The Utility Value of Information in Pig Production
Erik Jørgensen.
National Institute of Animal Science.
Department of Research in Pigs and Horses,
DINA1
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
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Danish Informatics Network in the Agricultural Sciences
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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.
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Marginal value
Production traits
Relative value
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, %
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
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Litter size, avg.
11.5
No culling
Parity
Sow
11
10.5
10
3
4
5
6
7
8
9
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,
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Improvement in litter size
0.6
constant involuntary culling
0.5
age dependent invol. culling
0.4
Optimal culling
0.3
Estimated age
used in
optimization
0.2
0.1
0.0
-0.1
-0.2
0
2
4
6
8
10
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
7
Empty
6
Pregnant
5
4
3
2
1
0
70
80
90
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
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Avg. duration pr observed sow
120
No preg. test
100
Pregnant sows
after preg. test
80
Empty sows
after preg. test
Duration of pregnancy test
60
40
20
0
0
5
10
15
20
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 50-76 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 afterwards2. 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 1991. 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
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Future value, DKR
6650
Traditional
Automatic
6600
Visual
Image analysis
6550
6500
6450
0
0.2
0.4
0.6
0.8
1
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
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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
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