertainty - III True energy content of silage is unknown The precision of the observed content depends heavily on the observation method (standard value from table, laboratory analysis etc.)

Silage obs.* Silage true Concentr.* Ration Milk yield* Herd size*

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Uncertainty - IV

Effects of decisions will be over-estimated if unceratainty about

• true state
• factor characteristics
• factor effects

is ignored. Wrong decisions may be made.

Slide 58

Uncertainty, V

Baysian networks with decisions and utilities added (student project).

Silage obs.* Silage true Concentr.* Ration Milk yield* Herd size* Method Mix Price Cost Rev.

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Uncertainty - VI Uncertainty is not the opposite of knowledge Uncertainty is a property of knowledge Reduction of uncertainty is often possible at some cost! Reducing uncertainty is not always profitable.