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How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
How the goat industry can benefit from NSIP
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How the goat industry can benefit from NSIP

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This is the second presentation from a six part webinar series on the National Sheep Improvement Program (NSIP). The presenter is Dr. Ken Andries from Kentucky State University. The date of the …

This is the second presentation from a six part webinar series on the National Sheep Improvement Program (NSIP). The presenter is Dr. Ken Andries from Kentucky State University. The date of the presentation was May 8, 2014.

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  • 1. Dr. Ken Andries Collage of Agriculture, Food Science, and Sustainable Systems Kentucky State University
  • 2. J.L. Lush, Animal Breeding Plans. 1945
  • 3.  Dairy herds started measuring performance in the 50’s and 60’s for milk production.  Beef cattle started BIF to standardize Performance programs in 1968.  Beef moved to performance pedigrees and then to EPDs in the 80’s  NSIP stared in 1986 and produced flock EPD for sheep and offered to Meat Goat Industry.  Research into use of DNA assisted selection (genomics) in the 2000’s for many species.
  • 4.  Average weaning weight has increased by 150% (175 lbs.) with only a small increases in birth weight.  Calving difficulty has been reduced greatly.  Carcass traits are now an economical trait for cow/calf producers  Most commercial beef cattle producers utilize data in selecting bulls.  AI has become very wide spread in the commercial industry partly due to performance data.  Key point: it did not happen over night and started with simple data collection.
  • 5.  Austria started in 1990.  Quality issues were identified as a problem as wool market was decreasing.  Research and selection resulted in increased carcass weight and increased economic value.  Most of the increase was due to improved performance (growth).  A common industry goal of improved meat quality was part of the success.  LAMBPLAN was key to allowing producers to improve selection effectiveness.
  • 6.  Reproduction has one of the greatest impacts on profitability.  Growth traits are often second in importance.  Health traits influence both reproduction and growth greatly so they are critical.  For meat goats, currently carcass traits have less value due to market trends.  To be profitable we must optimize reproduction, growth, and health traits.
  • 7.  Average goat producer keeps no records of performance.  Basic performance records from birth to weaning can be used to make progress.  Adjusted weights provide a better picture of performance, remove some environmental factors.  EBVs and EPDs are possible with meat goats if we have data to process.
  • 8. Variable Observations Mean Std Deviation Birth Weight 7791 7.55 1.73 Weaning Age 7041 89.31 20.13 Weaning Weight 7022 37.47 11.68 Average Daily Gain 6910 0.42 0.11 Weight per day of age 7022 0.52 0.12 90 day Weight 7023 38.23 10.55 Adjusted Weaning Weight 6986 43.41 12.65 Number Born 4501 1.84 0.65 Number Weaned 4376 1.59 0.69
  • 9.  Data collection gives you information to make progress towards goals.  Can impact health traits as well as performance.  Individual data such as on-farm performance data is not as effective as national evaluations.  Individual/on-farm data only apply to individuals within the contemporary group.  This limits their use when purchasing or comparing individuals from multiple groups.
  • 10. Year BWT WWT Age at Wean ADG 90DWT AWWT 2008 7.28 32.52 89.88 0.28 32.94 35.80 2009 7.65 26.35 71.50 0.26 31.02 36.07 2010 7.43 29.89 67.40 0.34 38.07 41.29 2011 6.95 25.38 60.98 0.30 34.10 36.87 2012 6.54 30.76 96.91 0.25 29.05 31.62 2013 7.01 27.08 84.02 0.25 29.49 31.34 • Purchase of a large number of yearlings that entered production in 2011 and 2012. • Introduction of the purchased doe kids and breed sires, without data, moved our selection back several years.
  • 11. 146 859 616
  • 12. ID Seasons BWT WN Wt 90D Wt ADJ WWT 146 3 7.24 34.15 35.28 39.5 616 7 8.2 38.71 41.84 43.87 859 5 7.62 33.53 35.76 39.59 Sires used in spring and fall breeding programs entered the herd the same year.
  • 13. ID WWT Value $ /20 kids $ /30 kids $ /40 kids $/50 kids 146 34.15 $58.05 $1,161.10 $1,741.65 $2,322.20 $2,902.75 616 38.71 $65.81 $1,316.14 $1,974.21 $2,632.28 $3,290.35 859 33.53 $57.00 $1,140.02 $1,710.03 $2,280.04 $2,850.05 * Price based on $1.70/lb kid price in the 40 to 60 lb range. • At this price with only 20 kids of average weight, $176.12 difference in income. • With 50 kids the difference is $440.30. • This value difference could cover the cost of membership for several years in NSIP.
  • 14.  Currently the NSIP version provides genetic evaluation of goats across different breeds and across different flocks within a breed group.  KIDPLAN is the goat version of LAMBPLAN, this is the program used by NSIP.  An Australian Sheep Breeding Value (ASBV) describes a goat’s genetic performance expressed in terms of the expected genetic performance of it’s offspring.  The breeding value is calculated by a BLUP analysis that can include information on the goat’s own performance and/or its relative’s performance.  ASBV data is submitted to KIDPLAN through NSIP by members.
  • 15.  Birth Weight (BWT) - direct genetic effects on weight at birth. It is positively correlated with weaning and postweaning body weight EBVs.  Maternal Birth Weight (MBWT) - genetic effects of the doe on the birth weight of her kids.  Weaning Weight (WWT) – direct genetic effect on preweaning growth potential.  Maternal Weaning Weight (MWWT) - estimates genetic merit for mothering ability, mainly reflects genetic differences in doe milk production though other aspects of maternal behavior may also be involved.  Postweaning Weight (PWWT) - combines information on preweaning and postweaning growth to predict genetic merit for postweaning weight at 150 days.  Yearling Weight (YWT) – genetic merit for growth to 12 months of age.  Hogget Weight (HWT) - genetic effects on body weight at 18 months of age.
  • 16.  Number of Kids Born (NLB) - evaluates genetic potential for prolificacy. This EBV is expressed as numbers of kids born per 100 does kidding. Selection on Number of Kids Born EBV is expected to increase prolificacy in the flock.  Number of Kids Weaned (NLW) - evaluates combined doe effects on prolificacy and kid survival to weaning. The EBV is expressed as numbers of kids weaned per 100 does kidding. Selection on the Number of Kids Weaned EBV is expected to increase weaning rates in the flock.  Scrotal Circumference (SC) - may be used to improve breeding capacity in males and reproductive performance in females. Selection of animals with positive Scrotal Circumference EBVs is expected to be most useful in improving reproductive performance in young does via desirable effects on rate of sexual maturation.
  • 17.  Worm Egg Count (WEC) - evaluates genetic merit for parasite resistance based on worm egg counts recorded at weaning or at early or late postweaning ages. Animals with low Worm Egg Count EBVs are expected to have greater parasite resistance, and selection to reduce Worm Egg Count EBVs is recommended in areas where internal parasites are a problem.  Postweaning Fat Depth (CF) – evaluates genetic differences in carcass fatness between the 12th and 13th ribs. It is derived from ultrasonic measurements of fat depth in live animals and adjusted to standard weights of 110 lb. (55 kg).  Postweaning Loin Muscle Depth (EMD) – evaluates genetic differences in muscling. It is derived from ultrasonic measurements of loin muscle depth between the 12th and 13th ribs in live animals and adjusted to standard weights of 110 lb. (55 kg).  Scanning data must be accompanied by a body weight and recorded at the same time as (or at least within ± 7 days of) the corresponding postweaning or yearling weights. Scans must be conducted by a certified technician.
  • 18.  BVs are designed to be used to compare the genetic potential of animals independent of the environment and location.  The traits to focus on will depend on your herd objectives and markets.  When selecting, consider your current performance and where you wish to move your herd.  The program provides some Selection Indexes that can be used as a way of weighing up different traits for a particular breeding objective.  Indexes can be helpful to narrow down selection however the individual trait values must be considered.  The program also provides percentile band reports can be useful to determine how an animal ranks relative to the rest of the breed for individual traits.
  • 19. BWT WWT PWWT YWT HWT CF WEC Top Value -0.2 4.4 7.1 8.7 9.8 -0.1 -2 Top 1 % -0.16 3.6 5.6 7 7.8 -0.1 Top 5% -0.12 2.9 4.6 5.8 6.6 -0.1 Top 10% -0.09 2.1 3.8 4.7 5.5 -0.1 -2 Top 20% -0.05 1.2 2.3 3 3.4 -0.1 -1 Top 30% -0.01 0.9 1.5 2.2 2.5 -0.1 -1 Top 40% 0.01 0.6 1 1.8 2 0 1 Top 50% 0.03 0.4 0.6 1.3 1.5 0 3 Top 60% 0.06 0.2 0.2 0.8 1 0.1 5 Top 70% 0.1 0 -0.1 0.4 0.5 0.1 6 Top 80% 0.13 -0.2 -0.5 0 0 0.1 10 Top 90% 0.19 -0.5 -1.1 -0.8 -0.8 0.1 10 Bottom value 0.39 -2 -4.3 -4.5 -3.4 0.2 12
  • 20. ID BWT WWT PWWTPFAT WEC MWWT Carcass Plus SRC Sire 1 0.3 1.2 1.9 -0.6 109 102 72% 79% 82% 20% 63% 39% Sire 2 0.05 1.8 208 -0.06 116 106 83% 86% 89% 36% 69% 43% Sire 3 -0.07 -0.1 -0.3 -0.03 98 99 80% 83% 87% 19% 67% 40% Sire 4 -0.01 -0.04 -1.1 -0.4 93 96 52% 41% 49% 10% 35% 22% Sire 5 -0.08 0.6 1.9 -3 -0.2 111 100 88% 93% 95% 61% 73% 75% 58% Sire 6 0.18 0.5 0.3 -0.4 103 90 81% 85% 87% 45% 69% 49% Sire 7 -0.01 -1.1 -1.2 -0.2 93 87 81% 85% 88% 55% 71% 54%
  • 21.  Easy to participate in the program.  Sign up information can be found at: http://nsip.org/.  Fees are explained on the enrolment sheet.  At this time there is a need for goat producers to participate.  We can make progress but we must have participation.
  • 22.  Genetic progress can only be made through evaluation and selection towards a set of goals.  EBV’s are a critical tool to use in comparing animals on genetic potential.  Progress from individual data is limited, participation in NSIP will greatly improve accuracy of selection and result in sustainable improvement in performance.
  • 23. “In each generation of animals in his herd or flock, the breeder must select those to be saved for breeding from those to be used for other purposes. Perhaps he will also select animals from other herds for use as breeders in his. These are the most important things he does.” Breeding Better Livestock. Rice, Andrews, and Warwick 1953

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