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From registration to information II Anders Ringgaard Kristensen - - PowerPoint PPT Presentation

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Department of Large Animal Sciences From registration to information II Anders Ringgaard Kristensen Department of Large Animal Sciences Outline The decision making process revisited Trends in livestock farming, data sources Value of data (and

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From registration to information II

Anders Ringgaard Kristensen

Department of Large Animal Sciences

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Outline

The decision making process revisited Trends in livestock farming, data sources Value of data (and information) Measurement errors, including Categorical observations and thresholds

Department of Large Animal Sciences

Advanced Quantitative Methods in Herd Management Slide 2

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Making decisions

Decision making is based on knowledge:

  • General knowledge: What you can read in a textbook on

animal nutrition, animal breeding, agricultural engineering etc.

  • Context specific knowledge: What relates directly to the

unique decision problem. Examples:

  • The milk yield of dairy cow No. 678 when considered for

culling.

  • The estrus status of sow No. 345 when considered for

insemination.

  • The current daily gain of the slaughter pigs in House 5

when considering whether or not to adjust protein contents of the diet.

When knowledge is represented in a form that may be used directly as basis for a decision, we call it information.

Department of Large Animal Sciences

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Information sources

General knowledge:

  • Look in a textbook
  • Ask an expert

Context specific knowledge:

  • Obtained through registrations (observations) in the

herd:

  • Traditional registrations
  • Test day milk yield, cow 567
  • Litter size of sow 123
  • Sensor based registrations
  • Conductivity or temperature of milk from AMS
  • Accelerations of a sow from a censor node in

an ear tag

  • Computer vision (image analysis)

Department of Large Animal Sciences

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From registrations to information

We refer to a collection (typically in a database) of registrations of the same kind as data. We don’t use data directly for decision making (huge amounts of data). Before we can use data we need to reduce it through some kind of processing. The resulting information is used for decision making (which again requires processing: optimization).

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A simple example of the path

Data: Test day milk yields Processing I: Calculating cumulated yields for individual cows over a standardized period and afterwards calculating the herd average. Information: Average milk yield in the herd. Processing II: Linear programming using the Simplex algorithm. Decision: Least cost feed ration for the cows. The path from test day milk yields to feed ration is not unique:

  • Both processing steps could be replaced by other methods.
  • Choosing a wise processing of data into information is an

important issue in herd management!

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Advanced example: Hogthrob

Activity Measurements – in Group Housed Pen

  • Accelerometer fitted on neck collar
  • Acceleration in 3 dimensions
  • Four measurements per second
  • Transfer PC via Blue Tooth
  • Gestation house and farrowing crate

Video Recordings

  • Four cameras used as web cam

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The Farrowing House

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Data Collected – Farrowing house

Farrowing

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Activity Classification – Farrowing

2 days before farrowing

Feeding: 7.15, 12.00, 15.30 Lying side 1 Lying side 2 Lying sternally Active

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Activity Classification – Farrowing

Farrowing day

Lying side 1 Lying side 2 Lying sternally Active Feeding / Rooting / Nesting

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Information retrieval – Farrowing (or heat)

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An advanced example of the path: Hogthrob

Data: Accelerations of a sow measured 4 times per second in 3 dimensions Processing I: Online time series analysis of the acceleration data using Dynamic Linear Models Information: Sow in heat? (yes/no). [Example: farrow] Processing II: Dynamic programming. Decision: Inseminate/leave open/cull Notice the reduction in the dimensionality of the information (one binary variable) compared to the data!

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The decision making process: Summary

The purpose of monitoring is to improve the decisions Processing of registrations into information is necessary Choosing the best processing is a key issue Information is a tractable representation of context specific knowledge. Monitoring is the sub-path from registration to information During this course we will follow the path from data to decision:

Department of Large Animal Sciences

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Trends in livestock farming

Over the last decades we have seen:

  • Computers available to farmers
  • Process computers (climate control, feeding systems,

milking systems)

  • Computer networks
  • On farms
  • The internet
  • Automatic registrations by sensors
  • Improved methods for data filtering
  • State space models
  • Bayesian networks
  • Improved methods for decision support
  • Decision graphs
  • Markov decision processes (dynamic programming)
  • Improved biological understanding

How do we include this in herd management? How do we evaluate?

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PigIT: Sensors as installed in two experimental pens

Advanced Quantitative Methods in Herd Management Dias 16

Department of Large Animal Sciences

16 pens in 4 sections are monitored by sensors and cameras

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PigIT: Data infrastructure in a herd

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Department of Large Animal Sciences

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PigIT: Sensor data – what does it look like?

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Department of Large Animal Sciences

Water, Feed Local temp. Section: Temp. Humidity

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Data sources: Brain storm

Live weight assessment:

  • …

Heat detection

  • …

Detection/prediction of farrowing

  • …

Detection of diarrhea

  • …

Detection of mastitis

  • …

…

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Sensor types

Flowmeters Climate sensors (temperature, humidity) Pedometers Accelerometers Vision Acoustic (e.g. coughing) AMS related Sensors provide data!

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Value of data

The potential positive value of data is that they lead to better information and, thus, better decisions: Compare to evaluation of a feed additive:

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Feed additive

Processing Mixing

Animals Response Data

Processing (twice)

Decision Response

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Value of data

Value of feed additive: Expected income with additive – Expected income without additive = Value of feed additive . Value of data: Expected income with data – Expected income without data = Value of data .

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Value of data

Data has only value if they improve the information and, consequently, lead to better management decisions:

  • Buying/selling of animals
  • Movement of animals
  • Insemination
  • Induction of events
  • Feed ration composition
  • Feeding level
  • Observing

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Value of data

Data has only value if they improve the information and, consequently, lead to better decisions. Data typically reduce uncertainty

  • In other words they improve the precision of the

information – cf. exercise on prediction of farrowings in sow herd.

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Example from exercises: Number of farrowings

Observations (data) s Number of sows inseminated (12) 1.66 + conception rate in herd (historical data) 1.39 + heat detection after 3 weeks (2 in heat) 0.98 + heat detection quality in herd 0.81 + ultra sound scanning of 10 sows 0.47

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The purpose of the Bayesian network was to predict the number of farrowings expressed by the mean and the standard deviation (s) More data increase the precision of the prediction!

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Definition

A record: λ Data: Λ = {λ1, λ1, … , λk}, Λ ∈ Rk Processing: Ψ() Information: I = Ψ(Λ), I ∈ Rm, where m << k Decision: Θ Decision strategy: I → Θ The processing of data into information typically implies a huge reduction of data. The processing is specific to the decision problem.

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Utility value of data – The two sow problem

Problem description A smallholder sow farmer has only housing capacity for one sow. Recently his sow died. He has no gilt, so he wants to buy a new sow. His neighbor has two pregnant 2nd parity sows for sale: Sow A and Sow B. Both of them will farrow two weeks from now. The price is the same. Decision problem Should the smallholder buy Sow A or Sow B from his neighbor? Initial comments The old sow is dead – nothing can change that. Everything that doesn’t depend on the decision can be ignored in the optimization (partial budgeting principle). In this case we can ignore the price of the new sow.

Dias 27 AVEPM 2013 Schwabe Symposium, Chicago

Department of Large Animal Sciences

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The two sow problem

What kind of data would the small holder farmer like to have? Litter size of first parity! Any correlation between litter size of first and second parity?

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Elements

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A record: Y1i Data: Λ = {Y1A, Y1B} Processing: Predicted litter size 10.0 0.2

8.5

Information: I = {

  • ,
  • }

Decisions: Select Sow A; Select Sow B Decision strategy:

  • : Select Sow A
  • : Select Sow B
  • : Select Sow A or Select Sow B

According to Example 6.1 the value of the data is 0.28 piglets. Would it be of any value to know the litter size of just one of the sows? If yes, what would the decision strategy look like? Refer also to exercises!

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Measurement errors

Registration failure – something is completely wrong Measurement precision – observation errors Indirect measure – what we observe is not exactly what we want to know Bias – systematic deviation from true value (calibrate!) Rounding errors – usually not a major problem Interval censuring – only thresholds known (date of farrowing versus time of farrowing)

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Categorical measurements

Variables observed only as a state: Heat detection: {In heat, Not in heat} Pregnancy test: {Pregnant, Not pregnant} Disease diagnosis: {Diseased, Not diseased} Body Condition Score: {1, 2, 3, 4, 5} State {d1, d2, … , dn}

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Categorical measurements

The measurement is (either literally or conceptually) a two step procedure:

  • 1. Measurement of a continuous variable y ~ N(µi, σi

2) if the

animal is in State di

  • 2. Assigning of a state di to the measurement depending on a

set of threshold values {τ1, …, τn-1}:

  • 1. τi-1 < y ≤ τi ⇒ State di observed

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Example – pregnancy diagnosis

State, di {Not pregnant (i = 0), Pregnant (i = 1)} Measurement Hormone level, h Distributions:

  • i = 0

h ~ N(10, 12)

  • i = 1

h ~ N(13, 12) Threshold: 11 Diagnose:

  • h ≤ 11

Not pregnant

  • h > 11

Pregnant

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As a Bayesian network (“ThresholdModel.xbn”)

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Conception rate Pregnant (no/yes) Hormone level Test outcome Threshold value The threshold value determines sensitivity and specificity!

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Overall performance of test – ROC

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By varying the threshold all combinations of sensitivity and specificity along the curve can be achieved. The circle corresponds to τ = 11.5, where the sensitivity is 0.93 and the specificity is also 0.93.

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Measure errors (continued)

Pre-selection (are animals representative) Registration of interventions (e.g. treatment or culling) Missing registrations as a source of information Selection:

  • Pigs with low daily gain stay longer in the herd (exercise)
  • High yielding cows are kept longer

Department of Large Animal Sciences

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