Utility value of management tools Advanced Herd Management Anders - - PowerPoint PPT Presentation

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Utility value of management tools Advanced Herd Management Anders - - PowerPoint PPT Presentation

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Utility value of management tools Advanced Herd Management Anders Ringgaard Kristensen Slide 1 What kind of tools? Complex systems Not just single pieces of information Management information systems Bedriftslsningen

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SLIDE 1

Slide 1

Utility value of management tools

Advanced Herd Management Anders Ringgaard Kristensen

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SLIDE 2

Slide 2

What kind of tools? Complex systems

  • Not just single ”pieces of information”

Management information systems

  • ”Bedriftsløsningen”
  • ”E-kontrol”

Monitoring tools

  • FarmWatch

Decision support tools

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SLIDE 3

Slide 3

Why do we want to evaluate?

Farmers who consider to buy a system would like to know the expected benefit Developers who wish to sell a system would like to be able to demonstrate the benefit Only very little research has been done in this field

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SLIDE 4

Slide 4

Basic problems Value of single ”pieces of information” is difficult to assess. Secondary effects

  • Positive: Increased focus
  • Negative: Decreased focus in other areas

The farmer perhaps doesn’t use the system in an

  • ptimal way.

Interactions production system/farmer/tool No control (what would have happened without the tool?)

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SLIDE 5

Slide 5

Methods (Verstegen et al. 1995)

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SLIDE 6

Slide 6

Methods (Verstegen et al. 1995) Normative approaches

  • Decision theoretical approaches
  • Decision tree analysis
  • Bayesian Information Economics
  • Control Theory
  • Decision analytical approaches
  • Simulation
  • Linear and dynamic programming
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SLIDE 7

Slide 7

Methods (Verstegen et al. 1995)

Normative approaches

Decision theoretical approaches

Decision tree analysis Bayesian Information Economics Control Theory

Decision analytical approaches

Simulation Linear and dynamic programming

Not value

  • f tools
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SLIDE 8

Slide 8

Methods (Verstegen et al. 1995)

Normative approaches

Decision theoretical approaches

Decision tree analysis Bayesian Information Economics Control Theory

Decision analytical approaches

Simulation Linear and dynamic programming

Not value

  • f tools

Examples

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SLIDE 9

Slide 9

Dynamic programming 1 333 1 299 1 327 1 304 ” , DKK/(1000 kg milk) 38 59 35 50 Annual replacement % 3b 3a 2 1 9 319 9 544 9 150 9 236 Net ret., DKK/cow/year 101.3 96.4 102.7 100.0 Number of cows 25 21 28 25 Average week of replac. 6 991 7 350 6 896 7 082 Milk yield, kg/cow/year Policy

Kristensen & Thysen (1991)

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Slide 10

Dynamic programming

Validity

  • What would the farmer do without the tool?
  • Would he/she follow the recommendations?
  • Are the registrations correct?
  • External validity:
  • Model versus real world
  • The tool tests itself
  • Bias for optimal policy
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SLIDE 11

Slide 11

Simulation (Markov chain)

Jalvingh et al. (1992)

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SLIDE 12

Slide 12

Simulation (Markov chain)

Validity

What would the farmer do without the tool? Would he/she follow the recommendations? Are the registrations correct? External validity:

Model versus real world The tool tests itself Bias for optimal policy

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SLIDE 13

Slide 13

Simulation (Monte Carlo) Jørgensen & Kristensen (1995)

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SLIDE 14

Slide 14

Simulation (Monte Carlo)

Validity

What would the farmer do without the tool? Would he/she follow the recommendations? Are the registrations correct? External validity:

Model versus real world The tool does not tests itself No bias for optimal policy

The preferred normative approach

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SLIDE 15

Slide 15

Empirical (”positive”) approaches

Verstegen et al. (1995):

  • Experimental designs
  • Field experiments
  • Experimental Economics
  • Quasi-experimental designs
  • Non-experimental designs

Use of data from herds

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SLIDE 16

Slide 16

Classification of designs

PO PP TS No control NPO NPP NTS Nonequivalent Control (N) TPO TPP TTS True control (N) Posttest

  • nly (PO)

Pretest- posttest (PP) Time series (TS)

Verstegen et al. (1995)

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SLIDE 17

Slide 17

Designs No control:

  • Only farms using the tool are included in the

study

Nonequivalent control (quasi-experimental design):

  • A control group is included in the study afterwards
  • As equal as possible to the farms using the tool

True control (experiment in the usual sense)

  • Farms are randomly divided into two groups
  • One group is told to use the tool
  • The other group is not allowed to use it
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SLIDE 18

Slide 18

Classification of designs PO PP TS No control NPO NPP NTS Nonequivalent Control (N) TPO TPP TTS True control (N) Posttest

  • nly (PO)

Pretest- posttest (PP) Time series (TS)

Verstegen et al. (1995)

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SLIDE 19

Slide 19

PO: Posttest only Not serious!

Result Time

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SLIDE 20

Slide 20

Classification of designs PO PP TS No control NPO NPP NTS Nonequivalent Control (N) TPO TPP TTS True control (N) Posttest

  • nly (PO)

Pretest- posttest (PP) Time series (TS)

Verstegen et al. (1995)

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SLIDE 21

Slide 21

NPO/TPO: Posttest only Manipulation

  • Confounding between

farmer type, production system and use of tool

Result Time } Effect

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SLIDE 22

Slide 22

Classification of designs

PO PP TS No control NPO NPP NTS Nonequivalent Control (N) TPO TPP TTS True control (N) Posttest

  • nly (PO)

Pretest- posttest (PP) Time series (TS)

Verstegen et al. (1995)

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SLIDE 23

Slide 23

PP: Pretest and posttest

Manipulation

  • Perhaps a general

trend: All farms may have improved as those being investigated

  • Confounding between

general development and effect of tool

Result Time } Effect

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SLIDE 24

Slide 24

Classification of designs

PO PP TS No control NPO NPP NTS Nonequivalent Control (N) TPO TPP TTS True control (N) Posttest

  • nly (PO)

Pretest- posttest (PP) Time series (TS)

Verstegen et al. (1995)

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SLIDE 25

Slide 25

NPP: Pretest and posttest Correction for

  • General trend
  • Confounding with

farmer type (partially, no randomization)

Result Time } Effect

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SLIDE 26

Slide 26

Classification of designs

PO PP TS No control NPO NPP NTS Nonequivalent Control (N) TPO TPP TTS True control (N) Posttest

  • nly (PO)

Pretest- posttest (PP) Time series (TS)

Verstegen et al. (1995)

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SLIDE 27

Slide 27

TPP: Pretest and posttest Correction for

  • General trend
  • Confounding with

farmer type (randomization)

Result Time } Effect

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SLIDE 28

Slide 28

Classification of designs PO PP TS No control NPO NPP NTS Nonequivalent Control (N) TPO TPP TTS True control (N) Posttest

  • nly (PO)

Pretest- posttest (PP) Time series (TS)

Verstegen et al. (1995)

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SLIDE 29

Slide 29

TS: Time series, no control

Confounding with farmer type Development over time

  • Value in the beginning

versus full value

Result Time

} Effect

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SLIDE 30

Slide 30

Classification of designs

PO PP TS No control NPO NPP NTS Nonequivalent Control (N) TPO TPP TTS True control (N) Posttest

  • nly (PO)

Pretest- posttest (PP) Time series (TS)

Verstegen et al. (1995)

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SLIDE 31

Slide 31

NTS: Time series, control

Development over time

  • Value in the beginning

versus full value

No confounding with farmer type

Result Time

}

Effect: b - a

a b

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SLIDE 32

Slide 32

Classification of designs PO PP TS No control NPO NPP NTS Nonequivalent Control (N) TPO TPP TTS True control (N) Posttest

  • nly (PO)

Pretest- posttest (PP) Time series (TS)

Verstegen et al. (1995)

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SLIDE 33

Slide 33

TTS: Time series, true control

Development over time

  • Value in the beginning

versus full value

No confounding with farmer type

Result Time

}Effect

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SLIDE 34

Slide 34

Example: NTS

Value of a management information system for sow herds

  • Response: Piglets/sow/year
  • Nonequivalent control
  • Time series