From registration to information, I Anders Ringgaard Kristensen - PDF document

From registration to information, I Anders Ringgaard Kristensen Slide 1 Outline 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 1

Focus of this lecture In last lecture, we discussed: • Utility concept • Planning, classical approach In this lecture we direct our attention towards the other side of the management cycle: • Production monitoring We shall, however, try to look at it from a decision making perspective. Slide 3 Making decisions Decision making is based on knowledge: • General know ledge : What you can read in a textbook on animal nutrition, animal breeding, agricultural engineering etc. • Context specific know ledge : 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 2

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 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 3

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 Advanced example: Hogthrob Activity Measurements – in Group Housed Pen • Accelerometer fitted on neck collar • Acceleration in 2 and 3 dimensions • Four measurements per second • Transfer PC via Blue Tooth • Twenty days (March 1st to March 20th 2005) Video Recordings • Four cameras used as web cam • Twenty days Slide 8 Cécile Cornou, LC-2373, IPH, KVL 4

Hogthrob: Acceleration data Eating Walking Sleeping Rooting Slide 9 Cécile Cornou, LC-2373, IPH, KVL DLM analysis Main Results – Sow 5 V-mask Specificity: 100 % Error Rate: 0 % (FP= 0) Tabular Cusum Specificity: 90 % Error Rate: 50 % (FP= 1) Cécile Cornou, LC-2373, IPH, KVL Slide 10 5

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). 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! Next step: Try another kind of processing. Slide 11 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 12 6

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 13 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 14 7

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 (Tuesday 8-10): • More or less “black box” approach Dynamic Linear Models based on Bayesian updated time series with Kalman filtering (Tuesday 10- 12): • Structured model • Data of same kind Bayesian networks (Tuesday afternoon) • Highly structured models • Data from different sources Slide 15 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 16 8

Focus on the monitoring process Slide 17 Principles of production monitoring Data recording Database Data processing Report with key figures Analysis • Statistical • Utility Decision Slide 18 9

Data recording Events: • Mating • Farrowing • Weaning • … Slide 19 A record (or ”registration”) Event (mating/ farrowing/ weaning … ) Identification (sow # / section # / batch # ) Registration level (animal/ section/ batch/ herd) Time (date or date/ hour/ minute) Property (what is measured) Value (numerical or categorical) Slide 20 10

A record, example Event: Farrowing Identification: Sow # 1234 Registration level: Animal Time: February 23rd Property: Live born piglets Value: 12 Slide 21 Principles of production monitoring Data recording Database Data processing Report with key figures Analysis • Statistical • Utility Decision Slide 22 11

Records: Collect in a table Basically a table for each event, but several properties • Example: Farrowing Sow # Date Liveborn Stillborn Course 1234 15/ 1 04 12 2 Easy 678 16/ 1 04 9 4 Diff. 1001 18/ 1 04 14 1 Easy … … … … … Slide 23 Database A collection of tables for different events Sow # Date Liveborn Stillborn Course 1234 15/ 1 04 12 2 Easy 678 16/ 1 04 9 4 Diff. 1001 18/ 1 04 14 1 Easy … … … … … Slide 24 12

Principles of production monitoring Data recording Database Data processing Report with key figures Analysis • Statistical • Utility Decision Slide 25 Data processing Ask questions to the database (the SQL language): • Number of events in a period • Matings per week: # matings/ # weeks • Farrowings per week: # farrowings/ # weeks • Averages over a period • Liveborn piglets per litter Slide 26 13

Data processing: Complex, I Total gain, slaughter pigs ( this period): • + Total weight, slaughtered pigs • + Total weight, dead pigs • + Valuation weight, end • – Total weight, inserted pigs • – Valuation weight, beginning Daily gain = Total gain / Total days in feed Data sources? Slide 27 Principles of production monitoring Data recording Database Data processing Report with key figures Analysis • Statistical • Utility Decision Slide 28 14

Interdependencies Key figures are heavily correlated: • Logically (refer to figure) • Biologically Slide 29 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 30 15