The document discusses the evolution of performance measurement and genetic evaluation in livestock, particularly focusing on dairy, beef cattle, and goat production. Key advancements include the adoption of performance data collection leading to significant improvements in traits such as weaning weight, health, and reproductive success. It highlights the importance of participation in national programs like NSIP for effective selection and genetic progress in goat breeding.

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