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11/15/2019 Department of Veterinary and Animal Sciences From registration to information Anders Ringgaard Kristensen Department of Veterinary and Animal Sciences Outline of Part 1 The decision making process The role of models Basic production monitoring as a source of information Key figures and their properties Interpretation of key figures Limitations of traditional production monitoring Slide 2 First focus of this lecture In this lecture we direct our attention towards the left side of the management cycle: • Production monitoring We shall, however, try to look at it from a decision making perspective. 1

11/15/2019 Department of Veterinary and 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. Slide 4 Department of Veterinary and 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) Slide 5 Department of Veterinary and 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). Slide 6 2

11/15/2019 Department of Veterinary and 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! Slide 7 Department of Veterinary and 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 Slide 8 Cécile Cornou, LC-2373, IPH, KVL The Farrowing House 3

11/15/2019 Department of Veterinary and Animal Sciences Farrowing Data Collected – Farrowing house Slide 10 Department of Veterinary and 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 Slide 11 Department of Veterinary and Animal Sciences Farrowing Activity Classification – Farrowing day Feeding / Rooting / Nesting Active Lying side 1 Lying sternally Lying side 2 Slide 12 4

11/15/2019 Department of Veterinary and Animal Sciences Information retrieval – Farrowing (or heat) Slide 13 Department of Veterinary and 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! Slide 14 Department of Veterinary and 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: Slide 15 5

11/15/2019 Department of Veterinary and Animal Sciences Uncertain information Classical methods assume certainty. In real life, certain information hardly exists. However, the degree of uncertainty varies. In general, we use distributions for representation of knowledge with uncertainty. For binary information, we may just supply the probability. For continuous information, we supply for instance a normal distribution with a mean and a variance. A small variance implies that we are rather certain about the value. For consistent processing of data, a model is needed. Slide 16 Department of Veterinary and Animal Sciences Uncertainty: Pregnancy status of a cow For the replacement decision we want to know the pregnancy status of a cow, but it is (most often) unobservable, so we have only indirect observations: • If a cow has not been inseminated, the probability of pregnancy is zero. • If it has been inseminated, the probability is 0.4, because the conception rate of the herd is 0.4. • If, after 5 weeks, the cow has not shown heat, the probability increases. Assuming a heat detection rate of 0.5, the probability increases to 0.7. • A positive pregnancy diagnosis will further increase the probability, but only a calving will increase it to 1. We need a method for consistently combining the indirect observations into an updated probability of pregnancy. We may use a Bayesian Network model for that! Slide 17 Department of Veterinary and Animal Sciences Models for monitoring under uncertainty Assessing the distribution of a key figure from the distribution of data (later this lecture): • “Black box” approach Statistical quality control models based on time series analysis: • More or less “black box” approach Dynamic Linear Models based on Bayesian updated time series with Kalman filtering: • Structured model • Data of same kind Bayesian networks • Highly structured models • Data from different sources Slide 18 6

11/15/2019 Department of Veterinary and Animal Sciences Basic production monitoring Traditional approach to production monitoring: • Registrations are collected systematically in the herd • Data is entered into a Management Information System (MIS). • At quarterly (or monthly) intervals, the MIS calculates a bunch of key figures which are presented to the farmer in tabular form in report. • The farmer looks at the key figure and decides whether or not to make adjustments to production. The information provided is the list of key figures. We shall briefly discuss how to interpret such key figures in a sound way. Slide 19 Department of Veterinary and Animal Sciences Principles of production monitoring Refer to Chapter 5 • Data recording • Database • Data processing • Report with key figures • Analysis • Statistical • Utility • Decision Slide 20 Department of Veterinary and Animal Sciences Report with key figures Key figures have 3 basic properties • Correctness • Are all registrations correct (right animal(s), right event, right value etc.)? • Validity • Does the key figure express exactly what we want to know? • Precision • Standard deviation of estimate – Exercise. Slide 21 7

11/15/2019 Department of Veterinary and Animal Sciences Correctness Examples … Slide 22 Department of Veterinary and Animal Sciences Validity Example: Reproduction in sows Discussion: • What do you want to know? • Which figure(s) will provide us with the desired information? Slide 23 Department of Veterinary and Animal Sciences Key figure: Farrowings per week Utilization of the farrowing department. One of the most important elements from an economical point of view. Presented as an average value. What is of interest is the distribution over weeks. Slide 24 8