Advanced Herd Management. Course introduction. Anders Ringgaard Kristensen

Transcription

1 Advanced Herd Management Course introduction Anders Ringgaard Kristensen 1

2 Outline Consequences of UR 2005 Competences: What are you supposed to learn? The framework and definition of herd management The management cycle Objectives of production, utility theory Classical production theory Classical replacement theory Limitation of classical theories Outline of the course Teachers The concept of uncertainty 2

3 Herd management at KVL Previous setup Praktisk produktionsstyring Registrations in practice Use of standard software ( Bedriftsløsningen ) Focus on existing tools UR 2005 Husdyrproduktion Basic production monitoring Use of existing models Tema: Animal Production Farmer preferences: Planning, Control Basic production monitoring Focus on problems to be solved using existing tools Advanced Herd Management Advanced monitoring Decision support Focus on construction and comprehension of management tools The problem is the starting point Advanced Herd Management Advanced monitoring Decision support Focus on construction and comprehension of management tools The problem is the starting point Tema: Husdyrvidenskab 2 Farmer preferences: Planning, Control Focus on problems to be solved using existing tools Use of advanced methods 3

4 Basic production monitoring In UR 2005 it is moved from Master s level to Bachelor s level. Most of you will never learn it, unless We cover it in Advanced Herd Management until the new structure is fully implemented. That s what we will do 4

5 Preconditions Previous setup Regressionsanalyse Lineær algebra (indirectly precondition) Produktionsøkonomi Some course on animal production. UR 2005 Husdyrproduktion Matematik og modeller Statistisk dataanalyse 2 Mandatory first year 5

6 Preconditions UR 2005 None of you have had a chance to fulfill the preconditions defined in UR Doesn t matter if you have had Lineær algebra and Regressionsanalyse. My guess is that very few have had those courses. As a consequence of this assumption, the course will start up with a brush-up course of Probability calculus and statistics Linear algebra This brush-up course was planned to be coordinated with Quantitative and Population Genetics, but that course has been cancelled (only 6 students). We are on our own! 6

7 Competences I Competences obtained within basic science: Comprehension of advanced methods for production monitoring and analysis as well as operational and tactical planning in livestock herds. Evaluation of various methods in relation to the solution of typical management problems in livestock herds. Make judgements concerning the choice of appropriate methods for different herd management tasks. 7

8 Competences II Competences obtained within applied science: Apply principles and advanced methods for production monitoring based on data from specific herds. Apply principles and advanced methods for operational and tactical planning in specific livestock herds. Apply principles and advanced methods in development of general herd management tools. Make judgements concerning the quality of commercially distributed general herd management tools. Competences obtained within Ethics & Values: Is aware of the relation between monitored production traits and the priorities defined by the farmer s utility function. 8

9 Framework and definition of herd management A pig (an animal) Medicine Meat Piglets Manure Feed Milk 9

10 Framework and definition of herd management Pig production = N a pig? Medicine Meat Piglets Manure Feed Milk 10

11 Framework and definition of herd management Pig production = N a pig? Medicine Meat Piglets Manure Feed Milk 11

12 Pig production Framework and definition of herd management Pigs: Ages Groups Individuals Buildings Fields Neighbors, society, consumers Owner Feed Farm hands 12

13 Framework and definition of herd management Elements of production I The factors (input to production) Animals Feed Buildings, inventory Labor Management Veterinary services Energy 13

14 Framework and definition of herd management Elements of production II Objectives: Maximization of the farmer s welfare: Income (personal) Leisure time (personal) Animal welfare (animals) Working conditions (farm hands) Environmental preservation (future generations) Prestige (personal) Product quality (consumers) 14

15 Framework and definition of herd management Elements of production III Constraints limiting production Physical (land, housing capacity, storage capacity) Economical (capital, prices) Legal (laws) Personal (skills, education) 15

16 Framework and definition of herd management Definition of herd management Having discussed the three key elements: Factors (input to production) Objectives (farmer s welfare) Constraints (limitations) we are now able to define what we mean by Herd Management: Herd management is a discipline serving the purpose of concurrently ensuring that the factors are combined in such a way that the welfare of the individual farmer is maximized subject to the constraints imposed on his production. A dynamic optimization problem under constraints. Decisions! We decide how to combine the factors. 16

17 The management cycle The management cycle: A never ending story 17

18 The management cycle The management cycle: Classical theories Utility Theory, Ch. 3. (Scarce Resources) Basic Production Monitoring, Ch. 5. Neo-classical Production Theory, Ch. 4. (Animal science, Production function) 18

19 The management cycle Herd Management Science Basic level: As we define the basic level, it consists of Utility theory Neo-classical production theory Basic production monitoring (Animal nutrition, animal breeding, ethology, farm buildings) What any animal scientist should know about management The starting level of this course Briefly revised this morning 19

20 Anders Ringgaard Kristensen, IPH Objectives of production, utility theory Utility theory We need a criterion for comparison of plans ( ways to produce). Several concerns: The farmer The staff Consumers The animals Environment Who decides the weighting? My answer: The farmer! Anders Ringgaard Kristensen, IPH Animals Farm buildings Land Feed Neighbors, society, consumers Farm hands Owner 20

21 Objectives of production, utility theory Farmer s preferences The farmer has/defines a list of concerns: Own direct concerns: Income, u 1 Leisure time, u 2 Prestige, u 3... Indirect concerns (because he cares for others) Animal welfare, u 4 Working conditions, u 5 Environment, u 6 Product quality, u 7 The farmer knows/decides the weighting The items on the list (u 1, u 2,, u k ) are called attributes of the farmer s utility. 21

22 Objectives of production, utility theory When is something an attribute? When it directly influences the subjective welfare of the farmer. May NOT be an attribute: Average milk yield of cows Average daily gain of slaughter pigs Animal welfare, because animals at a high level of welfare also produce at a higher level. May be an attribute: Monetary gain Leisure time Animal welfare, if the farmer is willing to accept that it to some extent decreases the levels of other attributes. 22

23 Objectives of production, utility theory Consequences measured by attributes Attribute 1 2 Stage T 1 u 11 u 12 u 1T 2 u 11 u 22 u 2T k u 11 u k2 u kt At any stage, the attributes will depend on the production Y t and the factors x t. The relation is given by the attribute function h: u t = h(y t, x t ) 23

24 Objectives of production, utility theory Aggregation of attributes: Utility function The utility function Aggregation over time Monetary gain Animal welfare Aggregation over attributes Expected Utility Theorem: Maximization of U is all we need to care about! Refer to Chapter 3 for details! 24

25 Classical production theory Production function 25

26 Classical production theory Production function In classical production theory, the uncertainty represented by the e s is ignored. 26

27 Classical production theory Neo-classical production theory Answers 3 basic questions: What to produce. How to produce. How much to produce. Marginal considerations Basic principle: Continue as long as the marginal revenue, MR, exceeds marginal costs, MC. At optimum we have MR = MC. 27

28 Classical production theory What to produce Two products y 1 and y 2 Prices p 1 and p 2. Fixed factor allotment Value of production: u = p 1 y 1 + p 2 y 2 Fixed value u : y 2 = u /p 2 (p 1 /p 2 ) y 1 The price ratio p 1 /p 2 determines the optimal combination! The general principle: Continue producing one more unit of Product 1 (reducing the output of Product 2 accordingly) as long as the marginal revenue exceeds the marginal costs. 28

29 Classical production theory What to produce: Two products y 2 p 1 /p 2 = 2 p 1 /p 2 = 1/2 y 1 29

30 Classical production theory How to produce Two factors x 1 and x 2 Prices p 1 and p 2. Fixed production Cost of production: c = p 1 x 1 + p 2 x 2 Fixed value c : x 2 = c /p 2 (p 1 /p 2 ) x 1 The price ratio p 1 /p 2 determines the optimal combination! The general principle: Continue adding one more unit of Factor 1 (reducing the output of Factor 2 accordingly) as long as the marginal revenue exceeds the marginal costs. 30

31 Classical production theory How to produce: Two factors x 2 0 x 1 31

32 Classical production theory How much to produce One factor x and one product y Prices p x and p y A production function y = f(x). Profit u(x) = yp y xp x = f(x)p y xp x Problem: Find the factor level that maximizes the profit 32

33 Classical production theory How much to produce Maximum profit where u (x) = 0. u(x) = f(x)p y xp x u (x) = f (x)p y p x u (x) = 0 f (x)p y = p x Maximum profit where: Marginal revenue = Marginal cost! 33

34 Classical production theory How much to produce 1 0,8 Total revenue, f(x)p y 0,6 0,4 0,2 Average revenue, f(x)p y /x 0-0,2 Marginal revenue, f (x)p y 34

35 Classical production theory How much to produce, logical bounds 1 0,8 Total revenue, f(x)p y 0,6 0,4 0,2 Average revenue, f(x)p y /x 0-0,2 Marginal revenue, f (x)p y 35

36 Classical production theory How much to produce, optimum 1 0,8 Total revenue, f(x)p y 0,6 0,4 0,2 Average revenue, f(x)p y /x 0 Price of factor p x -0,2 Marginal revenue, f (x)p y 36

37 Classical replacement theory Classical replacement theory The replacement problem in a broad sense is one of the most important decision problems in animal production. Dynamics: What we decide at this stage (keep/replace) may influence production in many future stages. Many other decision problems relate to the replacement problem: Insemination Treatment for diseases Feeding level A correct handling of the other problems implies that the question of replacement must be taken into account. 37

38 Classical replacement theory Definition Replacement: When an existing asset is substituted by a new one with (more or less) the same function. Examples: Light bulbs Cars Sows Milking robots 38

39 Classical replacement theory Replacement problems in animal production Female production animals: (Ewes, mink, goats) Sows Dairy cows Two levels: Optimal lactation to replace Optimal stage of lactation to replace Repeatability of milk yield over lactation rather high (as opposed to litter size in sows). 39

40 Classical replacement theory Replacement problems in animal production Technical: Examples: Farm buildings Equipment & machinery Very similar to the sow replacement problem, except for the biological variation Technological improvements probably more important than the corresponding genetic improvement in cows. Marginal/average considerations apply well 40

41 Classical replacement theory Replacement problems in animal production Slaughter calves: If housing capacity is limited and replacements are available, the problem is in agreement with classical theory. Marginal/average considerations Slaughter pigs: Two levels: When to deliver individual pigs (animal level) When to deliver the remaining pigs (batch level) Broilers: At batch level (no animal level) in agreement with classical theory. Contracts may limit the decisions of the farmer 41

42 Classical replacement theory A chain of assets Asset 1 Asset 2 Asset 3 Asset n t r How do we determine an optimal value for t r -the length of the period to keep each asset in the chain? 42

43 Classical replacement theory Optimal time for replacement Assume that the price of a new asset is S The salvage value of the asset at time t is s t The net returns from the asset in stage (time step) t is r t The total net revenue T(t) from the asset if it is replaced at stage t is then 43

44 Classical replacement theory Optimal time for replacement Average net revenue, if replaced at time t: Marginal net revenue at time t Optimal replacement time where Replace where: marginal revenue = average revenue 44

45 Classical replacement theory Graphical illustration The replacement problem Revenue Marginal Average -200 Time 45

46 Classical replacement theory Marginal revenue Typically decreasing because of decreasing productivity and increasing maintenance costs. The net returns adjusted for change in salvage value. The marginal curve crosses the average curve where the average is maximal. 46

47 Limitations of classical theories Limitations of neo-classical theory Static approach: Immediate adjustment Only one time stage Deterministic approach Ignores risk Biological variation Price uncertainty Knowledge representation (knowledge considered as certain): Unobservable traits Production functions Detached from production: No information flow from observations. No updating of knowledge. 47

48 Limitations of classical theories Limitations of classical replacement theory Uncertainty: The classical replacement theory assumes full certainty about the marginal profit function, the investment costs and all prices. As discussed in details in Chapter 2, uncertainty is an inherit property of the decision making process in herd management. The uncertainty is partly a consequence of imperfect knowledge, and partly of random variation. Uniqueness: The general theory implicitly assumes that the marginal and average profit functions are as shown in Figure 4.5 with a uniquely determined intersection. For several applications the intersection is not unique. This is, for instance, the situation in dairy cows, where the average and marginal profits are as shown in Figure 4.7. Availability: The theory assumes that a new asset for replacement is always available. 48

49 Background Structural development in the sector Increasing herd sizes Decreasing labour input Technological development Sensors, automatic registrations Computer power Networking Methodological development Statistical methods Operations Research 49

50 Outline of course - I Part I: Brush-up course on Probability calculus and statistics Linear algebra Advanced topics from statistics Basic production monitoring Registrations and key figures Analysis of production results 50

51 Outline of course - II Part II: The problems to be solved From registrations to information, value of information, information as a factor, sources of information Decisions and strategies, definition and knowledge foundation Consequences of decisions and states Assessing the utility value of tools 51

52 Outline of course - III Part III: The methods to be used State of factors Monitoring and data filtering Bayesian networks Decision support Decision graphs Simulation (Monte Carlo) Linear programming (low priority) Markov decision processes (dynamic programming) Mandatory reports 52

53 Teachers Part I: Anders Ringgaard Kristensen Part II: Anders Ringgaard Kristensen Part III: Anders Ringgaard Kristensen Cécile Cornou (PhD student of herd management) Jehan Frans Ettema (PhD student of herd management) Guests: Thomas Algot Søllested (TNM Teknik) Thomas Nejsum Madsen (TNM Consult) Lars Otto (Associate professor of economics, KVL) Others 53

54 Mandatory reports 4 minor reports must be handed in Based on the exercises At least 3 must be approved in order to attend the oral exam The 4 reports are distributed over the following methods: Linear programming Monitoring and data filtering Markov decision processes Bayesian networks (including decision graphs) 54

55 The web Herd Management at KVL: Home page of the course Course description Plan Pages for each lesson with a description of the contents, literature, exercises, software to use etc. 55

56 Exercise, uncertainty Production function: f ( x, x, x ) = c x + c x + c x + c x + c x + c x + c x x + c x x + c x x milk yield given energy, protein and fat Adding uncertainty, the actual milk yield is Y = f(x 1,x 2,x 3 ) + e 56

57 Uncertainty, II Adding uncertainty to production function: Considerable improvement, BUT Significant uncertainty about true energy, protein and fat content still ignored Example, only considering energy 57

58 Uncertainty - III Silage obs.* Silage true Ration Milk yield* Concentr.* Herd size* True energy content of silage is unknown The precision of the observed content depends heavily on the observation method (standard value from table, laboratory analysis etc.) 58

59 Uncertainty - IV Effects of decisions will be over-estimated if unceratainty about true state factor characteristics factor effects is ignored. Wrong decisions may be made. 59

60 Uncertainty, V Baysian networks with decisions and utilities added (student project). Silage obs.* Silage true Ration Milk yield* Method Concentr.* Herd size* Price Mix Cost Rev. 60

61 Uncertainty - VI Uncertainty is not the opposite of knowledge Uncertainty is a property of knowledge Reduction of uncertainty is often possible at some cost! Reducing uncertainty is not always profitable. 61