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 information) Measurement errors, including Categorical observations and thresholds Advanced Quantitative Methods in Herd Management Slide 2
Department of Large Animal Sciences 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. Advanced Quantitative Methods in Herd Management Slide 3
Department of Large Animal Sciences 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) Advanced Quantitative Methods in Herd Management Slide 4
Department of Large Animal Sciences 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). Advanced Quantitative Methods in Herd Management Slide 5
Department of Large Animal Sciences 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! Advanced Quantitative Methods in Herd Management Slide 6
Department of Large Animal Sciences 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 Advanced Quantitative Methods in Herd Management Slide 7
The Farrowing House
Department of Large Animal Sciences Farrowing Data Collected – Farrowing house Advanced Quantitative Methods in Herd Management Slide 9
Department of Large Animal Sciences 2 days before Activity Classification – Farrowing farrowing Feeding: 7.15, 12.00, 15.30 Active Lying side 1 Lying sternally Lying side 2 Advanced Quantitative Methods in Herd Management Slide 10
Department of Large Animal Sciences Farrowing Activity Classification – Farrowing day Feeding / Rooting / Nesting Active Lying side 1 Lying sternally Lying side 2 Advanced Quantitative Methods in Herd Management Slide 11
Department of Large Animal Sciences Information retrieval – Farrowing (or heat) Advanced Quantitative Methods in Herd Management Slide 12
Department of Large Animal Sciences 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! Advanced Quantitative Methods in Herd Management Slide 13
Department of Large Animal Sciences 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: Advanced Quantitative Methods in Herd Management Slide 14
Department of Large Animal Sciences 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? Advanced Quantitative Methods in Herd Management Slide 15
Department of Large Animal Sciences PigIT: Sensors as installed in two experimental pens 16 pens in 4 sections are monitored by sensors and cameras Advanced Quantitative Methods in Herd Management Dias 16
Department of Large Animal Sciences PigIT: Data infrastructure in a herd Advanced Quantitative Methods in Herd Management Dias 17
Department of Large Animal Sciences PigIT: Sensor data – what does it look like? Water, Feed Local temp. Section: Temp. Humidity Advanced Quantitative Methods in Herd Management Dias 18
Department of Large Animal Sciences Data sources: Brain storm Live weight assessment: • … Heat detection • … Detection/prediction of farrowing • … Detection of diarrhea • … Detection of mastitis • … … Advanced Quantitative Methods in Herd Management Slide 19
Department of Large Animal Sciences Sensor types Flowmeters Climate sensors (temperature, humidity) Pedometers Accelerometers Vision Acoustic (e.g. coughing) AMS related Sensors provide data! Advanced Quantitative Methods in Herd Management Slide 20
Department of Large Animal Sciences 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: Feed Processing Animals Response additive Mixing Processing Decision Response Data (twice) Advanced Quantitative Methods in Herd Management Slide 21
Department of Large Animal Sciences 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 . Advanced Quantitative Methods in Herd Management Slide 22
Department of Large Animal Sciences 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 Advanced Quantitative Methods in Herd Management Slide 23
Department of Large Animal Sciences 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. Advanced Quantitative Methods in Herd Management Slide 24
Department of Large Animal Sciences Example from exercises: Number of farrowings 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! 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 Advanced Quantitative Methods in Herd Management Slide 25
Department of Large Animal Sciences Definition A record: λ Data: Λ = { λ 1 , λ 1 , … , λ k }, Λ ∈ R k Processing: Ψ () Information: I = Ψ ( Λ ), I ∈ R m , 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. Advanced Quantitative Methods in Herd Management Slide 26
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