Jenny Patterson - Repeatability of Litter Size in the Sow Population

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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.

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Jenny Patterson - Repeatability of Litter Size in the Sow Population

  1. 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. 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. 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. 4. Between-litter variation1. Evidence for induced “litter phenotypes” in commercial sow populations.2. Low birth weight phenotypes3. Hyper-prolific and higher parity sows are most susceptible.
  5. 5. Litter phenotypes – between litter variation HIGH LOW Smit, 2007
  6. 6. Characteristics of High and Low average birth-weight litters (n = 1,094) “High” “Low” P-ValueAve BirthWeight 1.8 ± 0.01 1.2 ± 0.01 < 0.001Total born 12.3 ± 0.08 12.3 ± 0.07 0.91Born Alive 11.7 ± 0.09 11.0 ± 0.09 < 0.001Born Dead 0.6 ± 0.07 1.2 ± 0.06 < 0.001Weaned 10.8 ± 0.10 9.4 ± 0.10 < 0.001 (M. Smit, 2007. MSc thesis – Univ. Alberta / Univ. Wageningen)
  7. 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. 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. 9. Low birth-weight phenotype Genotype Ovulation Rate Uterine Capacity Embryonic/fetal Placental survival function Phenotype
  10. 10. Ovulation rate in multiparous sows (Patterson et al., 2008:J. Anim. Sci., 86, 1996-2004)
  11. 11. Evidence for early intra-uterine crowding and a wave of fetal lossesby 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. 12. Origin of litter phenotype Ovulation Rate Uterine Capacity Embryonic/fetal Placental survival function Average litter Identifying litterLimitations in birth weight phenotype – postnatal Develop selection growth Postnatal growth & production performance strategies
  13. 13. REPEATABILITY OF LITTER PHENOTYPE
  14. 14. Repeatability of low litter birth weight phenotype Knol E et al. 2010
  15. 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. 16. Variation in average litter birth weight controlled for total bornlitter size University of Alberta, unpublished data
  17. 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. 18. Distributions LOW Birth Weight MEAN HIGH Birth Weight 1462.71 ± 252Percent distribution 1179.33 ± 192 Average Litter Weight
  19. 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. 20. Z-Score normal distribution "low" phenotype "high" phenotype 65 % of data 95 % of data 99.7 % of data
  21. 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. 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. 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. 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. 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. 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 P3Q -Is it likely (probable) that a sow that is “low” is P2, will be “low”in P3?
  27. 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. 28. Prediction Probabilities – Low PhenotypeObserved Predicted Prediction 2 3 4 5 6Parity (ies) Parity Probability 2 3 L L - - - 0.630 Probability = 0.630 slightly higher than chance
  29. 29. Observed Predicted Prediction 2 3 4 5 6Parity (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. 30. Observed Predicted Prediction 2 3 4 5 6Parity (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. 31. Prediction Probabilities – High PhenotypeObserved Predicted Prediction Probability of a 2 3 4 5 6Parity (ies) Parity ABOVE average litter weight 2 3 H H - - - 0.607 H L H - - 0.440 2, 3 4 H H H - - 0.625If 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. 32. Prediction Probabilities – High PhenotypeObserved Predicted Prediction Probability of a 2 3 4 5 6Parity (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. 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. 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. 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. 36. Management Options --- Strategic culling? LOW Birth Weight HIGH Birth Weight 2 3 4 5 4 5 6 3 452
  37. 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. 38. The next generation – productivity of H or L female Selecting replacement females? Minimum birth weights?
  39. 39. Future: Is Phenotype repeatable within generation?
  40. 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. 41. Acknowledgements

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