The Utility Value of Information in Pig Production

Transcription

1 The Utility Value of Information in Pig Production Erik Jørgensen. National Institute of Animal Science. Department of Research in Pigs and Horses, DINA 1 Research Centre Foulum. P.O.Box 39. DK-8830 Tjele Abstract: The possibilities for registrations and the corresponding costs are steadily increasing. We need to treat registrations and the use of Decision Support Systems (DSS) as a production factor in line with feed. The quality or utility value of a registration can be measured on the improvement in decisions. In order to do this we need, however, to define and categorize the decisions made in pig production. In the paper the evaluation of production control, registrations used in culling decisions, pregnancy test and weighing of slaughter pigs is presented. These informations have only a low value, but the analysis indicates how their use might be improved. The continuation of the efforts of utility evaluation is important, if we are not to overtax the producers with information. Introduction Traditionally several traits are registered in pig herds. In sow herds such traits comprises, e.g., litter size and event dates (mating, farrowing, etc.). It is generally accepted that these traditional traits are useful for decision purposes. In contrast few traits are measured in slaughter pig production. The reason for this difference in registration detail is not clear. With the advent of electronic equipment a whole new range of registrations becomes possible. The possibilities comprises electronic identification; automatic weighing; temperature- and activity measurements; and several registrations via video recordings using image analysis techniques. (Van der Stuyft et al., 1991). The general attitude towards these new registrations is that they will improve, either income of the pig producer; welfare of the pig; reduce the environmental impact of pig production; or help to fulfil consumer demands for quality certification. As with the traditional registrations, the possible benefits of these registrations are not directly estimated. It seems that the choice of registrations has a large random element. In the author s opinion it is important to treat information as a production factor in line with, e.g., feed. We need to define the quality and value of information, just as we define quality and value of feed stuffs. The value of feed stuffs is measured by their effect on the output, i.e., daily gain, feed conversion and meat quality. The value of information should be measured similarly on the output, i.e., the improvement in decisions. The value of information is dependent on it s applicability in the decision process, that is to say, to what extent the information helps the producers to reach their overall goal. Each decision in the herd has its own demand for information. The value of information thus depends on the decision context, like the value of feed stuffs differs, whether they are used for sows or slaughter pigs. Statistical decision theory using bayesian techniques gives the necessary theoretical tools for measuring improvement in decisions. However, a large effort is needed in defining and categorizing the decisions made in pig production, and in estimating the necessary probability distribution of the relevant traits. The purpose of this paper is to present Danish efforts in the field of information evaluation. The presented examples cover several aspects of pig production. General approach A part of the statistical decision theory originates in the so-called game theory describing the situation, where 2 gamblers can choose between several actions. The loss of a gambler ( and the gain of his opponent) both depends on his choice of actions and the action his opponent chooses. A pig producer 1 Danish Informatics Network in the Agricultural Sciences -1-

2 can be viewed as taken part in a game against nature. His loss depends on the chosen action and the nature s "choice" of action, the so-called State of Nature. The Bayes Strategy for decision making prescribes to choose the action that minimizes expected loss. The expectation is calculated with respect to the probability distribution of the State of Nature. Information will influence this distribution. The distribution before information is obtained is called á priori distribution, whereas the updated distribution conditioned on the information is called the á posteori distribution. The updating is carried out using the so-called Bayes rule (refer to De Groot, 1970) When evaluating information in a decision context we need to specify: - possible decisions/actions - possible registrations - á priori distributions given the different actions - á posteori distribution given the different registrations (how the information influences the probability distribution). - loss function In the following this will be shown for several aspects of pig production Production control Production Control in sow units is carried out in order to detect deviations from a planned level. The decision to be made on the basis of the production control is whether to continue production according to the original plan or to change production plan. As an example, the Danish Efficiency control has been studied (Jørgensen, 1985). In this control system several traits are registered and presented to the farmers as quarterly averages. These averages fluctuate either due to random influences or due to systematic deviations from the planned level. By observing the production traits during a production period a better knowledge of the expected future levels is obtained. The deviation of the individual trait from the expected level can be weighed with the marginal value of the trait, and an estimate of the economic value of the deviation can be obtained. By comparing these deviations to the expected value of an other plan, the decision whether to alter the production in the future can be made. Similar approach has been used by Huirne (1990). The improvement in income from these changes in production plan is equal to value of the production control. The possible decisions are to continue with same production plan or to alter the plan. The first two moments of the á priori distribution are the expectation and variance for production traits and expected income under the other production plans. The first two moments of the á posteori distribution are the conditional expectations and variance given the observed level of production trait in the control. The loss functions are minus the expected income from the current plan and minus the expected income from the other plan with the cost of changing plan included. In the study by Jørgensen (1985) it was showen that the registrations in the Danish efficiency control could be expressed from 16 major traits. The distribution of quarterly averages of these traits were approximated with a 16-dimensional normal distribution. Based on registrations from 100 sow herds in the Danish field test organization Den rullende Afprøvning, the variance in these traits was divided into variance between herds and variance within herds. From these variance-covariance matrices it was possible to specify the á priori and á posteori distribution of the traits. The loss functions representing expected income in the herd was calculated using standard prices. From these parameters it was possible to calculate the value of the production control. The study showed an overall improvement of 6-7 % in total income from using the control. Furthermore, the marginal values of different production traits from a control point of view were calculated in the study. The sequence of the traits to be included was chosen to make the highest marginal improvement in value for each new trait. As shown in figure 1, the most important traits to register are piglet mortality; usage of farrowing department; litter size in parity 1, 2, and 3 or higher; growth rate of piglets; and pregnancy rate. It is important to note that the litter size in the different parities treated separately has a value on their own. The recommendations of Sundgren et al. (1980) of treating these traits separately was thus confirmed in this study. These 7 traits accounted for almost all of the value of the control. -2-

3 Marginal value Production traits Mortality, piglets % Farrowing section, usage % Litter size, parity >2 Litter size, parity 2 Litter size, parity 1 Growth rate, weaners Pregnancy rate, % Weaning to cull, days Matings before 21 days % Age at selling, weaners Weaning age Culled after weaning, % Mating to cull, days Feed pr dead weaner, est Weaning - 1. oestrus, days Culled after weaning, % Relative value Figure 1: Value of different traits in production control in sow units The conclusion on the study was that the use of the efficiency control did indeed improve the expected income, but this improvement could be obtained from fewer registrations than currently used. The emphasis should be on controlling the quantitative aspects of the whole herd s production, instead of a detailed control of individual performance. Recording of sow specific litter size As mentioned in the previous section, the registration of parity specific litter size in the herd is one of the most important registrations from a production control point of view. This might indicate that litter size of the individual sow is an important trait. Several authors have investigated the possibility for culling sow with respect to litter size, e.g. Strang & King (1970), Treacy (1987), Huirne et al. (1991). The decision is relatively straight forward; if the sow obtains a litter size lower than a specified norm at a given parity, it should be replaced by a replacement gilt. The improvement in expected income by using information concerning litter size could be used, or, as in the following, the improvement in expected average litter size. Jørgensen (1992) considered a modification of the method used by Huirne et al. (1991). From this paper results concerning detail of information will be presented. Three levels of information were considered. No information, i.e., only involuntary culling; Parity information, i.e, only the parity of the sow is known; and Litter size, i.e., the litter size in each previous parity of the sow is known. The relationship between parity and litter size and involuntary culling was assumed to be known as well as the relationship between litter size in subsequent parities. In figure 2 the relationship between involuntary culling and average litter size is shown using the three levels of information. The level of involuntary culling is measured by the average age in the herd, if only involuntary culling was used. The level of in voluntary culling is assumed to be slightly increasing with parity. As can be seen the culling strategy improves the average litter size, at least for the low level of involuntary culling (i.e., high average age). The difference between the strategy using sow specific information and the strategy using only parity specific information is very low (less than 0.1 pigs per litter). Furthermore, in figure 3 a more realistic situation is presented, where slightly wrong estimates of the influence of parity on involuntary culling is used when calculating the culling strategy. The use of these erroneous estimates results in a reduction in expected in litter size, compared to the situation where no Optimal culling strategy is used. The magnitude, 0.1 pigs per litter, is fully comparable to the maximum possible benefit of using the culling strategies. As a conclusion, due to the low value of sow specific information, DSS for sow culling do not need to include the variance between sows with respect to litter size. This gives a considerable reduction in necessary calculations and complexity of the model used. However, the problem is to estimate the -3-

4 Litter size, avg No culling Parity Sow Average parity without voluntary culling Figure 2: Influence of involuntary culling (measured as avg. parity) on effect of culling strategy influence of parity on the relevant traits in each herd. As this would have to be done on selected data, it is no trivial task. Efforts should be focused on establishing methods in this area. Pregnancy testing The use of pregnancy testing is an example where the decision process is not as clearly specified, and where the value of the information is dependent on the use of information. The result of the test can be used for different purpose. Many pig producers use the indication from the pregnancy test in order to cull the sows that are deemed not pregnant, whereas the results might as well be used to indicate sows where the effort of heat detection (and induction) should be intensified. The value of pregnancy test has been discussed by several authors, e.g., Meredith (1989) and Vedder et al. (1989) with different conclusions. Non-pregnant sows are only a relatively small proportion of the total number of sows. Only sows that does not come in heat three weeks after mating are tested. As an example, if 100 sows are mated approx. Sixteen will not be pregnant. Of these 16 sows, e.g., 8 will show heat. Only 92 sows will then be tested, where of only 8/92 or less than 9% will be non-pregnant. Even with low error rates of the equipment for pregnancy testing, a relatively large proportion of the sows, that are tested to be non-pregnant, is in fact pregnant. As shown in figure 4 approximately 50% percent of the sows that are deemed empty are in fact going to farrow, if they are not culled before, depending on the pregnancy rate in the herd. If the pig producer uses the information in order to cull the sow, he will obtain fewer farrowings in the herd, and he might even suspect that he has a problem with pregnancy rate in his herd. Depending on the proportion of pregnant sows between the culled, the value of the information from the pregnancy tester is low, and might even be negative, due to fewer litters produced, and lower average age in the herd with a correspondingly lower average litter size. On the other hand, if he uses the information in order to isolate a group of sows with pregnancy problems and subsequently intensifies the effort of inducing and observing heat among these sows, -4-

5 Improvement in litter size 0.6 constant involuntary culling 0.5 age dependent invol. culling 0.4 Optimal culling Estimated age used in optimization Average age without voluntary culling Figure 3: Effect of using wrong estimates when calculating optimal culling policy. (Constant involuntary culling - only the aver. par. differs from estimates; age dep. invol. cullling = invol. culling increases with parity) Diagnosed as empty, percentage Empty Pregnant Pregnancy rate, percentage Figure 4: Effect of pregnancy rate on proportion of pregnant sows in the group that are tested as nonpregnant the information might have a high, positive value. In figure 5 the increase in time spent on each non-pregnant sow are shown, as a function of average time per sow. It seems plausible, that the probability of detecting oestrus will increase with increasing time spent on it. As a conclusion pregnancy testing is a good means of ensuring that pregnant sows are pregnant. The positive value that Vedder et al. (1989) assigns to pregnancy testing is from this point of view. The -5-

6 Avg. duration pr observed sow No preg. test Pregnant sows after preg. test Duration of pregnancy test Empty sows after preg. test Avg. duration every sow Figure 5: Effect of pregnancy test on time spent on surveillance of empty sows only good indication of a sow being non-pregnant, is either oestrus signs or no farrowing at the expected time, and not a negative pregnancy test. The negative value that Meredith (1989) assigns to pregnancy testing is because of this. Precision in weighing of slaughter pigs Weighing of slaughter pigs is often considered necessary in order to obtain efficient production. From a decision point of view, weighing can be used as a control of growth, and as a means of deciding, when to deliver the slaughter pig to the slaughter house. In this paper the use of weighing when selecting pigs for slaughter will be analyzed. Several methods for weighing or estimation of live weight has been suggested: the traditional individual weighing; electronic weighing equipment with electronic identification of the individual pig; weight assessment using the dimensions of the pigs, e.g., through image analysis; and, finally, simple visual assessment of weight. These methods will be expected to differ in precision, or to put it in another term to have different variance. As the cost of weighing differs markedly, an estimate of the value of an increase in this precision would be of interest. In Denmark slaughter pigs are priced according to their slaughtered weight, and furthermore graded according to meat percentage. Pigs with a slaughter weight in the interval between kg. will obtain the highest price per kg. Thus, there is an incentive to deliver pigs with the right slaughter weight. A pig producer can only measure the live weight of the pig, and has to decide, whether to deliver or not, based on this criteria. He will necessarily have to cope with a variation around the desired slaughter weight. Furthermore, he has to report how many pigs he will deliver to the slaughter house approximately 3 days in advance. Usually he can only deliver pigs once or twice a week. The pig producer uses the decision rule that if observed live weight of the pig is larger than a threshold weight, the pig is delivered three days afterwards 2. If not, the pig is kept in the herd until the next weighing a week afterwards. However, if the expected return in the next week is lower than the extected return of a new pig the pig is also delivered, regardless of weight. After delivery the pig is replaced with a new pig after a week for cleaning of the pen. A probabilistic simulation model (Jørgensen, 1991) was used in order to calculate proportion delivered on each day after insertion, and corresponding expectation and variance-covariance matrices for total feed consumption and slaughter weight. Assumptions of multivariate normal distribution is used. The expected future value (FV) of the production using an interest rate of 0.1 is calculated for each threshold live weight and the optimal threshold live weight is found. Prices and costs correspond to the level in Denmark in the middle of December In figure 6 the future value is shown, for different values of the weighing precision, 2 It is realized that this decision rule is not optimal, a kind of regression equation would improve the decisions. However, this decision rule, is the rule generally recommended by advisers -6-

7 Future value, DKR 6650 Traditional 6600 Automatic 6550 Image analysis Visual Weighing precision, 1/kg Weighing No weighing Figure 6: Influence of weighing precision (1 divided by standard deviation) on future value of slaughter pig production. (1 divided with the standard deviation). The markers in the figure correspond to preliminary estimates from a study at our institute. With a weighing precision of 1 the value of the weighing is 150 DKR in FV compared to a fixed delivery date, or approximately 4 DKR per pig produced. Compared to simple visual estimation of weight the value of weighing is approx. 2 DKR. It must therefore be concluded that with the current pricing system there is not much economic value in weighing. Automatic weighing equipment in combination with electronic identification cannot even earn the cost for the electronic identification tag. The value of weighing might, however, be found in other production phases, e.g., in growth and feed control and estimation of growth and feed consumption curves for the individual herd. Conclusion This paper investigates the value of information of several aspects of pig production. As shown it is possible to get indications of the value of registrations from a detailed analyzis of the decision process, and the related stochastic variation in state of nature. The registrations used in their traditional context has shown only a slight improvement in expected utility, or as in the case with the pregnancy tester, even a negative effect. On the other hand, if the registrations are used in a different decision context, they can have positive influence. The estimation of utility value often indicates these other uses of registration. The notion of negative value of information is important. Researchers trying to develop DSS should have this in mind. Also the value of the system should be compared to systems with a lower need for information. Usually it is assumed, that the á priori distribution of the traits is known. We usually assume that we know how litter size depends on parity, we know the relationship between growth rate, feed consumption and age, etc. This knowledge is, however, based on experimental results or from other production herds, and might not be relevant for every herd. In practice, the most important problem seems to be how to obtain herd specific estimates of these relationships. If we are not to overtax the pig producers with information from the profusion of possible registrations, we need to identify the decisions made in the herd. Then we should consider, which information is used in the -7-

8 decision. Finally, we should investigate, whether some relevant data is missing, and whether the data can be obtained in a cost effective way. Then we might suggest to the pig producers, that he begins to use these data. The Danish efforts in this area will, therefore, be continued, primarily under the framework of DINA. References De Groot, M.H. (1970). Optimal Statistical Decisions. New York. McGraw-Hill. Huirne, R.B.M. (1990). Computerized Management Support for Swine Breeding Farms. Chapter 2. Ph.D. Thesis, Department of Farm Management, Wageningen Agricultural University, Wageningen, The Netherlands pp Huirne, R.B.M. & A.A. Dijkhuizen, J.A. Renkema. (1991). Economic optimization of sow replacement decisions on the personal computer by method of stochastic dynamic programming. Livestock Production Science 28 p Jørgensen, E. (1985). [Principles of production control in sow production, Ph. D. Thesis] in Danish. Licentiatafhandling i svinets fodring og pasning. Husdyrbrugsinstituttet, Den Kgl.- Veterinær og Landbohøjskole. København. pp 113. Jørgensen, E. (1991). Probabilistic Growth Model for Pigs. Paper presented at DINA-Workshop Ebeltoft 3-4 december. dupl. pp. 19 Jørgensen, E. (1992). Sow replacement: Reduction of State Space in Dynamic Programming Model and Evaluation of Benefit from Using the Model. Subm. to Livest. Prod. Sci. for publ. Meredith, M.J. (1989). Pregnancy diagnosis in higher performance pig herds - is it a waste of resources?. Pig News and Information 10, 4, p Strang, G.S. & J.W.B. King. (1970). Litter Productivity in Large White Pigs. Anim. Prod. 12 : p Sundgren, P.E., J.P. van Male, A. Aumaitre, E. Kalm & H.E. Nielsen (1980). Sow and Litter Recording Procedures. Report of a Working Party of the E.A.A.P. Commision on Pig Production. Livest. Prod. Sci. 7: Treacy, D.A. (1987). Optimum Culling Levels in a sow herd. in Proc. of the 6th Conf. Australian Ass. of Anim. Breed. & Gen. University of Western Australia, Perth, WA Australia, Dep. Agric. & Rural Affairs, box 125, Bendigo, Vic Australia, p Van der Stuyft, E. & C.P. Schofield, J.M. Randall, P. Wambacq, V. Goedseels. (1991). Development and application of computervision systems for uses in livestock production. Computers and Electronics in Agriculture 6 p Vedder, J. & U. Ellinghaus, B. Petersen. (1989). Nichttragende Sauen früher erkennen? Neue schnelltests zur Trächtigkeitskontrolle beim Schwein..in Die Landwirtschaftlichen Zeitschrift für Produktion - Technik - Management (dlz). 40, p