Repeatability of Litter Size in the Sow Population - Jenny Patterson, University of Alberta, from the 2012 Allen D. Leman Swine Conference, September 15-18, St. Paul, Minnesota, USA. More presentations at http://www.swinecast.com/2012-leman-swine-conference-material

1. Repeatability of litter phenotype
in the sow population
Jennifer Patterson
Swine Reproduction and Development Program (SRDP),
University of Alberta
John Harding
Western College of Veterinary Medicine, University of
Saskatchewan

2. Introduction
• Management advances and selection for
prolificacy have greatly increased litter size in
swine (Estienne, 2012) Early-in-life Here
experiences impact lifetime reproductive
Title
performance and longevity in sows
• Consequences have 12
been an increase in 11
the variation of birth 10 Born
weight & the Born Live
S
e
L
z
r
t
i
9 Weaned
proportion of low 8
birth weight pigs due 1990 1995 2000 2006
Year
to IUGR (Estienne, 2012) National Animal Health Monitoring
System, 2008 Title Here, Optional or
Unit Identifier
Taken from Estienne, 2012

3. IUGR --- Within-litter variation:
 Considerable negative economic impact for pork
production systems (Foxcroft et al., 2009).
 Effects of within-litter variation in birth weight on pre-
and post-natal development:
Intra-Uterine Growth Retardation

4. Between-litter variation
1. Evidence for induced “litter phenotypes” in commercial
sow populations.
2. Low birth weight phenotypes
3. Hyper-prolific and higher parity sows are most
susceptible.

5. Litter phenotypes – between litter variation
HIGH
LOW
Smit, 2007

6. Characteristics of High and Low average birth-weight
litters (n = 1,094)
“High” “Low” P-Value
Ave Birth
Weight 1.8 ± 0.01 1.2 ± 0.01 < 0.001
Total born 12.3 ± 0.08 12.3 ± 0.07 0.91
Born Alive 11.7 ± 0.09 11.0 ± 0.09 < 0.001
Born Dead 0.6 ± 0.07 1.2 ± 0.06 < 0.001
Weaned 10.8 ± 0.10 9.4 ± 0.10 < 0.001
(M. Smit, 2007. MSc thesis – Univ. Alberta / Univ. Wageningen)

7. Effect of average litter weight on body weight
0.56 Kg 3.05 Kg
difference difference
0.81 Kg 6.92 Kg
difference difference
It took the low bw litters 9 days longer to reach the same
slaughter weight as high bw litters
Smit, Leman Conference 2010

8. Impact on production systems.
 This constraint may reduce the lean growth potential
of the offspring of the entire litter not just the small
pigs!
– Increased pre-weaning morality,
– reduced survivability,
– reduced growth rates and efficiency
– Increased variation in pig market weights
– Slow growing pigs need to stay in barn longer to
hit carcass weight targets
Smit, 2010

9. Low birth-weight phenotype
Genotype
Ovulation Rate Uterine Capacity
Embryonic/fetal Placental
survival function
Phenotype

10. Ovulation rate in multiparous sows
(Patterson et al., 2008:J. Anim. Sci., 86, 1996-2004)

11. Evidence for early intra-uterine crowding and a wave of fetal losses
by day 50
20%
18% D30
16% D50
14%
12%
10%
8%
6%
4%
2%
%
O
n
p
c
a
e
v
r
)
(
t
l
0%
4 5 6 8 9 10 11 12 13 14 15 16 17 18 19 20 21 23 28
Embryo/Fetus No.
(From Patterson et al., 2008)

12. Origin of litter phenotype
Ovulation Rate Uterine Capacity
Embryonic/fetal Placental
survival function
Average litter Identifying litter
Limitations in birth weight phenotype –
postnatal Develop selection
growth Postnatal growth & production
performance strategies

13. REPEATABILITY OF LITTER PHENOTYPE

14. Repeatability of low litter birth weight
phenotype
Knol E et al. 2010

15. Identifying litter phenotype
 Obtained from a collaborating farrow to finish farm in
Saskatchewan
 Production nucleus and multiplier tiers (large
white/landrace females)
 8999 individual parity records, from 2223 multiparous
sows (parity <= 10) over 6 years (2006-2011).
 Total weight of piglets born alive was collected
 Average birth weight was calculated
as total born alive litter weight
divided by # born alive

16. Variation in average litter birth weight controlled for total born
litter size
University of Alberta, unpublished data

17. Summary Statistics (mean ± stddev)
ion.
e viat
da rd d
stan
an and
, me
u tion
s trib
e di
niqu
ha sau
ch cell
Ea

18. Distributions
LOW Birth Weight MEAN HIGH Birth Weight
1462.71 ± 252
Percent distribution
1179.33 ± 192
Average Litter Weight

19. Z-Score – comparing values from different distributions
Tells us how a single data point compares to the rest of the
population, represented by a normal curve.
It shows whether the point (weight) is above or below average,
but how distant the measurement is from the average.

20. Z-Score normal distribution
"low" phenotype "high" phenotype
65 % of data
95 % of data
99.7 % of data

21. High vs low phenotype -- litter size & weight.
Variable High Low P-Value
Total Born 12.7 ± 0.06 13.6 ± 0.06 0.0001
Born Alive 11.7 ± 0.06 12.6 ± 0.06 0.0001
Average litter birth
1523.6 ± 4.0 1141.5 ± 4.0 0.0001
weight (g)

22. Q - Is litter phenotype repeatable?
 Can litter phenotype after P1 be used to predict
phenotype in subsequent parities?
 Look at the correlation between parity records
Correlation
Descriptor
Coefficient
0.0-0.1 trivial, very small, insubstantial, tiny, practically zero
0.1-0.3 small, low, minor
0.3-0.5 moderate, medium
0.5-0.7 large, high, major
0.7-0.9 very large, very high, huge
0.9-1 nearly, practically, or almost: perfect, distinct, infinite
Hopkins, 2002

23. Correlation – P1 to subsequent parities
Parity
Parity Value
1 2 3 4 5 6
1 r 1 0.303 .274 .281 .206 .198
Gilts n 1232 1221 1218 1224 950 673
Low to moderate correlation

24. Correlation – P1 to subsequent parities
Parity
Parity Value
1 2 3 4 5 6
1 r 1 0.303 .274 .281 .206 .198
Gilts n 1232 1221 1218 1224 950 673
Decreasing ability to predict
Gilts at mating
Selection Take Home Message:
Body Weight Phenotype at Parity 1
Immunity Level can not be used to
Stall Acclimation predict phenotype in
Physiological Age
later parities.
Chronological Age

25. Q – Can P2 phenotype predict subsequent parities?
Parity
Parity Value
2 3 4 5 6
r 1 .355 .364 .373 .334
2
n 1233 1219 1225 951 675
r 1 .397 .407 .391
3
n 1230 1222 948 672
r 1 .420 .410
4
n 1236 954 676
r 1 .401
5
n 962 674
Take Home Messages:
highest correlations between subsequent parities
 correlations are stronger in more mature sows

26. A moderate correlation between P2 and P3 Z-scores (r = 0.355)
Low P2, High P3 High P2, High P3
Low P2, Low P3 High P2, Low P3
Q -Is it likely (probable) that a sow that is “low” is P2, will be “low”
in P3?

27. Probability – flipping a coin
 A number between zero and one.
 A probability of one means that
the event is certain (toss a coin, it
will be heads or tails).
 A probability of zero means that
an event is impossible (toss a
coin, you cannot get both a head
and a tail at the same time, so this
has zero probability).
 Probabilities do not tell you what
is going to happen, they merely
tell you what is likely to happen!
http://gwydir.demon.co.uk/jo/probability/info.htm

28. Prediction Probabilities – Low Phenotype
Observed Predicted Prediction
2 3 4 5 6
Parity (ies) Parity Probability
2 3 L L - - - 0.630
Probability = 0.630 slightly higher than chance

29. Observed Predicted Prediction
2 3 4 5 6
Parity (ies) Parity Probability
2 3 L L - - - 0.630
L H L - - 0.592
2, 3 4*
L L L - - 0.815
If the same sow delivered 2 consecutive “low” litters (P2
and P3) she was far more likely to deliver a below average
BW litter in her 4th parity (probability=0.82)

30. Observed Predicted Prediction
2 3 4 5 6
Parity (ies) Parity Probability
2, 3 4* L L L - - 0.815
L H H L - 0.444
L H L L - 0.621
2, 3, 4 5
L L H L - 0.647
L L L L - 0.824
 Sows that are classified “low” in P2-4, it is very probable, she
will be “low” in P5.
 Probability did not increase when a sow delivered “low” for 3
or 4 consecutive litters
L H L L L 0.698
2,3,4,5 6 L L L H L 0.655
L L L L L 0.805

31. Prediction Probabilities – High Phenotype
Observed Predicted Prediction Probability of a
2 3 4 5 6
Parity (ies) Parity ABOVE average litter weight
2 3 H H - - - 0.607
H L H - - 0.440
2, 3 4
H H H - - 0.625
If the same sow delivered 2 consecutive “high” litters (P2 and P3)
the probability of a “high” litter in P4 is a little above chance
(probability=0.63)
H L L H - 0.375
H L H H - 0.552
2, 3, 4* 5
H H L H - 0.579
H H H H - 0.756
If the same sow delivered 3 consecutive “high” litters (P2 – P4)
she is more likely to have “high” litter in P5 (probability=0.76)

32. Prediction Probabilities – High Phenotype
Observed Predicted Prediction Probability of a
2 3 4 5 6
Parity (ies) Parity ABOVE average litter weight
H L L L H 0.314
H H L L H 0.421
H L L H H 0.464
H L H L H 0.570
2,3,4,5 6
H H L H H 0.571
H H H L H 0.677
H L H H H 0.720
H H H H H 0.827
If the same sow delivered 4 consecutive “high” litters (P2 – P5)
she is more likely to have “high” litter in P6 (probability=0.83)

33. Is litter phenotype repeatable & predictable?
LOW phenotype
 Sows producing below average BW litters can be most
accurately predicted after parity 3.
 This is when intervention should be made.
 Do not wait until after parity 4, the accuracy of prediction
does not get any better.

34. Production strategies at sow/litter level :
 Segregate sows into farrowing rooms based on
expected birth weight phenotype.
 Segregate different birth-weight litters into different
nursery/grow-finish flows.
 Adjust nutrient requirements to reflect expected lean
growth potential
 Market progeny of different birth-weight litters at
different market weights or different ages
 Target nutritional interventions at sows with a
predicted low litter birth weight phenotype.

35. Management Options:
LOW phenotype
 Sows producing below average BW litters can be most
accurately predicted after parity 3.
 This is when intervention should be made.
 When sows produce the “low” phenotype for 5 consecutive
parities or fall “extreme low”, consider culling them.

36. Management Options --- Strategic culling?
LOW Birth Weight HIGH Birth Weight
2 3 4 5
4 5 6
3 452

37. Is litter phenotype repeatable & predictable?
HIGH phenotype
 Sows producing above average BW litters are most accurately
predicted after their 4th parity.
 This may be because uterine capacity could limit the full
expression of birth weight in younger parities.

38. The next generation – productivity of H or L female
Selecting replacement females?
Minimum birth weights?

39. Future:
 Is Phenotype repeatable within generation?

40. Summary:
 Litter average birth weight is predictable within sows.
 can be used as tool as a management tool
 Sows producing:
 below average BW litters can be most accurately
predicted after parity 3.
 above average BW litters are most accurately
predicted after their 4th parity.
 Management strategies are available to be used
 Take into consideration when selecting replacement
gilts.

41. Acknowledgements