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Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
Jenny Patterson - Repeatability of Litter Size in the Sow Population
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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. …

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

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  • 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 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. 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-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. 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 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. 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. 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 bornlitter 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 ± 252Percent 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 P3Q -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 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. 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. 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. 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. 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. 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

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